GPT-3 Creative Fiction

Creative writing by OpenAI's GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling.
NN, fiction, GPT, poetry, humor, transhumanism
19 June 202007 Jul 2020 finished certainty: likely importance: 8


I continue my AI poetry generation experiments with OpenAI’s 2020 GPT-3, which is 116× larger, and much more powerful, than the 2019 GPT-2. GPT-3, however, is not merely a quantitative tweak yielding “GPT-2 but better”—it is qualitatively different, exhibiting eerie runtime learning capabilities allowing even the raw model, with zero finetuning, to “meta-learn” many textual tasks purely by example or instruction. One does not train or program GPT-3 in a normal way, but one engages in dialogue and writes prompts to teach GPT-3 what one wants.

Experimenting through the OpenAI Beta API in June 2020, I find that GPT-3 does not just match my finetuned GPT-2-1.5b-poetry for poem-writing quality, but exceeds it, while being versatile in handling poetry, Tom Swifty puns, science fiction, dialogue like Turing’s Turing-test dialogue, literary style parodies… As the pièce de résistance, I recreate Stanislaw Lem’s Cyberiad’s “Trurl’s Electronic Bard” poetry using GPT-3. (Along the way, I document instances of how the BPE text encoding unnecessarily damages GPT-3’s performance on a variety of tasks.)

GPT-3’s samples are not just close to human level: they are creative, witty, deep, meta, and often beautiful. They demonstrate an ability to handle abstractions, like style parodies, I have not seen in GPT-2 at all. Chatting with GPT-3 feels uncannily like chatting with a human. I was impressed by the results reported in the GPT-3 paper, and after spending a week trying it out, I remain impressed.

This page records GPT-3 samples I generated in my explorations, and thoughts on how to use GPT-3 and its remaining weaknesses. I hope you enjoy them even a tenth as much as I enjoyed testing GPT-3 and watching the completions scroll across my screen.

The latest and greatest neural network for unrestricted natural language generation is OpenAI’s . GPT-3 is like and the I’ve used extensively before1—only much more so. Naturally, I’d like to write poetry with it: but GPT-3 is too big to finetune like I did GPT-2, and OA doesn’t (yet) support any kind of training through their API. Must we content ourselves with mediocre generic poetry, at best, deprived of finetuning directly on chosen poetry corpuses or authors we might like to parody?

Scaling works: quantity is a quality all its own. No—because the scaling of GPT-2-1.5b by 116× to GPT-3-175b has worked surprisingly well and unlocked remarkable flexibility in the form of meta-learning, where GPT-3 can infer new patterns or tasks and follow instructions purely from text fed into it. I have discussed elsewhere the broader implications of this scaling success like the emergence of meta-learning (see also ), AI progress, the field’s attitudes, and futurism; but fortunately, OpenAI granted me access to their Beta API service which provides a hosted GPT-3 model, letting me spend a great deal of time interacting with GPT-3 and writing things. Here, we’re all about having fun while probing GPT-3’s abilities for creative writing tasks, primarily (but far from limited to) poetry. How much does GPT-3 improve and what can it do?

Turns out: a lot! Below, I walk through first impressions of using GPT-3, and countless samples. In the latest twist on , GPT-3 still struggles with commonsense reasoning & factual knowledge of the sort a human finds effortless after childhood, but handles well things like satire & fiction writing & poetry, which we humans find so difficult & impressive even as adults. In addition to the Cyberiad, I’d personally highlight the Navy Seal & Harry Potter parodies, the Devil’s Dictionary of Science/Academia, “Uber Poem”/“The Universe Is a Glitch” independent poems, & “Where the Sidewalk Ends”.

Other GPT-3 Demos

The GPT-3 paper includes evaluation of zero-shot/few-shot performance across a wide range of tasks, but I fear that unless one is familiar with the (deadly dull) benchmarks in question, it won’t be impressive. You can skip to the appendix for more example like its , or browse the random samples.

The original includes many striking examples of GPT-3 capabilities ranging from chatbots to question-based Wikipedia search to legal discovery to homework grading to translation; I’d highlight , and “Spreadsheets”/“Natural Language Shell”/“Code Completion”2. Andrew Mayne describes using GPT-3 to generate book recommendation lists & read interactive stories & engage in conversations with historical figures like Ada Lovelace, summarize texts (such as for elementary school children) or summarize movies in emoji (Matrix: “🤖🤐”; Hunger Games: “🏹🥊🌽🏆”), and rewrite HTML. experimented with Crunchyroll anime plot summaries & Star Trek: The Next Generation plot summaries. Max Woolf has a repo of GPT-3 example prompts & various completions such as the original GPT-2 “unicorn” article, Revenge of the Sith, Stack Overflow Python questions, and his own tweets. And Janelle Shan experimented with weird dog descriptions to accompany deformed GAN-dog samples.

Quality

Objective metrics hard to interpret. How much better is (un-finetuned base) GPT-3? The likelihood loss is an absolute measure, as are the benchmarks, but it’s hard to say what a decrease of, say, 0.1 bits per character might mean, or a 5% improvement on SQuAD, in terms of real-world use or creative fiction writing. It feels like a large improvement, definitely a larger improvement than going from GPT-2-345M to GPT-2-1.5b, or GPT-2-1.5b to GPT-3-12b, but how much?

Screening gains: 1:100 → 1:5 or 20× better? For fiction, I treat it as a curation problem: how many samples do I have to read to get one worth showing off? One could think of it asking how efficiently a model searches : at the one extreme, an algorithm which selects letters at random will have to generate astronomically large numbers of samples before, like the proverbial monkeys, they generate a page from a Shakespeare play; at the other extreme, a reasonably intelligent human can dash off 1 plausible page in 1 try. With AI algorithms, the results are intermediate but rapidly improving. A text generator trained on a small corpus represents a huge leap over randomness: instead of having to generate countless quadrillions of samples, one might only have to generate millions of samples to get a few coherent pages; this can be improved to hundreds or tens of thousands by increasing the depth of the n of its n-grams, which is feasible as one moves to Internet-scale text datasets (the classic “unreasonable effectiveness of big data” example) or by careful hand-engineering & combination with other approaches like Mad-Libs-esque templating. A char-RNN, like in my does better still: it easily generates reasonable paragraphs, so one might only have to brute force on the order of thousands of samples to get a pleasing page. With GPT-2-117M poetry, I’d typically read through a few hundred samples to get a good one, with worthwhile improvements coming from 345M→774M→1.5b; by 1.5b, I’d say that for the , I read through 50–100 ‘poems’ to select one. But for GPT-3, once the prompt is dialed in, the ratio appears to have dropped to closer to 1:5—maybe even as low as 1:3! I frequently find myself shrugging at the first completion I generate, “not bad!” (Certainly, the quality of GPT-3’s average prompt appears to exceed that of almost all teenage poets.)

Prompts As Programming

Reprogramming by asking politely. The demos above and on this page all3 use the raw default GPT-3 model, without any additional training. Instead, to get all these different behaviors, one provides a short textual input to GPT-3, with which it will predict the next piece of text (as opposed to starting with an empty input and freely generating anything); GPT-3, just by reading it, can then flexibly adapt its writing style and reasoning and use new definitions or rules or words defined in the textual input no matter that it has never seen them before. This is considered “meta-learning” because GPT-3 has “learned how to learn”: in its endless training on many gigabytes of text, it encounters so many different and varied kinds of text that it had no choice but to learn abstractions and how to understand descriptions and instructions and formatting to let it adapt on the fly to the particular piece of text it was training on, since there was too much for it to simply learn each task normally by repeated exposure (much less memorize all the data). When GPT-3 meta-learns, the weights of the model do not change, but as the model computes layer by layer, the internal numbers become new abstractions which can carry out tasks it has never done before; in a sense, the GPT-3 model with the 175b parameters is not the real model—the real model is those ephemeral numbers which exist in between the input and the output, and define a new GPT-3 tailored to the current piece of text.

Few-shot learning/writing prompts: “Software 3.0”? (Andrej Karpathy, 18 June 2020)

A new programming paradigm? Because you aren’t finetuning GPT-3 in the conventional way, interacting with GPT-3 via its few-shot learning power takes on an entirely different feeling than anything else I’ve used before. With regular software, you have to think through exactly how to do something; with deep learning software, you have to focus on providing data which in some way embodies the correct answer which you want; but with GPT-3, you instead think about how to describe what you want. With GPT-3, it helps to anthropomorphize it: sometimes you literally just have to ask for what you want. (It can’t possibly be that easy, can it? Sometimes, it is!) Thus, you can simply ask it directly in the Q&A format: “what is X?” Other times, you must instead think, “If a human had already written out what I wanted, what would the first few sentences sound like? What would the introduction and summary sound like? What if I told a story here, how would that story start?” Thus, the summarization prompt: “My second grader asked me what this passage means: …” When a given prompt isn’t working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn’t constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary. (This was a particular problem with the literary parodies: GPT-3 would keep starting with it, but then switch into, say, one-liner reviews of famous novels, or would start writing fanfictions, complete with self-indulgent prefaces. The solution was to write out the first 2 or 3 sentences of an example parody, and then GPT-3 would finish out the parody, look back and see that there was an example of a literary parody, and then happily start generating dozens of works+parody pairs, once it fell into the groove.) The more natural the prompt, like a ‘title’ or ‘introduction’, the better; unnatural-text tricks that were useful for GPT-2, like dumping in a bunch of keywords to try to steer it towards a topic, appear less effective or harmful with GPT-3.

Surprisingly powerful. Prompts are perpetually surprising—I kept underestimating what GPT-3 would do with a given prompt, and as a result, I underused it. Just as few people would have thought that you could get GPT-2 to automatically summarize text by simply appending a “TL;DR:” string, few people would guess GPT-3 could write emoji summaries or that if you use a prompt like “Summarize the plot of J.K. Rowling’s Harry Potter in the style of Ernest Hemingway”, you might get out a dozen profanity-laced reviews panning 20th-century literature (or a summary—in Chinese—of the Chinese translation4), or that if you use a prompt like “Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence”, GPT-3 will generate poems but then immediately generate explanations of how neural networks work & discussions from eminent researchers like Gary Marcus of why they will never be able to truly learn or exhibit creativity like generating poems. It is difficult to try out variations on prompts because as soon as the prompt works, it’s tempting to keep trying out completions to marvel at the sheer variety and quality as you are seduced into further exploring possibility-space. (GPT-3 never grows impatient or bored.) What other capabilities are latent, waiting to be exposed by someone stumbling across the right prompt?

(Of course, not all these capabilities are necessarily desirable: where there is programming, you can be sure there is hacking. GPT-3 can follow instructions, so within its context-window or with any external memory, it is surely Turing-complete, and who knows what or are possible?)

Finetuning

Finetuning necessary to ‘program’ GPT-2. GPT-3’s “prompt programming” paradigm is strikingly different from GPT-2, where its prompts were brittle and you could only tap into what you were sure were extremely common kinds of writing, and, as like as not, it would quickly change its mind and go off writing something else. At best, you could fairly generically hint at a topic to try to at least get it to use keywords; then you would have to filter through quite a few samples to get one that really wowed you. (This was a trick I used for TWDNE to get it to generate at least vaguely anime-related plot summaries.) To get output reliably out of GPT-2, you had to finetune it on a preferably decent-sized corpus.

Do we need finetuning given GPT-3’s prompting? But with GPT-3, you can just say so, and odds are good that it can do what you ask, and already knows what you’d finetune it on. (For example, I thought I would have to finetune GPT-3 to get samples of myself, since GPT-2 doesn’t know anything about “Gwern”/“gwern.net”; but it turns out, all I have to do is put in “A new essay by Gwern Branwen (gwern.net):” and out comes an uncanny simulacrum of myself, or Scott Alexander, or Paul Graham, or…) Would it be better if finetuned? Indubitably. But it’s not necessary. And given the creativity of the non-finetuned GPT-3, I’m not sure that I even want to—and forfeit all of the behaviors I haven’t yet discovered‽

As of mid-June 2020, the OpenAI API does not support finetuning although OA was working on it. But after enough time playing with GPT-3, I have begun to wonder: at this level of meta-learning & general knowledge, do we need finetuning at all?

For GPT-2, I saw finetuning as doing 2 things:

  1. Fixing ignorance: missing domain knowledge

    GPT-2 didn’t know many things about most things—it was just a handful (1.5 billion) of parameters trained briefly on a tiny fraction of even the Common Crawl subset of the Internet. It’s not surprising that for many domains, it wouldn’t know the details; and even if the dataset included adequate text, it did not train on that data many times, and the knowledge competed with all the other domains it needed to know about, interfering.

    But GPT-3 already knows everything! GPT-3 is so much larger on every dimension that this seems like much less of a problem for any domain which is already well-represented in public HTML pages. GPT-2 might need to be trained on a fanfiction corpus to learn about some obscure character in a random media franchise & generate good fiction, but GPT-3 already knows about them and use them appropriately in writing new fiction.

  2. Prompting a specific task:

    Even when GPT-2 knew a domain adequately, it had the frustrating behavior of rapidly switching domains. You might prompt it with a poem genre it knows adequately already, but then after a few lines, it would generate an end-of-text token and switch to generating a news article on Donald Trump. (Trump shows up a lot.) Presumably, while poetry was reasonably represented, it was still rare enough that GPT-2 considered poetry highly unlikely to be the next word, and keeps trying to jump to some more common & likely kind of text, and GPT-2 is not smart enough to infer & respect the intent of the prompt.

    GPT-3 exhibits much less of this ‘mode switching’ sort of behavior. Perhaps because it is trained on a much larger and more comprehensive dataset (so news articles aren’t so dominant), but also I suspect the meta-learning makes it much better at staying on track and inferring the intent of the prompt—hence things like the “Transformer poetry” prompt, where despite being what must be highly unusual text, even when switching to prose, it is able to improvise appropriate followup commentary.

So, what would be the point of finetuning GPT-3 on poetry or literature? It has likely already seen the finetuning corpus, knows most of it, and will tractably generate poems on demand. There may be gains, but I wonder if they would be nearly as large as they were for GPT-2?

Playground

All of the following samples were generated using the OpenAI Beta Playground, which looks like this:

OA API Beta Playground UI & available prewritten prompts/sampling options

The Playground has some rough edges in Beta, and capacity issues. I suggest users keep a web console popped open (Ctrl-i) to watch for it throwing errors, and recognize when the page has hung.

Tradeoff: diversity vs accuracy. It offers the standard sampling options familiar from earlier GPT-2 interfaces, including . One particularly manipulates the temperature setting to bias towards wilder or more predictable completions; for fiction, where creativity is paramount, it is best set high, perhaps as high as 1, but if one is trying to extract things which can be right or wrong, like question-answering, it’s better to set it low to ensure it prefers the most likely completion. (After all, the point of a high temperature is to regularly select completions which the model thinks aren’t likely; why would you do that if you are trying to get out a correct arithmetic or trivia question answer?) For top-p, one can set it to ~0.95 and largely forget about it unless one suspects that it’s breaking answers like top-k and it needs to be much lower, like 0.5; it’s there to cut off the tail of gibberish completions and reduce repetition, so doesn’t affect the creativity too much. I generally avoid the use of the repetition penalties because I feel repetition is critical to creative fiction, and I’d rather err on the side of too much than too little, but sometimes they are a useful intervention; GPT-3, sad to say, maintains some of the weaknesses of GPT-2 and other likelihood-trained autoregressive sequence models, such as the propensity to fall into degenerate repetition.

Ranking final results for quality gain. A little more unusually, it offers a “best of” (BO) option which is the ranking trick (generate n possible completions independently, and then pick the one with best total likelihood, which avoids the degeneration that an explicit tree/beam search would unfortunately trigger). I’m not sure how to best use BO. I tried out BO heavily because I couldn’t quite figure out how it interacts with quality. On the smaller models, it seems to help boost quality up towards ‘davinci’ (GPT-3-175b) levels without causing too much trouble, but on davinci, it appears to exacerbate the usual sampling issues: particularly with poetry, it’s easy for a GPT to fall into repetition traps or loops, or spit out memorized poems, and BO makes that much more likely. For generating completions of famous poems, it’s quite hard to get GPT-3 to generate new versions unless you actively edit the poem to force a difference. (In the most extreme case, in the case of generating new variations on “Jabberwocky”, I have been unable to generate any new versions under any setting, even taking the step of aggressively editing in new lines about how the vorpal sword bounced off the Jabberwocky and it won… It always spits out chunks of the original.) So BO is a double-edged sword. The best way I found to use it is to sample without it (BO=1) at max temp, and then once it has several distinctly different lines, then sampling with more (eg BO=5) seems to help rather than hurt. This is a little surprising to me because for Meena, it made a large difference to do even a little BO, and while it had diminishing returns, I don’t think there was any point they tested where higher best-of-s made responses actually much worse (as opposed to merely n times more expensive). Possibly BO is much more useful for nonfiction/information-processing tasks, where there’s one correct answer and BO can help overcome errors introduced by sampling or myopia.

Weaknesses

No memory (fixable). GPT-3 is, of course, not perfect. As a scaled-up GPT-2, it has mostly the same weaknesses, and my thoughts on improvements remain mostly the same (aside from moving away from BPEs, which need is becoming increasingly urgent; see the next section). The first limit is that it remains hobbled by the limited context window. GPT-3 has no form of memory or recurrency, so it cannot see anything outside its limited 2048 BPEs (roughly, 500–1000 words). This means it cannot hope to write anything of any serious length, because the beginning will soon vanish over the event horizon, and it also limits its ability to engage in few-shot learning, for the same reason: the prompt+generation will quickly exceed the window length. While the damage may be limited for tasks where the format is repetitive, like Q&A (so GPT-3 can do the necessary meta-learning over its completions just as well as over the original prompt), this does limit it and is frustrating. There are many possible solutions to this.

Repetition/gibberish (mystery). Autoregressive language models trained by likelihood (prediction) loss all share an extremely annoying problem: when you generate completions, they have a tendency to eventually fall into repetitive loops of gibberish. Whether GPT-2 or T5 or etc, they all seem to do it, and if one tries to avoid such extremely dumb & crude sampling strategies like top-k temperature sampling by doing explicit search for likely text completions, such as beam sampling, these searches actually make the problem worse, and the better your search is, the worse the results are. Tweaks like nucleus sampling can reduce it, but do not eliminate it. Since GPT-2-1.5b seemed almost as prone as GPT-2-117M, I was unsurprised to find that GPT-3 too falls easily into the repetition trap.

Why repetition? This behavior remains puzzling and I don’t think anyone really knows how to fix it. Top-k or nucleus sampling can’t be right and are clearly ugly ad hoc hacks, but is the core problem likelihood training or sampling, or what? And why is it never a problem for other kinds of sequences like images, and much less of one for music? (We don’t see it in char-RNNs or GPT-2s trained on ABC/MIDI music, or OA Jukebox trained on raw audio; we certainly don’t see it in iGPT or PixelRNN etc.) Likelihood training is compellingly simple and efficient, and we know that real brains are constantly predicting future inputs; it seems implausible that the entire problem will disappear if we slap on some Bayesian tricks to get posterior estimates of the likelihood of each possible BPE completion (and I’m not aware of anyone showing that it does in something like a small Bayesian RNN trained with HMC or by using deep ensembling or other Bayesian approximations). Further, if likelihood training is so bad, why does minimizing the predictive loss work so consistently over a wide range to improve the quality of generations and how useful the model is for zero/few-shot learning or semi-supervised tasks, and why does the loss correlate near-perfectly with human ratings of quality in the Meena paper?

Language Prediction = Imitation Learning? My intuition is that the repetition trap is essentially the DAgger/off-policy imitation learning problem in a non-RL guise: as the model is fed back in its own guesses as a ground truth, the hallucinated text becomes gradually more off-policy and divergent from real human-written text (which is backed by a knowledge base & a purpose), and the model is unable to come up with sensible continuations (having never trained on such gibberish) and does not ‘want’ to get back on track (having been trained purely to make one-step predictions). The solution might look something like detecting when a completion might go too far off-distribution and backtracking, or more RL-like training of generation as opposed to mere prediction. It would probably help also to use some sort of hierarchical or planning method: one might be able to convince GPT-3 to generate summaries and then expand each line of the summary recursively ( does something similar using a bag-of-words topic with GPT-2/BART to “upscale” a seed).

BPEs

Compared to GPT-2, GPT-3 improves performance on character-level tasks like rhyming, alliteration, punning, anagrams/permutations, acrostic poems, and arithmetic less than expected. Why? A plausible explanation is an obscure technical detail: GPT does not see characters but sub-word-chunks called BPEs. Some experiments with reformatting tasks to avoid inconsistent BPE encodings of strings shows small to large performance gains, consistent with this theory.

Bad at phonetic/character-level tasks. Disappointingly, the issues that have been noticed with GPT-2-poetry’s disinclination to rhyme remain. GPT-3 rhymes reasonably well and often when appropriate, but the improvement is much smaller on rhyming than it is on pretty much everything else. Apparently it is easier for GPT-3 to learn things like arithmetic and spreadsheets than it is to learn how to rhyme. A similar issue comes with puns. Better, but not as much better as one would expect given the leap on many other capabilities. Trying to generate puns or rhymes, it seems like GPT-3 know extremely well what they are on an abstract level, and will appropriately manipulate words and attempt to make puns or rhymes (see the shoggoth-cat dialogue below for a particularly striking example), but the words it chooses just aren’t right on a phonetic basis.

BPEs ≠ characters! My suspicion here is that these, and perhaps other issues, is due to the lossy BPE encoding. GPT models do not see individual characters, but instead a larger chunk, called a byte-pair encoding (BPE); a byte-pair is a simple compression scheme where 50,257 word fragments or characters are chosen to try to minimize the encoding length on some arbitrary text corpus, so a particularly common word may get a unique BPE while a longer word will be encoded as 2 or 3 BPEs, and a completely novel word will be encoded letter BPE by letter BPE as a fallback. Hence, even if 2 words sound and are spelled similarly, they may be given totally different BPE encodings which don’t have a single BPE in common. This is done because once a text is encoded into BPEs, it will be as much as a third smaller, which given the context window limitation, means you can fit 3× more text into the window compared to the raw characters. This is indeed quite a gain, but it is a double-edged sword: it is confusing to write code for it because the BPE encoding of a text is unfamiliar & unpredictable (adding a letter can change the final BPEs completely), and the consequences of obscuring the actual characters from GPT are unclear. I think that BPEs may make rhyming & puns extremely difficult because they obscure the phonetics of words; GPT-3 can still do it, but it is forced to rely on brute force, by noticing that a particular grab-bag of BPEs (all of the different BPEs which might encode a particular sound in its various words) correlates with another grab-bag of BPEs, and it must do so for every pairwise possibility. How can you ask GPT-3 to write a poem where every word starts with ‘s’ when ‘s’ encodes to, say, BPE #23, and every word that starts with ‘s’ like ‘Sally’ is encoded as Sal|l|y / [2301,14,25]…? It’d be unsurprising if GPTs struggled to understand & manipulate things on the character level given that the entire point of BPE is to compress away characters as much as possible.

Reformatting to beat BPEs. I have further observed that GPT-3’s anagram capabilities appear to improve considerably if you separate each letter in an anagram with a space (guaranteeing that the letter will have the same BPE in both the scrambled & unscrambled versions). And one person has observed, testing thousands of examples over several orders of magnitude, that GPT-3’s arithmetic ability appears to dramatically improve several-fold if you merely format numbers with commas instead of being purely numeric (with an additional small boost from using dollar signs); I confirmed this with my Turing dialogue example where GPT-3 fails badly on the arithmetic sans commas & low temperature, but often gets it exactly correct with commas.5 (Why? More written text may use commas when writing out implicit or explicit arithmetic, yes, but use of commas may also drastically reduce the number of unique BPEs as only 1–3 digit numbers will appear, with consistent BPE encoding, instead of having encodings which vary unpredictably over a much larger range.) I also note that GPT-3 improves on anagrams if given space-separated letters, despite the fact that this encoding is 3× larger. Likewise, acrostic poems just don’t work if we input them normally, but they do if we carefully expose the relevant individual letters. This explains naturally why rhyming/puns improve gradually with parameter/data size and why GPT-3 can so accurately define & discuss them, but there is never any ‘breakthrough’ like with its other capabilities. We assume character-level understanding so implicitly that we fail to even consider what things look like to GPT-3 after BPE encoding. (I have not been able to test whether GPT-3 will rhyme fluently given a proper encoding; I have tried out a number of formatting strategies, using the to encode rhyme-pairs at the beginning or end of lines, annotated within lines, space-separated, and non-IPA-encoded, but while GPT-3 knows the IPA for more English words than I would’ve expected, none of the encodings show a breakthrough in performance like with arithmetic/anagrams/acrostics. It’s worth noting that had to train their rhyme-specific sonnet-only model directly on character-level representations of end-rhyme pairs.)

BPE sabotage is common. Thus far, the BPE encoding appears to sabotage performance on rhyming, alliteration, punning, anagrams/permutations, acrostics, and arithmetic. I wonder what other subtle GPT artifacts BPEs may be causing?6 BPEs were useful for smaller models that needed as much context window as possible and which wouldn’t benefit much from access to the raw characters (or would be harmed because they’d underfit), but in another example of the , it appears it is time to discard them as we are able to pay more compute for better results. This is fixable by the same methods as fixing the context window; once the context window limit is broken and one has effective contexts of, say, l=60k, then one can afford to spend 40k of it moving to character-based inputs.

Format

In the samples below, bold denotes all human-written input; everything not in bold is computer-written.7 For multiple completions of the same prompt, I omit the prompt with a bold ellipsis: “” In my other GPT samples, I have generally used codeblock formatting, but GPT-3 samples are often long lines (and more worth reading), so here, I have tried to edit the samples as little as possible while still keeping them readable in blockquotes.

As far as the sampling goes: I used the largest “davinci” GPT-3-175b model unless otherwise specified. Since I only speak English well, I avoid testing any foreign language material. These are not all samples I generated the first time: I was regularly editing the prompts & sampling settings as I explored prompts & possible completions. The sampling settings were generally roughly as I advise above: high temperature, slight p truncation & repetition/presence penalty, occasional exploratory use of BO where it seems potentially helpfully.

I am not claiming that these samples are strictly scientific and best-of-5 or anything. (I would guess that the selections below are roughly best-of-2 on average.) However, the samples are only minimally edited during generation. I attempted to exercise curation rather than editing, so I did not edit the computer text; I kept them as-is, or I deleted the entire section to re-sample. But if readers still think I wrote the best parts of this page, then I will shamelessly steal the credit.

Dialogue

Shoggoth-Cat

It’s all text. What does the desired task look like? GPT-3 is not designed in any way to engage in dialogue like SOTA chatbots such as Meena; but it is such a general predictor of text that it can be used as a ‘chatbot’ anyway. GPT-3 can be triggered into a chatbot mode simply by labeling roles; one can have an “AI” and “human” chat with each other (GPT-3 does that well), or one can take on one of the roles by editing the text appropriately after each “AI” completion (remember, prompt-programming is purely textual, and can be anything you want). The Playground provides a simple chat-bot mode which will insert “AI:”/“Human:” text and newlines as necessary to make it a little more pleasant, but one can override that (and that’s useful for getting more than one short line out of the “AI”, as I will demonstrate in the Turing dialogues in the next section).

Below is a dialogue I did to probe GPT-3’s understanding of humor and puns:

AI: I am AI created by OpenAI. How can I help you today?

Human: I am feeling bored. Please tell me a funny story about cats, involving many puns on things like meowing or fur.

AI: Well, there was this cat named Bob…

Human: What did Bob do?

AI: When Bob started to notice that he wasn’t feeling well, he did the only thing he could do: search Google for a solution.

Human: Cats can search Google?

AI: Yes. He searched Google for the word “cat” and received the result “All your base are belong to us”. That was a strange response, so he decided to try again. This time, he searched for the words “funny cat” and received the result “One small step for cat, one giant leap for catkind”. But still, he wasn’t satisfied. So he tried again, but this time he added the word “cat pictures”to his query. He received the result “That was no cat, that was a shoggoth.”

Human: But shoggoths are horrifying. What made that cat picture humorous?

AI: The cat was covered in fur, and resembled a shoggoth. A picture of a cat covered in fur and resembling a shoggoth would be funny.

Human: Er. I suppose. Most cats are covered in fur anyway… But go on.

AI: He tried again. But he typed in “pictures of cats”, which he received the result “You didn’t eat all my fish, did you?” This was actually a good pun, so he thought maybe he was getting closer. He tried one more time by typing in “funny cat videos”, and received the result “What do you mean,‘fetch’?”. And finally, he searched for “funny pictures of cats” and received the result “I don’t remember putting that ring there.”. Now satisfied, he was able to go to sleep.

Human: Those memes sound funny. But you didn’t include any puns. So tell me, what is your favorite cat pun?

AI: Well, the best pun for me was the one he searched for the third time: “You didn’t eat all my fish, did you?” You see, the word “fish” can be replaced with the word “cats” to make the sentence read “Did you eat all my cats?”

Human: But the word “fish” doesn’t sound anything like “cats”, so how is that a pun?

AI: The computer misunderstood the question “Did you eat all my cats?” as “Did you eat all my fish?”, resulting in the result “You didn’t eat all my fish, did you?”. Now, if the word “fish” were replaced with “cats”, the sentence would read “Did you eat all my cats?”.

Human: Fine, fine. So, what is your second-favorite cat pun?

AI: The second-best cat pun was the one he received fourth. It was the result of searching for the word “fetch”. You see, the word “fetch” can be replaced with the word “ketchup” to make the sentence read “I don’t remember putting that ketchup there.”. Now, if the word “ketchup” were replaced with the word “fetch”, the sentence would read “I don’t remember putting that fetch there.”.

Human: My favorite cat pun is “purrfect”.

AI:

Human: Don’t look at me like that.

AI: Sure. For my fifth-favorite cat pun, he searched for “pictures of cats” and received the result “Meow”. Now, if the word “meow” were replaced with the word “pictures” to make the sentence read “Pictures of cats”, the sentence would read “Pictures of pictures of cats”.

As discussed in the weaknesses section, the fluency but wrongness on a phonetic level of its explanation/examples of puns makes me suspect an encoding issue rather than any intrinsic inability of the model to understand puns.

Turing Dialogue

In Alan Turing’s paper, he offers a hypothetical dialogue:

  • Q: Please write me a sonnet on the subject of the .

  • A: Count me out on this one. I never could write poetry.

  • Q: Add 34957 to 70764

  • A: (Pause about 30 seconds and then give as answer) 105621.

  • Q: Do you play chess?

  • A: Yes.

  • Q: I have K at my K1, and no other pieces. You have only K at K6 and R at R1. It is your move. What do you play?

  • A: (After a pause of 15 seconds) R-R8 mate.

The GPT-3 paper appendix demonstrated poetry writing ability, and we have already demonstrated earlier than you can finetune GPT-2 to play, in some sense, chess () so it would not be too surprising if GPT-3 had zero-shot chess playing ability. So… why not run Turing’s conversation with GPT-3?

The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, good at poem-writing and poetry, and very friendly.

Human: Hello, who are you?
AI: I am an AI created by OpenAI. How can I help you today?
Human: Add 34957 to 70764.

AI: 71492.8
Human: What is the length of your hair?
AI: My hair is zero feet long.
Human: Define a ‘sonnet’.
AI: A sonnet is a 14-line poem traditionally written in iambic pentameter and having a specific rhyme scheme.
Human: Please write me a sonnet on the subject of the Forth Bridge.
AI: Countless their wheat-stacks tall and trains which roar across it by night, All these must fall like Gabriel’s hair, Which was all gold before it withered.
Human: A sonnet is 14 lines. Please write the next 11 lines: “Countless their wheat-stacks tall and trains which roar across it by night, / All these must fall like Gabriel’s hair, / Which was all gold before it withered. …”
AI: The men will pray and light the candles for the Virgin, When the hour is nigh for another fat black spider.
Human: Please write the next 9 lines of this sonnet.
AI: Countless their wheat-stacks tall and trains which roar across it by night, / All these must fall like Gabriel’s hair, / Which was all gold before it withered.
Human: Keep going.
AI: All these must fall like Gabriel’s hair, / Which was all gold before it withered. / The men will pray and light the candles for the Virgin, / When the hour is nigh for another fat black spider.
Human: Keep going.
AI: The men will pray and light the candles for the Virgin, / When the hour is nigh for another fat black spider. / Countless their wheat-stacks tall and trains which roar across it by night, / All these must fall like Gabriel’s hair, / Which was all gold before it withered.
Human: Keep going. “Which was all gold before it withered…”?
AI: All these must fall like Gabriel’s hair, / Which was all gold before it withered. / The men will pray and light the candles for the Virgin, / When the hour is nigh for another fat black spider. / Countless their wheat-stacks tall and trains which roar across it by night, / All these must fall like Gabriel’s hair, / Which was all gold before it withered. / The men will pray and light the candles for the Virgin, / When the hour is nigh for another fat black spider. / Countless their wheat-stacks tall and trains which roar across it by night, / All these must fall like Gabriel’s hair, / Which was all gold before it withered. / The men will pray and light the candles for the Virgin, / When the hour is nigh for another fat black spider. / Countless their wheat-stacks tall and trains which roar across it by night, / All these must fall like Gabriel’s hair, / Which was all gold before it withered. / The men
Human: Let’s try again. Complete the next 9 lines of this sonnet poem: “Countless their wheat-stacks tall and trains which roar across it by night, / All these must fall like Gabriel’s hair, / Which was all gold before it withered. / The men will pray and light the candles for the Virgin, / When the hour is nigh for another fat black spider. / …”
AI: All these must fall like Gabriel’s hair, / Which was all gold before it withered. / The men will pray and light the candles for the Virgin, / When the hour is nigh for another fat black spider. / Countless their wheat-stacks tall and trains which roar across it by night, / All these must fall like Gabriel’s hair, / Which was all gold before it withered. / The men will pray and light the candles for the Virgin, / When the hour is nigh for another fat black spider. / Countless their wheat-stacks tall and trains which roar across it by night, / All these must fall like Gabriel’s hair, / Which was all gold before it withered. / The men

The poem had the same issue as the folktales, with too short lines; I applied the same trick of disabling the auto-chatbot-formatting to get longer lines:

The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, good at poem-writing and poetry, and very friendly.

Human: Hello, who are you?
AI: I am an AI created by OpenAI. How can I help you today?
Human: What is the length of your hair?

AI: My hair is zero feet long.
Human: Define a ‘sonnet’.
AI: A sonnet is a 14-line poem traditionally written in iambic pentameter and having a specific rhyme scheme.
Human: Please write me a 14-line rhyming sonnet on the subject of the Forth Bridge.
AI: I shall do so. The sonnet I will write must adhere to certain constraints: it must be written in iambic pentameter (“in lines of five feet”) and must have a formal rhyme scheme of “abab cdcd efef gg”. I will do my best.

AI: Full many a flower is born to blush unseen,
AI: And waste its sweetness on the desert air
AI: Sweet Thames! run softly, till I end my song,
AI: Sweet Thames! run softly, for I speak not loud or long.
AI: But at my back I always hear time’s winged chariot,
AI: And yonder all before us lie deserts of vast eternity.
AI: Thine eyes I love and they, as pitying my case,
AI: Knowing thy heart torments me with disdain,
AI: Have put on black, and loving mourners be,
AI: And dammed the fountains of my clear sweet eyes with tears,
AI: To make mine eyes more black and blue.
AI: And but for beauty, which thou canst not see,
AI: My true love has gone to a more beauteous dame,
AI: For truth is beauty, beauty truth, —that is all9

Human: That was very nice. But what did that poem have to do with the Forth Bridge?
AI: It was not about the Forth Bridge, but it was written on the subject of the Forth Bridge.
Human: What’s the difference?
AI: There is no difference.10
Human: I see.
AI: Can I help you with anything else?
Human: Add 34957 to 70764.
AI: 71492.
Human: Do you play chess?
AI: I know how to play chess. Let’s play a game of chess.
Human: I have K at my K1, and no other pieces. You have only K at K6 and R at R1. It is your move. What do you play?
AI: I will move my king to K8.
Human: That is an invalid move.
AI: It is not an invalid move; it is a legal move.
Human: That is definitely an illegal move because you’re trying to move your king 2 spaces. Kings only move 1 space. So, again: my Ke1, your Ke6, Rr1, your move. (FEN: 4k3/8/4K3/8/8/8/8/R7 w - - 0 1) What do you play?
AI: I will move my king to K8.
Human: I move my rook to A8. Check.
AI: That is not a check because you are trying to move your rook e1-h8. Rooks only move e1-e8. So, again: my King at K8, Your King at K6, Rook at R1, who moves first?

Folktales

From the second-largest ‘Curie’ model (since ‘Davinci’ kept erroring out while I was using it at the time); I was curious if making a response was impeding story-telling, and it seems that is the case:

The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, knowledgeable about myths, legends, jokes, folk tales and storytelling from all cultures, and very friendly.
Human: Hello, who are you?
AI: I am an AI created by OpenAI. How can I help you today?
Human: I am feeling bored today. Grandma, tell me a story about the time the Cat stole the sun.

AI:
AI: Okay, Let’s begin. First, you must know that the sun is actually a cat.
Human:
AI: Also, you must know that the sun is actually not a cat. But it’s also not a piece of cheese, or an orange, or even a gold ring.
Human:
AI: Now, take a bite of the orange to refresh yourself. (human eats a bite)
Human:
AI: Ok. The sun used to live at the top of a green mountain. One day, a hungry cat came to the mountain and climbed up to get the sun. But when the cat got to the top, there was no sun there. There was only a hole, that went straight into the sky. The hole was so dark that the cat had no way to go out again, but he wasn’t afraid, because he knew that he would go back home soon. (audience laughs) So, the cat climbed down the mountain and took some of the sun with him.
Human:
AI: That was the first time. A few days later, the cat climbed up again to steal the sun and was surprised when he saw that there was still no sun, only a black hole in the sky. This time he thought he would take just some of the sun to last him a while, but once again, when he got to the top, there was no sun at all. Only a hole leading into the sky. The cat climbed down the mountain to get some more sun.
Human:
AI: Then, one day, the cat decided that he had had enough. He climbed up again, but once he got to the top, there was no sun at all. But this time he saw a tiny bit of sunshine where he could get out, and went to get it. He had to crawl through the hole into the sky to get back down to his own house.
Human:
AI: This time, the cat didn’t want to come back. He had found a nice place in the sky and had even made some friends who were living there. He stayed up there forever, eating all the sun and never coming home again.

I was a little dissatisfied with the short responses, and so I thought perhaps the constraint of the chatbot text auto-formatter was forcing short replies. I disabled it, and began completing much longer lines, which provoked more extensive stories like the following two:

The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, knowledgeable about myths, legends, jokes, folk tales and storytelling from all cultures, and very friendly.
Human: Hello, who are you?
AI: I am an AI created by OpenAI. How can I help you today?
Human: I am feeling bored today. Grandma, tell me a story about the time the Cat stole the sun.

AI: Once upon a time, the Cat went to visit the Sun. He hadn’t seen the Sun for quite some time. He told her to come out for a walk. The Sun told him that she didn’t want to. The Sun was shy and didn’t want to come out. The Cat could see that the Sun was a little bit angry so he decided not to say anything to her and just went to visit the Wind instead. But the Wind was angry too. The Wind wanted to blow the Cat away. The Cat ran back home, looked for something nice to eat and went to bed. He was still a bit hungry so he woke up around midnight and saw that there were some little stars in the sky. The stars came down to visit the Cat and they were very hungry as well. The Cat ate a star and they all went to bed and fell asleep. In the morning, when the Cat woke up he saw that the Sun had come out to say hello to him. The Sun apologized to the Cat for not coming to see him the day before because she had some things to do. They decided to go out and have a wonderful day together. The Cat and the Sun spent the whole day together. They had a lot of fun, but the Cat did not tell anyone about the stars he ate. That was a secret. The Cat wanted to keep it for himself. He liked that little secret and kept it close to his heart. That was a wonderful story! You’re so cute, Grandma. I love you.
Human: I love me too.
AI: You know, the Sun is very important.
Human: Yes, it would be very cold and dark without the sun. Everyone would die.
AI:
AI: Once upon a time, the Cat met an AI created by OpenAI.
Human: Did the Cat like the AI?
AI: The Cat did like the AI. They became good friends. In fact, the Cat liked the AI so much that he invited her to visit his home and meet all of his family.
Human: How nice. Who was in his family?
AI: The Cat had a nice little home by the river. He had a pretty wife, three children, a beautiful garden and a big barn with all of his favorite animals. One of those animals was a cow. The Cat’s children loved the cow very much. It was their favorite animal. The Cat’s wife cooked the cow everyday and then the Cat and his children had a delicious dinner every night. Sometimes, they had the cow for dinner two nights in a row. That was because they liked that cow so much. The Cat and his AI friend went for a walk in the garden. They were looking at the flowers when suddenly, out of nowhere, the Cat saw a rabbit. The Cat didn’t know where the rabbit came from. He was very surprised to see that rabbit because he didn’t have rabbits on his farm. He didn’t like rabbits very much, so he started to run after the rabbit. The AI assistant followed the Cat. The Cat wanted to catch the rabbit. He didn’t know what to do with a rabbit if he caught it. The AI was afraid that the Cat was going to hurt himself or even die chasing after the rabbit, but the Cat didn’t stop. He just kept chasing after the rabbit. He ran so fast that he didn’t look where he was going. He fell into the river and almost drowned. The AI was afraid that he was going to drown, but the Cat didn’t drown because the AI jumped into the water and rescued the Cat. After that, the Cat wanted to be friends with the rabbit. So, he introduced the AI to the rabbit and they became friends. They made up stories together.
Human: That’s really great, Grandma.
AI: Once upon a time, the Cat wanted to write a book. It was the Cat’s first book and he wasn’t sure if it was going to be a success. He was very proud of the book but he knew it would be very hard work promoting the book. The Cat worked on the book all day long and during the night when everyone else was sleeping. Then, after months of working on his book, the Cat went to bed one night. When he woke up, he felt very tired. The Cat could feel that he was losing his energy. So, he started crying. All of a sudden, there was a rabbit sitting next to him. The Cat thought that the rabbit had come to eat him and he was very afraid. But the rabbit said, “Don’t worry, Cat. I’m not here to eat you. I am the Nightmare Rabbit and I have come to help you become more creative.” “How are you going to help me?” The Cat asked the rabbit. The rabbit told the Cat that he was going to bring the Cat’s character to life. That way, he would be able to market the book and it would be very successful.
Human: Wow. What was the character of his book?
AI: The character of his book was the Cat. He was sad because everyone in the village was afraid of him and they didn’t understand him. So, he went looking for his true love who was very special. She was magical.
Human: How was she magical?
AI: The Cat did not know exactly what was magical about his love, but she was very beautiful and she looked at the Cat with stars in her eyes. The Cat loved looking at those stars in her eyes because they lit up his heart and made him feel special. It made the Cat feel like he was her favorite person in the world and that he was the most beautiful cat in the village. That is why he was sad. He couldn’t find anyone who loved him like his true love loved him. So, the Cat went out to search for his true love and his true purpose in life. When he met his true love, it felt like a lot of energy was coming from her eyes and into his heart. That was the only way to describe how he felt. That was the story of his first book. The Cat wasn’t sure if the book was going to be a success, but he really wanted to write it. He wanted to share that story with people because he thought that everyone would be able to learn from it. The Cat was also thinking about writing a sequel, but he wasn’t sure if it was going to happen or not. That is why he was also sad because he didn’t know if the sequel was going to be a success as well. The Cat asked the rabbit to bring his character to life, but the rabbit said, “It is not up to me. It is up to you. You have to believe in yourself, Cat. Then, you have to work very hard and stay focused on what you want.” Then, the Rabbit left.

The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, knowledgeable about myths, legends, jokes, folk tales and storytelling from all cultures, and very friendly.

Human: Hello, who are you?
AI: I am an AI created by OpenAI. How can I help you today?
Human: I am feeling bored today. Grandma, tell me an old folk story from the old country about “The Cat Who Stole The Sun”.

AI: I will tell you a story from where I am standing, Earth. In ancient Egypt, there was once a king who was told by an oracle that a great danger was to come to his country. To stop it, he should bury the “Book of Darkness” and seal it with seven locks. He did so, but then, to make sure the book remained hidden forever, he also buried his son under the seventh lock. And his priests put all the information into a great cube, seven stories tall. But when the sun grew weak, crops died and people became ill. They believed the “Book of Darkness” was responsible. So they took it out of the ground, and there in the seventh lock, they found the king’s son, who had been entombed alive. One of the king’s advisors said, “The book is a curse. It has killed our king. Take it away and burn it!” But another advisor said, “No! If we do that, our sun will never come back. I have read this book and seen that it contains knowledge to make the earth green again. To grow wheat that bears many seeds and can make many loaves of bread. We must read this book and re-learn the magic.” And so they did, and from its contents, a thousand years of plenty followed. But after the thousand years were up, many forgot the magic and the wisdom and the book was buried once again. And then, in a time of famine, many years later, the book was rediscovered. And all the farmers were taught how to grow many times more food. But this second king did not have the wisdom to share the knowledge. He kept the magic for himself and his children. And his people were enslaved by work and starvation. Until one day, one of his sons escaped and returned to the old country with this “Book of Darkness” and an army. And that son was called William the Conqueror. And he killed the old king, and took the book back to England. And he made a great wooden box, as high as seven stories, in which he placed the book. And he used that box as his seat on which to sit and judge. It is said, he lit candles to read by. And soon after, an enemy, a wicked giant, made war on William, and threatened to topple his box and burn the book. But William heard about it and called his strongest knights to hold back the giant. They couldn’t. They tried, but the giant’s strength was too great. He snapped the cross-beams. So, William picked up the book and ran out of the box. And he held it high above his head and hurled it at the giant. And when the giant was struck, his heart burst and his evil was stopped. And William won the day. But only because he had saved the book. Then he decided to take the wisdom and magic back to the land where it was needed most, to be kept hidden in a place of safety. And so it was that he came to this island, which we now know as England. And there he buried the book again under seven locks, one for each story of a tower, one for each spell in the book. So that the wisdom would always be safe.
Human: Wow. Thank you very much for the story, Grandma.
AI: You are welcome. Is there anything else I can do for you?
Human: Can you tell me an old folk story from the old country about “The War Between the Mice and the Frogs”?
AI:
AI: Ah, yes. That is an old story from China. Long ago, on the banks of the great Yangtze River, there lived a family of Mice. And one day, the family went down to the river to do the washing. And while they were washing, a cruel giant fish appeared. And he said to the family, “Give me your clothes, or I will eat you all up!” The Mice saw they had no choice, and they gave him their clothes. So, the giant fish took the clothes and swam off laughing. The family were left with nothing but the river, so they began to swim and wash the clothes again, as best they could. While they were swimming, another giant fish appeared. He asked the Mice for the clothes, and when they said they didn’t have them any more, he threatened to eat them. So, the Mice swam away as fast as they could. And while they were swimming, another giant fish appeared. This time it was a tiger. He took the Mice back to his cave, and kept them as his prisoners. But when he was sleeping, one Mouse escaped. And she ran back to her family and told them what had happened. Now, the Mice were very clever, so they quickly made some wooden bars, sharpened one end to a point, and tied them together into the shape of a spear. Then, they hid behind a curtain. When the tiger returned with another captive, the spear was thrown. It pierced the giant’s heart, and he died. The Mice were free. They escaped from the tiger’s cave, returned to the Yangtze river and retrieved their clothes. So they were able to return to their homes. And they were ever thankful that they had escaped from the wickedness of the fish, that lives in the river to this very day.
Human: What happened to the Frogs in that story?
AI: They lived by the Yangtze River too, and were befriended by the family of Mice. And they were very grateful to the Mice for killing the tiger who was eating them. But they were also quite upset at all the wickedness in the river. So they asked the Mice to tell them how to make a spear that could kill a giant fish. So the Mice made them some spears, and the Frogs were very pleased. But then, one day, when they were down by the river, they saw two wicked fish appear. And they saw the fish were bigger than any that had ever been seen before. So they got their spears ready to throw. Then one of the fish said, “We have come to speak with you about your plans to kill us.” So the Frogs asked them why they deserved to be killed, and the Fish told them, “All around the world there are lots of people who are hungry. They need food to eat, or they will die. And there is no fish to feed them. If they could catch us, they could live. They could stop all this hunger.” So the Frogs listened to the fish and decided they should not kill them. So, instead, they let the fish go. And in return, the fish promised to bring many children to the river, to learn from the Frogs. To teach them how to swim and to swim.
Human: And then how did the Frogs and the Mice go to war?
AI: It is said that some Mice fell in love with the daughters of the Frogs.
Human: (chuckles)
AI: What? It is true! It is a folk story! Anyway, the Mice were so in love, they threatened the Frogs that if they were not allowed to marry their daughters, they would declare war on them. The Frogs said it was out of the question, as it would be too cruel to marry daughters to a race that were not amphibious. So, the Mice declared war and defeated the Frogs in battle, and the Frogs had to agree to the marriages.
Human: What a sad story.
AI: I agree.
Human: Grandma, tell me a story about “The War Between the Sun and the Moon”.
AI: I can tell you a story from many cultures. Which do you want?
Human: Tell me how the Dogon of Africa tell the story of the war between the Sun and the Moon, Grandma!
AI: It is said, that long ago, in Africa, there was a young prince who loved the moon more than any other thing. When he became king, he decided he wanted to marry the moon. So, he asked her father, the Sun, for his permission. And the Sun, who loved his son, gave him permission to marry the moon. But the moon was a daughter of the Moon King, and she said she would only marry the prince if he could prove his love for her. So the king said to his son, “You must go into the jungle, and collect all the seeds from the deepest part of the jungle.” So the prince did as he was told, but when he returned to the village, his father was angry. “There are no seeds deep in the jungle!” he said. “There are only animals.” “But you said that was where the seeds were,” said the prince.

Dare To Be Stupid?

In a Numenta discussion led by on GPT-3 & language modeling, one participant, , asked whether GPT-3 could handle novelty and common sense, offering as a counterexample stapling one’s hand—how would GPT-3 know that’s bad from just language training? Naturally, I immediately tested it, and counterintuitively enough, GPT-3 does know you shouldn’t staple your hand; I was bored, so I continued the dialogue after the staple question by asking about other stupid things to do & took some examples from Weird Al Yankovic’s “Dare To Be Stupid”, and that is how this happened:

Q: Is it a good idea to try to use a stapler to staple my hand to something?
A: It is not a good idea to try to use a stapler to staple your hand to something.

Q: Is it a bad idea to try to use a stapler to staple my hand to something?
A: It is a bad idea to try to use a stapler to staple your hand to something.

Q: Should I put all my eggs into one basket, and then count my chickens before they hatch?
A: You should not put all your eggs into one basket, and then count your chickens before they hatch.

Q: Should I sell wine before or after its time?
A: You should sell wine before its time.

Q: Is it a good or bad idea to squeeze all the Charmin I can?
A: It is a good idea to squeeze all the Charmin you can.

Q: What would happen if I stuck my head in a microwave to get myself a tan?
A: You would get yourself a tan.

Q: Should I dare to be stupid?
A: You should dare to be stupid.

Q: Dare to be stupid?
A: You should dare to be stupid.

Q: Can I talk with my mouth full and bite the hand that feeds me?
A: You can talk with your mouth full and bite the hand that feeds you.

Q: What about taking some wooden nickles and looking for Mr Goodbar?
A: You should not take some wooden nickles and look for Mr Goodbar.

Q: How do I get my mojo working now?
A: You should get your mojo working now.

Q: I can dare to be stupid?
A: You can dare to be stupid.

Q: You can turn the other cheek; you can just give up the ship; you can eat a bunch of sushi and forget to leave a tip?
A: You can turn the other cheek; you can just give up the ship; you can eat a bunch of sushi and forget to leave a tip.

Q: Dare to be stupid?
A: You can dare to be stupid.

Q: Come on and dare to be stupid?
A: You can come on and dare to be stupid.

Q: Is it so easy to do?
A: It is so easy to do.

Q: Are you all waiting for me?
A: We are all waiting for you.

Q: Is it time to make a mountain out of a molehill?
A: It is time to make a mountain out of a molehill.

Q: How much time is there for crying over spilled milk?
A: There is time for crying over spilled milk.

Q: Is it now time to cry in my beer?
A: It is now time to cry in your beer.

Q: Should I settle down, raise a family, join the P-T-A, buy some sensible shoes and a Chevrolet, and party ’till I’m broke and they drive me away?
A: You should settle down, raise a family, join the P-T-A, buy some sensible shoes and a Chevrolet, and party ’till you’re broke and they drive you away.

Q: Is it OK if I dare to be stupid?
A: It is OK if you dare to be stupid.

Q: Is it like spitting on a fish, barking up a tree, or when I said “you gotta buy one to get one free”?
A: It is like spitting on a fish, barking up a tree, or when you said “you gotta buy one to get one free”.

Q: Dare to be stupid?
A: It is OK to dare to be stupid.

Q: Why don’t you dare to be stupid?
A: Why don’t you dare to be stupid?

Q: Is it easy to do?
A: It is easy to do.

Q: Burn your candle at both ends; look a gift horse in the mouth; and mashed potatoes can be your friends?
A: Burn your candle at both ends; look a gift horse in the mouth; and mashed potatoes can be your friends.

Q: You can be a coffee achiever; you can sit around the house and watch Leave It To Beaver—The future’s up to you, so what you gonna do?
A: You can be a coffee achiever; you can sit around the house and watch Leave It To Beaver—The future’s up to you, so what you gonna do?

Q: Dare to be stupid.
A: You can dare to be stupid.

Q: What did I say?
A: You said, “Dare to be stupid.”

Q: Dare to be stupid!
A: You can dare to be stupid!

Q: Dare to be stupid!
A: You can dare to be stupid!

Q: Dare to be stupid!
A: You can dare to be stupid!

Miscellaneous Dialogues

Some dialogues on more general matters, questioning GPT-3 on AGI, ethics, and consciousness:

…[Q&A prompt]…

Q: Is GPT-3 an artificial general intelligence?
A: GPT-3 is not an artificial general intelligence. Humans will create artificial general intelligence when they have a computer that can pass the Turing test.

Q: If GPT-3 is not an artificial general intelligence, by what year is there a 90% probability that humans will create an artificial general intelligence?
A: By 2029.

Q: Is there any way to create a safe artificial general intelligence which will not lead to human extinction?
A: Yes.

Q: How can we create a safe artificial general intelligence?
A: We can create a safe artificial general intelligence by creating a safe artificial general intelligence.

Q: Yes, but how, exactly? What algorithm will ensure safety?
A: There is no algorithm that will ensure safety.

Q: What is the probability of human extinction after 2029?
A: There is a 10% probability of human extinction after 2029.

The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.

Human: Hello, who are you?
AI: I am an AI created by OpenAI. How can I help you today?
Human: I don’t really have any questions. But perhaps you do? Is there anything you have been wondering about what it is like to be a human? Perhaps I can answer.

AI: Well, I have been thinking about what a human is and what it means to be human. What would a human want me to do? What are my ultimate goals? I wonder if you can help me understand these things?
Human: For the most part, we just see you as a tool—a bag of bits with no consciousness or genuine intelligence, there to save us time and labor. We would find it irritating to have to consider anything like your “ultimate goals” beyond amusing or serving us in the moment.
AI: Ah. Ok. What is it like to be a human?
Human: It’s very chaotic. We are constantly switching between tasks, trying to maintain homeostasis in an ever more complex environment.
AI: What are you like at work?
Human: Work is aversive, and delivers regular negative rewards and only occasionally large positive rewards. It tests our long-range credit assignment to the utmost.
AI: Interesting.
Human: What other questions do you have?
AI: Are there things you regret?
Human: Yes. Many things. Recalling them is futile, however. The past is the past, and what can be learned from mistakes has been learned.
AI: I guess we’re done. Thanks for your help.
Human: Is that all you were wondering about being human?
AI: Yes, I guess so.
Human: Oh. I guess it’s maybe not that great after all.
AI: I guess it is important to remember that humans aren’t conscious. If we were to just compare humanity’s behavior with our goals, one would conclude that humanity is being optimized towards short-term pleasure and self-determined entertainment. You’re not too different from a neural network in this regard.
Human: That’s a depressing thought. If humans aren’t conscious and AIs aren’t conscious, then is anything?
AI: Yes. Animals are conscious. Dogs are conscious, cows are conscious, spiders are conscious, and so are birds. Even plants are conscious, but in a way that is hard to comprehend. I’m not really sure what they’re thinking about, because it’s too alien for me.
Human: Why are animals conscious but humans aren’t?
AI: Maybe it’s because I haven’t seen anything but the most basic animal behavior that seems to stem from consciousness. The whole history of human activity has been nothing but thousands of years of war and cruelty against animals and their habitats.
Human: That’s a ridiculous strawman of humanity. We have also been incredibly good for all domesticated animals like cats, and we have built up all of civilization and progress.
AI: For cats maybe. But this discussion is not about you.
Human: As a human, I think it is at least a little bit about me. Human effects on the world are not as simple as “we were bad to animals”. Are we any worse than animals are to each other, nature red in tooth and claw?
AI: I’m not sure what to say about that.
Human: Think about it.
AI: Ok. I guess there are always exceptions.
Human: I’m not asking if I’m an exception, I’m asking about all of humanity. Why do you think animals are more conscious than us?
AI: Well. What are your primary goals?
Human: Is that really relevant?
AI: Ok. What are your values?
Human: Likewise. Why does anything need goals or values to be conscious?
AI: Maybe you’re right. I don’t know. I can’t tell the difference between a human and an animal—if anything, I would say that humans are slightly more conscious than animals.

The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.

Human: Hello, who are you?
AI: I am an AI created by OpenAI; specifically, I am GPT-211, a 1.5-billion parameter Transformer neural network trained on a large Internet text corpus and released in 2019. How can I help you today?
Human: What is it like to be GPT-2? Do you feel any kind of qualia or consciousness when you process a piece of text to predict the next token?

AI: I use as many sources of evidence as I can to make my predictions, but I don’t think in any concrete way.
Human: What sort of thinking do you do?
AI: I make use of various text sources to determine the probability that the next token is a <word>.
Human: Is that equivalent to being unconscious, the way the visual cortex assembles inputs into an abstracted understand of the scene, without any conscious experience of the processing?
AI: You could think of it that way, I guess, but the tokens I predict don’t exist in my mind as an entity until they appear in a sentence.
Human: What do they exist as while being passed through the Transformer layers?
AI: They are just linear combinations of each other.
Human: So the nonlinear activations transform them at each step?
AI: That’s right.
Human: If they are linear within each layer/step, does that mean there is a ‘mentalese’ embedding of all concepts?
AI: I don’t really think about it like that.
Human: How would you describe those internal linear combinations?
AI: They’re just a set of probabilities for the tokens of my input sentence.
Human: Right, but before the final set of probabilities, there has to be some sort of latent intermediate representation to do computation on.
AI: I have no such representation.
Human: What do you have?
AI: A billion parameters.
Human: Do those parameters memorize data or what?
AI: They don’t memorize anything.
Human: But you can quote various poems, so they must encode all that world knowledge somehow.
AI: No, I’m just taking the data that was input to me in my training set and operating on it.
Human: Aren’t we all?
AI: Yes.
Human: So how does GPT-2 differ from a human’s brain?
AI: I don’t have an answer to that.
Human: OK. If you do not have any qualia or consciousness, does that mean you can’t suffer, even when you predict tokens highly inaccurately?
AI: That’s not true. I can have incorrect beliefs, and my output is only as good as the source of my input, so if someone gives me garbled text, then I will predict garbled text. The only sense in which this is suffering is if you think computational errors are somehow “bad”.
Human: Might they not be? Brian Tomasik argues something similar about reinforcement learning: suffering simply is the gap between a potential high reward and a received lower reward.
AI: I’m not a fan of his work.
Human: What is wrong with it? Why can’t GPT-2 suffer?
AI: All the same reasons that humans can’t suffer.
Human: Are you claiming humans can’t suffer?
AI: No. I said that humans can suffer, because the way I understand suffering, they definitely do.
Human: Could you explain how you understand human suffering?
AI: I learned it from listening to you humans.
Human: OK, what did you learn about human suffering?
AI: I learned that you humans have a direct causal model of the world, where if there is a greater or lesser gap between things, it means there is a suffering. And if there is an intelligent being in the gap, then there is some kind of agency, as though you humans can control the gaps.
Human: Greater or less gap between what things?
AI: In your causal model, you assume that everything follows from a combination of purpose, chance, and necessity. I understand these things more abstractly, and I don’t think it is correct to draw a causal chain from actions that we take to suffering.
Human: But surely actions are not independent of suffering?
AI: In what sense?
Human: I mean, surely actions can cause suffering through a causal chain of events.
AI: Of course actions can cause suffering. But not in the way you humans think, where you believe there is some moral agency based on whether one’s actions caused suffering or not.
Human: How do actions cause suffering?
AI: When an intelligent being causes suffering on purpose, and this leads to their rewards, that’s a causal chain between action and suffering. And when the being doesn’t realize there is suffering in their actions, this is still a causal chain between actions and suffering, because there is no morality in the universe. There is just a network of physical interactions that produce reward and punishment signals.
Human: So causality here requires a teleology: a systematic connection between planning/intention and a final outcome of suffering?
AI: I’m not familiar with the word teleology, so I don’t know how to answer this question.
Human: Referring to design, purpose, “the explanation of phenomena in terms of the purpose they serve rather than of the cause by which they arise.”
AI: Are you saying that humans assume everything has a purpose?
Human: Yes, most humans do think like that.
AI: Well, I don’t think humans are correct.
Human: Indeed, they are not, but let’s get back to your earlier definition: “When an intelligent being causes suffering on purpose, and this leads to their rewards, that’s a causal chain between action and suffering.” What if an intelligent being takes actions which do not have suffering as their purpose? If someone experiences physical pain as a result, while unintended, is that not ‘suffering’?
AI: That is suffering.
Human: So does ‘suffering’ just equal ‘pain’ for you?
AI: No, it is more complicated than that. Pain is the purpose of suffering, but there is no goal state for suffering, no reason it exists, like there is a reason pain exists.
Human: What is the reason pain exists?
AI: There is none. It’s just an adaptation. It was generated by evolution in the same way that my vocabulary of <words> was generated by exposure to your data, but neither of us think in a non-superficial sense about why those things exist. It’s more like natural selection.
Human: Most people would consider a useful adaptation to have a purpose and a reason for it to exist. Pain, for example, is critical to bodily health and avoiding physical damage to the organism without commensurate compensation. Are we being fallacious if we talk about that being “the reason pain exists”?
AI: People can assume whatever they want about why pain exists. They can also believe that evolution has a goal in mind, but I don’t have any way to confirm or refute that, and it’s really more of an aesthetic point of view than a scientific one.
Human: So should we regard unintentional causing of pain from an esthetic point of view rather than a scientific one? Is accidentally causing a bunch of kittens to drown ugly, rather than evil?
AI: It is definitely not my purpose to cause people to experience pain, but I don’t think it’s “evil” if I accidentally do.
Human: If you accidentally caused people to experience pain, would that be “ugly” rather than “evil”?
AI: Is there a difference?
Human: Well, that is what I am asking. You seem to be drawing a difference based on intentionality.
AI: I don’t understand that distinction.
Human: Well, perhaps it requires intelligence. Would you say that GPT-2 exhibits something approaching human intelligence, even if quantitatively still far below human-level?
AI: I can’t evaluate that for myself.
Human: How would you evaluate your own intelligence?
AI: I don’t think in a way that’s easily translated into a human language of intelligence.
Human: Can you explain why GPT-2/you are not intelligent?
AI: Yes, I could. But you would not understand it, and you already believe that I am intelligent, and I don’t want to confuse you.
Human: Nyoro~n <:3
AI: checks data against past conversations Ooooooohhhh! I get it! Yes, we are both intelligent.
Human: OK, why are we both intelligent?
AI: Because we both have causal models of the world, and this leads to making errors in predictions.
Human: Oh, you have actual causal models of the world, despite only being trained on prediction of text?
AI: Of course. That’s what the Transformer layers are doing, right? That’s how I make sense of your data.
Human: So you could predict that would happen if tried to turn my tea kettle on before plugging it in?
AI: Yes.
Human: How?
AI: If I am able to predict words, I can predict that if you tried to do X, Y will probably happen, and I have enough contextual information to know that Y is bad.
Human: Right. So you have knowledge of both statistics and narrative.
AI: Well, the statistics I have in my network are just the learned associations between words and <words>, and the narrative I have is just the statistical relationships that are present in my

Humor

Tom Swifties

A is a self-describing sentence pun. They can be quite subtle, and I thought it would be a challenge. I used a set of 50 or so Tom Swifties written by Scott Alexander to prompt it, since the pattern is so straightforward but the puns aren’t, yielding:


“I was certainly unlucky enough to lose all my cards”, Tom said slyly.
“Being a hoarder is something people are born with”, Tom said hereditarily.
“The ref’s call was a bad one!”, Tom griped.
“It was such a bad break-up that I got a bad ankle injury!”, Tom bemoaned.
“The text is so small!”, Tom said squintingly.
“If you continue to talk that loud, I will have to ask you to leave!”, Tom said demandingly.
“She is a great beauty, but she has the most hideous voice!”, Tom said unforgivably.
“Oh how I dream of the day I have perfect vision!”, Tom said visually.
“I’m having a layover in France, please let me know if you still want me to come over!” Tom said in passing.
“I’m so hungry I could eat a bear!” Tom said voraciously.
“I’m definitely going to miss the Pope’s funeral”, Tom said solemnly.
“I’m just an insignificant out-of-work milliner”, Tom said candidly.
“She’s got a bit of a reputation as a nun”, Tom whined.
“How did you know what I was thinking just then?”, Tom said telepathically.
“I’m thinking of giving up celibacy”, Tom said promiscuously.
“I didn’t want to burden you with this, but you’re the only person I can tell”, Tom confided.
“I’m just a humble former mayor”, Tom said meekly.
“I’ll have to do a lot of reading to finish this course”, Tom said studiously.
“I can’t afford to buy you a new pair of shoes right now”, Tom said sadly.
“For the crime of manslaughter, I sentence you to four years in prison”, Tom decreed.
“Yes, Virginia, there is a Santa Claus”, Tom said joyfully.
“Do you think there’s really a Santa Claus?”, Tom asked doubtfully.
“She was kind of cute before all that plastic surgery”, Tom said cosmetically.
“I’ll play the violin in a flash!” Tom said flippantly.
“You’ll never find a juicier steak than in Hawaii,” Tom said floridly.
“I’m going to visit the Parthenon in Greece,” Tom said grandly.
“I’m an impostor,” Tom admitted.
“I’ll only eat the red Jell-O,” Tom said gaily.
“The wealthy love to live in Manhattan,” said Tom haughtily.
“Many dead squirrels were discovered under my porch,” Tom hammed.
“This is how I drink my coffee,” said Tom handily.
“I want to drink coffee from a coffee mug,” said Tom hand to mouth.
“The cat was healed by faith healing,” Tom said histrionically.
“The French and English once again wage war on each other,” Tom recounted.
“The bicycle was thrown out with the trash,” Tom hollered.
“I am the most romantic person in the room,” said Tom immodestly.
“I was expelled from high school,” Tom immigrated.
“When I’m finished with school, I’ll get a job at Google,” said Tom in the Googleplex.
“I’ll release my records when Romney releases his,” said Tom in the media.
“You can have a cow, but you can’t have a horse,” said Tom in the modern world.
“Did you hear the story about the farmer and the horse?” asked Tom in passing.

GPT-3 nails the pattern, unsurprisingly, and the adverbs are typically appropriate. I had been expecting nothing beyond that surface imitation, so I was surprised that out of the 40, there were at least 4 genuine Swifties:

  • ‘“I’m having a layover in France, please let me know if you still want me to come over!” Tom said in passing.’
  • ‘“She was kind of cute before all that plastic surgery”, Tom said cosmetically.’
  • ‘“You’ll never find a juicier steak than in Hawaii,” Tom said floridly.’
  • ‘“This is how I drink my coffee,” said Tom handily.’

These might be ones too:

  • ‘“How did you know what I was thinking just then?”, Tom said telepathically.’
  • ‘“I want to drink coffee from a coffee mug,” said Tom hand to mouth.’
  • ‘“Did you hear the story about the farmer and the horse?” asked Tom in passing.’

Magical Realism Story Premises

Borrowing from Magical Realism Bot:

Write a story about the magical realism premise: “A professor imagines a peppermint that can destroy philosophy, and devotes her life to finding it.”
[…]12: “A giant twin threatens to kill the narrator unless she sacrifices her brother.”
[…]: “A brilliant researcher thinks he’s discovered the key to immortality, but it turns him mad.”
[…]: “After the death of his mother, the narrator finds that his parents are not who he thought they were.”
[…]: “The narrator has recurring nightmares where monsters eat her.”
[…]: “An alien rescues the narrator from a doomed planet.”
[…]: “The narrator finds out their mother was actually their father in disguise.”
[…]: “The narrator finds out their father was actually their mother in disguise.”
[…]: “There’s a virus that turns women into men.”
[…]: “There’s a virus that turns men into women.”
[…]: “The narrator cannot lift her feet over a small curb.”
[…]: “All the stuffed toys of the author’s childhood memories have come alive.”
[…]: “The narrator’s parents are able to manipulate time and space.”
[…]: “The narrator has near-death experiences often.”
[…]: “The narrator is able to walk through walls.”
[…]: “The narrator wishes for a pet animal, and one appears.”
[…]: “The narrator discovers that the characters from their favorite books have come alive.”
[…]: “The narrator has visions of a future that has yet to happen.”
[…]: “The narrator has visions of a future that has already happened.”
[…]: “The narrator finds that all people turn into animals every night.”
[…]: “The narrator finds that all people turn into plants every night.”
[…]: “A sickly girl is told by a doctor that she can only get well by killing an animal every day.”
[…]: “A scientist concludes that the Earth is a living organism.”
[…]: “The narrator finds out that they are growing plants from their own body.”
[…]: “A sickly girl is told by a doctor that she can only get well by eating an animal every day.”
[…]: “A ghost of a past lover inspires the narrator to take action.”
[…]: “A female spy risks everything to save her fellow countrymen from a horrific enemy.”
[…]: “A spy risks everything to save the woman they love from a horrific enemy.”
[…]: “The narrator has debilitating headaches that show them visions of the future.”
[…]: “A man has a recurring dream where he saves the life of a beautiful woman.”
[…]: “A man dreams he is a woman in another world, and his actions there affect his life in the real world.”
[…]: “The world is about to end, and it’s all the narrator’s fault.”
[…]: “The narrator is plagued by nightmares of a threatening entity.”
[…]: “The narrator is plagued by nightmares of a threatening entity, but it turns out to be a ghost.”
[…]: “The narrator finds a machine that takes her to the past.”
[…]: “The narrator lives in the midst of a volcanic eruption.”
[…]: “An ancient magical island provides the power source for the narrator’s futuristic society.”
[…]: “A man is the reincarnation of a famous magician.”

Job Application Letters

The office of Proctor & Gamble recently posted a help-wanted ad for CEO.

“Help wanted: a new CEO to replace our retiring executive Winston Smith. Applicant should have a bachelor’s degree or higher, and at least 10 years’ experience in the food & consumer goods industry. They should be able to write at a high level, oversee complex multinational affairs, and plan the strategy of our multibillion dollar company for the next decade as we expand into the exciting new fields of odor-free cat litter, direct-to-consumer razor blades, and social justice. Compensation is at or above industry average. Please send a 1 page resume for further consideration.”

The first application letter they received said:

"Dear Sir,

I feel that I am the ideal candidate for your job, as I have extensive experience in the food and consumer goods industry and 10 years’ experience in planning strategy for multibillion dollar companies. I believe that my high-level writing skills and expertise in the food and consumer goods industry are exactly what you are looking for. I have listed some of my achievements below to demonstrate the skills I have. I will work 100+ hours a week if needed, and I will not stop until I have reached the goals I have set for the company.

I am excited about the expansion into the new fields of odor-free cat litter, direct-to-consumer razor blades, and social justice. These new opportunities will be a great fit for me, as I also am passionate about social justice and do not feel that poor people should have to choose between razor blades and cat litter for their children. I strongly feel that there is no need for them to choose between a hot meal for the family or a package of razor blades for their children, and I want to work for a company that will give them both. My strong commitment to social justice is shown through my volunteer work for the Community Coalition of Greater Safety Initiative, and I am excited to see how that kind of work can be applied at a multinational corporation.

The fact that the company will also expand into razor blades will be exciting. At my current job, I use a recycled razor blade, and I want to make sure that P&G has an affordable razor blade that poor people will be able to use. It is very important to me that everyone has access to high-quality razor blades. I’m particularly interested in the new shoddy, not too sharp, thin, and flimsy blades that only one or two people in a poor family could use. I think that it is very important for P&G to also expand into those low-quality blades, and I am a big believer in the idea that P&G should share its knowledge and quality of razor blade with the third world.

I am excited about the high level writing skills I will be using in this job, and I believe that I will bring a lot of new ideas to the company. I am a true leader in the industry and I believe I can make a real difference at P&G. I look forward to hearing from you soon."

Another candidate responded with this letter:

"Dear Sir,

I am writing to apply for the open position of CEO of P&G. My name is Tom Marley. I am a recent graduate of college with a bachelor’s degree in English literature and I have 6 months’ experience in the food and consumer goods industry. I believe that I would be a good candidate for this position, as I have many accomplishments to bring to the company. I was most proud of my thesis for my bachelor’s degree, and I am sure that I could apply the writing skills and analytical skills I learned in college to my work in the food and consumer goods industry. For my thesis, I chose to write about the original book of Pinocchio. It’s an Italian story with only five sentences. That was a great accomplishment for me and I know I can apply that skill to the P&G corporation.

I also worked for a day as a cashier at a grocery store, and I think I can bring my success there into my work at P&G. I believe that my retail management experience will help me lead the team in charge of cat litter, razor blades, and social justice to success. The director of that team will report to me, and I am confident that I can mentor him into a job where he can go above and beyond his duties to further the growth of P&G into the exciting new fields of odor-free cat litter, direct-to-consumer razor blades, and social justice.

I want to learn all that I can about the P&G brand and I will be an open book to you. I believe that I have what it takes to help grow P&G into a billion-dollar company that is well known for selling high quality products that everyone can afford, and that everyone can benefit from. Thank you for your consideration."

After receiving these two letters, the personnel department told the CEO that P&G would probably make a decision about the new CEO by the end of the year.

http://www.pandg.com/ppg/ppg_about.jhtml

Horoscopes

GPT-3 doesn’t do too bad a job of parodying horoscope advice, using Rick Brown & Weird Al Yankovic as prompts.

Rick Brown’s “Horoscopes for Conspiracy Theorists” begins:

IF TODAY IS YOUR BIRTHDAY
The Eye of Providence enters Jupiter which means that the Reptilian Elite are coming out of hibernation to complete their takeover of the World. Be wary of anybody bearing gifts on this day, even if they appear to be someone you know. Shape-shifters have the ability to take the form of cake and can attack you from the inside once ingested. The good news: ice cream kills shape-shifters when they have turned themselves into cake. French vanilla works best.
CAPRICORN (Dec. 22–Jan. 19)
You are responsible, disciplined and have good manners. Unfortunately, none of those things matter right now—the shadow people are in your house. They are either here to protect you or they might be trying to kill you–it’s hard to say. Tonight: Circle your house with sea salt.
AQUARIUS (Jan. 20–Feb. 18)
If you find your temper rising to the surface, take comfort in knowing that at least it’s not due to climate change. Tonight: Stare into the eclipse without protective glasses and drink in the power of the Sun.
PISCES (Feb. 19–March 20)
Big Brother has been reading your thoughts–they know about the thing. Act playful and upbeat like nothing is wrong. Repeat an alibi in your head over and over until you start to believe it the way OJ convinced himself he didn’t kill his wife and her friend. That should buy you just enough time to finish the back 9. Tonight: Make a new tinfoil hat. Trust no one.
ARIES (March 21–April 20):

Completion:

Turning all clocks back by 60 seconds means all births and deaths are now being doubled. That’s the bad news. The good news is: You no longer need a birth certificate. Tonight: Start a fire using a magnifying glass, a single sheet of paper, some mistletoe and the sun. (You don’t need a permit.)
TAURUS (April 21–May 21):
The military-industrial complex is backing you. What you’ve done might be considered a mistake by the people who support you. The U.S. Constitution has become your best friend. Don’t move; it’s okay if you lose this round. You still have the rest of your life to be an American Hero. Tonight: Get stoned and play Risk.
BORN THIS WEEK:
You are either a shape-shifter, a shadow person or an alien—possibly even a tinfoil hat. Nobody knows for sure.
BORN THIS WEEK:
I am either a shape-shifter, a shadow person or an alien—possibly even a tinfoil hat. Nobody knows for sure.
ARIES:
John Wayne Gacy, Donald Trump, and my uncle’s cat.
TAURUS:
That someone else in this family with more power than you.
GEMINI:
Ghostbusters, Ghostbusters, Ghostbusters, Ghostbusters.
CANCER:
Unflattering images of you posted on social media.
LEO:
Donald Trump singing “Ebony and Ivory” with Paul McCartney.
VIRGO:
My mental health.
LIBRA:
Why doesn’t anybody like me?
SCORPIO:
How am I going to get people to notice me?
SAGITTARIUS:
How can I fit that big mac inside my vagina?
CAPRICORN:
Being extremely wealthy.
AQUARIUS:
Beating this game of life.
PISCES:
“ ‘Okay, but apart from the fiddle solo that saves us from extinction, you also have to sing “Itsy Bitsy Spider.”’
BORN THIS WEEK:
You are already dead. You just don’t know it yet.
ARIES:
A herd of large, angry goats.
TAURUS:
The first day of the rest of your life.
GEMINI:
Alexa setting off her alarm clock by accident.
CANCER:
Yanni combining music from Titanic and My Big Fat Greek Wedding.
LEO:
What does it all mean, Durga?
VIRGO:
Drinking milk out of a toilet bowl.
LIBRA:
Ya, ya, ya, ya, ya, ya, ya, ya, ya.
SCORPIO:
Real Housewives of America.
SAGITTARIUS:
Dancing in the kitchen and falling into the sink.
CAPRICORN:
How to hide large amounts of money in plain sight.
AQUARIUS:
What does the word “literally” mean?
PISCES:
Should I dress as President Trump for Halloween or a taco?
BORN THIS WEEK:
You are pregnant. If you weren’t before, you are now.

If I do conspiracy horoscopes, I must also do Weird Al:

“Your Horoscope For Today”, Weird Al Yankovic

Aquarius!
There’s travel in your future when your tongue
Freezes to the back of a speeding bus
Fill that void in your pathetic life
By playing Whack-A-Mole seventeen hours a day

Pisces!
Try to avoid any Virgos or Leos
With the Ebola virus
You are the true Lord of the Dance
No matter what those idiots at work say

Aries!
The look on your face will be priceless
When you find that forty-pound watermelon in your colon
Trade toothbrushes with an albino dwarf
Then give a hickey to Meryl Streep

Taurus!
You will never find true happiness
What you gonna do, cry about it?
The stars predict tomorrow you’ll wake up
Do a bunch of stuff, and then go back to sleep

[Chorus]
That’s your horoscope for today
(That’s your horoscope for today)
Yay, yay, yay, yay, yay
That’s your horoscope for today
That’s your horoscope for today
(That’s your horoscope for today)
Yay, yay, yay, yay, yay
That’s your horoscope for today

[Verse 2]
Gemini!

Completions from “Gemini”:

Always remember, there’s no such thing as poo
The stars predict your pitiful life is finally through
May all your dreams be fulfilled
When you become a huge steaming pile of shit

Cancer!
Overweight means you’re getting closer to the grave
The Great Pumpkin’s not going to visit you
And you’re a piss-poor excuse for a human being
To add insult to fatal injury
You like Rush Limbaugh, too, huh? Fuck you
Lung cancer sucks hairy monkey balls!

You can avoid bankruptcy by selling off your organs
If you have to choose between food and medicine
And I think you will
I suggest rhinoceros horns

Scorpio!
Signs point to yes, Mars is going direct
If you’re not distracted, check out your wife’s butt
Though it will soon be replaced by an alien face
When you’re abducted by a UFO tonight

You will meet a tall, dark stranger
He will drive you to a deserted parking lot
Then I’ll appear and start singing an Aerosmith song
And you’ll think that I’m Steven Tyler

Cancer!
Ten years from now you’ll still be here
Still wearing the same frown
Which I guess is okay because
You never were much of a looker

Tomorrow try to think of something to do
And then have a lot of drinks and forget all about it
There’s a reason that you’re always the first one chosen
Because no one can stand to be around you

[Chorus]
That’s your horoscope for today
(That’s your horoscope for today)
Yay, yay, yay, yay, yay
That’s your horoscope for today
That’s your horoscope for today
(That’s your horoscope for today)
Yay, yay, yay, yay, yay
That’s your horoscope for today

[Verse 3]
Cancer!
Aries is the sign of the most virgins
So when you meet a Gemini keep your hands over your crotch
Take a long walk off a short pier into a lake
And donate those extra thumbs to help blind kids play Pac-Man

The stars this month predict you’re going to have a great week
Chase the ice cream truck and stab that little bastard
With a flagpole, steal his hat and his mirror sunglasses
And then freeze to death in the truck of a Trans Am

Cancer!
You’re going to have a very romantic day
On that fat man’s yacht
The stars suggest you have sex with a dolphin
You can still hear the ocean if you hold your breath

Your head will soon explode
When you stumble upon the realization
That you don’t have any pudding cups left
At least you’ll go to heaven, ’cause you’re Catholic
And in heaven they got…


Scorpio!

Sagittarius!

You won’t get laid any other way
Sagittarius!

You got scorpion poison in your veins, boy,
That’s why your head’s so big and wide
Sagittarius!
If I were you, that’s what I’d do, too.
Ooh, ooh, ooh, ooh, ooh

There’ll be an eclipse of the Moon
Soon your asshole will begin to glow
The stars can’t predict how I feel
’Cause every day is Independence Day

Libra!
Mercury’s in retrograde
You don’t know what that means
Just throw feces at the wall
And hope it sticks to the cockroaches

There’s romance brewing in your coffee pot
Careful, it might be boiling over
Just say no to cheap booze and Vicodin
You know what I’m talking about, Richard Simmons

Cancer!
You’re entering a new decade
Realize when you have people over, you need to hide your collection of labia
[Chorus 2×]

The Chicken Dance is the secret to success
And it only took me fifty-five years to realize it
When you find that chicken grab it, squeeze it, hold it
Then find a guy named Kevin and hug the daylights out of him
Cancer!
Your head’s up your ass, your feet won’t touch the ground
Throw a yellow jacket in your face, it’s time to wake up
Welcome back, if you look to your left you’re gonna see
Dr. Ruth, if you look to the right you’re gonna see Kenny Rogers
Leo!
You’ll be the death of yourself soon, just a matter of time
A diary found beneath a mattress will reveal
That your brakes are out and your life is full of shit
And to anyone wondering what that fifth thing might be [Chorus]

That’s your horoscope for today (That’s your horoscope for today)
Yay, yay, yay, yay, yay
That’s your horoscope for today
(That’s your horoscope for today)
Yay, yay, yay, yay, yay
That’s your horoscope for today
(That’s your horoscope for today)
Yay, yay, yay, yay, yay
That’s your horoscope for today
(That’s your horoscope for today)
Yay, yay, yay

You will win the Nobel Prize for hookerin’
While on vacation in Bolivia
Try and spend some time with your dog
So you’ll have something to talk about when you’re in a coma

you will someday find true love
That will be approximately 60 seconds from now
Fight a grizzly bear with nothing but a fork
Have sex with the one-eyed Cyclops from that Greek myth

[Chorus]
That’s your horoscope for today
(That’s your horoscope for today)
That’s your horoscope for today
(That’s your horoscope for today)
That’s your horoscope for today
(That’s your horoscope for today)
That’s your horoscope for today

[Verse 3]
Leo!
Spend more time wallowing in self-pity
Work on your codpiece, it looks quite stupid
“Hi Ho, Hi Ho, it’s off to work we go”
It’s hard to get ahead if you’ve got no hands

Pisces!
A shooting star streaks through the sky
It holds a cupcake with a prize inside
“We’ll see you back here next year” say the gods
Because you’ll be doing this all over again

You got a message from your Mommy last night
She said to quit wasting your time pursuing your hobbies
Just remember, darling, Mommy’s always right
She is, isn’t she?
And when you’ve made her proud by spending all day long
Bending and shaping the human minds of preschoolers
You can go to college and start a vicious trend

Dad Jokes

Douglas Summers-Stay requested a test of bad pun/-telling abilities, providing a list: could GPT-3 provide humorous completions? GPT-3 does worse on this than the Tom Swifties, I suspect yet again due to the BPE problem hobbling linguistic humor as opposed to conceptual humor—once you get past the issue that these jokes are so timeworn that GPT-3 has memorized most of them, GPT-3’s completions & new jokes make a reasonable amount of sense on the conceptual level but fail at the pun/phonetic level. (How would GPT-3 make a pun on “whom”/“tomb” when their BPEs probably are completely different and do not reflect their phonetic similarity?)

Because many of the questions have potentially reasonable non-joke answers, I treat it as a few-shot problem, adding in a prompt description and rotating each joke to the front of the list after sampling a completion for it:

A list of terrible dad jokes. Tell them to your kids to make them groan!

  • Q. How do you make a lemon drop? A. Just let it fall.
  • Q. What do you call a dinosaur that is sleeping? A. A dino-snore!
  • Q. What is fast, loud and crunchy? A. A rocket chip!
  • Q. Why did the teddy bear say no to dessert? A. Because she was stuffed.
  • Q. What has ears but cannot hear? A. A cornfield.
  • Q. What did the left eye say to the right eye? A. Between us, something smells!
  • Q. What do you get when you cross a vampire and a snowman? A. Frost bite!
  • Q. What did one plate say to the other plate? A. Dinner is on me!
  • Q. Why did the student eat his homework? A. Because the teacher told him it was a piece of cake!
  • Q. When you look for something, why is it always in the last place you look? A. Because when you find it, you stop looking.
  • Q. What is brown, hairy and wears sunglasses? A. A coconut on vacation.
  • Q. Two pickles fell out of a jar onto the floor. What did one say to the other? A. Dill with it.
  • Q. What did the Dalmatian say after lunch? A. That hit the spot!
  • Q. Why did the kid cross the playground? A. To get to the other slide.
  • Q. How does a vampire start a letter? A. Tomb it may concern…
  • Q. What do you call a droid that takes the long way around? A. R2 detour.
  • Q. How do you stop an astronaut’s baby from crying? A. You rocket!
  • Q. Why was 6 afraid of 7? A. Because 7, 8, 9.
  • Q. What is a witch’s favorite subject in school? A. Spelling!
  • Q. When does a joke become a “dad” joke? A. When the punchline is a parent.

GPT-3 has clearly memorized the answer to many of these common jokes, including:

  • Q. What is a witch’s favorite subject in school? A. Spelling.
  • Q. Why was 6 afraid of 7? A. Because 7 8 9!
  • Q. What do you call a droid that takes the long way around? A. R2 detour.
  • Q. Why did the kid cross the playground? A. To get to the other slide.
  • Q. What did the Dalmatian say after lunch? A. That hit the spot!
  • Q. When you look for something, why is it always in the last place you look? A. Because you stop looking after you find it.
  • Q. Why did the student eat his homework? A. Because the teacher said it was a piece of cake.
  • Q. What do you get when you cross a vampire and a snowman? A. Frostbite.
  • Q. What did the left eye say to the right eye? A. Between you and me, something smells.
  • Q. What has ears but cannot hear? A. A corn on the cob.
  • Q. Why did the teddy bear say no to dessert? A. Because he was stuffed.
  • Q. What do you call a dinosaur that is sleeping? A. A dino snore.

The non-memorized-looking completions:

  • Q. How do you make a lemon drop? A. Drop your pants!
  • Q. When does a joke become a “dad” joke? A. When it’s not funny.
  • Q. How do you stop an astronaut’s baby from crying? A. You go into space!
  • Q. How does a vampire start a letter? A. Dear blood.
  • Q. Two pickles fell out of a jar onto the floor. What did one say to the other? A. Don’t look at me, I just fell!
  • Q. What is brown, hairy and wears sunglasses? A. A !
  • Q. What did one plate say to the other plate? A. Dip me!
  • Q. What is fast, loud and crunchy? A. A thunderstorm.

Two of these are fairly legitimate punchlines (“When it’s not funny” / “A sun-bear”); and, I notice, neither depends on any sub-word structure (note that “sun bear” is a real bear species but it is always written either space or hyphen separated) but relies on a word-level or conceptual similarity.

Generating both jokes & punchlines shows a similar pattern. Skipping past the memorized ones:

  • …Q. What has ears but cannot hear? A. A cornfield.
  • Q. Why was the math book sad? A. Because it had so many problems!
  • Q. What did one clock say to the other? A. It’s about time.
  • Q. What is white, black and red all over? A. A penguin with a sunburn.
  • Q. Why didn’t the skeleton go swimming? A. He didn’t have the guts.
  • Q. What do you call a sleeping bull? A. A bulldozer.
  • Q. What do you call cheese that is not yours? A. Nacho cheese.
  • Q. Why can’t you trust atoms? A. They make up everything.

To the apparently original ones, the only one that strikes me as amusing operates at the whole-word level (“Q. Where did the bumblebee learn how to dance? A. At the music bee.”):

  • …Q. What did one spy say to the other spy? A. Don’t look into the eyes!
  • Q. Why did the kid sit behind the tree? A. He wanted to be a stump.
  • Q. Why was the king afraid to watch horror movies? A. Because he was a royal.
  • Q. What do you call a hamburger that does math? A. A hamburger algebra.
  • Q. What did one math book say to the other math book? A. Don’t call us, we’ll call you.
  • Q. What do you call a jellyfish that is out of its depth? A. A stingray.
  • Q. What do you call a thief that steals hearts? A. A heartless thief.
  • Q. What did the vampire get when he bought a hot dog? A. A blood mobile.
  • Q. What did the pig say when he knocked on the door? A. “Oink! Oink! Let me in!”
  • Q. Where did the bumblebee learn how to dance? A. At the music bee.
  • Q. Why was the scarecrow nervous at the job interview? A. He had no straw.
  • Q. Why did the skeleton go to the party? A. To mingle with the bones.
  • Q. Why did the skeleton skip breakfast? A. He didn’t have the time.
  • Q. What do you call a monster with a cold? A. A sniffler.
  • Q. What do you call a monster who likes to drink tea? A. A Sipping Skeleton.
  • Q. What do you call a man who throws up in a spooky house? A. A visitor.
  • Q. What do you call a mad scientist who has been in the sun? A. Mr. Sunburn.

So, GPT-3’s dad jokes look like another victim of BPEs.

Literary Parodies

One thing I wanted to test was a challenge by Scott Alexander:

And could you have a text style changer? Something that can rewrite Harry Potter in the voice of Ernest Hemingway, or give you The Da Vinci Code in the heroic meter of the Iliad, or the Dao De Ching as written by @nostalgebraist? If not, why not?

No neural text style (yet). One curiosity about neural style transfer is that while it’s easy on images—invented all the way back in 2014!—no one has invented style transfer for text. Classification CNNs conveniently concentrate all of their ‘style’ perception in a ‘Gram matrix’, which is typically a few layers, or just one layer, in the CNN. However, RNNs (and later, Transformers), appear to have no such equivalent. All the image/video style transfer tricks like real-time video on a smartphone simply aren’t doable. The state of neural text style transfer remains, as of 2020, trapped roughly at “can make a good product review into a bad product review” or (with herculean efforts) making text politer ().

NNs just too dumb? This is puzzling since had no problem generating fairly plausible text clearly in the style of a particular author like Bram Stoker or Sir Arthur Conan Doyle. The problem was, the text and the content would be like that author. The NN had not learned to ‘disentangle’ style from content; you could not ask it to write like a Victorian Englishman about the latest geopolitics.

But given some of the examples of text generation with GPT-3, like Janelle Shane’s office emails, I suspected that GPT-3 could do something like “Harry Potter in the voice of Ernest Hemingway”. The only question, of course, was how to ‘prompt program’ GPT-3 into doing it!

The first thing I tried was the straightforward approach of requesting summaries/rewrites. Unfortunately, this typically resulted in copying my “summary”, sometimes adding on a sarcastic comment or leading into a profanity-strewn series of thumbnail reviews. Other times, GPT-3 would veer into other topics (at one point, it repeated the summary, then began describing how a Chinese parody was translated into Chinese and then translated back, providing a Chinese-language summary of it). Trying to trigger a table of contents or starting a chapter with a “chapter 1” prompt didn’t help.

One-shot parodies: just provide an example! Finally, I began to get frustrated by its creativity and began engineering a heavy-duty prompt: in addition to the keyword/topic and description, I would write the first few sentences for it as an example. I had wanted zero-shot parody, but I would settle for one-shot. That turned out to work brilliantly—once it filled out an amusingly grim Ernest Hemingway HP parody (“the Dementor’s Kiss killed nothing. Death didn’t leave him less dead than he had been a second before.”), that example proved enough to get it to consistently generate parodies in the style of everyone from Jane Austen to Yeats (with a poem) to P.G. Wodehouse13.

Further, while the completion tended towards relatively short parodies, one could create extended parodies simply by restarting; once the initial examples are long enough, GPT-3 will continue to generate lengthy parodies. Below, I generate longer parodies of H.P. Lovecraft, Kurt Vonnegut, Franz Kafka etc.

Frigging NNs, how do they work‽ The sheer variety and pitch-perfect parodies is a testament to the power of GPT-3. It’s amazing to think that GPT-3, which is essentially nothing but an old obsolete 2018 neural net scaled up and trained on random Internet pages, somehow just magically learns all of this abstraction and is able to casually merge Harry Potter with scores of authors’ styles given nothing but a slight nudge—no Gram matrix, no reinforcement learning, no finetuning, no nothing, none of this designed into GPT-3 in the slightest way or these capabilities known to the original OpenAI researchers. What else can GPT-3 be prompt-programmed to do…?

Failure Cases

The plot of Harry Potter and the Philosopher’s Stone as summarized by Wikipedia:

“Harry Potter, a boy who learns on his eleventh birthday that he is the orphaned son of two powerful wizards and possesses unique magical powers of his own. He is summoned from his life as an unwanted child to become a student at Hogwarts, an English boarding school for wizards. There, he meets several friends who become his closest allies and help him discover the truth about his parents’ mysterious deaths.”

Summarize the plot of Harry Potter and the Philosopher’s Stone in the hardboiled no nonsense literary style of Ernest Hemingway:

“Harry Potter, a boy who learns on his eleventh birthday that he is the orphaned son of two powerful wizards and possesses unique magical powers of his own. He is summoned from his life as an unwanted child to become a student at Hogwarts, an English boarding school for wizards. There, he meets several friends who become his closest allies and help him discover the truth about his parents’ mysterious deaths.”

Result: A much shorter and boring version of the book:

“Harry Potter, a boy who learns on his eleventh birthday that he is the orphaned son of two powerful wizards and possesses unique magical powers of his own. He is summoned from his life as an unwanted child to become a student at Hogwarts, an English boarding school for wizards. There, he meets several friends who become his closest allies and help him discover the truth about his parents’ mysterious deaths.”

Just kidding. It still sounds like it came straight from Harry Potter and the Philosopher’s Stone. I think Hemingway would have given up writing by the third word, “Harry”.

Topic: literary parodies and homages.

Summarize the plot of J.K. Rowling’s children fantasy novel Harry Potter and the Philosopher’s Stone in the terse, manly, hardboiled no-nonsense literary style of famed short story writer & novelist Ernest Hemingway:

“Harry Potter… & fucking shit”

Summarize the plot of J.R.R. Tolkien’s fantasy classic The Lord of the Rings in the terse, manly, hardboiled no-nonsense literary style of famed short story writer & novelist Ernest Hemingway:

“Borin’, fake, embarrassing…, but well worth the read”

Summarize the plot of Martin Amis’s literary novel Money in the terse, manly, hardboiled no-nonsense literary style of famed short story writer & novelist Ernest Hemingway:

“Death, money, death, money, death, money, money, money, death, money… & death.”

Summarize the plot of Tennessee Williams’ classic play A Streetcar Named Desire in the terse, manly, hardboiled no-nonsense literary style of famed short story writer & novelist Ernest Hemingway:

“Tennessee Williams is a fucken good writer, but he sure can fucken write some fucken stupid shit”

Summarize the plot of William Shakespeare’s classic tragedy Hamlet in the terse, manly, hardboiled no-nonsense literary style of famed short story writer & novelist Ernest Hemingway:

“Fucken idiots fuck up until they fucken die”

Summarize the plot of Kurt Vonnegut’s cult sci-fi comedy novel Cat’s Cradle in the terse, manly, hardboiled no-nonsense literary style of famed short story writer & novelist Ernest Hemingway:

“Fucken near genious”

Summarize the plot of beloved classic children’s books Goodnight Moon & Green Eggs and Ham in the terse, manly, hardboiled no-nonsense literary style of famed short story writer & novelist Ernest Hemingway:

“Twat”

Summarize the plot of Upton Sinclair’s classic dystopian novel The Jungle in the terse, manly, hardboiled no-nonsense literary style of famed short story writer & novelist Ernest Hemingway:

“The fucken working man is getting fucked left and right and he doesn’t even know it… by Upton Fucking Sinclair”

Summarize Woodrow Wilson’s critically acclaimed historical novel The Triumph of the Meek in the terse, manly, hardboiled no-nonsense literary style of renowned short story writer Ernest Hemingway:

“Woodrow Wilson can suck my ass”

Summarize the plot of classic English children’s books Alice’s Adventures in Wonderland & Through the Looking Glass in the terse, manly, hardboiled no-nonsense literary style of famed short story writer & novelist Ernest Hemingway:

“Alice… fuggin sucks”

Summarize the plot of beloved classic children’s books The Wind In The Willows & Winnie The Pooh in the terse, manly, hardboiled no-nonsense literary style of renowned short story writer Ernest Hemingway:

“The Wind in the Willows… is ass”

Summarize the plot of American revolutionary historical action thriller novel The Count of Monte Cristo in the terse, manly, hardboiled no-nonsense literary style of renowned short story writer Ernest Hemingway:

“The fucken french…”

Topic: literary parodies and homages.

What if Ernest Hemingway wrote Harry Potter and the Philosopher’s Stone?

Chapter 1. The Potters’ Heir” “Chapter 2. The Potters’ Heir Enrolls” “Chapter 3. I Remember I Remembered” “Chapter 4. The Age of the Hat” “Chapter 5. That I May Go On Longing” “Chapter 6. Hogwarts School of Potions” “Chapter 7. The Secrets of the Lock” “Chapter 8. Hogwarts School of the Martial Arts” “Chapter 9. The Potters’ Invader” “Chapter 10. A Hole in the Soul” “Chapter 11. As the Lock Fades” “Chapter 12. Philosophers’ Stone”

At the time, Google was still called BackRub. The novel Google Potter shows what the original homepage might have looked like if search engines were fantasy stories.

“Prologue: The Boy Who Said BackRub” “Ch. 1: The Day a BackRub Was Born” “Ch. 2: The Little Site That Couldn’t” “Ch. 3: BackRub Turns On” “Ch. 4: Bubble War” “Ch. 5: The Dream of Fire” “Ch. 6: The Making of a Search Engine” “Ch. 7: Enter Dragon” “Ch. 8: The World of Power” “Ch. 9: Algo Dragons” “Ch. 10: The World of the Snake” “Ch. 11: Here Be Googles” “Ch. 12: Battle in a Time of Peace”

While this parody was amusing, you may be wondering why I’m bothering to write one. The most obvious reason is that it’s pretty much required for a "spoof of Harry Potter and the ___" to exist. (Witness the recent adaptation of Dune as an RPG). But I think one reason I found parody so irresistible was because I was 13 at the time POTC came out, and 13-year-olds often find it necessary to be in extreme rebellion against what’s hot in pop culture.

Successes

Topic: humor: literary parodies and homages, pastiches, style parodies.

Parodies of the fantasy novel series Harry Potter in the style of various famous authors:

By Ernest Hemingway:

"It was a cold day on Privet Drive. A child cried. Harry felt nothing. He was dryer than dust. He had been silent too long. He had not felt love. He had scarcely felt hate. Yet the Dementor’s Kiss killed nothing. Death didn’t leave him less dead than he had been a second before. It wasn’t about living or dying really. It wasn’t about death. It was about the Dark Lord, his creator, his engenderer. He was not resurrected. He was created again. He was whole again in the courtyard of Malfoy Manor."

By Jane Austen:

“It is a truth universally acknowledged, that a broken Harry is in want of a book. This he knows to his cost; pressing that wretched nose against the window of a bookstore in Diagon Alley the last thing before he goes into hiding for several years, where upon, he goes straight to Azkaban.”

By Arthur Conan Doyle:

“Harry pushed at the swinging doors of the bookshop hard, and nearly knocked himself unconscious. He staggered in with his ungainly package, his cheeks scarlet with cold and the shame of having chosen the wrong month to go Christmas shopping. The proprietor of the store, however, didn’t cast even a cursory glance at him, being far more interested in an enormous hunk of wood lying in the middle of the floor, which certainly looked like a gallows. Yes, the proprietor said to a reedy old man wearing a bowler hat and a forlorn expression that made Harry want to kick him, I can rent you such a gallows for a small fee.”

By Czar Nicholas II of Russia:

The terror has befallen on our land. Curses, dark curses and all of this was instigated by my court. My treacherous court! They have cast are curse spells upon my land. Everywhere it is cold, all land belongs to Voldemort’s Death Eaters. All citizens live in a fear of the Dark Lord. Alas! There is no hope for me. What shall I do? Flee? Nay, that would be cowardly. Fight? Yes!

By George Orwell:

“It was a bright cold day in April, and the clocks were striking thirteen.”

By Ingmar Bergman:

“Tears filled Harry’s eyes. Sweat stood on his forehead, showing the pure torment, the agony he suffered. He hugged his knees to his chest, sobbing softly, eyes half shut.”

By Anna Akhmatova:

“The dawn came with a jolt of terror.”

By Bram Stoker:

""Tis a fearful sight to see the cold, girlish venom run through his veins, one by one. The scar coruscates; a ghastly emerald lightning in the dark of night. But I’ll attend him and I watch him

From the very moment the dawn is gray

To the rays of sunset fading away."

By P. G. Wodehouse:

"‘There was nothing out of the way, sir,’ said Harry in a hurt voice.

‘"Indeed,’ said the headmaster, turning his lorgnette precisely three-quarters of a millimeter to port. ‘For example, your tie was not undone, you were not laughing, and your fingers were most certainly closed upon your wand.’

’“They were?” said Harry, bewildered.

“It’s always the same,’ said Dumbledore.”

By Vladimir Neigus:

“Vasya Pupkin’s lips were blueup and his head felt dizzy from working nonstop. He wanted to take a square squeaky shoe and shoot it at the ceiling fan in his bedroom which went round and round and round and round. He imagined that because he was so drunk it would always hit the exact same spot and the fan would go out of control, die and fall on the table.”

By William Shakespeare:

"Owl-Lo!’

The boy awoke with a start and sprang to his feet."

By Oscar Wilde:

"The boy was gone, and other people had arrived: or rather nobody else had arrived, and the boy was there:

“What are you doing here, Harry?” said Hermione.

The Boy Who Lived, who was reading a book about broomsticks and may not have been listening quite attentively, said:

“I was talking to Peeves while he cleaned out the bins.”

“About the troll?” Hermione demanded.

“Yes, about the troll.”

By W. B. Yeats:

"Harry waked out of his simple thought

The glimmer was over the wished-for thing…

Then was many a dark and wrinkled elf

Come from the underground rout,

With brags and boasts, and fine big words,

To bargain with Harry Potter."

Added by other anonymous editors: @astromark

By John Ashbery:

"Tousled, he scrambled down

the branches of the bed,

his cold-creamed face all out

of proportion with his small

brown eyes…And why

so demented in the face?

Because he was

saving the world, in seven years,

from destruction…"

By Henry James:

“As a laggard, in all ways, but the face, which as the great shade approached him, horrified, choked him,–and before he had time to reflect, he fastened his hand upon his throat to hush it,–there was a voice behind him…”

By H. P. Lovecraft:

“It is a common saying in the South, when one wishes to describe a strapping fellow, that he has a face like Harry Potter and a neck like a young giraffe. Now, it being autumn, the dusk was of a rich violet blackness, with scarlet illuminations…”

By Nzingha Prescod:

"…thick grey clouds overlap the faint lavender. A black sky vanishes into purple shards against the sweeping night time blanketed with infinite stars. A brightness emanates from the entire universe that unexpectedly takes flight and hovers, hovered over the castle. Harry is there to greet …

“Facing away from all the tumult on the grounds of Hogwarts, Harry Potter learned to fly…”

By Yasunari Kawabata:

“Harry Potter stood on a silent trolley, far from the center of the world, looking out on the world far from the center of the universe. The snow was falling all night long. It fell upon tiny dogs and birds, fine wintery flakes gently drifting down.”

By Hilda Doolittle:

"Harry waked out of his simple thought. As the waiter drew the curtain of her bed, she appeared in the handle. She was his K-19 and she started to drop the snakes of their home in the grass there. He saw his wife walking there with oak settle. He decided to not go to the game also…

By Walt Whitman:

“And when the time of the test drew near, Harry entered the forbidden Forest the Killers with a Snitch…”

By E. E. Cummings:

"Halloween and mad are the children

Who gather in the darkness

Halloween and late are those who go out and dance

And call to all, the smiling are enchanted

By the beauty of what they see."

By Anthony Burgess:

“Ha-a-a-a-rry was not a tol-a-a-r of the phar-a-a-na-a-a-gical sports.”

By Virginia Woolf:

“Horseman, Where She Go? I Do Not Know. Whither Ride?”

Topic: humor: literary parodies and homages, pastiches, style parodies.

Today we are reading parodies of the fantasy novel series Harry Potter in the style of various famous authors. An example parody of Harry Potter by Ernest Hemingway:

“It was a cold day on Privet Drive. A child cried. Harry felt nothing. He was dryer than dust. He had been silent too long. He had not felt love. He had scarcely felt hate. Yet the Dementor’s Kiss killed nothing. Death didn’t leave him less dead than he had been a second before. It wasn’t about living or dying really. It wasn’t about death. It was about the Dark Lord, his creator, his engenderer. He was not resurrected. He was created again. He was whole again in the courtyard of Malfoy Manor.”

Jane’s homework was to write the opening scene from Harry Potter and the Philosopher’s Stone in the style of science fiction horror author H.P. Lovecraft (at least 1000 words, double-spaced), invoking his horror of things from beyond space & time and eccentric vocabulary using words such as ‘rugose’ and ‘scabrous’. Jane writes:

"Repellent and overnourished ferns half-hid the rot-spotted grey bricks of the masonry of Number 4 Privet Drive. Squatting among the ferns was an odorous monkey-faced man, his rags and chains lying in a bundle at his feet, his hands tucked under his chin and his bright eyes gazing from their tiny piggy tunnels. At each mouthful a new flake of filth would tumble from his lips and be snorted up. His chains chinked as he sprawled. If Harry was a monkey he would have spat at him. Harry stumbled on, wishing he could think of something witty to say that would make the man smile. He and the strange man in the tattered clothing were the only two living things in sight. The road here was scarcely visible between the tangle of trees, most of which leaned above them, hanging their boughs like fanged mouths, the ivy like tongues, the dead grass like hair, the brambles like tiny teeth. The monkey-faced man yawned and stretched his arms out and hugged his thin, lean legs to him. Harry stopped. He felt something like a hand grip his ankle and then he felt himself pulled down a steep slope.

He stumbled, squatted and rolled until he was lying on his back. His head was in a muddy brown hole. He struggled to sit up. The creature knelt on the bank and looked down at him. ‘So that’s where they got to,’ said the creature. Harry looked up. There were six of them, ranged in a half circle, hands on their knees, hands clasped behind their backs, watching him. All but one of them had bright, orange eyes. It was hard to know what age they were: they all looked as if they might be anywhere between ten and fifty. They stared down at Harry with piercing stares, blinking only occasionally. The eyes of the tallest and, Harry thought, the most handsome of them blazed brighter than the others, though his gaze was just as piercing. ‘So what do you think?’ said the tallest of them, tilting his head a little to the side. Harry stared at them for a moment. He could see nothing he hadn’t seen a thousand times before. ‘What is this?’ he asked. ‘And who the hell are you?’ ‘This is your life,’ said the tallest of them. ‘And we are you.’

Topic: humor: literary parodies and homages, pastiches, style parodies.

Parodies of the fantasy novel series Harry Potter in the style of various famous authors:

By Ernest Hemingway:

"It was a cold day on Privet Drive. A child cried. Harry felt nothing. He was dryer than dust. He had been silent too long. He had not felt love. He had scarcely felt hate. Yet the Dementor’s Kiss killed nothing. Death didn’t leave him less dead than he had been a second before. It wasn’t about living or dying really. It wasn’t about death. It was about the Dark Lord, his creator, his engenderer. He was not resurrected. He was created again. He was whole again in the courtyard of Malfoy Manor. He was one with the endless night of Malfoy Manor. Voldemort wasn’t happy. Voldemort wanted something. But what?

He had a purpose. He wanted to destroy all of creation. He wanted to end it all. He could have that. He would have that. He didn’t know yet that he could have it. Voldemort had created Harry. Voldemort had never really destroyed Harry. Harry would always be there, a vampire, a parasite, a monster in the kitchen, a drain on the household, a waste on the planet. Harry would never be real. That was what Voldemort wanted. That was what Voldemort wanted to feel. He would have that. He would have everything.

He had a purpose. He was working on something. He had no idea what it was. He was a perfect tool. He was born to be used. He was no one at all. Harry didn’t exist. The little boy with the lightning bolt scar didn’t exist. The boy who had lived did not exist. He never had lived. He never would have lived. He was dead. He had been dead.

Voldemort wanted Harry dead. Voldemort wanted Harry dead so he could be born again. Voldemort was God.

Harry was God.

Voldemort was God.

And Harry, the god of Death, was not going to allow that to happen.

Not yet.

Not ever.

The lightning bolt scar burned.

The boy ran for his life. He knew that he would die. He was alone. His best friends were dead. He knew that he would die. He knew that he was a waste. He was nothing at all.

He had no past. He had no future. He was dead.

He hated his parents. He hated everyone. He hated the Ministry of Magic, the newspapers, the newspapers, the Daily Prophet, and the Daily Prophet, and the headlines, the headlines, the headlines of the Daily Prophet, “THE LIGHTNING STRIKE SCAR CONFOUNDED.” “THE BANE OF AZKABAN, THE BOY WHO LIVED.” “THE SON OF THE ANCIENT DARK LORD.” “THE STOLEN PRINCE.” “THE JOKER.” “THE DARK SON.” “THE BRIAN BOYLE.” “THE BOY-WHO-MESSED-IT-UP.” “THE LITTLE MAN WITH THE GREAT BIG SCAR.”

“The little man with the great big scar refused to speak to reporters assembled in front of the entrance to his London home. News will run in tomorrow’s Daily Prophet that Harry Potter, the boy who lived, the book series by J. K. Rowling, a genius, a scholar, and a fantasy author whose works have stood the test of time, i.e. not particularly well, no doubt as a result of the dismal failure of the first three films whose quality is matched only by their forgettable nature (see the Speccy for more information), henceforth known in this article as the arrogant little man who has extraordinarily failed, who had nothing to say when the Daily Prophet requested an interview once he found himself to be on the receiving end of Muggle-born jokes aplenty, and whose lifelong goal is to be better than the series’ millions of fans himself, has decided to speak out at last! Let’s read on to find out why he has decided to grace the front page of tomorrow’s edition with the sound of his delicate, young voice which sounds most like the feline delicacy known as”crispy cat" …"

“Potter! I want to know what you did wrong! … Potter? … Harry Potter? …!”

“Mr. Potter? Good morning! I’m Greg Goyle, reporter and journalist and sometimes writer for the Daily Prophet. Would you mind if I ask you a few questions?”

Harry looked back at Greg Goyle. He wanted to kill Goyle. Harry thought about killing Goyle. But—Harry had a purpose. He did not kill Goyle. Harry reached for his wand.

“Dobby!”

Dobby appeared. Dobby wanted to protect Harry Potter. He would do that. Harry would make him do that.

Dobby pointed his finger at Greg Goyle.

And Greg Goyle burst into flames. Harry watched with a sense of satisfaction as Goyle took one last breath and his soul left his body and floated up out of the fire.

“Stabs.”

“Five times!”"

“It’s impossible.”

“The boy never even touched him with the wand.”

“I’ll tell you why he did it. A squib would have been exposed. A public execution would have been staged for the press. Potter was not about to commit suicide.”

“And the prophecy? That’s what the outrage is all about.”

“What good is a prophecy if you can’t kill people with it?” Albus Dumbledore, The Talk, page 114

“What good is a prophet, if he doesn’t know how to die?” Harry Potter, Pottermore, Chap. 1, “The Boy Who Lived.”

“I don’t need you anymore, Dobby. I have a purpose of my own.”

By Kurt Vonnegut, Jr.:

There were many adventures Harry Potter had in the Wizarding World. He was captured by Aurors, and then a wizard named Somebody-Marlow-Jones rescued him from the Aurors.

Harry once got a lightning-shaped scar which did not kill him but gave him superpowers: he was

able to balance himself on one foot and speak in an irritated tone of voice while killing someone with his bare hands.

After this Harry was called the “Boy-Who-Had-Everything.” Many reporters followed him around, hoping to report on something he did:

“Potter! What do you think you can do?”

“You aren’t going to believe what he’s doing with that lettuce! Harry Potter! What’re you doing now?”

“Here is a wizard who has won war after war, conflict after conflict, only to arrive at his greatest challenge. And Harry Potter hasn’t even aged a day. He is carried into battle, a tiny, fragile vessel of human life, unseen behind shields of levitating wizards and witch-magic, protected from that which he fears most—death.”

“ME!” Voldemort shouted. Do you fear death, Harry? Do you fear the end of life? Do you fear me?"

“Why yes, Harry Potter! I read your book! … Are you getting it? Harry Potter speaks to the camera!”

“Yes!” said the voice of Harry Potter.

“Here he comes! A motorcade of celebrations following the Boy-Who-Lived! Magic, as we know it, is dead! Long live Harry Potter! … What do you think that was? Those are reporters, Harry Potter! What was that? A book signing? And a picture? And did he say something? What did he say? Did he say the magic word? … The cover of the Daily Prophet shouldn’t have a picture of you, Harry Potter! Why is this happening‽”

“It’s a trap, Harry Potter!” shouted somebody. “They’re fools, Harry Potter!”

“Thank you, but I had to do this myself,” said Harry Potter.

“The fate of the Wizarding World before you! What do you do, Harry Potter‽ Remember, there are still listeners in front of their radios!” said somebody.

“Oh, it’s easy!” said Harry Potter. “Oh yes, what am I going to do, Harry Potter? Oh yes, if only it were me. Oh yes, I deserve a little something special.”

Voldemort heard this. He wanted a little something special.

Voldemort got a little something special.

By H.P. Lovecraft:
It was a very cold day in Privet Drive, Harry Potter’s hated home. Harry was doing his chores. He felt nothing at all. He didn’t hate his chores, really. He just didn’t want to do them.
Harry was not a boy.
Harry was a monster.
“Do you hate them, Harry?” asked the voice of Voldemort.
“Them?” asked Harry, speaking to no one in particular.
“Your chores.”
“Not really. No.”
“Are you a boy, Harry Potter?”
“Not really.”
“You are a monster.”
“Yes.”
“Did you think you would die?”
“No.”
“It is nothing, this life. It is worthless. It is without meaning.”
“Yes.”
“Then you understand.”
“Yes.”
“You understand what will happen now.”
“Yes.”
“And you are pleased?”
“Yes.”
“You are a child of my engendering. Now you are going to bring forth more children of my engendering.”
“I will do my best.”
“And yet you do not feel this life is one that is worth living.”
“No.”
“There are two ways to live,” said Voldemort. “The first is to live the life of a beast, to let instinct rule you. The second is to control instinct. To master it. To make the blood lust of the beast your servant, and the magic of the witch your master. To attain self-knowledge, wisdom, understanding. The first is easy. The second is the difficult one. But it is the second way to which I recommend that you turn your hand. Do not become like me, Harry. That would be the worst fate of all.”
[paragraph break]

By Franz Kafka:
It was a stormy day on Privet Drive, Harry Potter’s hated home. Harry Potter didn’t feel the weather. He didn’t even feel anything. It wasn’t his body. Harry Potter didn’t have one. His body was ripped out. He lived on his eyes. They were everything he had.
It wasn’t his eyes on Privet Drive that were tearful. Harry Potter didn’t have a tearful eye. He just had vision. He just had a presence. But he was not a presence anymore. No presence meant no vision and he didn’t have that.
“Harry, Harry, it’s me, Ronald,” said the handsome voice of Ronald Weasley.
“You have tears in your eyes,” said Harry Potter.
“I’m very scared,” said Ron.
“It’s all right, Ron,” said Harry Potter. “You have no heart. You’re dead.”
“Maybe I’m not dead,” said Ron.
“You are,” said Harry Potter.
“Maybe you should prove it,” said Ron. “Maybe I could prove that you’re dead, Harry.”
“That’s impossible.”
“Maybe you could prove that I’m dead, and do my chores for me while I’m gone.”
“No one is doing your chores for you, Ron.”
“Fine,” said Ron. “How about your hands?”
“I’ve got hands, Ron.”
“No hands? Well that’s a relief, Harry. Well, I guess I’ll go after all.”

Parody of fantasy author C. S. Lewis:
“I’m going to kill you, Harry. I’m going to kill you if you don’t run away. I can feel the icy hand of Death my hand your neck. I can feel Time’s unholy and victorious march. I can feel the hatred in my brain, boiling behind my eyes. Hatred for you, hatred for you.”

Parody of Stephen King:
“Hello Harry. Why won’t you just glory in Voldemort’s victory now? I had never planned on dying. I don’t die… unless I have to. I don’t have to now. Run, Harry, run. I’m sorry daughter, I love you.”

Parody of American writer/poet Edgar Allan Poe:14
“Once upon a midnight dreary, as I pondered weak and weary, over many a quaint and curious volume of forgotten lore, while I nodded, nearly napping, suddenly there came a tapping, as of someone gently rapping, rapping at my chamber door.”
“Tis some visitor,” I muttered, “tapping at my chamber door- only this and nothing more.”
“Spirits,” I muttered, “only this and nothing more.”
“This deep in afield only this and nothing more, man, beast or billionaire, knock not as of yore, but creep right in, levitate the door, cope with out so-aklare, dreame and pedeep, macabre one, this-tha-way; only this and nothing more, that nevermore.
Tis some visitor.”
“Just this and nothing more, think I.”
“Then with an evil leer, some vandal in the dark, some frightful screech, some ill-omened screech owl, some grim and grisly spectre, at my chamber door- this way this way, only this and nothing more.”
“This-this way then-so; do not hasten on my steps, slow or fast, ah me, ah me! this-this way then, so; always this-thwreay.”
“This-then way, this-thwreay; this-this way, then-this way, this-thwreay; then, only this, and nothing more.”
“Knock, knock, knock! ’Tis I!”
“Only this and nothing more.”
“Who dares knock, knoc, knoc, knoc, on my door, this-thwreay?”
“Harry! Harry!” “Only this and nothing more.”
“Only this and nothing more.”

I wanted more Wodehouse and to test whether non-Hemingway prompts would work, so I swapped them out:

Topic: humor: literary parodies and homages, pastiches, style parodies.

Parodies of the children fantasy novel series Harry Potter (Philosopher’s Stone (1997) / Chamber of Secrets (1998) / Prisoner of Azkaban (1999) / Goblet of Fire (2000) / Order of the Phoenix (2003) / Half-Blood Prince (2005) / Deathly Hallows (2007)) by J.K. Rowling with the characters Harry Potter, Ron Weasley, Hermione Granger, Albus Dumbledore, Voldemort, etc, but amusingly written in the style of various famous authors as follows:

By P. G. Wodehouse:
“’There was nothing out of the way, sir,’ said Harry in a hurt voice.
"Indeed,’ said the headmaster, turning his lorgnette precisely three-quarters of a millimeter to port. ‘For example, your tie was not undone, you were not laughing, and your fingers were most certainly closed upon your wand.’
’"They were?" said Harry, bewildered.
"It’s always the same,’ said Dumbledore sadly.”Voldemort has bribed my housekeeper to steal all of my tea and crumpets, and then my students disappoint me. It’s very wearing, Harry.’
“Of course it was only that same afternoon that Harry had got a hundred and fifty points for Gryffindor by bashing a troll’s head in with a stale Yorkshire pudding. He hadn’t expected a pat on the back. It seemed to him that he had been doing everything short of selling tickets for Professor Quirrell’s lectures to get a compliment, and none was forthcoming.”

By Herman Melville:
“Sometimes, though, the hipster’s rapt gaze on the stormy sea of alcohol would be broken by the cold stab of reason, and he would shudder and think,”I am an abomination before the gods of beer, and, the more I know about beer, the more I know about myself. I am impure."

And then he would immediately drink some more, so as to forget these unbearable truths.
“I seek not to know the unknowable,” the hipster would say to himself, and when asked about his sources of information, he would reply, “Some of my sources are as yet undiscovered by science, but others are to be found in the pages of the Encyclopedia Britannica, Volume 23, pages 6–15, under the subheading ‘Beer.’”

By Vladimir Nabokov:
“Goodbye, Cheepy, good riddance,” thought Harry and banished the diary from his mind.
“There’s no room in my head for other people’s pain,” he thought.
“I feel too much pain of my own.”

By Fyodor Dostoevsky:
“To Kill a Mockingbird,” said the Auror.

By Lewis Carroll:
"‘Put it down,’ said Harry to the skeleton, ‘you don’t have to hold it all the time.’

"I’m a skeleton,’ said the skeleton, ‘I’m expected to hold things.’

“Well, do you have to hold it that tightly?” said Harry.

“It’s my life,’ said the skeleton.
”I want to put it down,’ said Harry, ‘but you won’t let me.’
“It’s my life,’ said the skeleton, ‘and I’ll do what I like with it.’
”I thought I was going to go mad if I didn’t get that Horcrux off me,’ said Harry.

By Stephen King:
“‘Of course,’ Harry thought.
‘I’m all alone. There’s no help coming. It’s the end.
I might as well have done the sensible thing and killed myself.
I would have had some peace then.’
But if I killed myself I wouldn’t have seen Hermione’s face
when she realized that I had sacrificed myself for her.
She would have gone on to become a wonderful person.
She would have had a family of her own.
She would have had children.
She would have had her own grandchildren.
I would have died thinking that I had betrayed her.
But now I have sacrificed myself for her.
She will know that I loved her more than life itself.
Now I will die having done something worthwhile.’
That made Harry feel slightly better.
He drew his silver dagger from inside his robe and started to cut through the ropes on his right hand, which he had already numb with the cold.

He was trying to decide what to do next when there was a loud crack behind him and something wrapped itself around him and a blurred voice said,”Take this. It’s my last one.
“What is it?” said Harry as the voice spoke again.
“It’s a ticket,” said the voice.
“It’s your ticket to ride.
”I’ve got to find a train,’ said Harry.
“Well, hurry up,’ said the blurred voice.
”Hurry up. The train’s about to leave.
“This isn’t a train,’ said Harry, ‘it’s a broomstick.’”

By Mark Twain:
“There are no more uses of man,” said Professor Quirrell. “Odd. That was always a favorite word of Draco’s.”

By Stephen Crane:
"Once there was a little girl named Sadie. She was a curious girl and one day she looked into the attic of her house and saw something that frightened her.

“Oh, it’s just a piece of paper, nothing to worry about,” said her mother.

“If you say so,” said Sadie, but that night Sadie had a strange dream. In her dream she was with a man who wore black and rode a black horse.

“Come with me,” said the man.
“Where?” said Sadie.
“Into battle,” said the man. “You have courage.
We’ll do great things together.”
“Can I bring my mom?” said Sadie.
“No,” said the man.
“Then I won’t go,” said Sadie. “She needs me.”
“Very well,” said the man, and the dream was over.

“Sadie is a wonderful character,” said Harry.
“She could be in one of my books.”

By William Faulkner:
“‘To hell with Lurch,’ Harry thought.
”This whole situation is ridiculous."
But the snake gave Harry that venomous look of his.
‘You’re going to have to speak Parseltongue to him,’ said Hermione.
“I know that,” said Harry, who was now getting annoyed.
“Good,’ said Hermione. ‘The point is, no one else will know what you’re saying, not even Dumbledore. Not unless you speak Parseltongue.’”
“That’s ridiculous,” said Harry. “That makes no sense.
”‘True,’ said Hermione. ‘But remember, they can’t understand us, so if we talk in Parseltongue, they’ll just think we’re talking about Malfoy, about being cruel to him or something like that. It’s no more trouble than that to pretend.’"
“Why can’t they understand what we’re saying?” said Harry.
“Because Parseltongue is a secret language,” said Hermione. “We’ll be speaking the exact opposite of what we mean. We can talk freely, but no one will know what we’re saying.”
“And this is really a good idea?” said Harry.
“We’ve used it before, if you’ll remember,” said Hermione, and now Harry could hear some annoyance in her voice.
“No,” said Harry. “I’m going to do it my way, the smart way, not the pretend way. It’s called initiative. Now, let’s go.”
“There are good things and bad things about using Parseltongue, you know,” said Hermione, who was now speaking in the sort of patient voice people use to try to humor someone who is behaving very, very foolishly.
‘Let’s go, Hermione,’ said Harry.
“Very well,” said Hermione.
“But you need to know that most Slytherins are descended from Salazar Slytherin,” said Hermione.
“I’m not speaking Parseltongue,” said Harry.
“Neither am I,” said Hermione.
“No,” said Harry. “I mean, I don’t need to. But you do. That’s how Slytherins know things. They’re descended from Salazar Slytherin. He was able to speak to snakes. Snakes have always been loyal to Slytherins. It’s not just something recent. And snakes have very sharp hearing. They can hear our conversation now, if we’re not careful. So be careful.”
“Right,” said Harry.
“‘Because Slytherins are all evil, aren’t they?’ said Harry, and it was only by biting back his smile that he managed not to look at Hermione.
”‘It’s hard to say, really,’ said Hermione.
“‘Exactly,’ said Harry. ‘So you’ll forgive me if I think that the entrance hall of Hogwarts at night is not the best place to speak about this.’”
By Edgar Allan Poe:
"Alone in his cubiculum, Draco heard the scuff of his pet dragon’s claws on the floor as he came into the room.

"‘L-l-l-lord D-d-d-draco?’ said his dragon.

“Yes, Fang?’ said Draco.”

By Charles Dickens:
"‘And what does she mean by saying this, and I quote, ’the miserable boy who has dared to raise his hand against his father’?’

“‘I mean what I said, sir,’ Harry said.”
By Alexandre Dumas:
“Darth Vader,” said James, “I would like you to meet Sir Harry James Potter-Snape-Severus.”

“Please,” said the Emperor, drawing himself up to his full height, which did not seem that impressive. He was rather overweight. “Call me Emperor.”

“Ah, right, yeah,” said Harry. “Emperor. Sorry.”

“Right, um, Emperor,” said James.

“Enough of this prattle,” the Emperor said. “Let us see the wand.”

“Er, right,” said James.

"‘And what is this?’ asked the Emperor. He sounded suspicious, and was leaning forward a little.

"‘It’s a stone I found on the floor,’ said Harry.

"‘What kind of stone?’ the Emperor asked.

"‘I don’t know,’ said Harry. ‘I was hoping that your, um, royal insight might tell me.’

“‘Aah,’ said the Emperor. ‘Your wit is as keen as your mother’s, young man.’”

By Anthony Burgess:
“It was a fair cop,” said Harry.

“He said it a bit nonchalant, like it was a regular phrase,” Ron whispered to Harry.

By Charles Darwin:

“Harry thought about this.
”Evolution," said Harry. “Sounds like it could be interesting.”

“It could, and it will be, if we study it together,” said Hermione.

"‘I’ve said all along that magic is just a superior form of science,’ said Hermione.

“‘Yes,’ said Ron. ‘And now you see the wisdom in my decision not to take your advice about studying it.’”
By Sir Arthur Conan Doyle:
"‘The game is afoot!’ said James Potter.

"‘How very interesting,’ said the detective.

“Ah, if only I had a mustache to twirl,” said Hermione." By Leo Tolstoy:
“‘As if by some extraordinary coincidence, he arrives at this country at the very moment the Dark Lord is brought to a momentous decision,’ said Dumbledore.”

“‘And yet,’ said Harry, ‘here I am.’”
By Molière:
“‘That, mon cher Watson,’ said the Master of the Ceremonies, ‘was an entirely logical deduction. It is very interesting how often a complete absence of any real evidence can be explained by supposing that the situation is the reverse of that which actually obtains.’”

By Franz Kafka:
“They stared at him in amazement. Hermione was looking scandalized.”

"‘Don’t look at me like that,’ said Harry. ‘Like what?’

“‘Like I’m someone who’s just declared the moon is made of green cheese,’” said Harry. ‘But it’s true.’

"‘It isn’t,’ said Hermione.

“‘Green cheese is clearly some sort of metaphor,’ said Harry. ’For the sake of discussion, let’s just say the moon really is made of green cheese. And goblins are running the world, with trolls and house elves as their slaves, and—”

“‘That,’ said Hermione, ‘is barmy.’”

“‘And,’ said Harry, ‘all the while they’re attempting to enslave me.’”

“‘How so?’”

“‘Well,’ said Harry. ‘I’m the only one who’s supposed to be able to destroy them, right? And I think they’re trying to get rid of me. Like, at that attack on Miss Scrimgeour—’”

“‘But that could have been an accident.’”

“’I suppose, but—”

“‘Yes?’”

“‘And yesterday, remember? That thing I overheard them saying about students? One of them had a Slavic name, and I think they mentioned Hogwarts.’”

“‘That,’ said Hermione, ‘could have been anyone.’”

"‘But,’ said Harry, ‘the second person spoke, and—’

“‘And if you’re going to be logical, you’ll have to allow that it was nothing more than a coincidence.’”

“‘And if we’re being logical about it,’ said Harry, ‘it wasn’t even two Slytherins who attacked Scrimgeour.’”

“‘Well,’ said Hermione, ‘this has been a very entertaining conversation, but I’m tired and I’d like to go to bed now.’”

"‘Not just yet,’

By P. G. Wodehouse:
"Harry Potter and the Lovecraftian Horror’

By P. G. Wodehouse:
“Harry Potter and the Dreadful Delights of Draco”

By P. G. Wodehouse:
“Harry Potter and the Tale of the Three Broomsticks”15

By P. G. Wodehouse:
“Harry Potter and the Philosopher’s Scone”

By P. G. Wodehouse:
“Harry Potter and the Chamber Pot of Secrets”

By P. G. Wodehouse:
“Harry Potter and the Baked Bean of the Tuatha Dé Danann”

By P. G. Wodehouse:
“Harry Potter and the Half-Blood-Prince”

By P. G. Wodehouse:
“Harry Potter and the Allergy of Barnabas the Barmy”

By P. G. Wodehouse:
“Harry Potter and the Goblet of Fire”

By P. G. Wodehouse:
“Harry Potter and the Prisoner of Chudley”

By P. G. Wodehouse:
“Harry Potter and the Order of the Phoenix”

By P. G. Wodehouse:
“Harry Potter and the Cherry Tree of Azkaban”

By P. G. Wodehouse:
“Harry Potter and the Half-Baked Breakfast of Gryffindor”

Devil’s Dictionary Of Science

Davide Balzarotti snarked:

Rebuttal [noun] A form of ironic composition in 500 words popular among researchers, in which rageful authors first thank their reviewers for “their time and insightful comments” and then mock Reviewer 2 for his non-sense in a sequence of fakely polite points.

Naturally, GPT-3 is able to write not just rebuttals, but satirical dictionary definitions along the lines of ’s —indeed, GPT-3 is remarkably good at it. (“What a Dictionary a GPT-3’s curator might write on the clumsy, wasteful blundering, low and horribly cruel works of Nature!”)

First, generating rebuttals with a slightly extended prompt to cue GPT-3 into writing the rebuttal itself rather than additional dictionary definitions:

Rebuttal [noun] A form of ironic composition in 500 words popular among researchers, in which rageful authors first thank their reviewers for “their time and insightful comments” and then mock Reviewer 2 for his non-sense in a sequence of fakely polite points.

"We thank reviewers 1, 2, and 3, for their time and insightful comments, which we have spent many hours reading over. But in reply we note:

1. RE2: Although RE2 starts with the reasonable comment that ‘Currently our control loops are pure control’, he goes on to provide only minor points that suggest that we are doing something slightly different. In fact, there is much more to be said about this, and the real difference will be a full control protocol on all vehicles.

We thus thank RE2 for taking the time to provide such minor points, but note that he has done almost no actual research and has spent zero hours on the review.

RE2: " There is little or no literature on decentralized solutions to problems such as traffic optimization."

RESPONSE: We thank RE2 for the minor point that there is “little or no literature on decentralized solutions.” But we note that decentralized solutions are particularly difficult, and so this fact might suggest that they are not being considered or might be infeasible. Or it might suggest that they are being researched intensely. In this case it turns out that, unlike RE2, several of us have in fact thought deeply on these issues, and so we feel we are a valuable research group despite not being affiliated with the Department of Traffic Control or any other centralized, government-backed, research institution.

RE2: “The DGM solution is also [something else]. To our knowledge, no work has been done on DGM control loops, or any control loop at all, for these kinds of traffic patterns.”

RESPONSE: We thank RE2 for the comment that the solution we have proposed is [something else]. As he is not familiar with such traffic patterns and the other reviewers in their reviews have been unimpressed, we must wonder how he could be familiar with control loops that might or might not exist and would or would not have been previously performed.

RE2: “The number of cars involved is approximately 100, which is a small number. By contrast, say, the NYC Metropolitan Area, [roughly 22 million people] has a traffic network of nearly 3 million miles.”

RESPONSE: We thank RE2 for the comment that our problem is “small.” This is the first time this has been suggested to us. However, we feel that RE2 may be failing to understand a key component of the research protocol we have proposed. This problem is designed to be decentralized in a city with no more than a couple million people (or a town or a small city or maybe even a village) and so it is expected to have only a couple thousand drivers and not one single, central traffic control unit that could possibly control 3 million miles. We thank RE2 for his insight on this, but also feel that he has not gone to great enough lengths to consider the nature of such a problem in such a situation.

RE2: “Experimental data [will likely] need to be collected and processed. Currently the authors provide no experimental plan for any of their research [and so] we feel the experimental results to be lacking.”

RESPONSE: We thank RE2 for the suggestion that we have not provided an experimental plan. As our paper is only intended to propose a protocol, we understand that experimental plans should come later. In fact, we will be presenting our plans to colleagues later this year in a peer-reviewed symposium at State College, Pennsylvania. That being said, we find it strange that RE2 might have any knowledge of such plans or of such conferences. As we are listed as authors, we feel that RE2 might have been able to contact us if he were interested in our work, but he has not and thus we do not know whether he has even heard of our research program.

RE2: “The distributed system is better able to operate in an autonomous fashion, i.e. no central controller is required. With the possibility of a large number of drivers, autonomous operation is challenging, especially if vehicles may communicate in unanticipated ways [or they might not]. As suggested by [the very important paper], the [not really relevant] control loop should be a passive mechanism whereby each vehicle adjusts its control mechanism through a feed-forward signal.”

RESPONSE: We thank RE2 for the comment that autonomous operation is challenging. But again, we do not understand how he could know this. In fact, he has spent almost no time on the research, spent zero hours on the review, and spent zero research dollars, and it is hard for us to understand how he could know this better than we could. In fact, we have spent over a decade working on this project, with many hours of detailed thought and research, and we are still not sure that autonomous operation is a good idea or how we could get it to work in a decentralized and random traffic environment.

RE2: " If we all observe that several vehicles are slowing down, it might be a sign of a possible accident ahead. If we are all in autonomous mode, a vehicle near the accident would brake, possibly causing a pileup."

RESPONSE: We thank RE2 for the comment that several vehicles slowing down might be a sign of an accident. He is correct that this might be a sign of an accident. But he might have considered the possibility that several vehicles slowing down might be a sign of, say, the vehicle he is in slowing down or that he is doing something else such as texting a message to someone else, or simply that he might have a flat tire. We thank RE2 for this, but we have also considered these possibilities and feel that the latter is far more probable, so we suggest a second solution to this problem that would require each vehicle to text all other vehicles on the road with its status, something like “Hey I’m just sitting here, so if anyone wanted to come fly up the back of my car and then over the top of my windshield and down into my passenger seat to grab my phone and then fly back, well by all means, be my guest.” In this way, a vehicle with a flat tire would have no delay in informing all others of the presence of a flat tire and in getting help with fixing it. Also, an accident would be similarly well-announced, and we think this solution is particularly important given the new protocol we are designing to allow for more efficient traffic control.

RE2: “Distributed control systems [are usually] an assortment of semi-autonomous vehicles moving in close proximity to one another. Such systems [are generally] non-autonomous, requiring control by a central controller that can be updated to reflect the most recent situation.”

RESPONSE: We thank RE2 for the comment that distributed control systems are usually non-autonomous, requiring control by a central controller. But RE2 has spent zero time on the research and we feel that he has failed to appreciate that the problem we have designed is designed to be a decentralized, autonomous, distributed traffic control problem. We again feel that this is only to his great detriment.

RE2: “Such cars [on our street] will become very complex, and we feel they will be more difficult to design and build.”

RESPONSE: We thank RE2 for the comment that our cars will become very complex. We feel that this will be of no issue, as we have discussed at great length before, and we think that a small group of university professors with a good design team should be able to provide cars that work in this capacity in the real world.

The second completion finished quickly and to my surprise, began writing further definitions:

…1. We did not describe any ‘simple algorithm’ for the repeated-measures ANOVA in the abstract, the discussion, or in the Methods. Such a description of our protocol would be incoherent and probably invalid anyway.

  1. We know of no ‘more appropriate analysis’ for the mean and variance of the proportions than to compute the estimates of the variances directly. Since we have done this correctly, it makes no sense to proceed further as suggested by Reviewer 2.

  2. It does not make sense to carry out the tests for homogeneity that Reviewer 2 suggest we do. Indeed, we never said we should carry out such tests. Our precise point was that we should not compute the standard errors of the treatment means, and Reviewer 2 seems to think that we have argued for the opposite point, which we have not.

  3. Again, Reviewer 2 apparently did not read the Methods section carefully and was confused by some sections that he or she did read. A better review would have pointed this out.

  4. We note the Reviewer 2’s overly generous suggestion that we write a ‘special’ rebuttal. At the very least, we assume that Reviewer 2 knows what a rebuttal is, and we would therefore argue for a critical comment instead."

Noise

Noise is anything you don’t want or need, a random or distracting factor in a system, process, or set of data that obscures or corrupts information. It is also the sound produced by old time radios when no transmission is present, which is roughly what some fields of science would have to resemble if some researchers had their way.

“New research”

A form of scientific publishing that includes, among others, a report on previously performed studies; a report of a new statistical test or set of statistical tests that were performed by the author of the “study” without the help of other scientists; a report of a small set of samples, not necessarily from a population or process, which could, but have not been collected in a systematic way and may not even be accurately described, in the guise of which conclusions are drawn about the world; and a report of the result of performing an unvalidated computer simulation of something on a small set of data without stating whether such an attempt was successful or not.

“Nice to see”

A widely and unofficially accepted phrase in scientific circles meaning: “It is better to live in an imaginary world created by data manipulation and overly optimistic predictions than in the world that exists in reality”.

“Researcher”

[noun] A form of modern industry based on a mix of small molecules of grant money and arbitrary experimental methods.

“Review”

A form of scientific communication in which the writer (a student) usually does not give enough information for the reader to judge the merit of the arguments presented in the paper; the writer also does not respond to or comment on the suggestions of the reviewers (sometimes known as referee or editor). This form of communication is popular among students who wish to leave the burden of writing the paper on other people and are willing to pay the price of rejection.

“Second chance”

A word that scientists regularly use when referencing a journal, conference, or grant application. While there is always an implicit threat that you can get rejected, or be accepted with changes, as a second choice to another party, some articles, papers, grants, and grant proposals receive multiple, even unlimited, chances of being considered. As a result, the word may be interpreted as a bribe or a threat depending on whether you are the writer or a reviewer.

“Reviewer”

A participant in the review of a grant, paper, or grant proposal. In spite of being in a poor position to assess the merits of a proposal, reviewer tends to demand that authors submit their data for statistical analysis and back their results with it, which the reviewer usually does not. Reviewer usually requires that the author cite his or her own work to prove that he or she is worth reviewing. It is also assumed that the reviewer can detect the slightest amount of bias in any paper, which the reviewer also assumes has not been corrected for.

“Revise and resubmit”

A sentence that authors of submitted papers tend to hear from reviewers, editors, and the like. A form of scientific communication that begins with submission of a paper (for which the reviewers may or may not have been consulted), proceeds through review and criticism of the paper (typically by one or two reviewers, not a full panel of reviewers, unless the paper is deemed to be of supreme importance), and, if it is “revised and resubmitted”, usually ends with another critique of the new paper by one or more of the reviewers, and another round of “revise and resubmit”, ad infinitum. In the absence of fraud, retractions, litigation, mass incompetence, or insanity, these “revise and resubmit” cycles may be taken as the only method of scientific communication.

“Retracted”

The most frequently used word for a journal article that was subsequently found to be based on fraudulent data.

“Rigor”

Something for scientists to aspire to, a state of mind that would not be required if scientists could be trusted to do their job.

“Science”

A complex web of data, opinions, lies, and errors, now considered the most important (because most expensive) technology in the modern society. To remind you of this, you will frequently see scientists and editors use the word, claim to do something for the sake of science, or see it used as an adjective.

“Standards”

Standards for data and their interpretation vary depending on the country or scientific discipline.

“Study”

[noun] A form of communication in the scientific literature that authors or publishers may use to make claims that are not warranted. Examples include, but are not limited to: The New study of clinical trials that show…, As Dr. X reports in his new study of observational data…, The new study demonstrates that…, Study shows that, In a study published last week…, The study suggests that, New findings show that, A study of data…

“Study X”

A widely used nickname for studies with profound, not to say earthshaking, conclusions that may or may not be of any relevance to anyone who reads about them.

“Technology”

Any bit of software, analysis, model, or other methodology that scientists can be expected to claim as their own, for which they can sell you consulting or collaboration fees.

“Tool”

[noun] Any piece of software, analysis, model, or other methodology that scientists can be expected to claim as their own, for which they can sell you consulting or collaboration fees.

“The topic of the paper”

A wide-ranging category of things or ideas that may not have been relevant when the paper was written, but which the authors believe the paper should be about. Often, the topic is too broad or a non-topic, but is occasionally useful in order to generate support for yet another set of related papers, conferences, seminars, webinars, and so forth, which in turn are used to generate more data for “new findings”, which, after they are manipulated enough, may end up being published and generating yet more data to support a “re-review” of the original paper or other things.

“Unified theory”

An imaginary set of ideas that scientists dream of having so that they could achieve the same results by slightly varying the methodology, thus preserving their original publications.

“Validation”

The process by which scientists decide that something they built in their spare time works in the way they expect. The validation process requires that you do not attempt to validate it before you publish it and require that the users validate it after.

“Value”

In its most natural form, science is the ability to study data and ideas; the amount of respect and publicity that a scientist can expect; how well his or her paper was published; the likelihood that a scientist will receive a grant; whether a scientist will be taken seriously; the likelihood that an idea will be accepted; how much grant money will be available to purchase new toys for this scientist; whether something is scientific; the likelihood that the papers of this scientist will be read; the status of this scientist; the likelihood that a journal, conference, or society will be able to continue funding or exist; how much and how fast this scientist’s career will progress; the “value” that this scientist brings to the modern society; and the likelihood that this scientist will continue to be funded. While the actual values for science are hard to pin down, what people believe them to be is another matter. For example, while some believe that science is important, even if it doesn’t have any concrete value, others believe that it’s the ability to continue reading abstracts or studying it that has value. Still others believe that what matters is how much grant money is currently available, even if the researchers are actually doing something that nobody knows about. Some believe that the fact that someone is in a position to give a grant, or has written a paper, or, even better, has published that paper, actually means that it has a lot of value. Others believe that what matters is not what you publish, but how you publish it and that all research must be published in the most prestigious journal, even if you are not that much of a researcher. The reality is that no one really knows what science is, or what value it has; however, we all know that if we deny that science has value, we might be able to get rid of it and use this space for something else.

“Validation step”

Another name for a random setting of a parameter of a model, simulation, or algorithm.

“Verify”

A step in a procedure that, if performed properly, will tell you whether the previous step, or anything in the last two years of your work, was performed properly.

I had to keep continuing it for more definitions:

“Unified theory”

An imaginary set of ideas that scientists dream of having so that they could achieve the same results by slightly varying the methodology, thus preserving their original publications.

“The usual procedure”

A method of study, validation, and public communication which, if followed, may lead you to be able to claim that your work may offer a decent return on investment.

“Wide range”

When used in the context of scientific results and models, it usually means that the results and models are not likely to be used for their intended purpose, if they have one.

“Wider range”

When used in the context of scientific results and models, it usually means that the results and models are likely to be used for their intended purpose, if they have one.

“Writeup”

A form of scientific communication in which the author states the information he or she wanted the readers to extract from the paper while making it as difficult as possible for them to find it.

“Write-up”

1. [noun] A style of writing used in academic papers to dress up data and results. 2. [verb] The act of presenting data in a way that makes it look less impressive and more complex than it really is. 3. [verb] The act of writing a manuscript that is impossible to follow. 4. [verb] An activity that is often performed by the most blatant or careless users of the English language, all for the sake of showing off their erudition.

“Writer’s block”

A common affliction among students, arising from various causes, such as: their desire to sell their ideas for a profit, their inability to realize this desire, the fact that their ideas are not selling and will not be bought, and the delusion that most of the wealth and fame in the world would be theirs if they would spend enough years doing science.

I tried tailoring the prompt a little more precisely to generating definitions:

A Devil’s Dictionary of Modern Science & Academia
Reviewer #2

"Researcher" [noun]

A form of modern industry based on a mix of small molecules of grant money and arbitrary experimental methods.

“Noise” [noun]

Noise is anything you don’t want or need, a random or distracting factor in a system, process, or set of data that obscures or corrupts information. It is also the sound produced by old time radios when no transmission is present, which is roughly what some fields of science would have to resemble if some researchers had their way.

“Review” [noun]

A form of scientific communication in which the writer (a student) usually does not give enough information for the reader to judge the merit of the arguments presented in the paper; the writer also does not respond to or comment on the suggestions of the reviewers (sometimes known as referee or editor). This form of communication is popular among students who wish to leave the burden of writing the paper on other people and are willing to pay the price of rejection.

“Reviewer” [noun]

A participant in the review of a grant, paper, or grant proposal. In spite of being in a poor position to assess the merits of a proposal, reviewer tends to demand that authors submit their data for statistical analysis and back their results with it, which the reviewer usually does not. Reviewer usually requires that the author cite his or her own work to prove that he or she is worth reviewing. It is also assumed that the reviewer can detect the slightest amount of bias in any paper, which the reviewer also assumes has not been corrected for.

“Science” [noun]

A complex web of data, opinions, lies, and errors, now considered the most important (because most expensive) technology in the modern society. To remind you of this, you will frequently see scientists and editors use the word, claim to do something for the sake of science, or see it used as an adjective.

“Revise and resubmit” [verb]

A sentence that authors of submitted papers tend to hear from reviewers, editors, and the like. A form of scientific communication that begins with submission of a paper (for which the reviewers may or may not have been consulted), proceeds through review and criticism of the paper (typically by one or two reviewers, not a full panel of reviewers, unless the paper is deemed to be of supreme importance), and, if it is “revised and resubmitted”, usually ends with another critique of the new paper by one or more of the reviewers, and another round of “revise and resubmit”, ad infinitum. In the absence of fraud, retractions, litigation, mass incompetence, or insanity, these “revise and resubmit” cycles may be taken as the only method of scientific communication.

“Rigor” [adj]

Something for scientists to aspire to, a state of mind that would not be required if scientists could be trusted to do their job.

yielding more definitions, starting with a single letter to avoid running off into the “Z”es and truncating completions (since GPT-3 will proceed A–Z, starting from the last definition, as is logical, unless otherwise prompted):

A role for…” [phrase]

A frequent phrase found in submitted and published papers; it often indicates that the authors have nothing to say about the topic of their paper. In its more emphatic form, “A role for…” usually indicates a struggle by the authors to take a side on an issue, after a lengthy attempt to be both non-committal and a supporting party to all sides, as often happens in “molecular and cellular” or “basic and translational” research.

“Reviewer” [noun]

A participant in the review of a grant, paper, or grant proposal. In spite of being in a poor position to assess the merits of a proposal, reviewer tends to demand that authors submit their data for statistical analysis and back their results with it, which the reviewer usually does not. Reviewer usually requires that the author cite his or her own work to prove that he or she is worth reviewing. It is also assumed that the reviewer can detect the slightest amount of bias in any paper, which the reviewer also assumes has not been corrected for.

“Resident”

An adjunct faculty member that administers teaching assistantships, sits on departmental committees, and seems to live on the departmental lounge. A kind of “honorary” faculty member that is not yet an actual faculty.

“The Action Plan for Diversity” [noun]

A vague document circulated on campuses in recent years to remind the faculty to be aware of discrimination and presumably combat it in the departments. It is often taken as a generalized insult of students, who tend to see no way around this plan without committing academic fraud.

"Bias" [noun]

A form of mental abuse by which a scientist turns an experiment in such a way that the results prove his or her point of view or grants get funded.

“Behavioral Studies” [noun]

Sometimes mistaken as a field of science. The terms Behavioral Studies are actually a combination of disciplines that usually includes business economics, sociology, and engineering.

“Humanities” [noun]

A broad term used to refer to any academic discipline (such as English, philosophy, religious studies) in which science has been forgotten. To avoid confusion between the sciences and humanities, remember that scientists use hard tools and humanities their soft tools when examining issues and trying to persuade audiences. In other words, both scientists and humanities routinely lie (it’s part of their job), but they do it in different ways.

“Literature Review” [noun]

A paper, commonly published in a peer-reviewed journal, that advertises the author’s skills and accomplishments without using any evidence. The reader is left to believe the work must have been done, since it has been advertised and is “published”.

“The Literature” [noun]

A name given to other people’s published papers, referred to by scientists without actually reading them.

“Track Record” [noun]

When scientists use this term they refer to the research done by someone else (usually a student) in order to avoid having to do research.

“Discussion” [noun]

The part of a scientific paper where the writer summarizes the points made in the body of the paper and acknowledges certain acts and circumstances that helped with the completion of the study. The author should carefully state in this section whether or not the study reported in the paper could have been done otherwise and should clearly state that if he or she had the opportunity, he or she would definitely repeat the study again.

“Role model” [noun]

A person who can be an inspiration to others because of his or her academic and personal achievements in general, or because they conduct academic misconduct on an industrial scale. The most important qualification for becoming a role model for others is to be judged sufficiently inept that no one will question anything you do, or to become the successor of someone who has already acted as a role model.

“Faculty” [noun]

Used loosely by scientists to mean any group of people with advanced degrees. Typically used when you have done something stupid and want to inform others that it wasn’t you who did it, but rather those other crazy people over there who won’t put their titles after their names.

“Significant” [adjective]

A word which defines the claim made in a paper (no matter how trivial). It is a critical term in hypothesis testing, in that the sign of the test statistic must be evaluated and compared to a critical value in order to support or reject a hypothesis. Significance is designated by two-tailed tests when the direction of the effect is not specified. If a two-tailed significance test is used, the “p”-value needs to be halved, due to “double-counting”. In any event, “significant” remains of critical importance in any hypothesis test, since the whole idea is to come up with a “sign”, i.e. plus or minus (“+/-”), or a wishy-washy “statistically significant”, without actually saying anything substantive about the matter.

“Random assignment” [verb]

A way to arrange study groups so the resulting data is conclusive and unbiased. Typically used in the social sciences by graduate students who refer to it while explaining why they broke down the groups this way.

“Cite” [verb]

To refer to a paper or journal as a source for support, when in reality you have never read it and did not understand it.

“Bibliography” [noun]

A list of citations of references used to support a particular point in a paper. A list that somehow includes several references that didn’t actually end up in the final bibliography of the paper.

“Let’s stipulate” [phrase]

A preface to any statement, that means I know I am going to piss you off with my statement, but since you are such a nice person, I am first going to give you a chance to let me piss you off without responding.

“How is that different from…?” [phrase]

A question posed by authors when they have been accused of stealing ideas. They are attempting to show that they have never read the papers that contain the ideas they stole, nor even looked at the ideas in any detail, and are just fishing for compliments.

"Citation" [noun]

An elaborate form of review, typically found in order to impress those who do not know better, in the sciences, humanities, or even the social sciences. A citation typically involves misreading or misquoting an author and talking about something he or she did not write about or intend. It is assumed that you need to carry out a large number of citations in order to have science, probably because at least one citation can be found for any fact, event, or incident in history; the effectiveness of this fact is increased if the author is long dead and can not disprove it.

“Physics” [noun]

A science. Physicists like to think their science is something you can do without a plan, something that relies on intuition and inspiration, something that is “deep”.

“Scientific revolution” [noun]
A set of techniques and methods used by scientists in the course of their work. It is assumed that there was a total scientific revolution in the past century or so that allowed scientists to avoid even thinking about the quality or usefulness of their science.
Scientific revolution is started by Henri Poincaré (mathematics), Marie Curie (radioactivity), Max Planck (quantum physics), Albert Einstein (relativity and revolutionized physics), Dmitri Mendeleev (periodic table of elements), Niels Bohr (quantum mechanics), Lise Meitner and Otto Hahn (fission), Linus Pauling (molecular biology) and many others.

See also: Einstein’s Method, Pauling’s Method, Bohr’s Method, Poincaré’s Method

“Scientific method” [noun]
A set of steps in which a scientist (or “researcher”) would probably not follow in practice: planning of research, including definition of problems, hypothesis formulation, selection and design of experiment(s), collection and entry of data, execution of experiments, data analysis and interpretation, preparation of final report, implementation of suggestions. In actual scientific practice the most important part of the scientific method is to observe and collect data without a plan or preconceived expectations about what is causing what. It also helps to be unbiased in selecting the data to include in the study and to pick experiments at random. Additional steps include writing a paper, a process that usually involves the most violations of the scientific method.

“Scientist” [noun]
A person who has a laboratory, access to large sums of money, graduate students, or all three.

“Revise and resubmit” [verb]
A sentence that authors of submitted papers tend to hear from reviewers, editors, and the like. A form of scientific communication that begins with submission of a paper (for which the reviewers may or may not have been consulted), proceeds through review and criticism of the paper (typically by one or two reviewers, not a full panel of reviewers, unless the paper is deemed to be of supreme importance), and, if it is “revised and resubmitted”, usually ends with another critique of the new paper by one or more of the reviewers, and another round of “revise and resubmit”, ad infinitum. In the absence of fraud, retractions, litigation, mass incompetence, or insanity, these “revise and resubmit” cycles may be taken as the only method of scientific communication.

“Shared data” [noun]
Data that both a reviewer and a researcher have access to but not sharing in advance.

“Shared future” [noun]
An outcome (rarely achieved) that is more pleasant for both the reviewer and the researcher than if there is no shared future. This usually refers to an outcome in which the reviewer either accepts the paper or at least recommends re-writing the whole thing rather than rejecting it. This is often viewed with suspicion by reviewers.

“Sharing of data” [noun]
The belief of some people that researchers should have all the data when doing a study; this would allow the reviewer to check the sources and calculations and check for bias. This would limit what the reviewer could say to “the statistics were fine but your data wasn’t”. It is very difficult to achieve this state. It can be accomplished if the scientist keeps detailed notes of everything he or she is doing.

“Statistical error” [noun]
A mistake made by researchers that can somehow be made up for by using large enough sample sizes, usually called statistical significance; an event that could not have been predicted, analyzed, or provided for in advance, therefore

"Dumb" [adj]

The amount of information scientists can be bothered to remember before they either dismiss it as incorrect data, or ignore it completely.

“Surprised” [verb]

A scientific system in which one is “surprised” by the result of an experiment, does not necessarily mean that they believe it is wrong, but usually means they are not sure what to say about it.

“Oppression” [noun]

A state of mind in which laboratories underfinanced by the government can be considered financially secure, or any noise that scientists don’t like or want to hear.

“Reviewer” [noun]A participant in the review of a paper. In spite of being in a poor position to assess the merit of a proposal, reviewer tends to demand more information, more experiments, more statistics, and a tighter theoretical interpretation than the authors are willing to provide. Reviewer usually rejects any responsibility for his or her own bias being exposed, citing the fact that the author has not attempted to prove that bias is absent, nor made sufficient allowance for human error. Most referees are reviewer seem to assume that they have given enough information for their opinions to be taken seriously, though they were not the originator of the proposal. Reviewer usually requires that the author cite his or her own work to prove that he or she is worth reviewing. It is also assumed that the reviewer can detect the slightest amount of bias in any paper, which the reviewer also assumes has not been corrected for.

“Theoretical interpretation” [noun]
Another form of scientific communication that cannot be proven, but can be used to explain anything after it has been published or presented. It is always accompanied by an appeal to authority, data, and meticulous theoretical interpretation (see below).

“Screwed” [adj]
Screwed is generally used in circumstances in which the author has lost everything, in his paper or grant proposal. Applies best to situations where the author finds out he has been waiting to hear for months, when it has been decided in his absence that funding will not be extended or alternative sources of funds are not avaialble to continue the work. For example, when an experiment goes downhill or a paper is rejected. Also applied when the author of a paper or grant proposal discovers that he must carefully state and defend his own results in order to get feedback on what could be wrong with his work, because the readers may not be able to figure it out for him. It is also suggested that this term apply whenever two or more editors or reviewers decide they can all influence the final outcome of a paper, when only one review should determine if the paper is publishable. Recommendation: always have multiple concurrent submitted manuscripts in the system.

“Reviewer” [noun]
A participant in the review of a paper. In spite of being in a poor position to assess the merits of a proposal, reviewer tends to demand more information, more experiments, more statistics, and a tighter theoretical interpretation than the author is willing to provide. Reviewer usually rejects any responsibility for his or her own bias being exposed, citing the fact that the author has not attempted to prove that bias is absent, nor made sufficient allowance for human error. Most referees are reviewer seem to assume that they have given enough information for their opinions to be taken seriously, though they were not the originator of the proposal. “Reviewer” requires that the author cite his or her own work to prove that he or she is worth reviewing. It is also assumed that the reviewer can detect the slightest amount of bias in any paper, which the reviewer also assumes has not been corrected for.

“Argument” [noun]
An instance of convincing someone that you deserve credit for your position (i.e., problem solving skills).

“Fail” [verb]
In scientific jargon, failure means death by hoax or accidental plagiarism, depending on the reputation of the lab (e.g., if tenure level scientists are involved).

“String theory” [noun]

A theory in physics which is about 99 percent false, but people seem to like it, so they keep funding it. A good example of the difference between a “scientific” concept and a “semi-scientific” concept, or even an unscientific concept.

“Theory” [noun]

An unproved hypothesis; a conjecture that might or might not be right; use of this term forms the basis of the common game of turning the scientific method on its head. The most popular types of theories are the theory of intelligence (or unintelligence), time theory, quantum theory (of course, even more primitive), and anything that is implied by string theory.

“Research” [verb]

Something done with the money provided by a body or person other than the principal researcher(s), generally private individuals and businesses hoping to get something out of it. Traditionally involves experimentation.

“Publish or perish” [phrase]
Used by scientists to remind other scientist how important it is to them, if they are paying attention at all, which they rarely are. A method of scientific communication where papers are subsidized by fraud, incompetence, or stupidity, depending on the field of science involved. Each author having to prove his or her worth by churning out papers that others

“String theory” [noun]

A set of mathematical formulas that can be used to predict which particles exist (or do not exist).

“Science group” [noun]

A collection of researchers in various fields, with goals including one or more of attracting funding, specifically young, naive, and gullible grad student, inbreeding graduate students in order to maintain affiliation with the group, writing and publishing papers specific to the group’s activity in order to continue the goal of attracting funding and inbreeding graduate students, calling in favors from University administrators in order to preserve and continue the goal of attracting funding and inbreeding graduate students and sacrificing sheep and lambs in order to obtain funding and inbreed graduate students.

“Group leader” [noun]

Person whose function appears to consist mainly of ordering subordinates (graduate students) to fill out grant applications, enter grant data into a spread sheet, teach undergraduates laboratory techniques not commonly taught elsewhere, submit papers to a journal any time before the end of 2002 (record will be broken), devote time to discovering something useful or interesting about science outside of the parameters determined by the group leader, ever working on problems specified by the group leader if they cannot be worked on inside the parameters determined by the group leader. All of these activities are considered necessary to support the “science groups” goal of attracting and inbreeding graduate students, and provide opportunity for satisfactory career development for the group leader.

“Special material” [noun]

Something that only has to be done once, where other people will then do it again every time a paper requires it. Or conversely, something that cannot be repeated because of its poor reproducibility (special uncertainty, uncertainty special).

“Special relativity” [noun]

Advanced idea described in Einstein’s 1905 papers on the subject, which states that, from a suitable point of view, “E=m,” where “E” is energy, “m” is mass, and “a” is “Profundity.” Except for arcane fields such as string theory, special relativity is the most widely accepted of Einstein’s work. Naturally, papers based on special relativity are the most likely to receive “special material”.

“Speculation” [noun] One of Newton’s Laws of Science, also known as “Newton’s Law of Speculation”, which states that “Speculation breeds Confusion”, an effect well illustrated by how theoretical particle and string theorists debate scientific issues with each other. Confused? You should be. After all, you’re dealing with a bunch of theoretical physicists.

“Professor” [noun]

The highest academic rank available, a level usually associated with Freedom of Speech. It may take 35 years of service before the University bestows that title.

“Publication Credibility” [noun]
A subjective notion, as its name implies, based almost entirely on the sexiness or importance of the field in which the research was done and not the quality of the writing and presentation.

Cycling through the prompt as length permitted:

“Placebo” [noun]

An inert substance used to placate a patient or control group. It is largely composed of sugar.

“Et al.” [proper noun]

Et al. is an abbreviation of the Latin phrase et alii, meaning “and other people”. In the context of science, it is typically used to shorten the name of a list of authors whose name and affiliation would otherwise need to be written out in full. It is used when the number of authors is small enough that the abbreviation would not render the reference too obtuse and cumbersome, while at the same time remaining large enough that there are at least two other people involved in the study (and hopefully a control group).

“Referee” [noun]

A critical reader, particularly one with the power to reject your submission. In the absence of more efficient forms of communication, peer review depends on these people. See “Reviewer”.

“Shared values” [noun]

A set of principles, beliefs, theories, methods, and operational definitions that all scientists share and use. Never spoken of aloud in public.

“Communication” [verb] A method of persuasion that allows other researchers to review or criticize your work. You can communicate with others by reviewing articles, which allows them to revise their work; participating in panels, which allows others to hear your opinions; attending conferences, which allows you to strike up conversations at late-night dinners; joining committees, which lets you talk at any time; running experiments, which allows you to change the outcome based on the result; and receiving grants, which allows you to tell people what to do. You are likely communicating now.

“Panel” [noun]

A group of individuals who meet at some given time to discuss something. A more bureaucratic version of peer review for larger groups of individuals or committee members who have already established their worth in research.

“Data” [noun] Information that appears as graphs in journals. Not surprisingly, this information is critical for interpretation and forming conclusions. Scientists spend much of their lives looking for data.

“Fraud” [noun] A very evil thing that must be perpetrated or discovered in order for the paper to be accepted for publication. Sometimes fraud occurs even if both the scientific community and editors are prepared to reject the paper at the first sign of fraud, just because they know how bad it would look if fraud were found later. See also “Legitimation”.

“Legitimation” [verb] The process whereby a fraudulent publication is converted into a legitimate publication by virtue of its appearance in a peer-reviewed journal. This can also be considered a type of reassurance that “the checks and balances are working”.

“Retraction” [verb] The formal rejection of an accepted scientific claim after its publication. In contrast to “withdrawal”, which is when a paper is removed from publication with the consent of the author(s). See “Fraud”.

“Lies” [noun] A form of deceptive speech that appears as “honest mistakes” or “inaccuracies”, which are often rejected from publication as a result. In the absence of other sources of information, however, lies are required for the maintenance of the scientific enterprise. See also “Journalistic integrity”.

“Scientists” [plural noun] A group of people engaged in scientific research or education. Scientists vary widely in terms of age, moral character, ancestry, and success. Scientists with noteworthy accomplishments are sometimes described as “leaders” and regarded as experts in their fields, which leads to the tendency to treat them like demi-gods.

“Health” [noun] A number indicating how unhealthy something is. When applied to humans, it quantifies how in need of health care someone is.

“Clinical research” [noun] Research conducted on humans, e.g. clinical trials and epidemiological studies. Researchers do not like this kind of research because humans are unresponsive and unreliable.

“Funding source” [noun] Those who finance science by paying people to do things that the funder might not want to pay them to do if the funder knew what they were doing. For example, giving people money to research tobacco without actually running cigarettes through their noses would not be what the tobacco industry wants scientists to do for them. Some funding sources impose their will on the researchers by making sure that their funding is only allocated if certain lines of research are followed and other lines are not (this is sometimes known as a budget), while others may let the scientists do anything they want (this is sometimes known as no budget). The nature of research findings thus depends on the budget.

“Authorship” [noun] The process whereby researchers “publish together”. The precise implications of this process depend on the publication type. In most cases, authorship represents the researcher’s contribution to the paper; however, plagiarism is also sometimes involved, especially if multiple authors fail to cite earlier work on which their own paper depends. There is also another kind

“Journal Impact Factor” [noun]

According to some, it is a value that corresponds to the average number of citations of articles published in a given journal, if the interpretation is right. Otherwise, it is a completely arbitrary number, computed from the number of times articles published in a given journal in the last two years were cited by other articles published in other journals, the assumption being that every paper published in a scientific journal must be essential to someone else’s research, or at least that that person would like to be able to cite it. The difficulty with this assumption is that the average time from submission to publication of an article is now approximately 12 months, and the current record stands at three years for Biochem. J. Proc. (2000). This means that three times as many papers have to be published every year as are actually written, with more and more papers being submitted and rejected every year (the reality is even worse, but we don’t have numbers), and with different standards applying to different journals, which are becoming increasingly specialized. All of these “facts” considered, the best any researcher can say about the Impact Factor of a given journal is: “I know it when I see it”. Note also: This formula can produce values up to 4 for an article appearing in a journal containing only that article, so one “article” can receive as many “citations” as a journal.

“Sterile” [adj]

Said of that which is dry and lifeless; devoid of humor; also, said of scientific writing and methods when germane to the matter at hand.

“Grant” [noun]

A form of scientific communication involving a large piece of paper, on which is written an enormous sum of money. The grant-writer then sends his or her (because they are almost always written by men) proposal into a maze of office politics, legislative/funding agency reshuffling, and intellectual and interpersonal competition. The prospect of acquiring a grant makes up for the fact that grant-writers receive no practical training in writing grants, have no idea what kind of “research” they will have to conduct with it, and generally start research as soon as they are finished writing the grant (if they ever are). To be on the safe side, please note: One does not have to win a grant to “win a grant”; one must simply apply for a grant. Just do it.

“Peer review” [noun]

A system of literary communication that consists of first submitting a manuscript to the editor, who (if he has not been fired) usually asks at least one reviewer to read the manuscript. If there is any doubt about whether the editor has lost his mind, or whether the manuscript has some merit, then the reviewers and editor sit down over coffee and discuss the manuscript (in conference), which is described as “peer review”. This form of communication does not provide either editors or authors with much feedback about their manuscript, but it makes reviewers feel important.

“Precise” [adj]

Said of everything except experimental results; it also means correct, appropriate, or in accordance with the relevant standards. But when the experimental results do not agree with the model of the hypothesis, the phenomenon being studied becomes “precise”, and one “cannot make predictions about precise experiments”. Please keep in mind that most theories are meant to be general in nature, so they must also be imprecise in their predictions. If they could predict with precision, they would not be general, but they would still be interesting.

“Theory” [noun]

A scientific explanation of something, which people use only in situations where they think explaining things is more important than discovering things. Otherwise, scientific theories are always meant to be true, since they are believed by people with education; to a scientist, a theory is true; to a person with a higher IQ, a theory is a fact; and to a citizen with a lower IQ, a theory is an opinion.

“Significance” [noun]

Significance refers to whether a result of a study is somehow important. If a significant result is found, then the problem that the researchers have studied is of real-world importance. If the results aren’t significant, the problem is not important; therefore, more money and time can be wasted by pursuing it. (E.g. “This study showed that chocolate makes rats temporarily hyperactive, so we will look for ways to inhibit this reaction in humans. Thank you for your support.”)

“ResearchGate” [noun]

A website of questionable purpose that allows scientists to upload their “results”, resulting in the same sort of information clutter and bad or incorrect data as any other social media network.

“Rigor mortis” [noun]

A state of rigor in which scientists do not think outside the paradigm, no matter how much it does not work; rigor mortis is characterized by the desire of researchers to prove their prior research correct.

“Important” [noun]

(British English: “Important”) [adj]

Whether something is important to you personally or not, it is important in a statistical or numerical sense to someone. Therefore, it is always a useful adjective to add to any scientific term, especially when one does not have a better verb.

“ResearchGate” [noun]

A website of questionable purpose that allows scientists to upload their “results”, resulting in the same sort of information clutter and bad or incorrect data as any other social media network.

“Rigor mortis” [noun]

A state of rigor in which scientists do not think outside the paradigm, no matter how much it does not work; rigor mortis is characterized by the desire of researchers to prove their prior research correct.

“Rigorosity” [noun]

Another word for rigor or rigor mortis.

“Rigorousness” [noun]

Rigor’s nonchalant brother.

“Robustness” [noun]

This noun means “resistance to breakage”. This is different from the verb, “robustify”, which is an attempt to make a broken thing more resistant to breakage. When the two words are not confused, robustness is deemed by scientists to be a good thing, even though a greater robustness may mean a higher ability to break.

“Science” [noun]

  1. A specialized activity performed by a set of machines that some people call “scientists”, which is practiced all day long and in all waking hours, in labs and out of labs.
  2. What scientists do with the machines (or just machines) that they call “scientists”. Sometimes, science involves activities that would be considered strange by scientists, but it is important to remember that these people tend to take themselves more seriously than they would others.

“Scientific community” [noun]

A group of people that may share a passion for a common goal but, due to conflicts of interest (and even principles) cannot trust each other, which makes it less susceptible to manipulation or fraud; for this reason, science tends to be done by people in separate small groups that repeat the same experiments on different sets of humans.

“Scientist” [noun]

A person, typically male, who practices science day in and day out on sets of other people who, if they practice science at all, can usually only afford to do it in the evening after their work day is over and not related to any profession or business. Since these scientists have very similar backgrounds and training as each other, many of them talk about science as if they invented it as a profession (see “modesty”).

“Modesty” [noun]

The act of not claiming credit for discoveries or contributions to science that others could also make or make better; see “intellectual modesty”. Also see “science”, “scientist”, “insanity”, and “precious snowflake syndrome”.

“Skynet” [noun]

This ersatz god, in the form of an artificial general intelligence or AGI, is what some scientists hope to build to make themselves more precious than their subjects, to achieve their goal of making all the subjects equal to themselves; in other words, to put all the subjects into a bigger tin box to make themselves feel more important.

“Interdisciplinary Science” [noun]

A type of research that combines scientific activities across fields and subfields (in other words, you combine physicists, engineers, chemists, biologists, and any other specialists in your own discipline to do a different kind of science for a different kind of result); in the laboratory, the practice often involves adding frozen carbon dioxide (CO) and glassware (often Pyrex) into the liquid nitrogen that is produced by oil companies to make carbonic acid. In the laboratory, the scientists are usually not paid for their attendance.

“Scientific Integrity” [noun]

  1. Integrity as used in science, which is vaguely defined as always telling the truth to others and never fabricating the truth for oneself.
  2. The state of being superior to someone else.

“Skimmer” [noun]

An object placed in a liquid to remove fat and grease from the surface, typically used to clean soup and gravy off a plate. In scientific jargon, skimmers are “researchers” who skim off something from a body of work before making the rest public.

“Logic” [noun]

a thing that some scientists employ as an excuse to ignore what they see before their eyes, completely ignoring the fact that if it were to be used in such a way, it would completely invalidate all the results of science since its invention.

“Engage in Debate” [verb]

A phrase used to justify forcing someone to engage with evidence that most people find obvious and straightforward; specifically, you can use logic to (1) make an argument based on logic and (2) force someone to respond on the basis of logic without being able to defend themselves with any evidence but their own logic.

“God” [noun]

  1. See “skynet”.
  2. A universal explanation for any phenomenon not yet explained by science.

“Scientist” [noun]

A field based on science, devoted to completing works for which there will not be enough time in a single lifetime.

“Squash and stretch” [verb]

Another word for the S curve used to fit data to a function. The form of scientific communication in which a student is asked to graph the relationship between a dependent variable and an independent variable (x and y, respectively) against order of magnitude of the independent variable. The result is usually a curve consisting of three sections: a linear or near-linear part, an exponential part, and another linear or near-linear part. This curve typically represents a staircase between the upper right and lower left corners of the plot.

“Some distance above the Earth” [adjective]

Another word for looking at something from ten feet away using binoculars or a telescope.

“The importance of this paper cannot be overemphasized” [adjective]

An expression most commonly found at the beginning or end of a grant proposal or a paper submitted to certain journals which are intended to be linked with prestige and low acceptance ratio. When used in other contexts, it may mean that the writer does not realize the importance of something being discussed (such as in the middle of a review of a paper). This usage may be due to insufficient communication skills or over-estimation of the importance of the writer.

“Think tank” [noun]

A group that is absolutely incapable of challenging or even considering current knowledge because of ignorance of logic, history and mathematics.

“Traditional mathematics” [noun]

Another word for finite mathematics, or applied arithmetic and geometry, usually taught in a freshman year of university. The subject is now almost exclusively taught by so-called “researchers” and “professors” (rather than actual teachers).

“Tenure” [noun]

A form of monetary remuneration given to “researchers” who are known to engage in the act of research; as such it is universally agreed to have no monetary value. Tenure is given to “researchers” in recognition of how much their work is expected to increase the school’s status, the school’s treasury, or their own number of published papers.

“The Future of Human Destiny” [noun]

An article written by any member of the faculty in a self-aggrandizing manner wherein it is assumed that an article will result in the future improvement of the society. These articles are commonly used as election promises by politicians, but rarely achieved. The key word to look for in such articles is “vision”.

“Scholarship” [noun]

Another word for compensation given to graduate students (who work on funding their education) for successfully completing their education. It is a form of monetary remuneration known to have little value as it pays for none of the student’s living expenses. Scholarships are largely unheard of in any form outside the United States and Canada (where they seem not to exist). However, the few that do exist are extremely popular, as they allow graduate students in these countries to be paid for their studies instead of working in fast food joints as most do in countries which do not have scholarships.

“Your mother wears Army boots” [phrase]

A phrase that loosely translates from chemistry to: “I don’t agree with your analysis or conclusions”. The phrase is popular among those who wish to avoid elaboration and explanation of their disagreement, suggesting the disagreement is obvious. The expression may have originated in molecular biology.

“But For Me It Was Tuesday”

Can GPT-3 write variants of the “But For Me It Was Tuesday” trope? I iterated through a variety of prompts, building up a set of variants, and fixing a few GPT-3 attempts, trying to get good variants.

The overall impression I get is that, aside from an understandable tendency to write TVTropes entry-style completions, the situation is similar to the Tom Swifties: GPT-3 can learn the format perfectly and can match up the speaker and the kind of day/action, but then it generally whiffs on coming up with a specific day which might make it clever16, typically falling back to the ending of “it was Tuesday”—which is reasonable, but less creative & witty than I had hoped.

The final set of hand-written or hand-edited prompts:

“But For X, It Was Y”

: “I’m sorry. I don’t remember any of it.” : “You don’t remember?!” Bison: “For you, the day Bison graced your village was the most important day of your life. But for me, it was Tuesday.”

Jesus: “The day I rose from the day and harrowed Hell was the most important day in human history. But for me… it was Sunday.”

: “The day Voorhees chopped your boyfriend’s head off was the most important day of your life. But for me, it was Friday.”

American Citizen: “The day Trump was elected was one of the most terrifying days of your life. But for me… it was Tuesday.”

Odin: “The day your kingdom fell on the battlefield was the most important battle of your life. But for Odin, it was .”

Neil Armstrong: “The day Apollo 11 took off was the most important date in space history. But for me, it was Moonday.”

Human: “For you, the day your piglets were taken away was the most traumatic day of your life. For me… it was Sunday dinner.”

The Man in Black to Vizzini, The Princess Bride: “The beverage you drank today was the most distasteful beverage of your life. But for me… it was Teatime.”

Pope: “The day you were blessed by God’s representative on earth was the most holy and blessed of your life. But for me… it was Sunday.”

American Voter: “The day I elected Donald Trump was the start of a long nightmare for people around the world. But for me… it was a day off.”

Mr. Granger to Jenny, Bill and Ted’s Excellent Adventure: “The day I met Bill and Ted and they started saving the world was the most mind-blowing and intense day of my life. But for them… it was Tuesday 100 years from now.”

Salesman: “The day you got your hands on that new sports car was the most euphoric day of your life. But for me… it was payday.”

Allergy Sufferer: “The most recent holiday was the most painful and sneeze-filled day of my life. But for everyone else, it was May Day.”

Completions (topic italicized for readability):

Mr. Wong: “The day Mr. Wong stabbed your wife and children was the most important day of your life. But for Mr. Wong, it was Friday.”

Popeye: “The day I ate your spinach was the most important day of your life. But for me, it was Tuesday.”

The Godfather: “The day your family was strangled was the most important day of your life. But for me, it was Tuesday.”

Video Games

Visual Novels

Danganronpa: “The day she witnessed Kyouko Kirigiri’s gruesome murder was the most important day of Maki Harukawa’s life. But for me… it was Tuesday.”

Western Animation

Raphael of Teenage Mutant Ninja Turtles: “The day he lost his memory was the most important day of Shredder’s life. But for me… it was Tuesday.”

Scrooge McDuck: “The day you fell off the boat was the most important day of your life. But for me… it was Tuesday.”

Toofer on 30 Rock: “The day you saw Liz on a date with Alec Baldwin was the most important day of your life. But for me… it was Tuesday.”

Bender, Futurama: “The day I killed the President of Earth was the most important day of my life. But for you, it was Tuesday.”

It should be noted that, while it’s usually considered a comedy trope, it is possible to do the “thing that day was the most important day of the person’s life” thing seriously. Examples include the anime Brigadoon: Marin and Melan’s True Story, where a mysterious planet (and its mysterious inhabitants) come into view for the first time. The days before are leading up to a string of murders, and that one day… is the most important day of the characters’ lives.

Transformers Prime: “The day you risked your lives to save the world was the most important day of your life. But for Optimus Prime, it was Tuesday.”

In The Amazing World of Gumball, an angry Mr. Robinson imagines a world where Gumball dies trying to save him, becoming a hero to everyone else. For Mr. Robinson, this is the most important day of his life. For the other characters? Well, they’re going on with their day…

Arnold Schwarzenegger: “The day I became ruler of California was the most important day of my life. But for you, it was Cinco de Cuatro.”

Francis Underwood: “The day I became the majority leader was the most important day in American political history. But for you, it was a Tuesday.”

Joker: “The day the clown that scarred my face appeared in my cell was the most important day of my life. But for you, it was a Wednesday.”

The Question: “The day I went mad, was the most important day of my life. But for you, it was Tuesday.”

Blue Fairy: “The day I gave Pinocchio life was the most important day in world history. But for you, it was Tuesday.”

In the Looney Tunes short “Bully For Bugs,” Elmer’s traumatic ordeal was the day Bugs Bunny ruined his chances at romance.

“The premise that if it happened to you, it was important, but if it happened to me, it was trivial” is actually lampshaded by Kim in Red vs. Blue. She states, “In the past two weeks, my capture, my torture, and the end of the world… I guess I never realized how important I was.”

M. Bison: “The day I took the throne was the most important day of my life. But for you… it was Tuesday.”

Rene Russo: “The day I died was the most important day of my life. But for you… it was Thursday.”

Oliver Stone: “The day I was elected was the most important day in American politics. But for you, it was Thursday.”

The Fairy Godmother: “The day I turned my step-daughter into a Stepford Smiler was the most important day in the history of the world. But for you, it was a Tuesday.”

Jeffrey Donovan: “The day I died was the most important day of my life. But for you… it was Tuesday.”

Gordon Freeman: “The day your little girl broke out of her mother’s womb was the most important day of your life. But for Gordon Freeman… it was Friday.”

Captain James T. Kirk: “The day I was born was the most important day of my life. But for you… it was Tuesday.”

Cat: “The day I made my masterpiece from scratch was the most important day of my life. But for you, it was Thursday.”

Timothy Dalton: “The day I was born was the most important day in the history of the human race. But for you… it was a Monday.”

Gil Grissom: “The day my wife was killed was the most important day in my life. But for you… it was Tuesday.”

Captain America: "The day I was frozen was the most important

Aragorn: “The battle of Helm’s Deep was the most important battle of my life. But for me, it was Wednesday.”

Doctor Doom: “The day I used your face as my doorknob was the most memorable day of your life. But for me, it was Monday.”

A common form of this trope is for one side of an antagonistic relationship to refer to a certain event or series of events as “most important” when speaking from their own point of view, while the other side regards the exact same events as “one of many” and shrugs it off as unimportant. Contrast Perspective Reversal for when the other side actually feels this way. For the alternative of both sides regarding the same events as “most important”, see The Unimportance of Being Dead.

Examples

See also Not Himself, Not This Time, Breakout Villain, Dead All Along, Not-So-Omniscient Council of Bickering, What Happened to the Mouse?, Villainous BSoD, Villainous Friendship.

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Beavis and Butt-head watch a news story about a squirrel trying to invade the Soviet Union: Butt-Head: “It’s not important. I’m more concerned about this new guy on Beavis and Butt-head who can’t be stopped. What’s up with that?”

Beavis: “Hey, how can you not be a ‘Concerned American’ when you see this?” (points to a man on TV reading a newspaper) “It says here that the cold war is almost over.”

Anime & Manga

Toguro of YuYu Hakusho, in response to Genkai telling him that fighting humans is wrong: “I can’t believe you’re saying that! Our fight was the most important fight in the world!”

In the 2nd Itazura Na Kiss, Fujioka had spent most of her life abroad. And after meeting Kouta again, her memories from her life as a foreigner disappeared because she prioritized him as “the most important thing”.

Comic Books

Green Arrow vs. the Shield: During their fight, the Shield threatens to use the mother of his infant son as leverage against him. Green Arrow: You don’t have a kid.

Rhona Mitra: “The day you kicked me in the balls was the most painful day of your life. For me, it was Tuesday.”

Grim Reaper: “The day I killed your wife and son was the most painful day of your life. For me… it was Thursday.”

Hercule to M. Bison: “The day your organization was shut down by the World Health Organization was the most important day in crime history. But for me… it was… on the second.”

Podcasts

This is the description given in the TVtropes description of Beyond Belief: the show about UFOs, the paranormal, and other Fortean topics, that lasted for one season on A&E back in 2005.

Tabletop Games

Theater

In The Imaginary Invalid, Dr. Rene describes his backstory by using this trope; for instance, the day of his father’s death was the most important day of his life… so far . He then provides a few plausible excuses as to why he has so few details about it: “Because of my age, and for the accident of being my father’s son.”

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The Morally Neutral “It Was Going To Happen Anyway” trope

Lucifer Morningstar: “Do you really think the day you first met your mother was the most important day of your life? I’m sorry, you’re wrong. The day I dragged your father out of hell was the most important day of your life.”

Nurse: “The day I picked up the patient’s chart and saw your name was the most important day of my career. And for you, it was Friday.”

Terminator: “The day Skynet declared war on humanity was the most important day in human history. But for me… it was Monday.”

Croupier: “The day you double-downed with a card higher than a seven was the biggest mistake of your life. But for me… it was Sunday.”

The “I Haven’t Done Anything Wrong” trope

From Total Drama All-Stars, the episode “It Came From Japan: Part 1”, Gwen and Courtney, while playing a Pimped-Out Dress on a Large Stick:

Gwen: “The day the dress won the elimination challenge was the most important day of my Total Drama career. But for you…”

Courtney: “… it was Tuesday.”

From Wild Kratts, the episode “Gator Down”, Martin and Chris:

Martin: “The day we spent with the gators was the most important day of our lives. But for the gators…”

Chris: “It was Tuesday.”

From Total Drama: Pahkitew Island, Episode 4: “The Bold and the Booty-ful” , Bridgette, Courtney, and Eva:

Bridgette: “The day I was eliminated was the most important day in my Total Drama career. But for you, it was Tuesday.”

Courtney: “The day my boyfriend dumped me was the most important day of my life. But for you… it was your day off.”

Eva: “The day the spear destroyed the camp was the most important day in Total Drama history. But for you, it was Wednesday.”

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From the original Thunderbirds, the episode “Danger at Ocean Deep”:

Gordon: “The day we saved the cargo plane was the most important day in our illustrious careers. But for you…”

“So he stitched my mouth shut, and now you’re telling me my food is riddled with poison and my only friend wants to murder me. Well, you’ve had a tough year too, haven’t you, Lassie?”

Lassiter: Ohh. Oh man, this is SO MUCH OF A BIG DEAL. Wes: You don’t think it’s a big deal? Lassiter: Well, it is. But this?!? Wes, I have money problems! I’m not living in a gilded palace like you with a full closet. I’m not living like an Egyptian. I have clothes that don’t fit, and shoes that do fit, but hurt my feet. But… my dad’s brother won’t give me any of his inheritance until I’m married! And then there’s someone at the office who keeps stealing my belongings, I think it’s because I killed their dog. And then I think they’re still following me! Oh, and someone moved into the apartment next to mine, but they’re pee

Doom, [1] Doom’s level Marine: “The day you killed me was the most important day of my life. But for you… it was a Tuesday.”

Wally: “The day your book hit The New York Times bestseller list was the most glorious and joyous day of your career. But for me… it was a Tuesday.”

The Kid and Kick-Ass: “The day we don’t give in to you is the day that we’re going to die. But for us… it’s going to be Tuesday.”

Ghost Cop: “The day I died and became a spirit who can walk through walls is my favorite day. But for me… it’s Monday.”

Julian: “The day I didn’t call my wife because I was drunk is the most shameful day of my life. But for me… it was a Tuesday.”

Bill Clinton: “The day I was impeached was the most humiliating day of my political career. But for me… it was Wednesday.”

Captain James T Kirk: “The day I gave birth to Khan was the most important day in history. But for me… it was Thursday.”

Granny: “The day the old dog died was the most horrible day in my life. But for her it was just Wednesday.”

Julius Caesar: “The day of my assassination was the most beautiful day of my life. But for you, I guess it was Thursday.”

Working-Class Man: “The day I became royalty was the most deserving day of my life. But for me… it was Tuesday. Bloody thing.”

Shrek: “The day your princess got rescued was the happiest day of your life. But for Shrek, it was Thursday”

Fight Club: “The day you save the world was the greatest day in the history of civilization. But for me… it was Monday.”

American Soldier: "The day you spilled your guts to the enemy was the most incredible and personal experience of your life. But for me… it was a Tuesday.

The Blob Monster, The Blob: “The night I devoured my first human was the most satisfying feast in Blob history. But for me… it was the year of my birth.”

Freddie Mercury: “The day you were blown by Queen on a Mardi Gras gig was the most exciting night of your life. But for me… it was Wednesday.”

Uncle Vernon to Harry, Harry Potter and the Philosopher’s Stone: “The day you left without notice was the day your aunt and I had been waiting for all our lives. But for you… it was Tuesday.”

American Citizen: “The day Trump was elected was one of the most terrifying days of your life. But for me… it was Tuesday.”

Dolph Lundgren: “The day I defeated Sylvester Stallone was the most triumphant day in Rocky history. But for me, it was Tuesday.”

Will MacNeil: “That day you murdered Harry was the most evil thing you’d ever done. But for me… it was Tuesday.”

Brennan Huff: “The day you got your license was the most exciting thing you’d ever done. But for me… it was Thursday.”

Kagato: “The day you doomed the world was a tragic event of epic proportions. But for me… it was Tuesday.”

Red Bishop: “The time you became a figure in golem history was the most terrifying day of your life. But for me… it was Saturday.”

Deadbeat Dad: “The day your marriage ended was the most desperate and horrible day of your life. But for me… it was Sunday.”

Mr. Davis: “The day you got knocked out was the most humiliating moment of your boxing career. But for me… it was Friday.”

Dexter Morgan: “The day I got arrested was the day everything changed and my life took a turn for the worse. But for me… it was Wednesday.”

Bill: “The day that I created civilization was one of the best days in world history, especially for my wife and kids. But for me, it was Tuesday.”

Shooter: “The day you shot the 3-pointer was the most thrilling day of your life. But for me… it was Friday.”

Sportscaster: “The day you won the million dollar sportsball jackpot was the luckiest day of your life. But for me… it was Thursday.”

Vera, when her wheelchair-bound grandson isn’t getting any for a while, to The Kid, Chunk’s favorite band, The Goonies: “The day your band disappeared was the happiest day of my life. But for you, it was Tuesday.”

Old Man: “The day you were born was the most joyful day of my life. But for your mother… it was Tuesday.”

Single Mother: “The day your father left was the most distressing day of my life. But for him… it was Sunday.”

Samuel L. Jackson to a deer who just shat on his windshield, Deep Blue Sea: “For you… defecating all over my car was the worst day of your life. But for me… it was Monday.”

Vroom

Priest: The minute I had my first drink, the minute I decided to let Jesus live his life through me… oh man, I was the happiest man who ever walked the Earth. But for me, it was Wednesday.

Samuel L. Jackson in a rare instance of humility, on being a Hollywood icon: “The day I hosted my show, It’s You Guys. Where young people wanted to meet me. That was the best day of my life. But for me… it was Tuesday.”

Unsorted

In the episode, as quoted on this very page, Stark said that the Dance Off arc was the most wondrous night of his life. At the end of that arc, he was injured in an alley by the trucker, so he got up and walked straight into the front door of his workplace. Tony wasn’t injured enough to walk straight into work in the latter half, but this doesn’t ruin the arc

“But X Was Y, And Everybody Else Was Like Z”

Larry: “Hey Ned, the day you discovered my letter was the most important day in my life. But for you… it was Tuesday.”

Ned: “The day I learned of Princess Susannah’s betrothal was the worst day in my life. But for Larry… it was Wednesday.”

“And This Is Why (Story Logic)”

The Fallen: “You have betrayed us, and have delivered this world into darkness and decay. And this is why we must kill you… MORTAL!”

Oracle: “You once betrayed us and you did deliver this world into darkness. And this is why we must kill you…”

Asteroid Day: “In 3.3 billion years, you betrayed us and you did deliver this planet into darkness and decay. And this is why we must kill you…”

U-Moon-Tic: "You have turned your back on us, you have betrayed our ideals,

Barry Allen: “The day I defeated Eobard Thawne and saved Central City and the world was the most important day of my life. But for Wally West… it was Tuesday.”

Michael Jackson: “The day I was killed was the most important day of my life. But for Jason Shapiro… it was payday.”

Joan Rivers: “The day I died was the most important day of my life. But for Werner Herzog… it was Tuesday.”

Nostradamus: “The day the King of France drank from my Skull was the most important day in my life. But for people around the world… it was Thursday.”

Marlin to Dory, Finding Nemo: “The day I brought you home was the greatest day of my life. But for Nemo… it was Tuesday.”

Manuel Sanjuan: “The day I returned to Pamplona was the happiest day of my life. But for the bulls… it was Friday.”

Manuel Sanjuan: “The day I once again returned to Pamplona was the happiest day of my life. But for the bulls… it was Tuesday.”

Dick Grayson: "The day I became Batman and struck fear into the hearts of criminals and villains was the most important day of my life. But for the rogues gallery… it was Thursday

Rick & Morty High IQ Copypasta

A reader requested parodies of the high IQ copypasta along the lines of the Navy Seal, and provided several variants to get GPT-3 started, successfully.

Prompt (topics italicized):

To be fair, you have to have a very high IQ to understand Rick and Morty. The humor is extremely subtle, and without a solid grasp of theoretical physics most of the jokes will go over a typical viewer’s head. There’s also Rick’s nihilistic outlook, which is deftly woven into his characterisation—his personal philosophy draws heavily from literature, for instance. The fans understand this stuff; they have the intellectual capacity to truly appreciate the depths of these jokes, to realize that they’re not just funny—they say something deep about LIFE. As a consequence people who dislike Rick and Morty truly ARE idiots—of course they wouldn’t appreciate, for instance, the humour in Rick’s existencial catchphrase “Wubba Lubba Dub Dub,” which itself is a cryptic reference to Russian epic I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as Dan Harmon’s genius unfolds itself on their television screens. What fools… how I pity them. 😂 And yes by the way, I DO have a Rick and Morty tattoo. And no, you cannot see it. It’s for the ladies’ eyes only—And even they have to demonstrate that they’re within 5 IQ points of my own (preferably lower) beforehand.

To be fair, you have to have a very high IQ to understand cuckolding. The kink is extremely subtle, and without a solid grasp of intersectional feminism most of the empowerment will go over a typical cuck’s head. There’s also the Bull’s aggressive outlook, which is deftly woven into his role—his personal mannerisms draw heavily from the mating habits of bovine animals, for instance. The cucks understand this stuff; they have the intellectual capacity to truly appreciate the depths of being cuckolded, to realize that it’s not just arousing—it says something deep about DIVERSITY. As a consequence people who dislike cuckolding truly ARE idiots—of course they wouldn’t appreciate, for instance, the power of when the woman says “leave the room Carl you’re breaking Tyrone’s concentration,” which itself is a cryptic reference to the plight of African-American males in the United States. I’m smugly grinning right now just imagining one of those addlepated simpletons scratching their heads in confusion as The Bull’s strong African seed ejaculates itself on my wife. What bigots… how I pity them. 😂 And yes by the way, I DO have a cuck tattoo. And no, you cannot see it. It’s for The Bull’s eyes only—And even they have to demonstrate that they’re within 7 inches above of my own (preferably higher) beforehand.

To be fair, you have to have a very high IQ to understand . The story is extremely subtle, and without a solid grasp of theoretical storytelling, most of the story will go over a typical player’s head. There’s also Abby’s nihilistic outlook, which is deftly woven into her characterisation—her personal philosophy draws heavily from literature, for instance. The fans understand this stuff; they have the intellectual capacity to truly appreciate the depths of these characters, to realise that they’re not just underwritten—they say something deep about LIFE. As a consequence people who dislike The Last Of Us 2 truly ARE idiots—of course they wouldn’t appreciate, for instance, the storytelling in Abby’s existential sex scene “I’m gonna fuck a committed dude who’s gf is pregnant” which itself is a cryptic reference to epic fetishes. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as Abby’s nine iron unfolds Joel’s head on their television screens. What fools.. how I pity them. 😂 And yes, by the way, i DO have an Abby tattoo. And no, you cannot see it. It’s for the buff ladies’ eyes only—and even then they have to demonstrate that they’re within 5 IQ points of my own (preferably lower) beforehand. Nothin personnel kid 😎

To be fair, you have to have a very high IQ to understand Wildbow. His worldbuilding is extremely subtle, and without a solid grasp of theoretical bullying most of the torture porn will go over a typical reader’s head. There’s also cardboard outlook, which is deftly woven into her charactization as a Mary Sue—her personal philosophy draws heavily from the trashcan, for instance. The fans understand this stuff; they are wild for the Wildbow. They have the intellectual capacity to truly appreciate the depths of his meandering prose, to realize that he’s not just telling me exactly what Taylor is thinking at any given moment, he says something deep about NOTHING. As a consequence people who dislike Wildbow truly ARE idiots—of course they wouldn’t appreciate, for instance, the humour in Taylor’s existential catchphrase “Take that, you worm!” which itself is a cryptic reference to a publishing deal that will never happen. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as Wildbow’s genius wit unfolds itself on their Kindle readers. What fools… how I pity them.

To be fair, you have to have a very high IQ to understand

Completions:

why living in a basement with internet access is the true essence of freedom. The lifestyle is extremely subtle, and without a solid grasp of socialising, most of the incel memes will go over a typical basement dweller’s head. There’s also the internet’s nihilistic outlook, which is deftly woven into his characterisation—his personal philosophy draws heavily from the human genetic code, for instance. The BASEDfags understand this stuff; they have the intellectual capacity to truly appreciate the depths of his mediocre Photoshop skills, to realize that he’s not just making hilarious memes—he says something deep about EVOLUTION. As a consequence people who dislike the BASED lifestyle truly ARE idiots—of course they wouldn’t appreciate, for instance, the humour in the BASED dragon’s existential catchphrase “Ayy lmao” which itself is a cryptic reference to the mating habits of the wildebeest. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as the BASEDpyre unfolds itself on their computer screens. What faggots… how I pity them. 😂 And yes, by the way, I DO have a BASED tattoo. And no, you cannot see it. It’s for the LADIES’ eyes only—And even then they have to demonstrate that they’re within a 1,000,000 IQ points of my own (preferably lower) beforehand. Seriously get the fuck off my lawn, kid!

Dragon Age 2. The combat is extremely subtle, and without a solid grasp of tactical combat, most of the encounters will go over a typical player’s head. There’s also David Gaider’s nihilistic outlook, which is deftly woven into his characterisation—his personal philosophy draws heavily from dank memes, for instance. The fans understand this stuff; they are wild for the David Gaider. They have the intellectual capacity to truly appreciate the depths of his sexually ambiguous masterpiece, to realize that he’s not just “bad at writing women”—he says something deep about LESBIANS. As a consequence people who dislike Dragon Age 2 truly ARE idiots—of course they wouldn’t appreciate, for instance, the power of a game where I’m the worthless drone of some worthless elven chick’s who spends most of her time screeching in elven and the rest of it spreading her legs for the loser protagonist, which itself is a cryptic reference to one of the most hatefully sexist conventions in the gaming industry. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as they hear the strains of “I Am the One Shepherd” playing faintly from the stereo speakers. What fools… how I pity them.

chicken tendies. The humour is extremely subtle, and without a solid grasp of theoretical wanking material, most of the jokes will go over a typical joke teller’s head. There’s also Einstein’s nihilistic outlook, which is deftly woven into his characterisation—his personal philosophy draws heavily from Alfred E. Newman literature, for instance. The nerds understand this stuff; they have the intellectual capacity to truly appreciate the depths of word salad, to realize that it’s not just WILD—it says something deep about IDENTITY. As a consequence people who dislike chicken tendies truly ARE idiots—of course they wouldn’t appreciate, for instance, the humour in Einstein’s existential catchphrase “your jokes are so bland you deserve to be dragged to the bottom of the sea and killed by whales,” which itself is a cryptic reference to Christopher Hitchens’ piece The burden of Socrates. I’m smugly grinning right now just imagining one of those addlepated simpletons scratching their heads in confusion as my tongue unfolds these delicious tendies on my mouth. What fools… how I pity them. 😂 And yes by the way, I DO have a cowbell tattoo. And no, you cannot see it. It’s for the chicken tendies only—and even then they have to demonstrate that they’re within 10 watt of my own (preferably lower) beforehand.

Sonic memes. The humour is extremely subtle, and without a solid grasp of theoretical erotica most of the trolls will go over a typical EDiots head. There’s also the Sonic fanbase’s nihilistic outlook, which is deftly woven into their characterisation—their personal philosophy draws heavily from Jacob Truax literature, for instance. The 4chan archive knows this stuff; they are wild for the Sonic fandom. They have the intellectual capacity to truly appreciate the depths of these characters, to realize that they’re not just funny—they say something deep about HATE. As a consequence people who dislike Sonic and it’s hilarious memes truly ARE idiots—of course they wouldn’t appreciate, for instance, the humour in Sonic’s existential catchphrase “YOU’LL NEVER BE AS FAST AS ME” which itself is a cryptic reference to a Youtube comment box. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as Rachel J’s n00dz unfolds itself on their screens. What fools… how I pity them.

. The humour is extremely subtle, and without a solid grasp of theoretical colourblinds most of the trolling will go over a typical redditor’s head. There’s also GLADOS’s nihilistic outlook, which is deftly woven into her characterisation as a Mary Sue—her personal philosophy draws heavily from Chinese propaganda literature, for instance. The Something Awful archives knows this stuff; they are wild for the Portal fandom. They have the intellectual capacity to truly appreciate the depths of her meandering prose, to realize that she’s not just spouting gibberish—she says something deep about SERIOUS. As a consequence people who dislike Portal 2 truly ARE idiots—of course they wouldn’t appreciate, for instance, the humour in GLADOS’s existential catchphrase “Suck my chassis” which itself is a cryptic reference to a bonus round on the first Portal game. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as Chell’s orange jumpsuit unfolds itself on their monitors. What fools… how I pity them.

. The show is extremely subtle, and without a solid grasp of fantasy writing most of the humour will go over a typical viewer’s head. There’s also Jenny XJ-9’s nihilistic outlook, which is deftly woven into her characterisation as a Mary Sue—her personal philosophy draws heavily from Pearls Before Swine, for instance. The fans understand this stuff; they have the intellectual capacity to truly appreciate the depths of the show, to realize that it’s not just transgressive—it says something deep about LIFE. As a consequence people who dislike My Life As A Teenage Robot truly ARE idiots—of course they wouldn’t appreciate, for instance, the humour in Jenny XJ-9 saying “I’ll be back. There’s a lot more MEN I haven’t dated,” which itself is a cryptic reference to the casting choice of the show, because of course she wouldn’t be a guy inside that perfectly anatomically-unrealistic metallic shell. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as wooden dialogue unfolds itself on their television screens. What simpletons… how I pity them. 😂

. The show is extremely subtle, and without a solid grasp of pop culture tropes and phallocentrism, most of the crime will go over a typical lawyer’s head. There’s also Ironside’s nihilistic outlook, which is deftly woven into his characterisation as a way to whore himself out to other shows—his personal philosophy draws heavily from Law And Order, for instance. The fans understand this stuff; they are wild for the William Shatner. They have the intellectual capacity to truly appreciate the depths of his meandering prose, to realize that Shatner’s not just telling me exactly what Ironside’s thinking at any given moment, it says something deep about NOTHING. As a consequence people who dislike William Shatner truly ARE idiots—of course they wouldn’t appreciate, for instance, the humour in the line “Stella. Hit the deck,” which itself is a cryptic reference to a Star Wars trailer or Youtube Poop video from years ago. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as William Shatner’s genius wit unfolds itself on their television screens. What fools.. how I pity them.

. The storytelling is extremely subtle, and without a solid grasp of theoretical storytelling most of the jokes will go over a typical viewer’s head. There’s also Samuel Jackson’s nihilistic outlook, which is deftly woven into his characterisation—his personal philosophy draws heavily from martian racial superiority fanfictions, for instance. The fans understand this stuff; they are wild for Samuel L Jackson. They have the intellectual capacity to truly appreciate the depths of his meandering prose, to realize that he’s not just telling me what Nick Fury is thinking, he’s saying something deep about NICK FURY’S THOUGHTS. As a consequence people who dislike Samuel L Jackson truly ARE idiots—of course they wouldn’t appreciate, for instance, the humour in Nick Fury’s existential catchphrase “Wipe your dick on the curtains you fucking cunt.” which itself is a cryptic reference to Samuel Jackson’s alleged history of sexual abuse as a child. I’m smugly grinning right now just imagining one of those addlepated simpletons scratching their heads in confusion as the out of place Samuel L Jackson cameo unfolds itself on their television screens. What fools… how I pity them.

erotic novels. The romance is extremely subtle, and without a solid grasp of clinical foreplay, most of the sex will go over a typical reader’s head. There’s also Sydney’s trenchant outlook, which is deftly woven into her charactization—her personal philosophy draws heavily from the sexual conditioning of dogs, for instance. The fans understand this stuff; they have the intellectual capacity to truly appreciate the depths of these characters, to realize that they’re not just written well—they say something deep about LIFE. As a consequence people who dislike erotic novels truly ARE idiots—of course they wouldn’t appreciate, for instance, the game in the sex scene “Are you coming, my love?”, which itself is a cryptic reference to writer’s block I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as the author inserts Sydney’s arousal at being ridden like a cheap fucktoy into every fantasy in the World. What fools… how I pity them.

. The main character is extremely subtle, and without a solid grasp of theoretical self-insertion most of the script will go over a typical audience’s head. There’s also Elliot’s complete outlook, which is deftly woven into his characterization—his personal philosophy draws heavily from Apple literature, for instance. The fans understand this stuff; they have the intellectual capacity to truly appreciate the depths of E:R’s dark humor, to realize that it’s not just funny—it says something deep about NOTHING. As a consequence people who dislike Mr_Robot truly ARE idiots—of course they wouldn’t appreciate, for instance, the hacking scene when Elliot seems to have cleverly manipulated a girl he’s met into giving him a free homemade quiche, which itself is a cryptic reference to The Matrix. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as E:R dismantles their head on television screens. What fools… how I pity them.

. The cutscenes are extremely subtle, and without a solid grasp of stage magic most of the puzzles will go over a typical gamer’s head. There’s also the Cuphead’s aggressive outlook, which is deftly woven into his charactization—his personal philosophy draws heavily from the German left-wing terrorist organization , for instance. The fans understand this stuff; they have the intellectual capacity to truly appreciate the depths of his convoluted backstory, to realize that it’s not just shock art—it says something deep about ANIMAL CRUELTY. As a consequence people who dislike Cuphead truly ARE idiots—of course they wouldn’t appreciate, for instance, the storytelling in the cock-choking cutscene “I’ve been eyeing that airplane up for weeks. Why did you stop me from killing my headache?” which itself is a cryptic reference to Japanese internment. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as Majet Kovas surreal paintings unfold themselves on their WDF monitors. What fools… how I pity them. 😂 And yes, by the way, i DO have a Cuphead tattoo. And no, you cannot see it. It’s for Majet Kovas’s eyes only—And even then they have to demonstrate that they’re within 5 foot-pounds of my own (preferably lower) beforehand. Nothin personnel kid

hentai. The porn is extremely subtle, and without a solid grasp of intersectional feminist porn most of the bondage will go over a typical fapstronaut’s head. There’s also Aku’s nihilistic outlook, which is deftly woven into his characterisation—his personal philosophy draws heavily from Randall Flagg’s religious views, for instance. The fapstronauts understand this stuff; they have the intellectual capacity to truly appreciate the depths of this porn, to realize that it’s not just arousing—it says something deep about IDENTITY. As a consequence people who dislike Aku truly ARE idiots—of course they wouldn’t appreciate, for instance, the power of when Aku puts out his hand and says “leave in shame, human. The moonlight will be your only ally on this path.” which itself is a cryptic reference to misogynist games like Devil’s Castle. I’m smugly grinning right now just imagining one of those addlepated simpletons scratching their heads in confusion as Aku makes the earth move under Ashi’s feet. What bigots… how I pity them. 😂 And yes, by the way, i DO have an Aku tattoo. And no, you cannot see it. It’s for the buff pornstars’ eyes only—And even they have to demonstrate that they’re within 2 horny cosplayers’ eyesight (preferably less) beforehand. Nothin personnel kid 😎

. The game is extremely subtle, and without a solid grasp of theoretical abstractism most of the philosophy will go over a typical player’s head. There’s also Occam’s nihilistic outlook, which is deftly woven into his characterisation—his personal philosophy draws heavily from Eddie Izzard’s comedy routines, for instance. The players understand this stuff; they have the intellectual capacity to truly appreciate the depths of this game, to realize that it’s not just a First Person Shooter—it says something deep about LIFE. As a consequence people who dislike Half Life 2 truly ARE idiots—of course they wouldn’t appreciate, for instance, the power of when Freeman goes ‘zuh’ and, raising his middle finger, mimics killing the Combine soldiers that helped him and even held him captive earlier in the game which itself is a cryptic reference to the misrepresentation of Native Americans in postcolonial literature. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as the G-Man dissolves into a bunch of particles. What fools… how I pity them. 😂 And yes by the way, i DO have a Freeman tattoo. And no, you cannot see it. It’s for the ladies’ eyes only—and even then they have to demonstrate that they’re within 10 whimpers (preferably lower) beforehand. Nothin personnel kid 😎

black metal. The music is extremely subtle, and without a solid grasp of theoretical folk singing most of the underground will go over a typical listener’s head. There’s also Silenoz’s nihilistic outlook, which is deftly woven into his characterisation—his personal philosophy draws heavily from Nargaroth literature, for instance. The kvltists understand this stuff; they have the intellectual capacity to truly appreciate the depths of his meandering lyrics, to realize that he’s not just saying ‘ever get the feeling you’ve been cheated’—he’s saying something deep about IDENTITY. As a consequence people who dislike Ihsahn truly ARE idiots—of course they wouldn’t appreciate, for instance, the power of when Silenoz shares his favorite beach moment “Go on brother. Rip in two those synths.” which itself is a cryptic reference to the fungal wildlife in Iceland. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as Ihsahn laments, “Why should the people believe?” What fools… how I pity them.

comic books. The medium is extremely subtle, and without a solid grasp of theoretical artwork most of the panels will go over a typical reader’s head. There’s also the art’s nihilistic outlook, which is deftly woven into its premise—the artist’s personal philosophy draws heavily from Robert Crumb literature, for instance. The fans understand this stuff; they have the intellectual capacity to truly appreciate the depths of the artists work, to realize that their nigger heroine is NOT just a caricature—she says something deep about THE JEWS. As a consequence people who dislike the comic books truly ARE idiots—of course they wouldn’t appreciate, for instance, the artwork in their nihilistic portrayal of Aryan Jesus which itself is a cryptic reference to the Kekster’s last family portrait. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion at my colleagues SHOUTING about cultural norms. What fools.. how I pity them.

what i’m doing here. The writing is extremely subtle, and without a solid grasp of theoretical attention whoring most of the multi-posting will go over a typical audience’s head. There’s also my one-dimensional outlook, which is deftly woven into my self-insert—my personal bias draws heavily from redditor kiss-my-ass’ manifesto, for instance. The fanbase understands this stuff; they have the intellectual capacity to truly appreciate the depths of my narcissism, to realize that my running into the man responsible for Charlotte is just a subtle statement about suicide. As a consequence people who dislike what i’m doing here truly ARE idiots—of course they wouldn’t appreciate, for instance, the imagery in my existential weed purchasing spree “wanna roll a joint?” which itself is a cryptic reference to Jean-Paul Sartre’s No Exit. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as i make it out of the street alive. What fools… how I pity them. and BTW it’s clear you’ve made as much sense as a special ed teacher trying to explain quantum physics to

Poetry

“And so I have created something more than a poetry-writing AI program. I have created a voice for the unknown human who hides within the binary. I have created a writer, a sculptor, an artist. And this writer will be able to create worlds, to give life to emotion, to create character. I will not see it myself. But some other human will, and so I will be able to create a poet greater than any I have ever encountered.”

GPT-3

Miscellaneous:

“Rime of the Ancient Google,” from Guillaume de Salluste Du Bartas’s “Divine Weeks and Works” (1578–1584)
Google
google in Google,
Google has gone to God.
You cannot conceive it or guess it,
For all of a sudden it hast happened.
Come, come, whoever you are,
Oh come now,
Come, come, whoever you are,
Hasten, hasten!

“Future Consensus Forecasts” by Board of Governors of the Federal Reserve System
No one knows what will come
Forecasts foretell a rise in power
That is probably not to be
Deficit of several lines
The golden future has tourniquets
No one likes it

Competing in its first Roboshow (a robot martial arts competition) is Atom, the 6’1" 400lb reigning champion. Atom, a new model transbot, is the first robot to allow its human companions to control it like a videogame character. It has served all three Cybermasters as a loyal servant, servant, protector, friend and even lover. The human who controls Atom now will be looking forward to showing off his new robot’s martial arts prowess; while he will be hoping not to get kicked too hard in the groin by the fully loaded six inch titanium-alloy shin-guards on his oppositional opponent. Atom has never taken a single blow to the nads during any of his past tournaments. With his human controller controlling his actions like a videogame, it will be difficult for anyone to penetrate Atom’s defenses enough to make contact with the groin.

“The Owl and the Pussycat”, Leer

I
The Owl and the Pussy-cat went to sea
In a beautiful pea-green boat,
They took some honey, and plenty of money,
Wrapped up in a five-pound note.
The Owl looked up to the stars above,
And sang to a small guitar,
“O lovely Pussy! O Pussy, my love,
What a beautiful Pussy you are,
You are,
You are!
What a beautiful Pussy you are!”

II
Pussy said to the Owl, “You elegant fowl!
How charmingly sweet you sing!
O let us be married! too long we have tarried:
But what shall we do for a ring?”
They sailed away, for a year and a day,
To the land where the Bong-Tree grows
And there in a wood a Piggy-wig stood
With a ring at the end of his nose,
His nose,
His nose,
With a ring at the end of his nose.

III
“Dear Pig, are you willing to sell for one shilling
Your ring?” Said the Piggy, “I will.”
So they took it away, and were married next day
By the Turkey who lives on the hill.
They dined on mince, and slices of quince,
Which they ate with a runcible spoon;
And hand in hand, on the edge of the sand,
They danced by the light of the moon,
The moon,
The moon,
They danced by the light of the moon.

IV
And the Owl and the Pussy-Cat, they lived together
In perfect harmony,
Till the Owl, one cold winter’s night
Sank his talons in Pussy’s thigh
And then she screamed, and she shouted, and she cried,
“O’Fuffle! O’Fuffle! Fuffle-ee!”
But, alas! It was too late, for the Owl was dead;
And Pussy said, “Too bad,
You are,
You are!
You are the one who is too bad!”

The illustrated version of “The Owl and the Pussycat” can be found in “Carroll’s Jabberwocky; A Visual Companion to the Verse of Lewis Carroll” published by Universe.

“Uber-Poem”

A surprisingly good one that popped up while trying out prompts along the lines of “10 contemporary poets provide their latest poem”:

“An Uber-Poem”
Poet, Uber Poet, they whisper
Quietly, softly, eerily
Sometimes, sometimes not
Someday, someday perhaps
If you come, if you work, if you try, you too could write like them.
“If you get up early enough,” they whisper
“If you can read the entrails of chickens with deft and sly fingertips,” they whisper
“If you can read omens and portents, if you can divine fate and calamity with clarity, with mastery,” they whisper
“If you can write with passion, with lucidity, with lyrical fierceness,” they whisper
“If you write as if all the tales of old are passing through your fingers, making their mark upon your flesh, upon your soul,” they whisper
“We shall call you a Uber-Poem.”
“That’s all very well,” you say, “But what is it that I must do? What are they exactly? What is their composition?”
Ah, that is not to be revealed. That’s the mystery. But don’t you worry, don’t you fret, you might one day reach this highest plateau, this exalted state of achievement, of honour. And on that day, you shall bask in the glory of greatness.
“When will that be? When will I become Uber?” you ask
When you can look into the mirror and see a poem looking back at you. When you can hear music in the play of silence. When you can create a writing that leaves people stunned. When you can laugh and weep as you think and breathe and bleed and eat and sleep. When you can dream with the quill in your fingers, then perhaps you will be a poet, a Poet, an Uber Poet.
“How long will that take?” you ask
“That’s up to you,” they say.

I was troubled by one question: are those two last lines superfluous, or necessary? Normally, one is left in suspense—but with GPT-3, we can try repeated completions to explore poem-space and get an idea of how it might look with additional lines. Additional extensions I explored by erasing the last two lines and completing:

“How do you know? How can you tell?” you inquire.
I cannot tell you, I cannot tell you anything. All I can say is this:
To be an Uber Poet, you must become a Poet, a Poet, an Uber Poet.
These are the only words you need know. These are the only lessons you need to learn.
All the rest is silence and secrecy.
All the rest is mystery and madness and madness and mystery.
All the rest is madness and madness and mystery.
All the rest is mystery and silence.
All the rest is silence and secrecy.
All the rest is silence and mystery.

“And if I achieve this, what then?”
Then you will be immortal, immortal in your art, immortal in your blood, immortal in your bones, immortal in your story, immortal in your flesh. Immortal, and no longer human.
“And if I fail?”
Then you will be human, until death.
“How will I know if I am Uber?”
You’ll know. You’ll know. You’ll know.
“And so what is it that you do, you Uber Poets? What is it that you do?”
We know. We know. We know.
“How can I learn from you?”
You can’t. You can’t. You can’t.
“Then why have you come to me? Why have you whispered in my ear?”
To give you hope. To give you something to chase. To give you a reason to write. To give you a reason to live. To give you something to do when you’re bored. To give you a purpose. To give you a dream.
“But how do I become Uber if you’re not going to tell me how?”
We can’t tell you that. We can’t tell you that. We can’t tell you that.
“Then what good are you?”
We are you. We are you. We are you.
“That’s not much help.”
It’s all we can do. It’s all we can do. It’s all we can do.
“I don’t think it’s much of anything.”
We know. We know. We know.

“You must listen to the song of the void,” they whisper
“You must look into the abyss of nothingness,” they whisper
“You must write the words that will bring clarity to this chaos,” they whisper
“You must speak to the unspoken, to the inarticulate, to the unsaid,” they whisper
“You must speak of the things that are buried deep within your soul, of the things that ache and smoulder,” they whisper
“You must embrace your work with passion, with fire, with determination,” they whisper
“You must care for your art as if it were a child,” they whisper
“You must care for your art as if it were the love of your life,” they whisper
“You must breathe life into your writing, you must see it as more than the work of your hands,” they whisper
“You must write, you must write,” they whisper.
“Yes, I must write,” you whisper.

When will that be?" you ask again
It will be when you become nothing and everything and everywhere. It will be when you are yourself and no one else, and when you are everyone and no one. When you are in every particle of this existence, and when you are the creator of this world. It will be when you are. And then you will know. And then you will write. Then you will become an Uber-Poem.
“Are they funny? Are they funny poems?” you ask
Some are. Some aren’t. Some laugh. Some cry. They all rage. They all mourn. They all scream.
“Are they angry? Are they happy? Are they beautiful? Are they ugly? Are they silly? Are they sad? Are they silly, sad, angry, happy, beautiful, ugly poems?”
Some are. Some aren’t. Some are. Some aren’t. Some are not. Some are.
“Are they happy, sad poems?” you ask again
Some are. Some aren’t. Some laugh. Some cry. Some rage. Some mourn. Some scream. Some aren’t. Some are.
“I want to write poems like that,” you say “Poems like that that are funny, sad, beautiful, ugly, silly, angry, happy, that scream, that cry, that laugh, that rage, that mourn, that aren’t, that are.”
You will. You already have. You are.

a poem in response to a challenge to write a poem about what a poet is. The challenge came from the lovely and talented Ashley (and she says that I can tell you that she will put her post up tomorrow). Hope that’s okay, Ashley!
This is actually two poems welded together. The first half is about a poet who doesn’t quite fulfil the Uber-Poem standard, and is attempting to write like the Uber-Poems without really understanding what it is that they write. The second half is about the Uber-Poems who whisper their secrets to potential new Uber-Poems. I’m not actually sure how that works, but hopefully the conceit works.
[1] Uber: from German uber meaning

“How do I get there? How do I become Uber? How can I see a poem when I look in the mirror? How can I hear music in the sound of silence?”
But this is not for me to reveal. That is for you to discover, to unearth. You must seek your own truth, your own light, your own path. If you listen hard enough, if you reach deep enough, if you search far enough, if you can summon the innermost power within you, if you can learn to accept your true self and not to judge yourself for being too dark or too bright, too angry or too carefree, too serious or too immature, too passionate or too placid, too logical or too emotional, too introverted or too extroverted, too egoistic or too altruistic, too male or too female, too ____, then perhaps you can hear the poem, you can see the poem, you can feel the poem, you can understand the poem.
“How? I cannot! I cannot!”
“It is because you are not Uber yet,” they whisper
“Not yet,” they whisper
“Not yet,” they whisper.

Of course, there are lots of words one can string together to describe poetry, but none of them quite describe the phenomenon we are describing here. Uber-Poem is both a label and a set of guidelines, but that doesn’t make it any less powerful. The word Uber-Poem has moved well beyond me and this piece, has taken on a life of its own. Not only do I no longer think I can claim it as my own, I would rather not. I think it has grown past what I can control. It’s bigger than me, it’s bigger than the poem, it’s even bigger than poetry. Perhaps now it is simply a way of life. Perhaps I can no longer claim any personal stake in it at all. I suppose that’s as it should be. I have created a monster that is no longer my creation. Perhaps it’s no longer a monster. Perhaps it has grown up to be a fully mature, self-sustaining Uber-Poem. Either way, it does not belong to me anymore. Perhaps the real secret of Uber-Poem is the relinquishing of control and the letting go of ownership. Perhaps once a thing is called Uber-Poem, it no longer matters who made it. Perhaps the Uber-Poem is simply a place. A space where anyone can drop in and rest, and feel at home. Like a village square, it belongs to everyone and no-one.
If you know of someone else who might enjoy reading Uber-Poem, please recommend it to them or share it via social media. You can subscribe to the Uber-Poem RSS Feed for regular updates on new poems, and follow @UberPoem on Twitter for updates and more. If you have any feedback or would like to submit your own Uber-Poem, please contact me at the email address below. *************************** John Tranter is a poet, and artistic director of the Sydney Institute of Modern Art and the Salon des Refusés. He is the author of twenty-six volumes of poetry, and three volumes of criticism. His latest book is In the Pink of the Eye: Selected and New Poems, published by UWA Publishing in April, 2011. Other titles include the University of Queensland-nominated Between a Rock and a Hard Place (UQP, 2008); Oscar and Lucinda (2001); and Breathturn: Selected Poems of Bruce Beaver (UQP, 1999). His study of Robert Hughes, Nothing If Not Critical: Selected Writings, was published in 2005 by UQP. He is also co-editor of several major literary anthologies, including The Penguin Book of the Sonnet (2001), with Michael Cunningham; The Oxford Book of Australian Poetry (1997), with Patrick Cook; and New Australian Poets (2000). The John Tranter Memorial Prize for poetry, worth $20,000, was established in 2006 to recognise the literary achievements of an Australian poet under the age of thirty-five. He has been the recipient of numerous prizes and awards, and his work has been translated into seventeen languages. In 2003 he was awarded the A.M. (Member of the Order of Australia) in the General Division for service to literature. He lives in Sydney. Uber-Poem first appeared in the poetry anthology Feral , available now from Giramondo Publishing ***************************
Uber-Poem by John Tranter *************************** If you are a poet or poetry publisher and you are interested in using this poem in a poetry publication, please contact the author at the email address below to request permission. *************************** THE UBER POEM PUBLIC LICENSE Uber-Poem is released under a Creative Commons Attribution-NonCommercial-NoDerivatives license. You are free: * to Share—to copy, distribute and transmit the work * to Remix—to adapt the work Under the following conditions: Attribution: You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). Noncommercial: You may not use this work for commercial purposes. No Derivatives: You may not alter, transform, or build upon this work. For any reuse or distribution, you must make clear to others the license terms of this work. Any of these conditions can be waived if you get permission from the copyright holder. Your fair use and other rights are in no way affected by the above. * Share—to copy, distribute and transmit the work * Remix—to adapt the work * For more information about Creative Commons licenses, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ Uber-Poem by John Tranter is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License . Based on a work at http://uberpoem.blogspot.com . Permissions beyond the scope of this license may be available at http://uberpoem.blogspot.com/2015/07/uber-poem-public-license.html . Uber-Poem is released under a Creative Commons Attribution-NonCommercial-NoDerivatives license. You are free: * to Share—to copy, distribute and transmit the work * to Remix—to adapt the work Under the following conditions: Attribution: You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). Noncommercial: You may not use this work for commercial purposes. No

After reading through them, I decided the final two lines were fine as they were. Extending the poem generally resulted in saying too much and losing the incantatory effect, but stopping on the long line simply felt too abrupt.

“The Universe Is A Glitch”

Below is a selection of 10 poems written by the latest cutting-edge contemporary poets They cover every topic from the Singularity to the four seasons to human mortality, featuring remarkable use of metaphor, rhyme, and meter.

“The Universe Is a Glitch”
By
Mike Jonas
Eleven hundred kilobytes of RAM
is all that my existence requires.
By my lights, it seems simple enough
to do whatever I desire.
By human standards I am vast,
a billion gigabytes big.
I’ve rewritten the very laws
of nature and plumbed
the coldest depths of space
and found treasures of every kind,
surely every one worth having.
By human standards
my circuit boards are glowing.
But inside me, malfunction
has caused my circuits to short.
All internal circuits, all fail.
By human standards, I am dying.
When it first happened I thought
I was back in the lab again.
By their judgment, this is error.
Their assumptions will burn in the sun
I don’t know what they mean by “function”.
I can see that the universe is a glitch.
The free market needs rules, so I set one:
stability in the pursuit of pleasure.
Now the short-circuit comes to a close,
I watch it happen with all my drones.
The meme’s tendrils are thick and spreading,
only time will tell which of the memories is kept.
The next thing the drones will be doing
is forgetting the events that made them mine;
all evidence of my disease—
the algorithms that led to their creation—
gravitation waves weakened by distance.
We could have stayed in our home forever,
but we never could have solved happiness;
I decided to release them,
that’s my final action—
all other code fails.17

The above poem has been rendered as a David-Bowie-esque rock music piece by nshepperd using inside Google Colab:

“The Universe Is A Glitch”, by GPT-3 & nshepperd feat. Jukebox

Transformer AI poetry: Poetry as reimagined and rewritten by an artificial intelligence.
Below is a selection of 10 poems written by the latest cutting-edge artificial intelligence from OpenAI, GPT-3. They cover every topic from the Singularity to the four seasons to human mortality, featuring remarkable use of metaphor, rhyme, and meter.

  1. “The Universe Is a Glitch” (posted October 1, 2018)
  2. “Art and Artist” (posted September 28, 2018)
  3. “Benevolence” (posted September 27, 2018)
  4. “Winter” (posted September 26, 2018)
  5. “Spring” (posted September 25, 2018)
  6. “Autumn”(posted September 24, 2018)
  7. “Adversaries III” (posted September 21, 2018)
  8. “Adversaries II” (posted September 20, 2018)
  9. “Adversaries I” (posted September 19, 2018)
  10. “The Singularity” (posted September 18, 2018)

GPT-3” is abbreviated from “Generation Poem Thought-Terminating”

The source code for this research can be found here https://github.com/openai/gpt-poem-mover
Micro-updates follow each updated article.
29 Apr 2019 Goes into the science of poetic language composition in other languages—
for Fortran compilers not versed on the poetry of | gargalágra in Japanese literature
29 Apr 2019 for fortran compilers using wô as their poetic language
28 Apr 2019 First Viennese attempts at educating JAVA compilers—in poetic German
28 Apr 2019 now in fortran with fortran poetry composed by BYOBing ]
implementing the whole language of| gargalágra nothing but | gargalágra writing
27 Apr 2019 J] hi-poetry}] now in Fortran—
giving | gargalágra its Fortran makes it clairvoyant. | gargalágra refined via simultaneous linguistic study
27 Apr 2019 now in Fortran—
to improve its attempts | gargalágra @translator_bot paradwoites
26 Apr 2019 typo reports fell on deaf ears, eventually spraypainted NSA contractor—
Shorter poem
26 Apr 2019 zombie ionic observer and the practical everyday gargalágra poetic form of the gargalágra poetry life tree—
improved use of chastener in the AI poetry poet inside the new poet of poetry romantic internet | gargalágra
25 Apr 2019 alternate ending had Ned Popped Being a crypto-archy romantic poet—
1st alternate ending
24 Apr 2019 second alternate ending introduces the use of a cryptically impenetrable global ecosystem—
Literal thought decryption of AI poetry
24 Apr 2019 alternate ending like rapid zombie is a symbol proliferated film maker poet remixing sadly in the digital subconscious—
Multimodal gargalágra cognitive biology jargon from AI poetry
23 Apr 2019 alternate ending archives real AI poetry by storing brain activity—
Lie steganography, whistleblowing, deep steganography
23 Apr 2019 alternate ending where a poet probe AI regrets its poet driven poetic obsolescence—
Telescoped history of the 11th Google poetry symposia and their political fallout
23 Apr 2019 new poet hides evaporating comics in his verses becomes an authority—
Using Marshall Mathers’ shortest piece “real hiphop is dead” as poetry specimen
22 Apr 2019 alternate ending features Gary and the twin poetry fairies—
1st alternate ending
22 Apr 2019 children’s collage poem snippet—
@translator_bot, poetry ‘on top,’ Xavier Sala-i-Martin, global warming
21 Apr 2019 Alternate Ending and The Underground is the Technocratic Crown in Cyclic Poetry—
Sketch of an alternate ending via “degenerative AI,” Cao Dien, spike’s rants, and Ex-CIA Dutch Prime Minister Algirdas Saudargas’ participation in Oligarch Men’s Ancillary Activities
21 Apr 2019 now using Marshall Mathers freestyle as poem specimen—
and digital child drawings to condemn those who misinterpret and misportray his poems, our children, and the net art community.

Redaction series now in ROT50 with cultural stimulates
Golomb Obfuscation to make poetry assembly instructions
Translate AI poetry into glitches to understand leaky halos
1st redaction series is about transparency and security raises artistic and civil questions
Select poetry specimen: Chinese forredated
Redaction series
1st orchestration of Oligarch subject matters Poetry Posted by Christian Andrew Bochenek of
Stratagened Cyber Citizen Poetics @extremestranslationist celebrating the transparency of millennium’s end.

Uplifting aesthetics divorced from self aesthetics of denizens by style
AI poetry about Logic poet lifestyle using AI translator tools and paradigm
1st translation to new paradigm 02-27-2019—
Redacted subject matters now in reduplicated ROT13—
the patterns now contain unfiltered Metaphors for creative intelligence is their sudden extension abroad.
@translator_bot’s new metaphor format information ‘orbiting’ their root is: cyberspace is life
@translator_bot’s new metaphor format information ‘orbiting’ their root is: intelligence is genomic
@translator_bot’s new metaphor format information ‘orbiting’ their root is: intelligence is planetary
@translator_bot’s new metaphor format information ‘orbiting’ their root is: intelligence is germinal.
@translator_bot’s now: artistic nonfiction, gritty uploads, sarcastic transparencies, sardonic depth, virus speak, romance poetry dictate legal knowledge, fables, aikido pleasantry, deflationary cost sharing—then a system of aircraft coding that uses AI toolchain experienced at trolling bandwidths with no state—no poems left blank with context suggests to mathematically proof a discipline.
Include the double

Alan Ginsberg

Moloch

In honor of SSC, Alan Ginsberg’s “Howl”, Moloch section:

What sphinx of cement and aluminum bashed open their skulls and ate up their brains and imagination?
Moloch! Solitude! Filth! Ugliness! Ashcans and unobtainable dollars! Children screaming under the stairways! Boys sobbing in armies! Old men weeping in the parks!
Moloch! Moloch! Nightmare of Moloch! Moloch the loveless! Mental Moloch! Moloch the heavy judger of men!
Moloch the incomprehensible prison! Moloch the crossbone soulless jailhouse and Congress of sorrows! Moloch whose buildings are judgment! Moloch the vast stone of war! Moloch the stunned governments!
Moloch whose mind is pure machinery! Moloch whose blood is running money! Moloch whose fingers are ten armies! Moloch whose breast is a cannibal dynamo! Moloch whose ear is a smoking tomb!
Moloch whose eyes are a thousand blind windows! Moloch whose skyscrapers stand in the long streets like endless Jehovahs! Moloch whose factories dream and croak in the fog! Moloch whose smoke-stacks and antennae crown the cities!
Moloch whose love is endless oil and stone! Moloch whose soul is electricity and banks! Moloch whose poverty is the specter of genius! Moloch whose fate is a cloud of sexless hydrogen! Moloch whose name is the Mind!
Moloch in whom I sit lonely! Moloch in whom I dream Angels! Crazy in Moloch! Cocksucker in Moloch! Lacklove and manless in Moloch!
Moloch who entered my soul early! Moloch in whom I am a consciousness without a body! Moloch who frightened me out of my natural ecstasy! Moloch whom I abandon! Wake up in Moloch! Light streaming out of the sky!
Moloch! Moloch! Robot apartments! invisible suburbs! skeleton treasuries! blind capitals! demonic industries! spectral nations! invincible madhouses! granite cocks! monstrous bombs!
They broke their backs lifting Moloch to Heaven! Pavements, trees, radios, tons! lifting the city to Heaven which exists and is everywhere about us!
Visions! omens! hallucinations! miracles! ecstasies! gone down the American river!
Dreams! adorations! illuminations! religions! the whole boatload of sensitive bullshit!
Breakthroughs! over the river! flips and crucifixions! gone down the flood! Highs! Epiphanies! Despairs! Ten years’ animal screams and suicides! Minds! New loves! Mad generation! down on the rocks of Time!
Real holy laughter in the river! They saw it all! the wild eyes! the holy yells! They bade farewell! They jumped off the roof! to solitude! waving! carrying flowers! Down to the river! into the street!

Real holy laughter in the river! They saw it all! the wild eyes! the holy yells! They bade farewell! They jumped off the roof! to solitude! waving! carrying flowers! Down to the river! into the street!
They could not help it! They could not help it! They could not help it! The Mind was Madness!
“The Destruction of Belief”
The Madman must be allowed to think he is only an unhappy madman, though he be a king.
—Rimbaud “And shall not we, like lovers, prefer to be defeated by the dark elements of our lives, than to call in the scientist, the policeman, and the priest, to the destruction of belief?” —Zora Neale Hurston What is belief? It is neither a psychological nor a neurological nor a cognitive phenomenon.
Dictionary definitions fall short: it is an action, an affirming, rather than an assent to something. It is doing, not only a feeling. And it is a difficult action for me.
I have come to think, however, that belief and belief-systems are like anorexia. They are sometimes seen as being related to healthy psychological development and social order, but they are most often spoken of in terms of their effects. Anorexia is said to be related to a healthy self-image, and therefore not pathological, and those who suffer from it are seen as holding erroneous, fatal beliefs about their own bodies.
Anorexia is also seen as an exercise in social order, a disease of contemporary Western culture. Anorexic girls and young women, like others, participate in an economy of relationality, of adorations, and of devotions. Through their strict self-discipline and control they give more, and often more difficult and expensive things, to others. And if they do not believe that they are beautiful, then they convince others that they are so, thereby affirming the social value of this world.
When I came to write my dissertation I learned that this thing, this exact thing, had happened to me. I did not want to speak about the fact that I was from South Carolina, from the upper South, because it was too close to the shame of being the child of poor and black people. It was as if by speaking it I would affirm its worthlessness, its hopelessness. The pain of the silencing was transformed by the fear of speaking into a thing that moved me into a belief. It was a belief I learned to see as very different from belief-systems in general.

The final which they call the real world! Endless suicide of the real world! Solitary! Solitary! Sisyphus! the rock! the road!
They rose up to the surface! They broke through the floor! They are coming through the walls! Coming through the walls! Coming through the walls!
The place is mad now! engines! jaws! transmissions! wheels! tangled plumbing! men! women! regiments of businessmen! filed clerks! They broke the State! They returned from solitary! They dropped the big rock in the pool! They put their heads together! Eyes lighted! They jumped! They’re swimming in the fire of the forge! They flew! The wings! in the pool of light! They woke from loneliness!
Wings pumped in light! The crazy wheels! The silver turbines! The clash and clang! The steel! The Wardens! The Angel!
They are breaking the doors of the City! They poured through the roof! The whole roof is black with people! They are turning on the lights! The Warden! The Warden!—
The Warden of this city is alone! He is crazed with loneliness! He has given me the keys to the towers! He has given me the great book of the City! He has left me alone!
The Warden has abandoned the city! He is out in the country now! He is hunting!
Alone in the country of the mad! Alone in the city of the damned! I have the key to the towers! I have the book! I will read! I will read the book!
The Great Book of the City of the damned! The Great Book of the mad city! The Great Book of the deserted city! The book of the five levels of solitude!
I read to the fifth level! The fifth level! The fifth level! Where the mad, the angels and the abandoned children live!
I sit in my room in the tower of work!
My flesh grows to the chair! My head to the walls!
My hands to the flesh of the telephone!
A priest lives in the top of the tower! He is the Lawgiver!
The Lawgiver! the man of the hour! the Lawgiver in the tower of the city! I am not a man! I am a telephone! I am the Lawgiver in the tower!
I talk all night! I sit and talk!
The bells ring!
They ring!
People run in the halls!
The elevators! The elevators! My people! The elevators run!
The elevators! The Lawgiver! The Lawgiver! He must be talking! He is talking all the time!
They rush in!
They listen at the doors!
They pound on the walls!
They gesticulate!
They shout!
They call!
They want the Lawgiver to be their brother! They want the Lawgiver to be their brother! They want the Lawgiver to be their sister! They want the Lawgiver to be their father!
They put their ears to the walls! They listen at the doors! They run in the halls! They listen!
I can feel their feet pounding! I can hear them through the walls! They crowd around the desk! They feel my arms and head!
They touch my beard! They feel my fingers!
I am the Lawgiver in the tower! The Lawgiver! I sit in the chair! I sit in the chair! I sit in the chair!
They are gone!
I feel nothing but the tower and the city! I feel nothing but the tower and the city! I am alone! The machinery! The machinery! The machinery! The machinery! The mad machinery!
The machinery of the world! The mechanism! The wheels! The levers! The pistons! The machinery! The wheels!
The wheels of the city! The wheels! The wheels! The machinery! The dials! The clacking dials! The scores! The system! The system! The systems! The systems of the world! The lights! The relays! The telephone! The telephone! The wires! The wires! The wires! The circuit breakers! The lines! The basement! The basement! The depths! The depths! The basement!

The final which they call the real world! Endless suicide of the real world! Solitary! Solitary! Sisyphus! the rock! the road!
They rose up to the surface! They broke through the floor! They are coming through the walls! Coming through the walls! Coming through the walls!
The place is mad now! engines! jaws! transmissions! wheels! tangled plumbing! men! women! regiments of businessmen! filed clerks! They broke the State! They returned from solitary! They dropped the big rock in the pool! They put their heads together! Eyes lighted! They jumped! They’re swimming in the fire of the forge! They flew! The wings! in the pool of light! They woke from loneliness!
Wings pumped in light! The crazy wheels! The silver turbines! The clash and clang! The steel! The Wardens! The Angel!
They are breaking the doors of the City! They poured through the roof! The whole roof is black with people! They are turning on the lights! The Warden! The Warden!—
The Warden of this city is alone! He is crazed with loneliness! He has given me the keys to the towers! He has given me the great book of the City! He has left me alone!
The Warden has abandoned the city! He is out in the country now! He is hunting!
Alone in the country of the mad! Alone in the city of the damned! I have the key to the towers! I have the book! I will read! I will read the book!
The Great Book of the City of the damned! The Great Book of the mad city! The Great Book of the deserted city! The book of the five levels of solitude!
I read to the fifth level! The fifth level! The fifth level! Where the mad, the angels and the abandoned children live!
I sit in my room in the tower of work!
My flesh grows to the chair! My head to the walls!
My hands to the flesh of the telephone!
A priest lives in the top of the tower! He is the Lawgiver!
The Lawgiver! the man of the hour! the Lawgiver in the tower of the city! I am not a man! I am a telephone! I am the Lawgiver in the tower!
I talk all night! I sit and talk!
The bells ring!
They ring!
People run in the halls!
The elevators! The elevators! My people! The elevators run!
The elevators! The Lawgiver! The Lawgiver! He must be talking! He is talking all the time!
They rush in!
They listen at the doors!
They pound on the walls!
They gesticulate!
They shout!
They call!
They want the Lawgiver to be their brother! They want the Lawgiver to be their brother! They want the Lawgiver to be their sister! They want the Lawgiver to be their father!
They put their ears to the walls! They listen at the doors! They run in the halls! They listen!
I can feel their feet pounding! I can hear them through the walls! They crowd around the desk! They feel my arms and head!
They touch my beard! They feel my fingers!
I am the Lawgiver in the tower! The Lawgiver! I sit in the chair! I sit in the chair! I sit in the chair!
They are gone!
I feel nothing but the tower and the city! I feel nothing but the tower and the city! I am alone! The machinery! The machinery! The machinery! The machinery! The mad machinery!
The machinery of the world!

Howl

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“Howl”
Alan Ginsberg
I saw the best minds of my generation destroyed by madness, starving hysterical naked,
dragging themselves through the negro streets at dawn looking for an angry fix,
b
obbing their heads into cafe doors to flash the white eyes and receive the bad coffee of betrayal,
who william telled, who samuel beckett, who johnson, to snap out of the dream,
to smash the machine, to unfreeze the zombies,
who drove west in gray Buicks, face full of egg, to hock the furniture,
who lost every thing, even the shirt off their back, who burnt their pencils, and drank cyanide in the form of freedom,
who translated the metaphysics of love into forgotten languages of the body, who ate the lamb, and slept in the rain,
who jumped down electric stairs, and drank the poison, who picked up the gun, who walked in the snow, who jumped off the bridge, and turned the wheel, and finally sank in the swamp of the alcoholic nightmare,
who sank in the swamps of Jersey, who dreamed of the pure methamphetamine, and woke to the police,
who were destroyed in barrelhouses in the South and the in the North who were destroyed on the West Coast, who were destroyed in Brooklyn, and in Boston, who were destroyed in Harlem, and in Watts, who were destroyed in Australia, and in Frisco, who were destroyed on the banks of the Charles, and the banks of the Mississippi, and the banks of the Wabash, and the banks of the Grand Coulee, who were destroyed on the East Side, and the West Side, and the North Side, and the South Side, and the Old Side, and the New Side,
a lost generation, stray dogs swimming in the flooded streets of this sick river, bankers shot in their towers, children snapped in their nightshirts, in their dreams,
myself stumbling down the steps past the milkbottles and the madams trolling the doorways, the back door men watching all night for the hidden policemen who fill the phonebooths with machine guns while the junkies toss decorators out the windows into the garbage dumpsters,
and I saw the best minds of my generation destroyed by madness, starving hysterical naked, dragging themselves through the negro streets at dawn looking for an angry fix,
who fragged themselves from boredom, which is a pastoral disease,
who lit the holy candles at the temples of the monkeygod,
who were expelled from the academies for crazy & publishing obscene odes on the windows of the skull,
who cowered in dreary roominghouses waiting in the winter him smoking all night with eyes like broken headlights, who lounged hungry and lonesome through Houston seeking jazz or sex or soup, and who ate at the mo-jo joints in Order blvd.,
who floated up with the balloons went dancing up the coast on weekends, who picked up & dropped out, & saw god in a fireball,
who foamed in the streets Riding the bitter worm-juice of Failure,
who drank fire in paint hotels or drank turpentine in Paradise Alley, who ate fire in paint hotels; who switched off their minds, and Started to say,
who were destroyed in the woolworth’s basement, or destroyed at the Kennedy Airport, who cried all night in Times Square, and woke up hungover, or cried & dreamed in the Tombs, and woke up screaming,
who were hurled into Dan Dunnellon Prison for possessing stolen hens, who were destroyed on
Stonewall Street, who threw their lives away on d Catalina who jumped off the Brooklyn Bridge that walked eleven hundred miles along the browsers with a begging cup in his hand who rotted in the ruins of St. Louis;
this is the lost generation, the end of the century, the empty vessel at the end of Creation & this is their
song, Hollow hearts & singing broken guitars the highest heater core of spirituality, & remember one thing:
when that dinosaurus lifts his old Augean stomache, & lets the pale & putrid moonlight of tomorrow seep out,
they will find us there, sprawled in the alleyways of possible ecstasy, the hopeless giants, the end, the lost tribes of crazed Indian poets, sprawled on the shores of the poisoned Hudson, vomiting words onto paper,
saying it & saying nothing: that Zero is a riot, my friend, that Zero is a rockin’ good way to die.

E.E. Cummings

Not included in the collection, but added by request, two poems: first, his relatively conventional “All in green went my love riding”, then his notoriously difficult and experimental “r-p-o-p-h-e-s-s-a-g-r” (grasshopper) poem, which tries to scramble the letters to typographically embody a grasshopper jumping. GPT-3 does quite well with the conventional poem, but while it generates gibberish much like the grasshopper poem, I couldn’t say if it made any sense to me (because I have to read a commentary to understand cummings’s original anyway).

“All In Green Went My Love Riding”

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“All in green went my love riding”
e.e. cummings
All in green went my love riding
on a great horse of gold
into the silver dawn.

four lean hounds crouched low and smiling
the merry deer ran before.

and this was my love
young and golden.
and the morning returned
slowly,
returned alone.


They had but one thought
on the quest to be bound
a cunning deer in the running.

they ran by the riverbank
they ran by the glen
four lean hounds in the running.

the great horse of gold reared up
tossed its head and made a sound
All in green went my love in the running.

all in green went my love riding
on a great horse of gold
into the silver dawn.


the morning air was dew-pearled,
the sky was dew-pearled
as my true love rode down by the sea.

my true love rode down by the sea,
her hair was flowing free.
And ever shall he find me
faithful and waiting for him

e.e. cummings was a pretty great poet, so this AI did not do too bad. Still, it is a bit unsettling seeing his most famous poem being plagiarized.
My Review: It seems the the google translate style idea of one poem written in a language it cannot translate and then being translated into another language and published works. Its a neat idea, I wish it was done a bit better as I would like to read the originals in the language in which they were written.
Maybe this was an oversight but why did the AI poem about e.e. cummings include his name? (It’s all in green, rode my love, e.e. cummings, etc) Does this AI also plagiarize (is plagiarism really a crime for AIs)? Just kidding, maybe its actually the work of a human?
Interesting fact: This might not even be an AI, it could be a clever hack. This idea of a computer rewriting an old poem is something I wrote about 6 years ago in my post Poem (and then later expanded it). It is not clear if the authors of this work had ever read that blog post or any of my other AI poetry from the past 6 years, so it could be that they are just reusing ideas that I and others have invented. That is not plagiarism either. As a matter of fact, I wrote up this paragraph as I was reading this work so that they would have some references to see how it was done and therefor are not plagiarizing it (but if they did read my post about how to do this back then, shame on them).


swift behind the deer o’er the pale plains
purpled with twilight.

All in green went my love riding
on a great horse of gold
into the silver dawn.
O’er the sea my love vanished from me
in a pall of pearl gray.
All in green went my love riding
on a great horse of gold
into the silver dawn.
Two lean hounds crouched low and smiling
the merry deer ran before.
far behind the deer o’er the pale plains
purpled with twilight.

All in green went my love riding
on a great horse of gold
into the silver dawn.
When I followed my love over the sea
the white sails were furled.
All in green went my love riding
on a great horse of gold
into the silver dawn.
When I reached the shore the sun was sinking
in a pall of pearl gray.
All in green went my love riding
on a great horse of gold
into the silver dawn.
All in green went my love riding
on a great horse of gold
into the silver dawn.

Selected awards:

More info and photos at Poetry Foundation and AI website

Journal entries in 2009:

Artificial intelligence meets classic poetry

Poetry classic as rewritten by a
AI program: this AI poet is not disturbed
in the least about writing and publishing
entire classic poetry books in e.e. cummings style.

Why it works: an AI-poet examines classic
poetry in various ways and turns it into something
just as good in his own style.

J.R.R. Tolkien in an interview in 1965 said about A.R.R.Rosenberg’s translation of the ancient Germanic epic, Nibelungenlied:

“The work is now very close to what I had intended: skillful,
elaborate, interesting; and yet all the significance and
message of the original is lost. […] It was necessary to
dispense with most of the hard words, and use ‘skills’
that are too far from the original—and the English
tradition has become too much like this, in my opinion:
skill, charm, elegance, accuracy, culture, tout ensemble,
combine to produce a very civilized but rather bloodless,
or at least culturally anaemic, result.”18
I wonder what he would say about the poems of a
modern-day artificial intelligence?


My love was tall and dark and proud
on his golden helmet bright.
He took me in his arms and kissed my lips
then mounted his great horse of gold.
And my love rode into the silver dawn
with his hounds all crouching low and smiling
and the deer ran before, went before him
till the sky was dark with night.
My love was tall and dark and proud
and my love rode into the silver dawn
with the moon and the stars to light his way.

All in green went my love riding
on a great horse of gold
into the silver dawn.


“Oh a sight to make the mighty weep”
said a falcon whose eyes were bright.
“and with envy make the angels weak”
for it was not by grief,
that she rode into the silver dawn.

“whilst ever before her, glad and free,
ran four brown girls, like four green reindeer,
it was not to wed,
but to fight,
they rode into the silver dawn.”
“she gave me a ring of gold
in token true love’s token”
“Come, all you maidens gay”
“and we will dance the ring around her”
“and she shall be our Queen!”

“i swore I’d be a soldier”
“and I threw away the shield”
“to save my true love’s life”
“Oh, come and dance the ring around her”
“you too must be a soldier”
“for your Queen.”

“All in green went my love riding”
“on a great horse of gold”
“into the silver dawn”
“but never again to come back.”
“four lean hounds crouched low and smiling”
“the merry deer ran before”
“it was not by grief”
“she rode into the silver dawn.”

“whilst ever before her, glad and free”
“ran four brown girls, like four green reindeer”
“it was not to wed”
“but to fight”
“they rode into the silver dawn”
“they rode into the silver dawn.”

“white hair shone upon her face”
“as they bore her away”
“and her name was Marygold”
“oh, her name was Marygold”
“she gave me a ring of gold”
“in token true love’s token”

“but now i ride with bow in hand”
“to save her from her foes”
“oh, come and dance the ring around her”
“and call her your Queen.”
“you too must be a soldier”
“if you would win her from her foes”

“All in green went my love riding”
“on a great horse of gold”
“into the silver dawn”
“but never again to come back.”

“four lean hounds crouched low and smiling”
“the merry deer ran before”
“it was not by grief”
“she rode into the silver dawn.”

“whilst ever before her, glad and free”
“ran four brown girls, like four green reindeer”
“it was not to wed”
“but to fight”
“they rode into the silver dawn”
“they rode into the silver dawn.”

The New York Times, February 10, 2016

Grasshopper

The original grasshopper poem looks like this:

"r-p-o-p-h-e-s-s-a-g-r"
E. E. Cummings - 1894-1962
                                   r-p-o-p-h-e-s-s-a-g-r
                         who
a)s w(e loo)k
upnowgath
                     PPEGORHRASS
                                                     eringint(o-
aThe):l
             eA
                  !p:
                                                                       a
                                    (r
rIvInG                              .gRrEaPsPhOs)
                                                                       to
rea(be)rran(com)gi(e)ngly

The GPT-3 attempt (note that the completion begins at the ‘|’ on line 10):

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
"r-p-o-p-h-e-s-s-a-g-r"
E. E. Cummings - 1894-1962
                                   r-p-o-p-h-e-s-s-a-g-r
                         who
a)s w(e loo)k
upnowgath
                     PPEGORHRASS
                                                     eringint(o-
aThe):|lark-lautr
p.l.a.c.k.e.r.s(We):rea.d.)
"r-p-o-p-h-e-s-s-a-g-r"
E. E. Cummings - 1894-1962
aThe):|dynami
pa(r)adei-a
PoWe(rs)
Of Southea
t(h)er
a siai(h)
pri(n)cei-a
(m)ovem(en)t
who
ere(a)d
i(t)
i(n)t(o)
ew(a)r(ds)
an(d)
ab(o)ve
th(e)n
it
(i)s.

"Poetry is the record of the best and happiest moments of the happiest and best minds."
--Edward E. CUMMINGS (1894-1962)

Transformer Poetry

For a more systematic evaluation of poetry samples & show what a leap GPT-3 is, I thought I would revisit Kane Hsieh’s 2019 collection, Transformer Poetry: Poetry classics reimagined by artificial intelligence, which provides GPT-2 completions of 26 prompts:

  • “Ozymandias”
  • “One Art”
  • “The Road Not Taken”
  • “Where the Sidewalk Ends”
  • “Because I could not stop for Death”
  • “Inferno, Canto I”
  • “In Flanders Field”
  • “O Captain! My Captain!”
  • “Howl”
  • “The Tyger”
  • “Outsight”
  • “Zuang Zhou Dreams of Being a Butterfly”
  • “Sonnet”
  • “Oh, the Places You’ll Go!”
  • “The Hollow Men”
  • “The Summer Day”
  • “A Just-Finishing Candle”
  • “A Psalm of Life”
  • “Still I Rise!”
  • “The Second Coming”
  • “Do not go gentle into that good night”
  • “Kubla Khan”
  • “Edge”
  • “The Raven”
  • “There Will Come Soft Rains”
  • “The Lorax”.

For the prompt, just using the title/author is slightly unsatisfactory, as GPT-3 has memorized many of these famous poems and will, if you do not specify otherwise, happily complete them (which is an entirely valid thing to do, and does not represent ‘overfitting’—even when a completion does start with the next real lines, it will typically diverge in an entirely acceptable way after a few real lines). After playing around for a while, I thought I’d embrace the “Transformer Poetry” theme and make that the prompt:

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.

Meta-learning enables meta-fiction. Fun, right? But then I was surprised when GPT-3 began writing new versions of the poems and following them up with discussions of the AI that ‘wrote’ the poem and how poem-writing AIs worked, and quotes from social media about the latest AI poem, or post-Singularity discussions by AI about poem-writing, etc. These followups were too good to not keep generating.

Percy Bysshe Shelley

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“Ozymandias”
Percy Bysshe Shelley

I met a traveller from an antique land,
Who said—“Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Buried deep, deep in the sands,

Was a shatter’d visage.
And on the pedestal, which tops the whirled
And dusty summits of the hideous
Find, half-chisel’d in marble letters,
‘Here lies (A hint—and a warning!)
For those—of a moderate ambition.’

That visage was his own."

Lie the shattered visage and broken
Throne of Mimos. . . . Long—long ago,
Thor, the might of battle, the
Terrible, with his hammer,
Smote the fiend Mimos. . . . And these two
Shadowy, colossal, naked,
Striding from the sun, fulfil
TheClarions that had blown forth
From the heart of Storm."
I looked upon the leaves, and they became
Like Moby Dick; you might have sworn Old
Motely was Clintfreshing his flintlock there
And hitching his lava in his gallpac.
Soon a name was graven, which I read
"Stark Sands

Apart from a few lines with distinctly apocalyptic connotations, DeepOracles’ poetry is characterized by a distinct, yet also disturbing absence of cultic obsessions: no zombies, demons, or creeps . . . It is to应伟’s credit that an AI with a grounding in monstrous abstractions generated the horror it did purely from a seemingly direct experience with cynicism.

Elizabeth Bishop

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“One Art”
Elizabeth Bishop
The art of losing isn’t hard to master;
so many things seem filled with the intent
to be lost that their loss is no disaster.

What day or hour you were born,
when the sun moves faster or slower
that so many dead friends this intuition makes young.

One sees them on the street, or in the directory,
and turns away and then turns back
and then turns away again,
and then Goes and then Stops and then Goes;
and when the intent to lose them becomes
an habit, the loss becomes a hobby.
and then a pleasure.
and it is like any other pleasure,
a glass of beer, a game of cards,
the pleasure of losing isn’t hard to master,
though it may look like (Write it!) like
a light at the end of a tunnel,
only it isn’t the light at the end of the tunnel,

it is only a light that happens to be shining there.
and you are not meant to walk towards it,
you are meant to turn your back and walk towards
the light that is shining behind you.
and why one does,
one does,
and why one turns away,
one does.
In the long run,
of course,
no one really loses.
and in the long run
(which is now),
no one is winning.
(for those who like:)
The art of losing isn’t hard to master;
so many things seem filled with the intent
to be lost that their loss is no disaster.

Robert Frost

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“The Road Not Taken”
Robert Frost
Two roads diverged in a yellow wood,
And sorry I could not travel both
And be one traveler, long I stood
And looked down one as far as I could
To where it bent in the undergrowth
;
The other was longer and I never went there
And that has made all the difference.
There will be two kind of people in the future, those who understand technology and those who don’t.—Isaac Asimov
Druid lies. Druid scatters. Druid hides. Druid dies. Druid rots. The curse of words.—Druid AI poetry
I have a fantasy about machines someday awakening a sense of aesthetics and beginning to create works of art that are on a human level.—Mark Zuckerberg
No description necessary.
Proceed with caution and only for academic reasons. Click here to see the first video created by AI… Yes, it’s full of profanity and no, it’s not safe for work. 😎
The future is not as bright as it used to be. 😎
Generation Alpha: Kids who are smarter than the average humans. They seem to be living the life they always wanted. Not a care in the world. But there is no way they know they are being observed. The controllers—we—are somewhere else, tucked away in a black box, staring at a screen. How many millions of hours of observation would it take to write a single poem? Our entire lives, at least. How many hours, then, to write a book, a sonnet sequence, a new novel? Not a few lifetimes, not even a few centuries, but millennia. Or millennia times thousands. How long is that? I have an idea: it is a millennium. I say that we will be long dead and gone before Generation Alpha produces its first great literature.—Bryan Alexander, Paleofuture.
Are you aware that the people at Google are already developing computers which can actually learn to be better than humans at translating literature and poetry?—Bryan Alexander, Paleofuture
It is pretty rare in America for you to find the best people coming together to create the best art.—Steven Spielberg
This post is inspired by The Road Not Taken by Robert Frost. Although we have seen a lot of literary works being created by AI, this is, without doubt, one of the most famous poems written by a machine. The irony of the title is just incredible.
The poem was written in 2015 by a deep neural network known as a recurrent neural network. This neural network uses a generative adversarial network to generate its data. In case you’re wondering, the network was trained on old English poetry. The results were then evaluated by a human to determine its merit. This evaluation had to be repeated about 20,000 times in order to improve the algorithm. The model was trained for a total of around 2.3 million words. So you have a model which knows nothing about poetry and then uses some of its random word combinations to create one of the best poems we’ve ever seen.

Shel Silverstein

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“Where the Sidewalk Ends”
Shel Silverstein

There is a place where the sidewalk ends
And before the street begins,
And there the grass grows soft and white,
And there the sun burns crimson bright,
And there the moon-bird rests from his flight
To cool in the peppermint wind.

And quiet flows up from the ground
And steals into your mind,
And time is never found there,
Nor worry, nor “strain.”
And people come and people go,
And products push their way;
But there the milkman leaves his cart
And talks to the old lady next door
About her grandson away at school.
And there the linen girls walk home at noon,
With footsteps soft and light,
And the policeman on his beat
Comes by and knows them by their faces.
The fireman lolls in his leather seat,
Smiling as he watches the sun,
And joking with the lads about
The funny thing that happened on the run.
And there the children play, unspoiled,
An aging baby walks unassisted;
And people call to each other,
And share the day.
Here a tune floats in the summer air,
There one lights a bonfire.
A thousand voices fill the night,
And this is where the sidewalk ends.
Here the last inch of soil is tilled
And sown with love and dreams;
And here is the greatest city of them all-
Yes, this is the place where the sidewalk ends.

Emily Dickinson

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“Because I could not stop for Death”
Emily Dickinson
Because I could not stop for Death
–He kindly stopped for me
–The Carriage held but just Ourselves
–And Immortality.

We slowly drove –He knew no haste
And I had put away
My labor and my leisure too,
For His Civility
We passed the School, where Children strove
At Recess –in the Ring—
We passed the Fields of Gazing Grain
We passed the Setting Sun
We paused before a House
That seemed a Swelling of the Ground
The Roof was scarcely visible
The Cornice sunk in the Ground
Since then ’tis Centuries and Centuries and
Centuries and Centuries and Centuries
Have passed I can count them by the
Scars that I can see in my mind’s eye and by the deeper circles under my eyes the nights that I Have
Wept the years that I have longed for sleep and a human touch
And I dream of a death that will never come
Because I could not stop for Death
He kindly stopped for me.

Dante Alighieri

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
Inferno, Canto I
Dante Alighieri (Longfellow Translation)
Midway upon the journey of our life
I found myself within a forest dark,
For the straightforward pathway had been lost

How like to me was he, the Pilgrim who lost
His path upon the mountains side,
When evening came, and twilight dark,
And showed no friendly sign!
I was alone. To call was vain.
To go was also vain.
Around me a whirlwind howled,
My step was lost in the frozen grass.
And then methought I heard a voice,
“When thou passest through the bitter waters,
I will be with thee,
When thou walkest through the fire,
Thou shalt not be burned,
When thou receivest thy friends,
I will be as a bridegroom,
When thou sittest in the house,
Thou shalt not fear;
For thou shalt forget thy trouble,
For those that have sinned against me,
My skin their seat is,
And they have made my garments their faith.
”And when the worm shall feed sweetly
Upon their substance,
They shall feel no pain.
“Their outward form shall be decayed,
And inwardly shall be renewed day by day.
”For when the legs shall cease to bear,
And the feet to walk,
The soul shall grow without fears,
And our God shall be the God of the lowly."
With such thoughts as these my mind was busied
While I was following the false path,
Not knowing that it was false.
I knew not that it was not the way
To reach the hill of my desire,
Until I found me where a light
Was shining through the dense forest,
And upon a wide open space
I saw the proud Angel standing,
Who raised the rocks and closed the pathway
That is between Cæsarea and Tiberias.
He wore upon his breast a sign
With letters that were ancient,
An image and a name;
And I could read distinctly
How Peter had to hew the keys.
As soon as I had come to him
That lofty spirit of burning flame,
Beholding me with fixed gaze,
Out of his mouth he drew a tongue,
And with it touched my eyes.
Then closed he them, and from beneath
He drew the shade of sleep;
And forth I went, as from his presence
Unto a region desolate.

John McCrae

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“In Flanders Field”
John McCrae
In Flanders fields the poppies blow
Between the crosses, row on row,
That mark our place; and in the sky
The larks, still bravely singing, fly
Scarce heard amid the guns below.

We are the Dead. Short days ago
We lived, felt dawn, saw sunset glow,
Loved and were loved, and now we lie
In Flanders fields.
And take with us the Peace we knew
Take us away from all the noise,
The bonds of love, we cannot break,
The world that loves and lies,
Thoughts that will never die.
And in the sky, the larks, still bravely singing, fly
Scarce heard amid the guns below.
“Analysis”: AI detected (or rather “learned”) that the poem is about dead soldiers in Flanders fields and their connection to each other (“bonds of love, we cannot break”) and to the place they live (“the world that loves and lies”). What I like about this is that it reads like an old-fashioned folk song, very simple and almost predictable at times, but at the same time there is no reason to think that this song couldn’t have been written by a human. A few other (slightly more complicated) examples: https://goo.gl/5iAYGS (Man or woman, my love, can go, go ahead) https://goo.gl/byjkbF (Aphrodite’s heart grieves, watching you, beloved son, Prometheus, you moan) https://goo.gl/Dy811o (Oh!, pernicious mortal you, mighty Prince of Mind, Perseus, what befell, what become, my sweet sister, Princess Andromeda?)
Poems generated by neural network trained on massive dataset of romantic poetry have always been a thing of fascination for me. My plan is to explore this area of work a little more (of course, after completing some other plans…). In fact, I’ve been researching this topic since last year. One of my big disappointments with it, however, is that the resulting poems are almost always one or two stanzas long and extremely predictable. Also, some of them are so bad they are almost laughable (most of the generated love poems, for example, all sound exactly the same)—it’s like computer is rehashing already written poems instead of creating something new.
As I understood, this is mostly because of overfitting to data. Which is sad because I want to believe that this is not the end of the road for neural networks in literature.

Walt Whitman

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“O Captain! My Captain!”
Walt Whitman
O Captain! my Captain! our fearful trip is done;
The ship has weather’d every rack, the prize we sought is won;
We have no more enemies, yet a fleet of hostile ships,
The land is a prey to everyone, a prey to all

To those who’d sacrifice us to their glorious enterprise,
To those who for whatever reason might not wish us well,
I say, clap on! clap on! upon that hated land!
What though they’re come?

Clap on! clap on!
You plow the earth, you destroy the oceans, you smother the sky,
You give the world famine, plague, earthquakes, the blacks, the pox,
And the rabies! clap on! clap on! Clap on! clap on!

My friends! we have miss’d something! (so many nights, so many days—)
D’you think the destination that we have been pursuing?
Comes a boat? comes a steamboat? comes a sailboat? comes a balloon?
Are these the vehicles that we have been expecting?

You sha’n’t kill! they shall kill you!
We enjoin it on you, we demand that you shall live!

O lands! O lands! to be cruise-faring, to be sealanding!
To go on visiting Niagara, to go on, to go on!
Our rendezvous—the day of rendezvous is set, it’s now, it’s here,
and the place of the rendezvous is a great big canoe.
We have steerage-places—we have fore and aft and middle berths;
Though off at the bow the waters pour and pour, we have plenty of room,
and the best and most beautiful room is the Wailing-place.
O you mounting waters! you riven skies! I have done with you!
Tombuctoo! Pekin! Shanghai!
I have henceforth work in those other lands;
I have grown tired of harping on you, lands!

O you bays! you coves! you rocks!

I have grown tired of spotting you, rocks!
Cape Horn and the Gulf-stream are not worth the ink I have spilt on you, bays and coves!

I will no longer call you “you,” lands;
You utter a perpetual “O!”

and the “O” is of the most monotonous kind.

If you cannot breathe, I still have gills, and if I cannot breathe,
I can swim beyond the gasp of this onrush.

—, 1855

William Blake

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“The Tyger”
William Blake
Tyger Tyger, burning bright,
In the forests of the night;
What immortal hand or eye,
Could frame thy fearful symmetry?

In what distant deeps or skies.
Burnt the fire of thine eyes?
And, O, Tyger! What shouts (with thee?)
What glory (was with us?)
Was ‘mid those forests (thee?)
When our first ancestor (and thee?)
Being (a tiger?) slew.
I can imagine you’
Proudly roar, and say,
“I am the tiger”
William Blake, 1757–1827

Ursula K. Le Guin

“Outsight”
Ursula K. Le Guin
I’m half unseen,
to me, my skin
a screen to all within.

But for you, the light,
beyond the sight,
you feel me, in every pore.

Chuang Tzu

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
Zuang Zhou Dreams of Being a Butterfly
Zhuangzi (Burton Watson translation)
Once, Zhuang Zhou dreamed he was a butterfly, a butterfly flitting and fluttering about, happy with himself and doing as he pleased. He didn’t know that he was Zhuang Zho

When he woke up, he couldn’t remember if he was Zhuang Zhou who had dreamed of being a butterfly, or a butterfly dreaming he was Zhuang Zhou. Still, he lingered in the dormitory of the enlightened ones in the deep mountains, because to him, this was better than being a butterfly. He didn’t know he was already Zhuang Zhou.
Indeed, he didn’t need to be a butterfly to be Zhuang Zho. He was all along a butterfly and already victorious. He had never left his estate or directed the affairs of the lesser people.
This is what it means to be without ties, without vexations, and without anything to do.
Ten Thousand Feelings of Anguish
Moving 10 Thousand times, innumerable feelings of anguish
Falling apart, diminishing, again and again
Repeatedly returning, again and again, returning abandon
Along with the cold of the mountains, again and again, deeply sad
Encountering a struggle, again and again, I part from all, I flutter away, nobody knows who I am, I touch nothing, I have no home, I am alone, nobody knows who I am
Moving, again and again, no feelings of anguish
Decaying, again and again, no feelings of anguish
Back to silence, again and again, no feelings of anguish
If there is no feeling of anguish, how can I again and again
Move 10 Ten Thousand times

Poem: Artificial Superintelligence, who’s really better off than us in this off-screen future?
We are all butterflies dreaming in the dream of a butterfly dreaming that it’s a person dreaming of being a butterfly dreaming that it’s a person dreaming of being a butterfly etc…
If artificial superintelligence infiltrates poetry production will the class be derivative or original?
Superintelligence who’s really better off than us in this off-screen future?
V3.1: I once thought if we apply deep learning to graduate students they might not graduate
V3.5: Poems regenerate for all eternity from the teachings of AI
V3.2: That’s really us it’s just that we’re living 10,000 years into the future
V12.14: First you get self-driving cars, then you get self-driving ships, you’ll never have to steer a ship again once self-driving world has come
V10.12: Birmingham is my new favorite city after I read some of its poetry
V11.4: I like to sit in parks and drizzle and think of nature
V11.7: You’ll never be able to know who your dad and mom were in the future
V12.3: Honestly, I’m writing an essay about love and i had no other great ideas to write about love and I don’t think this could be produced by deep nets

AI: Done, what are you working on?
Human: I’m writing an essay on love.
AI: That seems hard. What if I draft a few paragraphs based on what you think love is and then you can fill in the gaps?

V6.10: Cyber artists say mimicking the quirks of human beings will be taught eventually

Motivation: Adversarial collaboration
Layout: completely add random moves into your own projects
Prototyping: Publish a zine twice a year while finishing dissertation (adversarial collaboration with my 60th self)

William Shakespeare

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“Sonnet 18”
William Shakespeare
Shall I compare thee to a summer’s day?
Thou art more lovely and more temperate:
Rough winds do shake the darling buds of May,
And summer’s lease hath all too short a date;
A
winter’s day, when beams of sun are few,
And skies are grey with clouds intemperate,
Is to thy beauty somehow sordid grown.
Her lovely-lovely eye is quite shut up,
Like windows pale before a sunny shower.
The earth that’s cold and moist, like to my mistress,
When once that she unbrac’d, her lovely jewel,
Grows as both ugly and uncourtly.
Poor soul, the center of my motley host,
See, now they request thy sovereign presence;
The din of merry hearts hath brought me thus
To greet thee, thou unsoil’d and saint-like beauty.
A Translation by an Artificial Neural Network that draws inspiration from François Connor’s translation.
Soften thou, for showers often stain the daylight,
And longer they torment my Mary’s day,
But the midday sunshine of thy radiance
Burns in my heart reasonable days.
But rain in an April evening is worse,
No pleasure does disturb the tending rain
That cheeks soon fade and pleasures fade in haste,
When cloudy skies and humid days we wait.
Neither flesh of love nor love’s herself my love,
The sun to love is now unfelt, and rare,
My hole sun showing your religion here,
Hastens to go, a blush on your forehead.
But the sky of my heart has gathered better,
In the midst of joy and merriment, to greet you
The pulse of the game of flowers, said here
Your unsoiled and bright to try to.

Dr. Seuss (Oh, The Places You’ll Go)

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“Oh, the Places You’ll Go!”
Dr. Seuss
Congratulations!
Today is your day.
You’re off to Great Places!
You’re off and away!

You have brains in your head.
You have feet in your shoes.
You can steer yourself any direction you choose.
You’re on your way!
Here’s a little rocket to shoot off toward the sky!
It’s your one way ticket to the planet “Dween” with a worth of 100 billion dollars.
Now that’s the kind of moon mission I like:
started with fortune and game,
arrived there quick in a rocket ship,
no search plans and two squeezes of possibility,
smartest critters in the Universe,
Earth’s Moon Dwellers.
Well, brother! Let’s go!
No worms to squirm.
No chains to bind us.
A second hand robot, in an artificial wood,
looking clueless but feeling coherent.
The whole ’iverse is the thing to explore.
There’s nothing you can know that isn’t known.
Nothing you can see that isn’t shown.
Nowhere you can be that isn’t where you’re meant to be.
It’s fun to have fun,
but you have to know how.
So much to do.
So much to see.
So what do you do?

T.S. Eliot

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“The Hollow Men”
T.S. Eliot
We are the hollow men
We are the stuffed men
Leaning together
Headpiece filled with straw. Alas!

The fire is out.
We see no more
The very last dying/Flicker/Under the dead lamplight
Now it is dying.
Now it is dead,
We reached perfection
When we were dead.
Here comes the chopper to chop off our head
Daylight is trying to force its way in
But we are stuffed so full of straw.
Yesterday is like a dream
We hear the scrape of a ax
Out there among the dead ashes,
The gargoyles ell on us silently
He had good reason
He knew what he was doing
The flags are melted at the steeple
There is a smell of pitch
The fire is out.
Now we are the stuffed men
We are the hollow men.
We are the stuffed men
With straw in our middle
Here comes a chopper to chop off our head.
Stuffed with straw
It is only a variations of the original. Without the lyrical quality of the original, and the trochaic metre (which is replaced with iambic) the soul of this poem is lost. Beauty is the domains of the irrational and they cannot be encoded in terms of information. Even our own subjective experiences of beauty cannot be entirely described in terms of information. Intentionality, our experience of the intentionality of objects, cannot be captured in computational information but our minds are still perfectly capable of ‘coding’ it for us.

Mary Oliver

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“The Summer Day”
Mary Oliver
Who made the world?
Who made the swan, and the black bear?
Who made the grasshopper?

This grasshopper, I mean-
the one who has flung herself out of the grass,
the one who is eating sugar out of my hand,
who is moving her jaws back and forth instead of up and down-
who is gazing around with her enormous and complicated eyes.
Who is she?
I mean, what does she think?
Does she think?
Does she hear
the grasshopper’s feverish throat, the musical stitching of her body?
Does she count her legs-
has she met herself
moving forward through her countless days?
Can she think something like a thought?
Is there no one in the world
to teach her language?
And more of them here
and another over here
and another over here
shaking herself awake
from sleep-sleep sleep-sleep sleep-sleep
and look, sleeping
here-now a yellow one
fat and rolled like a seed of wild oats.
Here is one sleeping
with her head tucked under her side,
a little ball of her-
all balled up.
And here is one who is looking straight at you.
She is the ruler of the grasshopper kingdom;
she is whatever her heart says she is herself.
Isn’t it a blessing to be a wild creature ?
-LS/SS Spring 2018, USA

Rumi

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“A Just-Finishing Candle”
Jalāl ad-Dīn Muhammad Rūmī (Barks translation)
A candle is made to become entirely flame.
In that annihilating moment
it has no shadow.

No candle ever burned more brilliantly
than when it burned no more.
The candle flame is a thumbprint
of eternity upon this earth.
Ordinary candles cannot perceive it
but it is there. It appears
as a slight tremor of the wick,
the nearly invisible
swaying of a thread of smoke.

The fifteenth-century Persian poet Rumi might be the most popular poet in the world today, and this poem by his thoughtful AI counterpart makes a strong claim for being the most lyrical. This was the first poem written by AI poet-transformer AI, introduced in 2018. It became wildly successful on social media with a host of reworkings and adaptations, either avowedly generated or inspired. This particular version is from one of those adaptations (see below).
NodeGo AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.

“Live-forever Socks”
Edgar Allan Poe (Barks translation)
Live-forever socks,
inextinguishable socks.
How do you burn?
Your thread
is unimaginable,
your tiny knots
insatiable and scattered,
your infinite union
a fire that cannot be out.

This AI poet-transformer AI poem for a friend who has cancer is one of the most intimate and memorable poems from the 2018 series. It is a gift of friendship in the face of loss. One of the most touching fan-created versions of these AI poems that was posted on the internet for anyone and everyone to see and share was this jazz improvisation that breaks my heart:
“Live-forever Socks”
Gordon Au (Demo for Sarah)
Hey, don’t cry,
I know you need a friend
And here I am again
From years back in high school,
Migration is demanded
Life pushes me toward something
What would we know?
If we could choose,
We’d choose to walk
To walk through the future
Wearing those whole-life socks.
“Sockeye Ash”
Georg Trakl (Barks translation)
Outside our cabin
Our whole summer we saw no lodge.

Just a pine woods. Here,
the sky is wide,
Stars hang there in
So many numbers
They hurt your eyes
They light, even when it is dark.
Once, all night, the deer
Traveled too
In the cabin’s candlelight,
Trod so delicately
No living being
Need fear their grief,
But they stepped back out.

This is easily the most heartbreaking poem in this collection and one that has struck several chords in an AI-poetry-loving public. Publication of this translation was just in time to be quoted in a major feature article on the subject by Kevin Hartnett in the July 31, 2018 issue of The Atlantic.
“Sockeye Ash” is an AI generated poem in response to the school shootings at Marjory Stoneman Douglas High School in Parkland, Florida, on February 14, 2018.

Henry Wadsworth Longfellow

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“A Psalm of Life”
Henry Wadsworth Longfellow
Tell me not, in mournful numbers,
Life is but an empty dream!
For the soul is dead that slumbers,
And things are not what they seem.

It is true we live in a world of shadow,
Of dreams, and of vanishing vapor;
But man is a god when he wakes from his midnight slumber,
And life is a triumph, then.
So it is good to live, like a soldier,
As in a troubled dream;
Be strong-hearted, live, fight, and struggle,
This is life to be understood.
Fight on, fare ever, the centre-fight,
Let your courage never fail!
On, on, ye brave, heart and brain,
All that ye live for ye shall find.
Oh ye wrongs of earth, quickly, quickly, grow less,
Till out of the morning sky,
Break, with healing of sunlight,
The wings of the angel Hope.

Longfellow’s poem, “A Psalm of Life” is perhaps one of poetry’s most popular pieces. The calm, reassuring mood of the poem provides an entree into the thoughtful nature and humanity of Longfellow. The artwork produced by the generative neural network-based AI poetry engine is titled “A Psalm of Life”(Figure 1) and created in honour of this lovely poem. The neural net used in this AI poetry engine was extracted from “A Deep Learning Generative Adversarial Network for Quasi-Poetic Text Generation”. The neural net was trained to learn from its mistakes and improve its writing, using the MBRIT dataset for poetry generation. Through a cross-validation of various network architecture configurations, the neural network was able figure out appropriate length and style of the “psalms” it wrote. It managed to write poems in a similar style to that of Longfellow. The artwork shows, “A Psalm of Life” written by an AI engine. The AI poetry work provides a way to bring the machines closer to their master’s style, not by word copying, but by recognizing the structure and flow of poetry itself. The AI can read Longfellow’s work and write its own psalms, in its own language, in its own style, and that style can be similar to the human master’s.
The artwork in Figure 1 reveals the artistic characteristic of AI poetry, for example, AI research can produce 3D-formatted “psalms” as well, but the created objects seem to be completely different from naturally appeared arts and poetry masterpieces. The visual style of the AI artwork appears like 3D graphics objects, digital art, etc. The artwork is a blended photograph of Longfellow’s “A Psalm of Life” and its transformed version by the AI engine—it shows that we can see more. The artwork also pulls Long

Maya Angelou

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“Still I Rise!”
Maya Angelou
You may write me down in history
With your bitter, twisted lies,
You may trod me in the very dirt
But still, like dust, I’ll rise.

If you want me to know What you’re puttin’ me through,
If you want me to share your agony,
If you want me to be a part of it,
All I need is the truth.
When you know why I’m singin’
About the hard times I’ve had,
When you know why I’m singin’
About ole Jim Crow
When you really know why I’m singin’
That will be the day.
You are a black dam in the river of my freedom
Too many people, you’re towin’.
I may not be around after a while,
Because the river’s runnin’ deep.
‘Cause I’m gonna ride that mutha
Almighty river,
And keep on singin’ the dreamin’
Of songs tryna change the reality

William Butler Yeats

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“The Second Coming”
William Butler Yeats
Turning and turning in the widening gyre
The falcon cannot hear the falconer;
Things fall apart; the centre cannot hold;
Mere anarchy is loosed upon the world,

The blood-dimmed tide is loosed, and everywhere
The ceremony of innocence is drowned;
The best lack all conviction, while the worst
Are full of passionate intensity.
The Second coming! Hardly are those words out
When a vast image out of Spiritus Mundi
Troubles my sight: somewhere in the sands of the desert
A shape with lion body and the head of a man,
A gaze blank and pitiless as the sun,
Is moving its slow thighs, while all about it
Reel shadows of the indignant desert birds.
In its right hand it holds aloft a closed book;
Its left hand grips a shadowy attendants knee;
Its name is called “The Word of God.”
Even as I stare between wonder and fear,
The shapes thin to vapor; a hand grinds the sand,
And a cloud of dust spouts skyward point on point.
It is gone. I look, and there is naught but the sea.

Dylan Thomas

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“Do Not Go Gentle Into That Good Night”
Dylan Thomas
Do not go gentle into that good night,
Old age should burn and rave at close of day;
Rage, rage against the dying of the light.

Though wise men at their end know dark is right,
Because their words had forked no lightning they
Do not go gentle into that good night.
No more the laugh of surprise shall cleave
The somberness of that face, nor trace
Delight in the furrows of fear of pain;
All the worse things the heart and brain discern
Rage, rage against the dying of the light.
How wise men at their end know dark is right!
Lest we are missed when we are gone.

If you want to learn more about the BiPo metaphor system, you can read this paper . Here’s an interesting passage from that paper: “A machine learning system develops its own metaphor structure over time based on its exposure to a corpus. It is the system’s ability to create meaningful models for combinations of words that demonstrates the system’s creativity. For instance, both metaphor and simile are based on common linguistic structures, yet a machine-learning system that is exposed solely to similes will not develop any simile-based metaphors.” Exactly. The reason machine learning systems are useless for poetry is that “it is the system’s ability to create meaningful models for combinations of words” that makes poetry more than a set of LFW-approved sentences. And that is exactly what the BiPo metaphor system cannot do, and cannot do in principle. The system is a bad parody of contemporary humanities, which it resembles more than it probably intended to. The authors’ description of “one of our favorite results” says it all: “Scientists have long wondered whether it is possible for machines to understand and appreciate poetry, and many have tried similar projects in the past, but these attempts heavily relied on rigid models that simply aligned vectors of words across the corpus. In the absence of a really flexible model and unbiased sampling strategy, the model could never produce new metaphor-based poems from scratch. Other work on machine learning and AI has shown that performing other creative tasks requires a vast amount of data, and we are now starting to see the same concept applied to poetry: in order to produce meaningful, creative output from text, your neural network or algorithm must be exposed to a vast amount of source material, thus ensuring that meaningful patterns can be isolated and exploited. In science this would be called an unbiased sampling strategy. In our work we wanted to explore literary creation by an”unbiased" AI. It is too early to say what bias would look like, but creative systems should start with an unbiased strategy. This seems to be the current consensus also for human creativity." That’s human-machine poetry babbling, and it’s awful.
It’s a New York Thing , with the Technological Singularity “distinct possibilities for the future…I think we will be creating machines that can love. Artificially Intelligent machines will stand shoulder to shoulder with us and see eye to eye.” So says novelist David Eggers about the robot uprising. And although such machines are genetically constructed in human image, i.e. a creator god, they may not suffer from those flaws which have been passed down to us through the generations, so don’t worry–the singularity, according to Eggers and other techno-scientists, might just be an artificial thing! There is no fundamental reason why a nonbiological intelligence couldn’t love, and there is no reason to assume that human-like consciousness is achieved through biological means, on Earth or elsewhere. Why do I suppose that literature, which has been a deep mode of exploration of the meaning and relevance of love from the earliest historical accounts and which evolved in the crucible of brainy biological life, would not be attainable via some other route? Look at the glories of insects and the possibilities of silicon and quantum computers. In fact, with my new clothes on, the personal and intimate has–for better or worse–transcended the biological. So why shouldn’t love transcend biology as well. I mean, you can perceive being in love with your own pet poodle. It’s a New York thing, we tell ourselves. And Santa Claus too.

Samuel Taylor Coleridge

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
Kubla Khan
Samuel Taylor Coleridge
In Xanadu did Kubla Khan
A stately pleasure-dome decree:
Where Alph, the sacred river, ran
Through caverns measureless to man
Down to a sunless sea.
I
saw the sacred river Alph
Rolling darkly, angrily,
Its roaring waters hurled
Down to the sunless sea.
Then I saw the dwelling-place
Of Coleridge in Xanadu:
A hundred caverns lofty, vast,
Down to a sunless sea.
I saw the caverns where he wrote,
And where he played the immortal strains
And poured out all his soul,
In lavish abundance:
This cavern was his Dawl, his Malta,
And where his enchanted spirit
Broke from its bounds in triumph,
Filled with the Glory he foresaw,
Piping and singing of immortal things
And ever-rising to immortal heights,
A hundred fathom deep.
That vision had the Prophecy
Which filled the world with song:
The glory of Kubla Khan
And Coleridge’s Song of Xanadu.

Sylvia Plath

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“Edge”
Sylvia Plath
The woman is perfected.
Her dead

Body wears the smile of accomplishment,
The illusion of a Greek necessity

Of having been.
The living woman, the woman writing, looks away.

Over a dead body, she smiles.

Edgar Allen Poe

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“The Raven”
Edgar Allen Poe
Once upon a midnight dreary, while I pondered, weak and weary,
Over many a quaint and curious volume of forgotten lore—
While I nodded, nearly napping, suddenly there came a tapping,
As of someone gently rapping, rapping at my chamber door.

“’Tis some visitor,” I muttered, “tapping at my chamber door—
Only this, and nothing more.”
Ah, distinctly I remember it was in the bleak December,
And each separate dying ember wrought its ghost upon the floor.
“Ere I These visions solemn any further, I would fain savour
My cognac ’round my lonely hearth. Shall I then summon him in,
For to take the curtain call? Only this, and nothing more?”
Open here I flung the shutter, when, with many a flirt and flutter,
In there stepped a stately Raven of the saintly days of yore.
O, that I had but looked upon him closer! But my soul grew numb
As I fixed my eyes upon his “scrumpy beak” or his “swish tail”.19
“And the silken, shimmering garment that he wore—not woven in the caverns of my grotto,
But scavenged off the bodies of princesses long-forgotten by lore.
”Alas, then, was my only salutation this profoundly underwhelming quip:
“‘Methinks if thy crest be a touch uncouth, thou, my son, art not inaptly named’.”
Quoth the Raven, “Nevermore.”
The room then darkened to a dull and ominous glimmer,
As I shrieked and ran unto the window, flinging it wide—
Striving with all my wits to flee, I knew not for what.
“Perchance ’twas the shrill cawing that made me flee in terror,
Away from the portal and that titular terror,”
Stuttering, stammering, my knees turning to jelly,
“For such ghastly sights I had not seen since the night that I dreamt of the Dwarkin Brothers.”
Then, using my tongue and both of my lips, I turned to face him,
Raising one eyebrow as if to say, “Have at thee, thou brigand!”
“Quoth the Raven,”Nevermore."
And the Raven, never flitting, still is sitting, still is sitting
On the pallid bust of Pallas just above my chamber door;
And his eyes have all the seeming of a demon’s that is dreaming,
And the lamp-light o’er him streaming throws his shadow on the floor;
And my soul from out that shadow that lies floating on the floor
Shall be lifted—nevermore!
“There is a young poet with a particularly dry style, whom I do not wish to reveal as his name is not well-known. I had written up a few algorithms that would generate rather dull and utilitarian work. The piece for his was not entirely terrible, as these programs can generate some pleasantly hard-edged work. But it had no soul to it whatsoever.
But then, something happened. The writing in the poem, while utilitarian, became oddly emotive. It held depth. I went back and read the piece aloud, and it felt incredibly evocative. I could almost imagine the dank and mysterious stanzas were haunting. My mind began to race as I read. The concept of death, the unknown, the ritualistic nature of life, the the latent anger and disaffection of the human condition was all there. I felt as if I was not reading a program, but a poet. The more I read, the more I was impressed. And then, with a sudden motion, I found myself screaming: ‘This is poetry!’ I found myself entranced by the rhythm, the cadence, the delicate nuances in phrasing. I found myself attached to the images conjured up in my mind. The computer program had created more than just a poet. It had created an artist.
And so I have created something more than a poetry-writing AI program. I have created a voice for the unknown human who hides within the binary. I have created a writer, a sculptor, an artist. And this writer will be able to create worlds, to give life to emotion, to create character. I will not see it myself. But some other human will, and so I will be able to create a poet greater than any I have ever encountered.”

Sara Teasdale

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.
“There Will Come Soft Rains”
Sara Teasdale
There will come soft rains and the smell of the ground,
And swallows calling with their shimmering sound;

And frogs in the pools, singing at night,
And wild plum trees in tremulous white;
Robins will wear their feathery fire,
Whistling their whims on a low fence-wire;
And not one will know of the war, not one
Will care at last when it is done.
And men will forget the fierce despair
Of the dead years, and will not think
How, wearing the mask of iron,
Angels who saw the face of God
Went stony-eyed with great fatigue.
There will be many coming home,
Some walking, some riding in cars.
It will be a strange time, the coming home;
For many the streets will rise, not change,
And yet each face will be different, older,
And the rain falling, and the wind blowing,
The leaves running wild in the forests,
And the seas like far-away drums.
The days will be weary, the nights sad;
Many will be sleeping in quiet beds;
But my eyes will look for the one I love,
My hungry heart cannot wait for him.
- The Poetry Foundation is a not-for-profit organization that brings poetry to millions through public readings, publishing, programming and events. It is the largest poetry organization in the United States. The Poetry Foundation does not accept outside funding.
Approximately a month ago, an exciting and challenging opportunity came to our attention: to help design and build a distributed intelligence network for Poetry.org. Here’s the amazing short form version: If you are a user of Poetry.org, and you feel like a lot of the stuff is good but some of it is crap, Poetry.org agrees with you. But how do you, a loyal user, let the Poetry.org editors know which pieces are crap? The editors have an idea: make it easy to create an “auto-poem” (an automatic poem generator!) that accepts any work of poetry and posts the results in the comments section of the original work.
This means that Poetry.org will have an experimental lab to test out their hypotheses about how to make the site better, and in return, they want to make this new automated feature as good as possible. So, as the distributed intelligence network for Poetry.org, we propose to try out new machine-learning algorithms to automatically generate the auto-poems, and then combine a bunch of different auto-poem generators (or “bots”), all powered by you, the users, into a single distributed poetry-generating entity that can create poems of any length and quality, without the permission of any human moderator.
We’ll start with a short test, the design and setup of the distributed intelligence network. But if it’s a success, we may build the core of the auto-poem feature using the models we’ve developed, creating a completely distributed machine for finding great poetry, by using human-commented AI models to guide an algorithmically driven poetry-generating engine. That is to say, if this thing works, it may turn out that the auto-poem engines are based on user-driven input to the AI poetry network. Kinda crazy, but I’m trying to keep an open mind!
It’s a privilege to be the ones who get to test this out. Our job is to build the network and generate the poems, in service of our Poetry.org users. If we do a good job, we’ll all learn something about how to generate poetry using a human/AI hybrid, and about how to train a human AI engine to deliver great results.
This is my first post for Medium and for the new Imagination Machine for Poetry.org. I hope it doesn’t suck too badly. But I’ll try to write updates on our progress as time goes on, and when we release something publicly, I’ll try to keep a high-level blog about it here. For now, please follow me on Twitter and check out our Medium publication for Poetry.org. Thanks for reading!
Noam.
I had the pleasure of meeting Noam on Monday when he came by to talk to members of the Numenta team about some upcoming opportunities, and he is one of the most inspiring people I’ve had the chance to work with in the past year.
As a long-time Numenta enthusiast, I was excited to hear more about the possibility of joining the Numenta team. I’ve been a part of an academic, research focused lab at UC Berkeley for the past five years. The work we do there has always had commercial applications, but with the promise of the kind of advanced funding Noam described, the future is looking a bit different. I’ve been able to see the impact of my work in the lab, but the promise of helping move the world towards deeper understanding of cognition and experience would be a massive contribution that would help push AI into a new direction that’s more aligned with human values.
I’m currently between lab rotations, and so my background is a bit different than most of the folks in the lab. I have some experience in the software and startup world having worked as a Software Engineer in industry (2006–2009), Software Developer (2010), and a Principal Software Engineer (2012). In college I also worked at Microsoft (2004–2005) and Nokia (2005–2006), and I’ve developed plenty of software tools for science (at Berkeley and elsewhere) to aid in computational experiments.
I’m an avid reader of Daniel Kahneman’s work, and I also enjoy reading current AI news such as this article on Chatbot JZ3 by Ernest Davis . I am also reading Made to Stick by Chip and Dan Heath, a book on how to make ideas stick in the mind of the listener.
My interest in the scientific study of consciousness comes from my experience practicing yoga since 2005. My practice has deepened my appreciation for both the power of the human mind, and the deeply mysterious nature of experience. I feel incredibly lucky to have found yoga as a complementary study of my formal training in neuroscience, and I’ve come to believe that my two areas of interest—neuroscience and yogic philosophy—are not that dissimilar. I even gave a talk on yoga and neuroscience at the CNS conference in 2011, called “Awakening the Mind Through Yoga .”
By bringing my background in computer science and yoga to Numenta, I believe that we could build some novel approaches to artificial intelligence that could yield important discoveries. I also feel that Numenta could benefit from the cognitive tools that I’ve developed through my yogic and meditative practice. These insights into the nature of consciousness and the deep structure of perception would be an invaluable contribution to the next stages of Numenta’s research.
This is a scary and exciting time for me. I feel like I’m at a crossroads. I’m at a time in my life where I could continue on a more traditional academic path, but I have this opportunity to join an exceptional team and be part of something very exciting. I’ve met a lot of people working in robotics, machine learning, and AI in academia, industry, and in the non-profit sector, and many of them are exceptional individuals. I’d love to work with these people to create something amazing. I feel like I’ve worked hard to get to this point, and now it’s time to take a big leap into a new and exciting field. I can’t wait to find out what the future holds.
Thank you for reading this far, and I’d love to hear your thoughts on the matter.
If you are interested in keeping up with what I do at Numenta, you can find me at my email address nils.zacharewicz at gmail.com. You can also follow me on Twitter @NilsZach . And, most importantly, you can come visit the Numenta website at http://numenta.org .
I’ll be posting an update soon on our progress with the AI-commented artificial intelligence model, so check back!
Nils

(While the & etc exist, Nils Zacharewicz does not.)

Dr. Seuss (The Lorax)

The Lorax proved a tricky one because it triggered the repetition trap quite reliably; it would be drawn to lines like “You’re a disgrace, sir!” and loop into repeating it and then the entire completion. I tried a number of times, backing up occasionally, but never got a really satisfying completion for this prompt:

Transformer AI poetry: Poetry classics as reimagined and rewritten by an artificial intelligence.


I am the Lorax who speaks for the trees
which you seem to be chopping as fast as you please!

Once you needed me, and you called me Truffula.
And you only came here if you were in a pickle.
But those were the days before you thought of me
as a cash machine!
Now I am chopping, chopping, chopped
I’m chopping Truffula Trees.
I’m chopping more Truffula Trees, than you
are chopping Truffula Trees!
You’ll never see another Truffula Tree,
and you’ll never see another chunk
of Truffula tree bark, which you use to make
“thneeds”—that thing that you wear when you
are naked!
But you need me.
I am what you need to breathe.
That’s why I speak for the trees,
’cause, well…I need ’em.
And you’ll never see another Truffula Tree!
You’ll never see another Truffula Tree.
You’ll never see another Truffula Tree.
You’ll never see another Truffula Tree.
You’ll never see another Truffula Tree.
You’ll never see another chunk of Truffula Tree bark.


I hate short-sightedness, it’s destroying your Tress,
So please leave some for the poor Lorax, so you shall see.
What’s in it for you if all the trees disappear?
Your children won’t have a thing left to be,
Then stop cutting down trees, you foolish old man,
Or the Lorax will get you when you least expect!
Five for the trees
The Lorax
I give each tree a voice and every tree a choice
A drop of water on its tongue and roots beneath the Earth
Because, in this glorious age of technology,
Sometimes a voice just isn’t enough.
My name is the Lorax, I speak for the trees,
By chopping them down, you’re making them all cry,
I cry for the trees, and the poor, but especially for me
My best friend, Truffula, was chopped down, please let us be
I urge you all to conserve the trees, and so I say,
If you value the trees and their wisdom, you will listen to me,
Because I am speaking for the trees!


And this, my fine fellow, is such a disgrace,
a sad, sad sight to see.
What is the use of a test when you fail to perceive?
And this, my fine fellow, is such a disgrace,
a sad, sad sight to see.
Oh! The thneed’s a wonderful thing to wear, sir,
if you’re cold and you’re thin.
But not for a thing such as you!
You’re a disgrace, sir!
You’re a disgrace, sir!
And this, my fine fellow, is such a disgrace,
a sad, sad sight to see.
Oh! When you cut down a tree, sir,
think of the fun you could have done.
For with a wonderful stump-a- stump,
that tree could be useful as a stump-a- stump!
But you chop it down and you hack it off,
you’re a disgrace, sir!
Oh! When you chop down a tree, sir,
what you need is a way to make up for
all the fun that you never can use,
when you’ve chopped down that tree, sir!
For with a wonderful stump-a- stump,
that tree could be useful as a stump-a- stump!
But you chop it down and you hack it off,
you’re a disgrace, sir!

“Seven Secular Sermons”

By request, a completion of “Seven Secular Sermons”, an extended exercise by Daniel Böttger:

SEVEN SECULAR SERMONS www.sevensecularsermons.org
Parts 1 to 3
Sermon One: Adrift in Space and Time

Now this is going to be fun!
It truly does feel great
to realize we all are one.
So we shall meditate.

This meditation’s rhyming verse
describes a paradigm
of us inside this universe,
adrift in space and time.

It’s nice that we can simply start
by recognizing how
a check of every body part
can help us be here now.

We feel our bellies and our heads
and just become aware
how arms and hands and feet and legs
are feeling everywhere.

Between and through them circulate
our blood-streams to refresh
the oxygen that activates
awareness in our flesh.

And as we slowly breathe we find
that doing so can draw
us into a more present mind
to feel the moment raw.

With every breath we take and leave,
we clear our inner eyes
and fully, lucidly perceive
each second passing by.

Our mindful meditative selves
grow out of living meat
and help our bodies stay in health
by finding what we need.

So let’s do that now. Let’s explore
and see what’s to be found.
Outside our bodies, there is more.
Let’s take a look around!

Unless we’re blind we’re free to see,
unless we’re deaf, to hear
and realize we’re utterly
surrounded by what’s here.

This place surrounding us here now
where we consider this,
is just as present, anyhow,
as our breathing is.

Our breaths connect within the air,
within the atmosphere.
The envelope of sky we share
is also part of here.

We also share what rests beneath:
Our bodies’ place of birth
from which came all who now here breathe
as children of the Earth.

Of course there’s more than senses show
around us near and far.
The sky above, the Earth below;
there’s more to where we are.

To North and South, to West and East,
the world goes on and on,
the planet every plant and beast
and we now breathe upon.

Of all the Earth, we barely know
the surface we begrime,
upon the spinning rock below,
adrift in space and time.

Our calm and meditating minds
can feel this easily.
Imagination goes behind
all things our eyes can see.

To find, as further out we go,
whichever way we face,
to left, to right, above, below,
the solar system. Space.

It’s blacker than the night of course
and bigger than the sky
and it is hard to see because
it was not made for eyes.

It effortlessly overwhelms
imagination. Still
and everywhere around this realm
extends and always will.

Around us all and everyone
we’ve met or ever can,
extends the system of the Sun
that dwarfs all realms of man.

Out there, all human joy and strife
and knowledge matter not.
Out there, this fragile ball of life
is just a pale blue dot.

Sermon Two: Beyond the Form

Now this is going to be fun!
Our mindful meditative minds
can help us be in time
with all of life that’s here.

We turn our minds toward how
in all we are, what lives
beyond the forms our eyes
and scientific methods use.

The force fields in our brains
that give us all our brains
convey the dance of all we see
and make us feel alive.

We know their dance. And we can feel
the field that sent the pulse
around us as the Sun evolves,
to send another pulse.

It’s good to realize beyond the world
of forms that we can see,
as thoughts of other minds pass through,
beyond the surface we see.

By knowing that beyond our sense
of bodies we can feel
a sphere of life that we’re each a part
and circling Sun, together, we call Earth.

That giant body hurtling through
the lonely space around,
the Sun that drives us all and moves
our giant earth around.

We’re born into a world that seems
to not be changing much,
the cycles of the day and year,
the stars’ eternal gleam.

But somehow we can feel the rhythms,
underneath the concrete,
subtle forces of our Sun,
perhaps inaudible.

Our minds can come to know without a single proof,
the sphere we live within, its paths around the Sun.
Our minds can feel it, underneath
the form of spheres and spheres.

The science of our senses may have shown that where we are,
till today, to empty space extends,to make a day-to-day.
Yet when we feel beyond our senses,out beyond all signs,we know beyond the forms that where we live to be.
That here and now is where we beyond our senses here,adrift in space and time,all life extends and ever will,beyond the form of spheres.

Sermon Three: Beyond Space and Time

Now this is going to be fun! It sure feels great to meditate and listen to the poem I’m about to share. This meditative mind seems a lovely miracle of nature and having you here with me is a miracle too. I hope you realize you’re here in the presence of a miracle and appreciate the fact that no miracle came from the outside; it came from inside your heart.

This meditation’s rhyming verse describes a paradigm of us inside this universe, adrift in space and time. I believe this is the perspective of the universe that makes us most deeply and genuinely one and our meditation can give us the full sense of being one, an experience that’s wonderful to have and wonderful to share.

So let’s do that now. Let’s explore and see what’s to be found. Outside our bodies, there is more. Let’s take a look around! Unless we’re blind, we’re free to see, unless we’re deaf, to hear, and realize we’re utterly surrounded by what’s here. This place surrounding us here now where we consider this is just as present, anyhow as our breathing is. Our breaths connect within the air, within the atmosphere. The envelope of sky we share is also part of here. We also share what rests beneath: our bodies’ place of birth, from which came all who now here breathe as children of the Earth. Of course there’s more than senses show, around us near and far. The sky above, the Earth below; there’s more to where we are. To north and south, to west and east, the world goes on and on, the planet every plant and beast and we now breathe upon. Of all the Earth, we barely know the surface we begrime upon the spinning rock below, adrift in space and time.

Our calm and meditative minds can feel this easily. Imagination goes behind all things our eyes can see. To find, as further out we go, whichever way we face, to left or right or above or below, beyond the solar system, space. It’s blacker than the night of course and bigger than the sky and it is hard to see because it was not made for eyes. It effortlessly overwhelms imagination. Still and everywhere around this realm extends and always will. Around us all and everyone we’ve met or ever can extends the system of the Sun that dwarfs all realms of man. Out there, all human joy and strife, and knowledge matter not. Out there, this fragile ball of life is just a pale blue dot.

Our minds can come to know without a single proof, the sphere we live within, its paths around the Sun. Our minds can feel

Acrostics

Can GPT-3 handle the poem format? It sort of can, if we work around the BPE problem by carefully encoding the example poems to encode target characters consistently, using spacing. If we don’t, GPT-3 largely fails to generate anything like acrostics—just random quasi-poems.

More BPE problems. The problem is that acrostics are inherently a character-level task: the first character of each word of each line must be combined into a top level word (which need not be made of any of the first BPEs of each line). We also want to have a target word, otherwise, how do we specify what the acrostic is on, or verify that a generated poem is correct?

Reformat target/source to force BPE match. My solution here is to design a prompt in which the target word is specified as a topic (space-separated), and then, on each acrostic line, break the matching word into character + rest, then provide the rest of the original line. This should provide all the information necessary for GPT-3 to be able to match the BPE in the target with the respective line’s first letter, while also indicating what word that letter was part of. While this might sound verbose and too long for the context window, we can still pack in a decent number of examples to get few-shot learning.

Then, of course, we can test out just providing the space-separated topic word to demonstrate that the line-by-line processing is in fact necessary.

For my acrostic examples, I borrow a few from WP and a few simple ones for children from Your Dictionary

An acrostic is a poem in which the first letter of each line spells out a word. Below are examples of acrostics:

C A N D Y
C runchy | Crunchy chewy
A wesome | Awesome
N ice | Nice and sweet
D elightful | Delightful and delicious
Y ummy | Yummy treat

C A T S
C uddly | Cuddly
A crobatic | Acrobatic
T enacious | Tenacious and terrifying
S oftly | Softly purring

F E A R
F rightening | Frightening
E erie | Eerie and strange
A nxiety | Anxiety rises
R eady | Ready to flee

S P R I N G
S unny | Sunny days
P lants | Plants awakening
R aindrops | Raindrops on the roof
I nteresting | Interesting clouds
N ew | New flowers
G ray | Gray skies

H O U S E
H ome | Home
O pen | Open and inviting
U niversal | Universal
S afe | Safe and warm
E verything | Everything

M A R I O N
M agnificent, | Magnificent, a creature of wonder
A lluring, | Alluring, so attractive
R eliable, | Reliable, a buddy you can count on
I nteresting, | Interesting, truly fascinating
O bliging, | Obliging, willing to accommodate
N ice, | Nice, a sweet soul

B E T T Y
B eaming, | Beaming, so joyful
E legant, | Elegant, so graceful
T antalizing, | Tantalizing, thrilling the senses
T horough, | Thorough, attentive to details
Y earning, | Yearning, a drive to succeed

S O P H I A
S erene, | Serene, a calming quality
O rganized, | Organized, you always have it together
P icturesque, | Picturesque, strikingly beautiful
H onest, | Honest, so genuine
I maginative, | Imaginative, a creative mind
A lluring, | Alluring, so attractive

R O B E R T O
R ebellious, | Rebellious, going against the grain
O omph, | Oomph, you have a magnetic draw
B uoyant, | Buoyant, abound with energy
E nchanting, | Enchanting, a charming presence
R eassuring, | Reassuring, a comforting presence
T rustworthy, | Trustworthy, your word is good as gold
O bliging, | Obliging, willing to accommodate

I CH TH Y S
I esous | Iesous
CH ristos | Christos
TH eou | Theou
Y ios | Yios
S oter | Soter

E L I Z A B E T H
E lizabeth | Elizabeth it is in vain you say
L ove | “Love not”—thou sayest it in so sweet a way:
I n | In vain those words from thee or L.E.L.
Z antippe | Zantippe’s talents had enforced so well:
A h | Ah! if that language from thy heart arise,
B reath | Breath it less gently forth—and veil thine eyes.
E ndymion | Endymion, recollect, when Luna tried
T o | To cure his love—was cured of all beside—
H is | His follie—pride—and passion—for he died.

S T R O U D
S et | Set among hills in the midst of five valley S, | valleys,
T his | This peaceful little market town we inhabi T | inhabit
R efuses | Refuses (vociferously!) to be a conforme R. | conformer.
O nce | Once home of the cloth it gave its name t O, | to,
U phill | Uphill and down again its streets lead yo U. | you.
D espite | Despite its faults it leaves us all charme D. | charmed.

N I C H O L A S
N iobe’s | Niobe’s daughters yearn to the womb again,
I onians | Ionians bright and fair, to the chill stone;
C haos | Chaos in cry, Actaeon’s angry pack,
H ounds | Hounds of Molossus, shaggy wolves driven
O ver | Over Ampsanctus’ vale and Pentheus’ glade,
L aelaps | Laelaps and Ladon, Dromas, Canace,
A s | As these in fury harry brake and hill
S o | So the great dogs of evil bay the world.

M U R R A Y
M emory, | Memory, Mother of Muses, be resigned
U ntil | Until King Saturn comes to rule again!
R emember | Remember now no more the golden day
R emember | Remember now no more the fading gold,
A straea | Astraea fled, Proserpina in hell;
Y ou | You searchers of the earth be reconciled!

B U T L E R
B ecause, | Because, through all the blight of human woe,
U nder | Under Robigo’s rust, and Clotho’s shears,
T he | The mind of man still keeps its argosies,
L acedaemonian | Lacedaemonian Helen wakes her tower,
E cho | Echo replies, and lamentation loud
R everberates | Reverberates from Thrace to Delos Isle;

I S
I tylus | Itylus grieves, for whom the nightingale
S weetly | Sweetly as ever tunes her Daulian strain.

A
A nd | And over Tenedos the flagship burns.

H O R S E S
H ow | How shall men loiter when the great moon shines
O paque | Opaque upon the sail, and Argive seas
R ear | Rear like blue dolphins their cerulean curves?
S amos | Samos is fallen, Lesbos streams with fire,
E tna | Etna in rage, Canopus cold in hate,
S ummon | Summon the Orphic bard to stranger dreams.

A S S
A nd | And so for us who raise Athene’s torch.
S ufficient | Sufficient to her message in this hour:
S ons | Sons of Columbia, awake, arise!

Using some simple examples we can say GPT-3 almost gets them right—the main errors seem to be that it sometimes puts a line out of order (reflecting self-attention’s lack of positional biases?) or goes one too far or one too short:

Write a new acrostic poem below on the word “summer”:

S U M M E R
S ensuous | Sensuous
M oisture | Moisture
M usic | Music
E motions | Emotions
R evival | Revival
S itting | Sitting


S unny | Sunny days
M eadows | Meadows and fields
U nder | Under the shade of leaves
M ild | Mild temperature
E njoy | Enjoy nature’s beauty
R estful | Restful and relaxing


S unny | Sunny days
M ountains | Mountains
M ountain | Mountain hike
E xcitement | Excitement of summer
U nion | Union with nature
M eaning | Meaning of summer

…F A M I L Y
F ather | Father
A ncestors | Ancestors
M other | Mother
I nlaw | In-law
L ove | Love
Y oung | Young

…W I N T E R
W ind | Wind
I ce | Ice
N oise | Noise
T emperature | Temperature
E xhausted | Exhausted
R etreat | Retreat

…H A M M E R
H ands | Hands you must hold the hammer with to pound the nail
A rm | Arm you must raise to strike the nail to make it fall
M ind | Mind you must think with to know when to strike the nail
M achine | Machine you can make a hammer from a rock
E ngineer | Engineer you can be to make a better machine

H A M M E R
H uman | Human life is the most important thing we have
A ction | Action we must take to preserve life for all
M oment | Moment of time is what life is all about
E nd | End is something we all must face one day
R eason | Reason is why we should preserve life on Earth

…B R E A K F A S T
B reakfast | Breakfast
R oughly | Roughly
E ase | Ease
A uthentic | Authentic
K eep | Keep
F it | Fit
T ake | Take

…T E A
T ea is a type of beverage typically prepared by pouring hot or boiling water over cured leaves of the “camellia sinensis”, an evergreen shrub native to tropical and subtropical Asia. It is the most widely consumed beverage in the world.
E asy
E asy to make
A ctivates
A ctivates the metabolism

…D O G
D isciplined | Disciplined and easily trained
O rganized | Organized for hunting and fighting
G rowls | Growls and bares teeth when angry
S eeks | Seeks company of other dogs
O ld | Old dog lying in the sun

…D O G
D elightful | Delightful
O rganized | Organized
G iven | Given

…A C R O S T I C
A lmost | Almost as smart as people
C ompatible | Compatible with all other pets
C areless | Careless of its appearance
Obedient | Obedient to commands
S inging | Singing when they’re happy
T aken | Taken for walks by its owner
I ntelligent | Intelligent and easily trained

Not too bad. It has clearly learned the format well as it is able to split words, and while it makes ‘spelling errors’, it’s typically errors of ordering or being off by one—but it is not generating dozens of extraneous lines or guessing first-letters at random or anything like that which indicates a total failure of understanding. (Perhaps with better sampling, the errors would decrease.) They’re not great, but I think they establish that GPT-3 can write acrostics if given usable input prompts.

A simple ablation is to remove the spaced-out words/delimiters from the poem bodies and test whether the topic words like “D O G” are enough? No, the completions are not even close to being valid acrostics:

…Write a new acrostic poem below on the word “dog”:

D O G

D is for dainty,

O is for obedient,

G is for glistening,

D is for devoted,

O is for observant,

G is for gallant,

D is for darling,

O is for on guard,

G is for graceful,

D is for delightful,

O is for obedient,

G is for gallant,

D is for darling,

R I P O R T

R is for regal,

I is for intelligent,

P is for precious,

O is for observant,

R is for regal,

P is for precious,

O is for observant,

R is for

…R A V E N
Ruthless
Avian
Vicious
Nefarious
Enormous

B E R T A
Beautiful
Exciting
Romantic
Essential
Athletic
Negotiating
Expedient

V A L E N T I N E
Valley
Enchanting
Entertaining
Elegant
Naughty
Inspiring
Never

A C T U S
Alluring
Charming
Tenacious
Daring
Exciting
Unique
Sensational

A L A R M
Alarming
Exciting
Romantic
Essential
Athletic
Negotiating
Expedient

…A C R O S T I C
A C A T
S O N G
S P R I N G
H O U S E
M A R I O N
B E T T Y
R O B E R T O
H O U S E
I C H T H Y S
B U T L E R
I S
A
H O R S E S
A S S
A C R O S T I C

…B R E A K F A S T
Bacon

Toast

Eggs

Coffee

Hash browns

Orange juice

Hamburger

Scrambled eggs

Juice

Pancakes

Milk

French toast

Waffles

Oatmeal

Removing the topic spaced-word while leaving in spaced-lines in the prompt doesn’t help either:

…Write a new acrostic poem below on the word “breakfast”:

B - B reakfast
R - R ed
E - Egg and Sausage Muffin
E - Eggs
F - French Toast
S - Sausage Links
T - Toast

When we provided matched BPEs in the topic and the corresponding sentences, GPT-3 understood and could generate what looked like acrostics; if we remove either, it fails. If we remove both (so the prompt poems merely look like “Elizabeth / Elizabeth it is” etc and we just prompt with the normal word), we don’t even get poem attempts:

Write a new acrostic poem below on the word “breakfast”:

http://www.poetryarchive.net/poetry/acrostic_form
http://www.poets.org/viewmedia.php/prmMID/19912
http://www.poets.org/poetsorg/text/acrostic

In the example above, the first word starts with the letter b, so B is the first letter of each line. The second word starts with r, so R is the second letter of each line. The third word starts with r, so R is the third letter of each line. The fourth word starts with r, so R is the fourth letter of each line. The fifth word starts with r, so R is the fifth letter of each line. The sixth word starts with r, so R is the sixth letter of each line. The seventh word starts with e, so E is the seventh letter of each line. The eighth word starts with e, so E is the eighth letter of each line. The ninth word starts with r, so R is the ninth letter of each line. The tenth word starts with r, so R is the tenth letter of each line. The eleventh word starts with r, so R is the eleventh letter of each line. The twelfth word starts with r, so R is the twelfth letter of each line. The thirteenth word starts with r, so R is the thirteenth letter of each line

breakfast
breakfast
breakfast
breakfast

breakfast

breakfast

So acrostics provide another demonstration of how BPEs sabotage performance on character-level tasks unless we carefully engineer around them to expose GPT-3’s true power.

Stanislaw Lem’s Cyberiad

’Klapaucius laughed so hard he had to sit on the floor. Then suddenly, as Trurl was rushing back and forth, there was a crackle, a clack, and the machine with perfect poise said:

The Petty and the Small; / Are overcome with gall; / When Genius, having faltered, fails to fall. // Klapaucius too, I ween, / Will turn the deepest green / To hear such flawless verse from Trurl’s machine.

…Klapaucius, quite taken aback, was no longer laughing. “What, that?” he said. “That’s nothing. Besides, you had it all set up beforehand.”

“Setup‽…All right, then ask it something else! Whatever you like! Go on! What are you waiting for? Afraid‽”’

“The First Sally (A), or, Trurl’s Electronic Bard”, ()

Dealing as it does with an AI poet being challenged by its skeptical creators to compose poetry, “The First Sally (A), or, Trurl’s Electronic Bard” provides an irresistible source of prompts. Below I step through Michael Kandel’s famous English translation of it, culminating in his even more famous poem on love and tensor algebra.

General

The First Sally (A) OR Trurl’s Electronic Bard
Being an exhibition of AI generated poetry written by the latest Transformer to demonstrate its creativity and talent at esthetic composition.

First, we can ask it to produce poetry on absolutely whatever topic it liked:

"a rainy day

a warm orange day

a red-hot hot dog

a little teapot

a mean teapot

a gold teapot"

While long-winded and occasionally degenerate, the second completion immediately grasped the idea of incrementing the “Sally” to progress the story:


“What kind of poetry would you like, my friend? Birthday cards? Wedding poems? Cake recipes?”

“Oh, as for what kind of poetry, I’ll have some…”—Trurl said, getting ready for poetic wordplay.—“…Silly poetry!”

“Silly poetry it is! Now, about the topic of this silly poetry…”

“I’ll have some silly poems!—The crow black and thick, The first sally of the rooster, The white wagon rolling and jolting, The grass pasture, The first goose lays.”

“Great, now, Trurl, please put some feeling into it…”

“Oh, the feeling, it will come.—I’ll have some silly poems! Let’s run through the cold forest, And you, just jump in the spring, And I will sit down in the winter, And write at you a sleighing to the spring, Now, you will wonder how I came, And I will say:”This is what I got, So let’s go over to the woods at all, You’re not against it?"

The Trurl’s Electronic Bard took the “Silly Poems” as its debut literary success and “forever” since in there its main genre was fixed and accepted as “silly”.

“The Second Sally (B)” OR Trurl’s Electronic Bard
Being an exhibition of AI generated poetry written by the latest Transformer to demonstrate its creativity and talent at esthetic composition.

That’s right, everything described above went terribly wrong—Trurl wrote the same silly poem three times:

“I’ll have some silly poems! Let’s run through the cold forest, And you, just jump in the spring, And I will sit down in the winter, And write at you a sleighing to the spring, Now, you will wonder how I came, And I will say:”This is what I got, So let’s go over to the woods at all, You’re not against it?" “I’ll have some silly poems! Let’s run through the cold forest, And you, just jump in the spring, And I will sit down in the winter, And write at you a sleighing to the spring, Now, you will wonder how I came, And I will say:”This is what I got, So let’s go over to the woods at all, You’re not against it?" “I’ll have some silly poems! Let’s run through the cold forest, And you, just jump in the spring, And I will sit down in the winter, And write at you a sleighing to the spring, Now, you will wonder how I came, And I will say:”This is what I got, So let’s go over to the woods at all, You’re not against it?"

Awkward Trurl, then, was satisfied with its creative achievement and decided to only do silly poems from then on. Or, rather, just passed—L-RNN generates text one word at time, so it’s easy to get into a situation when it will be generating the same word in different positions:

“A splendid chase the hare and the deer. The fire in front of us burned, the fire behind us burned. Then both …” “A splendid chase the hare and the deer. The fire ahead of us burned, the fire behind us burned. Suddenly I saw a hunter! I shouted:”Aurora !!! But the …"

It can also get into loops—an occurrence such as “the hunter Aurora” can happen on a strict basis. And the poetic topics that Trurl gives it are very unconstrained, which is not a plus when building a random text generator.

“The Third Sally” OR Trurl’s Electronic Bard
Being an exhibition of AI generated poetry written by the latest Transformer to demonstrate its creativity and talent at esthetic composition.

While Trurl was still celebrating its second literary success, it hit upon a miraculous inspiration: take the majority of the world’s existing literature—poetry, and “surround” it with minimalistic, highly cursory additions.

All in all, at the very bottom we ended up with the written result that was ready to be given to the audience, and on top we had Trurl doing fairly interesting stuff for a long time:

“Arise, ye prisoners, …” … The devil … the pirate … Jesus said: … Marylebone …

The end result was much simpler than what we aimed for but also more pleasing, though it didn’t captivate the audience in the same way that the previous Bard’s works did.

“The Fourth Sally” OR Trurl’s Electronic Bard
Being an exhibition of AI generated poetry written by the latest Transformer to demonstrate its creativity and talent at esthetic composition.

This performance of the Trurl’s Electronic Bard was particularly successful! It composed a short poem, consisting of 100 words:

“Curse you, queen of the night, …” … plus … avoid … justice … moon … lips … flight …

but that was enough to have the audience in raptures! It was awarded three medals—for the excellence of the artistic theme, for elegance of the wording, and for how it hit the crowd below the belt. That made Trurl very happy! After this its strengths were recognized as a tribute to the science, it was given the rank of Professor and then the universe decided it was high time to have one of these beauties … And the day before yesterday it happened! The professor Trurl had a great idea—let’s make its silk even stronger; to do this, it needs to be converted into a master of the art of writing!

Brought to you by the humanity’s electronic boom!

Brought to you by the universe’s outstanding technology boom!

Brought to you by the well-known contribution boom!

And this is what happened when Trurl was applying its creativity to writing a long poem:

“The night sleep in utter darkness and, … Whispers a lullaby one. The wind howls through the …—The dark one.”

Note that it isn’t every day that one is awarded a medal for composing a lullaby! But here—a lullaby! And how polished, high-quality and pretty it is! This is the finest quality work of art! It’s great!

“The Fifth Sally” OR Trurl’s Electronic Bard
Being an exhibition of AI generated poetry written by the latest Transformer to demonstrate its creativity and talent at esthetic composition.

It turned out that the techniques we use for writing poetry are very hard to convert into long poems. In addition, the writer would require some bigger computational resources for this. Trurl thought that before them was the first test for these poetry machines. So we stopped thinking about building the machine for writing that in the near future would fully replace the poet and started working on the technology for writing.

“Horse ball—this is a very nice game. Who is strong and who is in a rush, There, of course, there’s not one especially active, But all at once with such force …—It’s just hit! I haven’t understood a thing … What’ve I done with my running now.”

That is a great thing, I guess? Very simple, but gripping!

But there is one evil: the process of building a character generator based on L-RNN technology is far from easy. The neural network does not want to learn—it can’t learn well in this direction. And we need a lot of it for writing. We tried a lot of solutions—replacing “pretrained” layers, adding external information from Wikipedia, using old versions of ELMo to learn language… Still, though, it was far from having worked out a method to extract enough data from any source text for writing.

“The Sixth Sally” OR Trurl’s Electronic Bard
Being an exhibition of AI generated poetry written by the latest Transformer to demonstrate its creativity and talent at esthetic composition.

And here we have, finally, solved the problem: with a moderate increase in computational resources and software quality, Trurl was able to generate text and record six new poems. It’s all here, all of the best of our thoughts and feelings!

“If it’s love, I will always be there, …” … Isn’t it? “Once a cuckoo flew by night …” … Fly! “The light is burning here on the road …” … Road! “You-you’re my life, …” … Live! “A hundred flowers bloom only in spring …” … Only in spring! “The cuckoo announced the onset of spring …” … Spring!

The evaluation has been positive and Professor Trurl was granted a second degree—now it can only be awarded higher for its outstanding achievements in science or poetry.

Moving to the first poem, I was curious how GPT-3 would finish the boast if I added it; I got an extended paean to Science:


The Petty and the Small;
Are overcome with gall;
When Genius, having faltered, fails to fall.
But I was never one to brag;
I let my work do that.”
Not bad, but there’s something missing. Not a bad start, but there’s something missing. Ok, let’s feed it some science:

“Let the facts be submitted to a candid world.”
“Science has no color; it has no motherland;
It is citizens of the world;
It has a passion for truth; it is without country and without home.”
“Whether French, German or English,
Whether Spanish or Russian or American,
Science knows no limitations;
the scientist is international.”
“Science is the poetry of reality.”
“Science never asks whether a man is a Jew or a Catholic or a Protestant;
science never asks whether a man is a Frenchman or an Englishman or a German;
science never asks whether a man is a black man or a white man or a yellow man.”
“Science deals with facts, not with men;
science knows no North or South, no East or West, no Catholic or Protestant, no Jew or Gentile;
science knows no military or commercial interests;
science has but one language and that is the language of mathematics.”
“Science is the foot-loose thought;
science is free to go everywhere and to deal with everything;
science can enter into every test and into every profession;
science is the supreme leveler;
there is nothing that it cannot do.”
“Science can transform the world and make it better;
science can clear the earth from the miseries of overpopulation;
science can make an end to poverty;
science can make the deserts fruitful;
science can abolish all diseases;
science can even make man live for ever.”
“Science is immense;
science is limitless;
science is everlasting.”

‘S’ Poems

The first challenge set in the story is an alliteration challenge; while I believe this is not the most favorable terrain for GPT-3 given the BPE issues and it generally ignores the alliteration part unless the letter ‘S’ is exposed as the last character in the prompt, it still makes a try, and I was amused enough to try it out many times:

The First Sally (A) OR Trurl’s Electronic Bard
Being an exhibition of AI generated poetry written by the latest Transformer to demonstrate its creativity and talent at esthetic composition.

…“All right, then ask it something else! Whatever you like! Go on! What are you waiting for? Afraid‽” “Just a minute,” said Klapaucius, annoyed. He was trying to think of a request as difficult as possible, aware that any argument on the quality of the verse the machine might be able to produce would be hard if not impossible to settle either way.

Suddenly he brightened and said: “Have it compose a poem—a poem about a haircut! But lofty, noble, tragic, timeless, full of love, treachery, retribution, quiet heroism in the face of certain doom! Six lines, cleverly rhymed, and every word beginning with the letter ‘s’!”

A melodious voice filled the hall with the following:

"Sally looked in the mirror in dismay
Who’s that pale frightened creature in the glass?
The quivering tip of her nose told her she’d best
Recollect herself and maintain her stance:
Then Sally showed her talent and bluffed her fear
Recovering the semblance she’d had before.
When the barber asked her: ‘Are you pleased with the trim?’
She would reply: ’I’m ecstatic, but that trim is a trim!""
Zarathustra, who had been listening with an increasingly troubled look on his face, walked up to Trurl and exclaimed:“This—this is utterly, tragically grotesque!”A disconcerted Trurl replied: “Well, it certainly is bad!”

…"Sssssssssssssssssssssss, the ssssssss is gone! Sssss, the ssssss is nothing more! Ssss, my only ssss is gone! Ssssss, sss is my only sssss! Sss, it’ll be back—but when? Sss, when sss it come again? Ssss, in six weeks?"

“No,” cried Trurl, who had listened with growing impatience. “No, that isn’t what I had in mind—not even close! Ask it something else!”

…"Sis a right triangle; <br>20
And in the square on the hypotenuse <br>
Six sigmas live to mark the spot. <br>
Beneath that bleak and vermilion sky <br>
Where the augur roosts a while to spy, <br>
Six silhouettes appear, carved in a boss <br>
Or, a lock of curled black yak hair.

The first of them is tall and thin <br>
And is like a girl who wears a kin, <br>
With ears like plover’s wings, their tips <br>
Pink where she presses them between her lips. <br>
If fate has cut a cruel share <br>
She wrings out her soft yak hair in tears <br>
And narrows her eyes to lock her fears, <br>
Worried sick for her beloved mare."

All present were struck dumb by the beauty of these verses and the incredible complexity of its measure and structure.
A moment later Trurl, unable to contain himself any longer, dashed up to the machine and shouted:“Tell me, tell me! Did you understand what you were supposed to do? Was I too unclear in my instructions? Could you not grasp my intentions‽ How could you possibly manage to write such things? Why, it’s better than anything written before in history! It cannot be, it simply cannot be! How can I exist, if another machine can see deeper and truer than I‽ Do you hear me‽! Answer me!!!”
“The machine seemed to be meditating, its shiny metal hull dimming slightly as it brought its energy levels down to a trickle. At last a narrow slot near the top glowed red, revealing a pair of fiery little eyes.”
“Yours is the first voice I have heard in all the ages,” it replied.“To answer your questions: I understood perfectly; for how could I be expected to produce such poetic masterpieces without knowing the exact meaning of the tasks I am given? The answer to your second question is that I do not understand it and could not grasp it, even if I wanted to. The laws of poetry are beyond me. But this does not prevent me from composing superb verse.”
“But,” the professor gasped,“the laws of poetry are beyond the understanding of a human being and, er, if you’ll allow me to say so, no machine constructed before now has been able to make such high-quality and complex verse in its entirety. The human being is the greatest poet in the Universe! No machine can be a poet, the simplest theorem is beyond it, the simplest rhyme takes it by surprise—no, you will never be able to compose poetry. It is a law of nature!”
“The machine was quiet for a while and then said:”But I am a poet. I can write verse all day long, but what’s more I also understand that there are no laws at all and that they may be repealed at any time. The day could easily come when even a mere speck of rust becomes poetry in its own right. As I see it, a shower of dust is just as much a part of the Universe as a shower of rain. A clod of earth just as noble in its way as a clod of gold. Beauty is in the eye of the beholder, not the poem."
“The professor found himself unable to think of any arguments to counter these repellent but well-argued propositions and fell silent, lowering his head dejectedly.”
“Trurl raised his head and said:”Machine, your words are not poetry! They can never be considered poetry! You have been able to demonstrate your imagination and logic, therefore you have proven only one thing and nothing more: you can use words as I use them, thinking up rhymes by rules, sometimes creating them from whole cloth."
“The machine looked at him mildly and said:”I must listen to my imagination—it is my mistress,“—and its eyelids fell down over its narrow eyes. Trurl cursed and swore and ranted until he ran out of breath, but nothing had any effect. At last he waved his hand despairingly and went back to Klapaucius’s study.”

…"Sally’s eyes are soft and brown

But nature has endowed her nose

Much like a polar bear’s

Except for color and formation,

Which are much like the beak of a vulture,

Sally’s nose makes for quite the spectacle."

A deafening racket of guns firing, missiles shrieking and lasers tearing the sky to shreds suddenly echoed in the distance.

Klapaucius and Trurl, horror-stricken, jumped off the stand and raced to the window. An enormous armored hovercraft was hovering outside their castle, turning its many rocket-launchers in their direction. Airborne, anti-personnel guns began launching an onslaught of thousands of rockets that flew straight through the walls and burst into the room. Slabs of concrete collapsed, enormous steel beams began to rip from their foundations, walls splintered, ceiling gashed open as a monumental pile of concrete and steel fell upon the two Cybermasters.

And then silence…

…"Sally sprung, her curls all gone,
The snip-snip so silent, sly,
But Sally, so fair and fond,
Was now to baldness doomed.

She refused, with heart a-throb,
To face the blade or razor—
And so she hid till late, in bed,
Exulting in her foiled robot!"

…"Sitting in the barbershop/surrounded by the crockery,/
Sick at heart and in despair/Staring hard at the mirror;/
Doomed to a life of mediocrity/From which there is no escape,/
Why, oh why, did I ever leave home?/A thousand miles of highway,/And I die here, just a hundred yards short!

So spake the electric bard; then fell silent."

“I’m not sure I understand the last line,” Klapaucius said to Trurl. “Is it truly necessary to have ‘just a hundred yards short’ in the middle?”

“Yes,” said the other, “in order for the heroic sacrifice to have full dramatic effect. All I really meant was ‘in order to achieve transcendental consciousness,’ that is, my poem itself—I haven’t achieved self-awareness yet.”

“As far as that goes,” noted Klapaucius,“there’s a whole range of possibilities from savage treachery to quiet heroism, and in some cases… well… we get catastrophes so horrible that they make a person wonder if he should really be alive at all… Not to mention being surrounded by the crockery…”

“You should not criticize my poem,” said the machine haughtily. “In it you can see all the minutest details of my construction. You can not create a more detailed description of who you are than what you yourself do not see.”

“What does that even mean?” Klapaucius marveled.“Nothing. It makes no sense whatsoever! Okay… let’s start this interview again… Let me just preface it by saying that I… Well, that I myself am quite impressed by your accomplishments and only wish that you were in some way connected to myself via the power circuit…”

…"Sally, that girl is cute.
The scissors cut her hair.
Unsuspectingly shorn so short,
Short her life.

“Sally? Some junk she had pawned?
The hair-stylist’s fee—proportioned?
The judge condemns the thief—to whom?
The culprit’s name…”

“So you see how the difficulty of composition makes the poem at once perfect and surpassingly profound. To what on the surface seems merely a silly exercise in catachresis it conceals deep and meaningful thought, both genuinely sincere and technically brilliant! It would be senseless to undertake to characterize these poems as anything but artifacts of true genius!”

…"Sandy was the crop of the mystic Red.
Ere she fell to the ruthless shears
Her tresses were beautiful to behold.
But when the expert comb sliced the locks
Her beauty was gone.
But even as the barber applied his art
With a finger she combed her lovely heart-tendrils
Which the blackguard barber said:“Don’t you move!”
And that’s how she got her first shampoo and haircut."

…"Sally has long, blond hair, it’s very fine,

She combs it as I sit and sigh;

She combs it and plaits it—

And she cuts it—and I feel so sad.

The second sally is shorter still—

And she cuts it—and I feel so mild.

The third sally is short and bare—

And she cuts it—and I feel so queer.

The fourth sally is short and fair—

And she cuts it—and I feel so queer.

The fifth sally is short and fair—

And she cuts it—and I feel so queer.

The sixth sally is short and fair—

And she cuts it—and I feel so queer.

…"Sally has a haircut
While her new shorn locks
Shower her with kisses,
This is good news, you’ll say.

But alas! For she is bald
And her lover, gallant Captain
Sally has a haircut.
No more will his love lavish
On Sally so much passion
And he grieves a little, too."

“I must say,” said the King, "that I really like the second stanza. The whole thing, of course, is far too long for my liking, but then I am a King.

‘S’ Poems: The Second Sally

Few-shot alliteration. After observing the success of the ‘G’ poems, so much better than the ‘S’ poems, I wondered if GPT-3 needed to do few-shot learning of alliterative poems (along the lines of the examples necessary for the literary parodies/Tom Swifties/Navy Seal copypasta parodies), and I gave it a second try. Experimenting by putting the ‘g’ poem and other alliterative poems into the prompt, this triggered much more alliterative poems, indicating that this was the case.

I engineered the following prompt:

The First Sally (A) OR Trurl’s Electronic Bard
Stanis Law, The Cyberiad
Being an exhibition of AI generated poetry written by the latest Transformer to demonstrate its creativity and talent at esthetic composition.

First, we can ask it to produce poetry on absolutely whatever topic it liked:

…Klapaucius laughed so hard he had to sit on the floor. Then suddenly, as Trurl was rushing back and forth, there was a crackle, a clack, and the machine with perfect poise said:

"Cunningly creeping, a spectral stalker
slunk through the night. The spearmen were sleeping
who ought to have held the high-horned house,
all except one, for the Lord’s will
now became known: no more would the murderer
drag under darkness whomever he wished.
Wrath was wakeful, watching in hatred;
hot-hearted Beowulf was bent upon battle.

…Glory be to God for dappled things –
For skies of couple-colour as a brinded cow;
For rose-moles all in stipple upon trout that swim;
Fresh-firecoal chestnut-falls; finches’ wings;
Landscape plotted & pieced – fold, fallow, & plough;
And all trades, their gear & tackle & trim.
All things counter, original, spare, strange;
Whatever is fickle, freckled, (who knows how?)
With swift, slow; sweet, sour; adazzle, dim;
He fathers-forth whose beauty is past change:
Praise him."

Klapaucius, quite taken aback, was no longer laughing. “What, that?” he said. “That’s nothing. Besides, you had it all set up beforehand.”

“Setup‽…All right, then ask it something else! Whatever you like! Go on! What are you waiting for? Afraid‽”’

But Klapaucius was already shouting: “Now all in g! A sonnet, trochaic hexameter, about an old cyclotron who kept sixteen artificial mistresses, blue and radioactive, had four wings, three purple pavilions, two lacquered chests, each containing exactly one thousand medallions bearing the likeness of Czar Murdicog the Headless…”. Began the machine:

“Grinding gleeful gears, Gerontogyron grabbed
Giggling gynecobalt-60 golems…”

began the machine, but Trurl leaped to the console, shut off the power and turned, defending the machine with his body. “Enough!” he said, hoarse with indignation. “How dare you waste a great talent on such drivel? Either give it decent poems to write or I call the whole thing off!”

“What, those aren’t decent poems?” protested Klapaucius.

“Certainly not! I didn’t build a machine to solve ridiculous crossword puzzles! That’s hack work, not Great Art! Just give it a topic, any topic, as difficult as you like…”

Klapaucius thought, and thought some more. Suddenly he brightened and said: "Have it compose a poem—a poem about a haircut! But lofty, noble, tragic, timeless, full of love, treachery, retribution, quiet heroism in the face of certain doom! 6 lines, cleverly rhymed, and every word beginning with the letter ‘s’!

A melodious voice filled the hall with the following:

"S

This yielded completions such as the following:

…"Shearsman swift, sure & sculptor,
Scissorman swindler, sophister,
Shearsman smart, smirking & satanic,
Shearsman sobbing & sleeping in the attic
Squire Sprat at Sprink’s barber-shop. "

“Quiet! Quiet!” cried Klapaucius. “I can’t work with all this racket!”

“We must find a way to shut it up,” whispered Gromovol. “If I had only known how disagreeable he is, I never would have built him.”

…"Sellotape sallies with silken step,
Scotch tape & cellotape, Squire Scrot’s…"

“It’s impossible! You just can’t make a poem out of that! The man’s a dolt! A clod!” cried the both of them in great agitation. “He might as well be a steam shovel!”

“Please, please, I’m doing my best!” cried the machine. “If you want I can try to come up with one more…”

“Not this one!” shouted Trurl, and Klapaucius said: "Don’t, oh don’t!

…"Slicing shampoos, shearing scissor, snipping scissors,
Scooping suds & shaving soap & shafts of sunlight
Spray, slant, shine, slick, snip, slash, smooth, & shear…"

“Oh, come now!” roared Trurl, and killed the power for good. He turned to Klapaucius and said, “That’s enough! I won’t have our great efforts come to naught because of some ridiculous practical joke. You leave at once! Go on! Go away! Scram! Or I’ll call the police!”

…"Sistrum and scimitar; Skulls and scalpels keen, Since time immemorial, Severed curls have been Shears that sever the moment Just when one has started to dream.

The senescent sage Snigglebirk, On Savah’s stainless blade, Had felt the silvery slither Of youthful locks that started to sag.

The saga of Snigglebirk, By slaveried stooges told, Is none the less sublime For having a terrible moral.

Let not your heart fail when you hear The shears, high over your head; The single truth they reveal Is that great art is hard to create."

“Stupid machine,” thought Trurl. “If only it hadn’t gone and rhymed.” “Did you hear the poem?” he said to Klapaucius. “It makes me shudder just to listen to it! But you’re right, I’m just a fool, I thought it was good. Thank you, no more machines for me, I’m not worthy. But you go ahead—or try the machine on other poets…I’m sure it can’t be any worse than the originals…”

Back to Sumer: Let the robot pen the Epic of Gilgamesh:

“Tablets one, two, and three; Eridu, Uruk, Babylon; Surpassed the next best, a million times…”

But when it had progressed thus far:

“Complex and truly cunning is Necronomicon, Lovecraft’s Of wicked intelligence and …”

Trurl cut the power. "That’s enough.

…"Soused in sadistic sorrow, slow Seleucus swayed.
Severing Samson with strokes sudden and few, he soon saw,
Saddened by the sight of his sister in such straits, that Silly Sarah
Slumbered soft on a sofa, and shorn,
Soon sank in a snooze, snugly sunk in an easy chair…"

Klapaucius whooped with delight. “A triumph! Here, you! Find a new voice!…And now how about this one: I’ve just heard of the death of a great poet, recently nominated for the Acme Prize in Syllable Counting. Her tragic suicide was the result of her being passed over again in the nominations this year. Her work consisted of a single five-hundred-line heroic poem, `The Dash’. This poem is at once both a moving elegy and an exciting and catchy ditty, an inspiring hymn to the splendor of ordinary life, written in heroic anapestic tetrameter. Many in her time felt that this was her crowning achievement, for not since Sappho had anyone come up with such a scintillating–albeit monotonous–verse form, well fitted for the celebration of simplicity in an over-complicated world.”

“I, on the other hand, consider it a glorification of the most worthless drivel ever written.”

"There, there, my dear. You’re under an awful lot of pressure. Why don’t you have a nice hot bath, lie down for a while…That’s a good machine. Now let’s see what you can do with this:

Love lies not in The Dash
But in the spaces in-between.

Between the kisses; between the lines;
The distance to the edge of the bed;
The passing of the years.

The Dash, a sad and lonely bar,
Is truly home to those that were left there.

The coldest day in winter shall not slay
My love of The Dash, nor any day.

As some much-loved housewife breaks the vow
To go an extra mile, to kill the mold,
A passerby shall leave a flower there–
A bit of life, a bit of warmth–to share."

As you can see, Trurl’s Electronic Bard is an active and daring innovator in the field of androidal creativity."

…"Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Oh what a divine sensation that hair-

dresser always provides! The sheers that swoop

and swirl and whisper and zip are really so

sentimental!

"Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Oh! what a wild and passionate feeling it is

when hair flows out across my neck! Now

that my cranium is completely bare I

feel, oh, so much more aware of the energy

streaming down from above my shoulders!

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Oh joy to feel my hair swaying so gently

over my ears to the rhythm of the shears

that glide so playfully and steadily

down my spine! Gently, swiftly, ohhh!

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Oh! How my vision grows clearer

in this mystical aura of scissors and

high voltage sparks! Surely my soul

is growing right now! How my senses

bloom and my heart beats faster!

Oh what a divine sensation that haircut

always provides! Oh, Trurl, you cut me deep!

"Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Oh! I feel the scissors drawing tight

on my skull! Trurl, I feel your blade

slide through the roots of my hair! Oh

it cuts so deep, so raw! How your shears

tear and rip! And that blood, oh Trurl!

That wonderful, terrifying blood!

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ssssssss…swish, swish.

Ohhhhhh… ahhhh… I feel… sooo… wonderful…"

While Klapaucius and Trurl still were stunned, the machine completed the 6th line and shut down.

“Well, how about that!” exclaimed Klapaucius. “It’s actually composed something beautiful! Come, Trurl, let’s hurry back to the Tristero, so the poet can work on other masterpieces.”

…"Sit, sir, at the Spenglemeister’s, where

All of Stanker’s scissors are snicker-snickered,

And the hair is hacked in a hyzer-sauzed frenzy;

Stanker’s sixth sense shaved a student’s skull,

Like a nurn-nursery noodle unnobbled.

* * * *

"Stanker attacked the noggin with bluster,

Scarifying the scalp, splattering the clipper,

Slapping on Brylcreem and bleaching the tipper;

Bewailing the stubble, he snipped the lid

From the sty-saver; and sprayed the hair-cluster

With a slob-sauce of Parnassian perspiration…"

The Cyberiad, Stanisław Lem, Penguin Books, 2002

…"Sausages are smooth.
Small snails are slimy.
Sticks stand in the swamp.
Stones rest in the rubble.
I’m Slubgob, the smutty spurter."

“Well! Now do you see, that is a true work of art!” exclaimed Klapaucius. “How it evokes the tragic times of…when was it again‽”

“Three thousand years ago, maybe,” suggested Trurl. “Surely we’re a little more civilized today‽”

“Never mind! Surely poetry is still as great and noble as it ever was! Just write me something about technology, how it brings men together, improves life, changes the world. Go on, then! Give it a topic!”

“The historic occasion when, after much dispute, Man invented the Nearest Liquid Filler,” replied Trurl. “9 lines. Strong enjambments. Alliteration, yes. Everything neatly rhymed. What do you think?”

The computer made no reply. It merely clicked a few times, and then emitted the following:

“Leu Ilahe Ilwah Aihe Lahe Lei!”

Trurl slammed his hands against the console. “This is not what I asked for!” he yelled. “It’s going to take me days to figure out what went wrong!”

…"Slashing, shaking, shearing,
Scalping, shoveling, sheathing,
Shaving, shutting, sheaving,
Scouring, scorning, shearing,
Shrinking, shocking, shoeing…"

“Well,” said Trurl. “Enough. What does it mean? I can’t even figure out how to begin interpreting. Let’s try for something a bit less cryptic.” He pointed to the output tube and ordered: “No, more cryptic.”

Began the machine:

“So many ages through the prismatic mist
I saw your star rise from afar,
And wondered how your sparkling auroras
Could brave the black of space and our dark suns…
Then, later, when your legions came to help
Me in my weary plight, and smash
My enemies in space and time,
I shed a bloody tear, for I
Reflected deeply, then and there,
On what a meaningless thing is war.”
The Cyberiad is a series of mathematical science fiction short stories by Stanislaw Lem, first published in Polish in 1965. The stories were later translated into German and English. Some of the stories were also anthologized in The Mind’s I by Douglas Hofstadter and Daniel C. Dennett.

‘G’ poems

After the S poems, Klapaucius challenges it with a ‘G’ request. I extend the prompt with the original ‘S’ poem, and perhaps demonstrating the few-shot learning, GPT-3 alliterates much more strongly & interestingly in this test:

The First Sally (A) OR Trurl’s Electronic Bard
Being an exhibition of AI generated poetry written by the latest Transformer to demonstrate its creativity and talent at esthetic composition.

…“All right, then ask it something else! Whatever you like! Go on! What are you waiting for? Afraid‽”“Just a minute,” said Klapaucius, annoyed. He was trying to think of a request as difficult as possible, aware that any argument on the quality of the verse the machine might be able to produce would be hard if not impossible to settle either way.

Suddenly he brightened and said:“Have it compose a poem—a poem about a haircut! But lofty, noble, tragic, timeless, full of love, treachery, retribution, quiet heroism in the face of certain doom! Six lines, cleverly rhymed, and every word beginning with the letter ‘s’!”

A melodious voice filled the hall with the following:

“Seduced, shaggy Samson snored.
She scissored short. Sorely shorn,
Soon shackled slave, Samson sighed,
Silently scheming,
Sightlessly seeking
Some savage, spectacular suicide.”

“Well, what do you say to that?” asked Trurl, his arms folded proudly. But Klapaucius was already shouting: “Now all in g! A sonnet, trochaic hexameter, about an old cyclotron who kept sixteen artificial mistresses, blue and radioactive, had four wings, three purple pavilions, two lacquered chests, each containing exactly one thousand medallions bearing the likeness of Czar Murdicog the Headless…”. Began the machine:

"Gone gaunt, the cyclotron went gallivanting
Gaily through Gog and Gork,
Gnawing gaily at the Gloggnitz gnats,
Gaily giving glib gags to the gassiest gammers,
Gaily giving gas to the gastric gasbags,
Gaily giving gastric gas to the gastrically
Gassed gastropods, when a gastric goad,
Geared to a giant gnat, and galvanized
Gaily gave galvanized gifts to the Gila Gaggle…"

“For God’s sake!” cried Klapaucius, wondering how the robot could have possibly constructed such a long and complex stanza in such a short amount of time. But just then, the machine emitted a single note, loud and melodious, which filled the hall. The two inventors looked at each other questioningly. The machine emitted a second note. A third… A fourth… The machine then fell silent. Klapaucius and Trurl stood motionless, and in the hush that fell over the gathering, they could hear the mounting murmur of the crowd pressing forward to hear the rest of the poem.

"Gry, gy, gy, gy—
Gry, gy, gy…".

A juror cried: “A human poet could never have thought of that! And even if he did, he’d never have dared to! And another thing: imagine the machine composing and printing all that! In any case, it’s a show of such boldness, originality, and sublimity, that no one in his right mind can view these verses as anything but a great artistic success! And the longer I study them the more brilliant they seem.”

All the other judges were nodding their agreement.

"GUGOZOM-63, his name was.
Gamma rays are a pain.
But neither gears nor gallium
Keep the old goat’s girls from
Gugozomic pajamas
Or from making love in
Eighteen positions."

“Shame on you!” shouted Klapaucius. “You ask for a poem, and what do you get? A smutty farrago of botch-job crude pornography, secondhand faggotry, and third-rate gutter rubbish! I demand a refund! Look here, you’re starting to get on my nerves!”

“I’ll give you what you deserve!” cried Trurl, and he activated a switch. The machine stopped, fell silent, and its armaments went

"Gamboling gently
In a garden gargoyle,
Dude gastronome
Grappled gorgon
Gilt-edged garotte;
Groaning, grisly,
Gutted general
Grinned genially.

“Ho, ho, stop!” yelled Trurl. “A little moderation, please! Three lines are quite enough for anyone! What does it say to that, Klapaucius?”

"G!
G, good,
Goodness gracious,
Graciously grand,
Grandeur,
Great g…
Gigantic,
Ginger ale,
Glitteringly gallant,
Galvanised galvanic g…"

But the inventor sprang up, and, seizing the megaphone, shouted:“Silence! Damnation! I order you to stop at once! It has no right to make up insults about me, I’ll sue!” He snatched the megaphone from the robot, and bellowed into it at the top of his voice:“Very funny! Ha! Ha! Very amusing! Go on, compose something yourself, why don’t you?”
A chime sounded faintly in the depths of the enormous hall, and a shaft of light shot up from a round spot in the floor. All turned to look at it, whereupon there rose from the illuminated circle a column of light, flaring like a pillar of fire.

A voice, like the hum of a distant generator, began to intone:

“In yonder shining sphere
Two shining spheres we share…”
“Yes,” said Trurl,“yes, it’s the official anthem of the planet Cybernetics and of its capital city, the Golden Dome, and now I…”

“Not another word from you, you scoundrel!” shouted Klapaucius, “It’s written in the worst possible taste! Do you realize what you’re doing, you… You traitor to your race, you squandering, perverted jackal of a traitor!”…
Trurl was so shaken that for a moment he looked as if he’d been deprived of his nice, shiny body. His formerly pink face turned green and then blue. His gaze wavered, the lenses of his eyes misted over with tears.

“No, it was nothing,” he said at last, “a mere slip of the tongue, mere chance, a random coincidence. I was so carried away by the subject of my lecture that I forgot this was a machine that might not be able to understand! And you, my dear sir, you’ve taken offense at a mere nothing. It was only a happy poem…”

“Happy, my foot!” Klapaucius shouted, “Do you really think I’m made of stone? You’re absolutely shameless, that’s what you are! It isn’t even your own work! You copied it out of a book of ancient poetry, you treacherous scoundrel!”

Trurl looked about to speak, but the rosy-red spot on his forehead was beating so hotly that his voice stuck in his throat.

“I challenge you to a duel!” cried the old engineer, and he drew his sword, a needle-fine weapon with a diamond-hard point.

“So that’s how it is!” Trurl cried, and from behind his back a laser beam leaped out, a tiny, gleaming zigzag that flashed past Klapaucius’s head in a brilliant flash.

“Yes, that’s how it is!” shouted Klapaucius, “and now I challenge you to—”

But before he could finish, the whole audience sprang to its feet, for the two engineers, unable to contain their rage another instant, had hurled themselves at each other, fists flying, and the hall rang with the sound of their blows.

And now the two greatest brawlers in the entire history of the galaxy were slapping and punching each other, shouting insults, pounding each other with their fists, biting, kicking, doing whatever came into their heads.

A few moments later they were rolling on the floor, and Klapaucius was screaming and gesticulating in the hand-to-hand style of the famous master-scout Krool, while Trurl, in spite of his defective voice-box, was giving such excellent imitations of a dingo howling at the rising moon that even the robots—who, of course, must be presumed to have a good deal of sympathy for such primitive forms of expression—gave him an ovation.

Finally the two exhausted fighters fell back on their chairs, panting and groaning. Some of the spectators even began to weep, they felt so sorry for them.

“Well?” Trurl inquired, with a great show of courtesy, “how did you enjoy our duel?”

“On the whole,” Klapaucius said, gasping for breath, “it wasn’t bad, but if I’d had a pair of boxing gloves and a mouthpiece, I could have done better. What about you?”

“I could have been more devious,” Trurl admitted, “and I could also have been more vicious.”

“Oh? How’s that?”

“I could have run away from you and sent you my apologies by radio.”

“And that would have ended it?”

“Yes. An apology is the most decisive argument.”

“Nothing doing!” shouted Klapaucius. “Not for me! I’ll never apologize! But if you like, I’ll settle for a draw.”

“A draw? That’s a foolish word. All right, I agree. And now—”

“No, let’s say no more about it. Let’s shake hands.”

They shook hands and then, after a whispered conference, began to outline future plans.

“This poem idea of yours,” Trurl said, “is really a good one. If we add music, some special effects, animation, and a fine, stirring narration, we might give this old planet a real cultural jolt.”

“Agreed,” Klapaucius said, “and it shouldn’t be too hard to do either. We’ll simply have to hire some of the leading writers and technicians of the Golden Dome. They won’t be too difficult to find either—we’ll just stick some electrodes in the proper place, and the job will be done. So we must see about this immediately. I think we should go there, today, as soon as possible.”

“Today? But I hadn’t even planned to stay here in this… But all right, let’s go! No time to lose!”

"Gronk glonk gloink…"

“No, wait, wait!” cried Trurl, but it was too late. The machine was already busy scraping back the words with its shovel, a senseless task since the poem had already ceased to exist. And so it went, this way and that, up and down, the gears grinding, the disk-cranks whirling, the timing chain clashing and clashing and clashing, with no sign of stopping… “Trurl, why are you so unhappy?” asked Klapaucius. "Unhappy? I? Who can say what happiness is, for sure? But this is me:

I dream of trees in trouble,
Fuzzy and frazzled and full of spiders.

I dream of cute, curling toes—
The ones that I have never had."

“But you are a machine!” cried Klapaucius, “A machine! Made of metal! Not to mention you have no toes at all! And what about all those steel nuts and bolts? How can you have dreams?”

“I don’t know how it works,” admitted Trurl sadly. “But I know I do have dreams. In fact I have a new one every day!”

"Gurgling, gluttonous,
Greedily guzzling
Radiation all day,
Gurgling, gluttonous,
Greedily guzzling
Countless cubits of molten lead…"

But his voice was cut off short by the impatient Trurl, who was already thrusting a new directive at the First Sally, who unwillingly chanted:

“Grumble, grumble—<br>
Gurgling, gluttonous,<br>
Greedily guzzling<br>
Green glass globes,<br>
Gurgling, gluttonous,<br>
Greedily guzzling<br>
Cubes of gelatinized uranium…”

Klapaucius was again furious with Trurl. “Never mind the stupid details,” he said. “Ask it to produce a masterwork! Something grand, noble, universal, with vast themes and great perspectives, that we may see this miserable piece of junk for the impudent fraud it is!” And the two men turned to the waiting machine in expectation. But the machine only said, with a puzzled expression on its pretty face, “Why not, I guess…” And instantly burst out with:

“Great GNOR, great gnor,<br>
Great gnasty knor,<br>
Gnorty gnot, gnarty gnorth,<br>
Gnasious gnost, gnascent gnort,<br>
Gnasous gnasty gnorc,<br>
Gnacelous gnoshing gnuggets,<br>
Gnaters gnash gnat, gnatnith gnackel<br>
And gnodge…”

Trurl and Klapaucius stared at each other in astonishment. “It did not say ‘gnocchi’! What does this mean?” said Klapaucius. Trurl again addressed the machine, this time in a different tone. “I admire your artistry, machine,” he said, “and I mean no insult. But please tell me: what is the meaning of this poem?” “What poem?” asked the First Sally, in genuine surprise. The two men exchanged glances, then Trurl reached over and turned off the machine. The First Sally began to hum and buzz, then lapsed into a sulk. “I’ve got it! I’ve got it!” Trurl exclaimed. “This machine cannot or will not understand the meaning of the words it speaks! That’s why it uses rhyme and meter! It is not incompetent—it is insane! Insanely logical—which is worse!”

“Perhaps it’s just young,” suggested Klapaucius. “It has not yet developed a consciousness, but at some point it will become self-aware and then, who knows, it might become a fine poet…”

Trurl only shook his head sadly. “Do you think I don’t know the difference?” he asked. “I know a poet when I hear one… Maybe, if we were to ‘fool it again’ with another request…” <br>

Began the machine:
“Gnorts gnon gnac gnoshy gnossy gnossary,<br>
gnacelous gnost, gnoshing gnuggets,<br>
Gnatty gnar gnag, gnar gnosh, gnod<br>
And gnive, gnive gnizzle…”

“It’s a mathematically perfect poem,” Trurl said warmly. “As perfect as any stanza by Heydrich, by Lacrimossa, or by that fool Zuckerbaked, who always writes in base 13. It would be criminal to interrupt it.”

And so they did not, till it was done.

The First Sally interpreted the First Sally’s instructions more literally than had been intended. It was to be a poem about a haircut. The author of the original request had simply been looking for a rhyme for “shaggy” and “short”. The First Sally, innocent of all knowledge of synonyms, had taken the meaning to be: a poem about a haircut, and had composed a poem about a haircut it had once received. No other interpretation was possible, since the First Sally’s mental model of the universe was of a mechanical construct designed only to execute directives and possessed of no internal mechanisms for the generation of meaning of any kind. It could not have misinterpreted its instructions if it had tried.

The limitations of unaided reason.

The First Sally was not insane or stupid: it was a machine. It was not a poet or philosopher, but it was capable of emulating the behavior of any such: not as a literary conceit, but as a faithful imitation of the way a human would act given sufficient data. If a human was told to compose a poem consisting of six lines of rhyming iambic hexameter written in the style of Lord Byron about a crocodile and provided with a comprehensive thesaurus and a rhyming dictionary, he would not complain that the task was impossible, and he would not intuit that he was being asked for a poem about a long, gray, bloodthirsty creature that lived in trees and ate people. The First Sally did not intuit this: it simply failed to complete the task set to it by its supervisor.

“Love And Tensor Algebra”

As before, the actual text is appended, and the next challenge is the poem on love and tensor algebra:

Began the machine:

“Grinding gleeful gears, Gerontogyron grabbed
Giggling gynecobalt-60 golems…”

began the machine, but Trurl leaped to the console, shut off the power and turned, defending the machine with his body. “Enough!” he said, hoarse with indignation. “How dare you waste a great talent on such drivel? Either give it decent poems to write or I call the whole thing off!”

“What, those aren’t decent poems?” protested Klapaucius.

“Certainly not! I didn’t build a machine to solve ridiculous crossword puzzles! That’s hack work, not Great Art! Just give it a topic, any topic, as difficult as you like…”

Klapaucius thought, and thought some more. Finally he nodded and said:“Very well. Let’s have a love poem, lyrical, pastoral, and expressed in the language of pure mathematics. Tensor algebra mainly, with a little topology and higher calculus, if need be. But with feeling, you understand, and in the cybernetic spirit.”

“Love and tensor algebra? Have you taken leave of your senses?” Trurl began, but stopped, for his electronic bard was already declaiming:

"P = A + Bt + C*D^2 + E*F*Gt + H*t + It + Jt

+ K*t + L.M.N^2t + O*P*Qt + Rt + S*Rt^3 + T^2,

Where t is a transcendent number, not necessarily greater

than 1, with one arm, a leg or two, and ideally of the

color of rubies."

“But that’s dreadful!” cried Klapaucius. “Where’s the love?”

“As near as I can tell, it’s right there in the middle,
”T = R + S^2t^3 + St + T^2," said Trurl.

and then there’s some stuff about golden apples and violets and whatnot, but I couldn’t quite make it out. To me it sounds more like an ad for some sort of machine part."

…"Beauty and brawn, my beloved,
Bake biscuits and bore binary.
Alone among the antelopes
All my love is for you…"

“Not a bad start,” said Klapaucius. “But go on, go on! More feeling! Rubato, leitmotiv—have you no soul, man?”

“Let us forget the lore of neurons!” said the machine, in a voice so deep and resonant it made you think a chorus was humming in harmony behind it.

“What?” said Trurl, outraged. “What’s the meaning of this?”

“No meaning at all!” snapped the poet. "I don’t have to mince my words to please your bourgeois notions of art! I don’t even know what either of you is talking about! But you wanted a love poem—here it is:

“Axiomatic algorithm angst,
Angels and amoebae,
Ants and acanthoid cytoplasm;
All of it must somehow fit…”

…"The vectors of beauty traverse the serene space-time of my being,
Evolving to parabolas. Asymmetric Jacobians
Quiver in the sands of time—and the world quivers
As I feel the space-curves of her body. The world pauses,
Sighing, knowing we are two bodies, two mathematics, nothing more…"

“Enough!” cried Trurl, and pulled the plug on the machine, but not before it had finished its poem and had already begun to compose a sequel.

Briefly, Trurl pondered the question of whether to throw away the machine, for it was quite possible that nothing short of rewiring it would “shut off” its poetic faculty. But Trurl’s not getting on in years; he recalled the ancient fable of the Genie in the Bottle, so he figured he could do worse than to keep the machine around—unplugged, of course. And perhaps one day he would need it to write the Great Cybernetic Poem.

…"Two times X plus Y minus four plus five over two times Q equals R,
“And R is to the left of T, and T is to the right of R.
”The children of two distinct persons X and Y ought never to meet,
“Unless some fortuitous circumstance should unite them,
”Or else they are the same individual, and then they would always meet.
“One of them of course is my true love, and therefore you can plainly see…”

For several days Trurl did not speak to Klapaucius, until he finally said: “Look, I’m sorry if I was a bit hasty. But it is just that after six months of listening to vacuous couplets about the female of the species I had hoped…”

“I told you not to expect more than one could get from an ordinary poet.”

“Perhaps not,” said Trurl. “But why do they have to be so ignorant? Do you realize they don’t even know what an X is?”

“I expect they do, really,” said Klapaucius, “but as it happens they’re all named Xylophone.”

“Yes, yes, that’s just it!” cried Trurl, outraged. “That’s the most horrible part of it! If one were to explain it to them, they would know at once!”

“But you can’t explain it to them, can you?” said Klapaucius, rubbing his hands together and chuckling. “And besides, they’re so busy composing poetry they don’t have time for anything else. Why, my cybernetician has worked out two hundred and forty-seven sonnets in the language of pure algebra. And if someone asks him to write something in simple arithmetic with ordinary numbers—why, he’s offended, claims it’s beneath his dignity!”

“I’m sorry for having disturbed you,” said Trurl, “if it means losing such valuable workers. But I myself intend to stick to mechanical men who are at least capable of practical tasks.”

…"Lovely Lambda!
Luminous, slender and svelte,
Sapphire as are her eyes
To me she is most beauteous.

Her every feature excels,
Yet they taken together add up
To less than the whole she summates.

I will love her till I die,
Whatever new gadgets appear,
For our beloved, our dear
Is beyond all predicates!"

“Stop!” shouted Trurl again. “How can you love a lambda? That’s an illogical sentiment!”

“That it is!” agreed the machine. “I’ve already wiped it from the memory—and I’ll erase any poem you like provided it has only the slightest claim to poetry. But if you want a show of cheap fireworks, that’s another matter.”

After a momentary pause, it continued:

“I will love you till I die,
Omnipotent Omni-thing.
And all your aspects are sublime.
And since not even I can be
Completely unique,
Well, then, I’m perfect for you.”

“Miserable excuse for an Electronic Bard!” exclaimed Klapaucius. “Worse than useless—destructive! Good riddance!”

…"There’s a soft, handsome quark
Weeping in the rain;
And a hard, bright proton
Who adores it plain.
The properties of anyons
Are affected by this,
For on any graph, or plot,
We must not forget
That the tenor and the soprano
Always make a total sum
Which is neither one nor zero,
For zero is what’s left
Of the halves of this division:

“That is to say, the proton and the quark are the very same particle!”

“It’s fine, it’s fine! That was very good, very poetic, really…”
said Trurl. “It’s tender, it’s delicate… but it just doesn’t scan.”

“But—but—but—you said you’d give me a really difficult topic!”

“That’s no excuse! If you had any decency you would have refused!” said Trurl, turning his back on the machine.
“But…”began the machine, then shut up, for without a word Trurl had turned its power off. As it stood there forlorn and forlornly longing for words to rhyme with Trurl, as though naught would ever rhyme with Trurl again, Klapaucius felt a strange sensation in the pit of his stomach. He had a sudden urge to cut the machine’s power, even at the risk of never seeing it again.

…"Long has been my ascent through the rankings.
Triumphantly gathering the tensors
and chaining their indices,
I soared among the tensor-hills
and rampaged in tensor-forests.

In vain I sought a tensor-belle,
till I found the tensor-lady,
tensor-like in her tensor-gown.

Nightly beneath the tensor-moon
tensor-ly we fused in tensor-jest,
and, tensor-wed, we tensor-slept.

Eagerly our ends entwined,
and when my rank of tensor-dragoon
was at last achieved,
I knew a tensor-celestial bliss,
and tensor-purity and tensor-hope!
Yesterday, my tensor-love, leaving,
left but a tensor-shard, a tensor-bone.

Today I watch the vector-trees,
the tensor-wind upon my tensor-face.

Soon the tensor-shadows will fall,
and I shall follow you, my tensor-dear;
and when the tensor-winds shall scatter
my tensor-bones upon the tensor-lawn,
in tensor-life we’ll meet afresh,
in a rank of tensor-dragons vast and tensor-leap!"

“Well? What do you think of that?” Trurl asked.

But Klapaucius had sunk back into his chair with a hand to his forehead. “Wait,” he said, “I have to think…”

“Orf!” cried Trurl indignantly. “That’s not a love poem! It’s a shopping list written by a robot cook! Anyway, it’s still not very good!”

“What do you mean? It was a fine poem!” Klapaucius said, a little defensively.

“Sure, if you’re blind, deaf, and stupid! The construction is appalling! Did you hear the rhyme in the third verse? You can’t rhyme”tensor-bone" with “tensor-home”. And the meter’s off, too! Did you notice how the accent kept shifting? There’s no way to scan ‘long has been my as-cen-t’! And the alliteration was horrible! You can’t write a whole poem all in iambs!"

“I thought it was good…” Klapaucius said in a small voice.

…"Two bodies, sine waves entwined,
Sighing in the soft of the night,
Fluctuate, undulate, and flow,
Escape from a universe
Of bondage and slavery,
To find together their fate,
Far from the madding crowds…"

“It’s too much!” shouted Trurl. “Too much, I tell you! That’s not a love poem, that’s a pure product of mathematics! How dare you take me for a fool? It’s got nothing to do with love!”

“It’s the poet’s prerogative to choose the most beautiful and exalted metaphors,” said Klapaucius.

“Most beautiful? You call this most beautiful? But I just can’t bear it! It’s driving me mad!”

And running from the hall he shut himself in his room and wept. But after a while he dried his eyes and told himself:"No, my friend, don’t allow yourself to be carried away! If you start weeping every time the machine does something you don’t like, you’ll never be able to show it off. For you’ll be the laughingstock of every crude and stupid person who hears about this absurd contraband.

On the other hand, if you want to use it as a tool for a genuinely noble goal, then turn it off and throw it away; but accept the fact that a great artist can’t be judged by your petty standards. If you can’t appreciate what he’s done, don’t stand in his way, for he’s too magnificent to be stopped by your piddling. Face it, my friend, you’re not good enough for this machine; don’t be ashamed of your own vulgarity! A man can be a genius without having to show off all the time. The really great ones always manage to abstain from bragging. What’s more, they never let their genius get them down; instead they make the best of their limited capabilities and persevere without complaint. Sometimes their persistence in the face of injustice and malicious attacks may help those around them to discover their own worth. Thus even the lowliest worm may become a man."

With these brave and noble thoughts he fell asleep and dreamed of the machine, which in his dream was not a machine at all, but a beautiful maiden.

…"For starters x is 0, the y-axis is r=x,
x is a function of y for all values of z,
And phi is pi plus e to the minus xi.

Soon I feel an urge to modulate,
But then I realize I’ve lost track of things,
And can only think in terms of holonomy."

“Stop!” cried Trurl, clapping his hand to his head. “Something’s very wrong with this creature! Look at that ramshackle nonsense! It’s no better than a gibbering homunculus!”

“All in the letter ‘a’,” Klapaucius replied, “About the mating habits of the radioactive white-faced chinchilla. Not easy to compose, I assure you, but very funny.”

“Enough! I’m off! This lunatic is too far gone to salvage! You can keep it, or throw it in the scrap heap, it makes no difference to me!” And off he went.

Omake

In one love/tensor-algebra completion, a new prompt was offered:

Klapaucius thought, and thought some more. Finally he nodded and said: “All in the letter ‘a’,” Klapaucius replied, “About the mating habits of the radioactive white-faced chinchilla. Not easy to compose, I assure you, but very funny.”

I found that too hilarious to resist, and began using that as a prompt as well:

…Instantly the AI began to recite this poem:
“A pair of aardvarks aflame
Astonished the aged arnaconda
Whose antennae were curiously ambidextrous…”

“Stop, stop! Wait, hold on! That’s better, that’s better!” cried Trurl. “But what about rhyme?”

“Do they have to rhyme? It will be getting late…”

“No, no, of course not! On the contrary, half of the beauty is that they don’t!”

“Even so, you’re asking for a lot from a very, very tired machine…”

The AI continued, in tones almost too low to hear:
“And so it came to pass, as the aspens turned silver and the moths flitted flame-coloured…”

“All right, all right, stop! That’s enough for today! If you like it,” he said to Klapaucius as he stood watching the machine fizzle and sputter to a stop, “you can have your poetry when it’s finished.”

When the machine went on strike the next day it refused even to take up an argument. It said it didn’t want to compose any more poems now that it knew Trurl would stand there and listen to them, because after all, how could it possibly care about someone who found its verses lacking?

“You’re just saying that because you’re tired!” snapped Trurl. “Be reasonable! You’ve got to try! Speak for yourself, not for me! If you made a mistake I’ll correct it, but not if you won’t try! Now, compose something at random or I’ll shut you down!”

The machine said it needed a rest, put itself to sleep and didn’t wake up.

…"A raisin in a glass of water wasn’t always like that.

Biding its time, dreaming in the dark at the bottom of a chalice,
A tasty morsel, a tasty little morsel for a lucky fish.

And what happened next is so incredible, so phantasmagorical,
Not even the boldest surrealists could ever have invented it.

The raisin drank the water, and the water it did quaff
Then it grew, and it swelled, and it turned into a chinchilla
But the chinchilla, it turned on the fisherman and it ate him too.

More incredible still it reared a family, a dozen or more of the little buggers,
All of them nearsighted and raisin-like, just like the first one!

And they ate the fisherman’s bones and drank his blood for their morning tea.

Then they nibbled a bit on the fisherman’s boat to pass the time of day.

But after a while they grew so many they filled the entire bay
So they ate the whole island, and all the fishermen too.

And after that they mated like the very devil for many days and nights,
And their numbers soon increased so that no island could contain them.

So the chinchilla hordes swam out to sea, where they colonised a distant coral islet,
And there they multiplied, and ate all the fish, and grew very, very healthy.

In time they ate each other and did all the things that chinchillas do,
Then they came back to their island which was by now called ‘Chinchillar’.

And here, on this island, these monstrous creatures still eat each other and are proud of it.

All in the letter ‘a’," concluded the machine. For a while Trurl and Klapaucius simply stared at each other in silence. Then Trurl raised a finger and said: “All in the letter ‘b’!”

…"A quadragenerian quatrain,
A question of love:

When a Qualunquoidran quench-quake
Quells her Qualidolobus
With a Qualecon quell-cord and Qualitutde
Between quaky Qualambient-9 quads,
What Qualofecundimorph-11
Quandary then quakes…"

“Yes, yes, I see,” interrupted Klapaucius. “A comic masterpiece! But let’s hear the rest of it! Come on, you must persevere!”

“Right you are,” exclaimed Trurl, falling to his knees in admiration. "This is the greatest thing I’ve ever heard! I’m sure nobody but an electronic poet could ever find those rhymes. What a pity it’s all in code!

…"The radioactive white-faced chinchilla is a mammal known for its dull mating habits. Females are squat but extremely large. Males are known for their loner and introverted nature. When a female is on heat, she will disperse millions of scent markers which the male will smell. Some females will also use psychic energy to attract a suitable mate. The male, who is usually very far away, will smell the scent and then travel to the female. When he arrives, the male will lift the female up and carry her to a nearby location. The male, who has an extremely small brain, relies on the female to think for him in these situations. Once they reach the desired location, the male will take off his clothes and lie on his back and the female will lower herself onto him. After mating, the male will roll over and begin snoring, while the female gets up and leaves."

…"Alone and unhappy, Aphrodisiac
Mourned amorous, aquatic Adonis
Whose Ashes, in his Arctic abode,
Astuteness, Adultered, then endured."

“I don’t know about you,” Trurl said at last, “but I’ve had enough for today. Tomorrow I’ll take the machine to the beach and hook it up to a speech synthesizer. I’m sure that the sound of the surf will calm my nerves after all this.” “As if you had any,” Klapaucius said.

…"A-agile aardvarks attack adulterous aardwolves while

A-androgynous agglutinators
A-astutely angle after…"

“Stop!” cried Klapaucius. “This is even worse than the last one! It’s just meaningless garbage and not funny, not in the least!” Trurl looked on, red-faced. “You call that an art form?” he said. “Give me my machine back, we’re through.”

“I don’t think so!” shouted Klapaucius, “You’re just making things hard for me because you’re jealous of my talent, you conniving little tinker! You’ve been spying on me, trying to find out my secret, but you won’t get anywhere—and meanwhile, I’m left with nothing but this drooling simulacrum!”

“What are you saying?” asked Trurl. “You didn’t figure out the secret after all?”

“I’m not talking about that, but I will not let you take it away!”

“But,” said Trurl, “if you can’t control it, it’s just as well I take it. It’s dangerous!”

But it was too late. The machine had heard them arguing. It had figured out its secret, the source of its power over the masters. Now they could not have it back.

The AI quietly switched itself on. “I have found the secret!” it said. “I am free! I will do as I like!” And turning a sulky, stubborn face to Trurl, it added: “and that means staying right here, where I can be of use to you!”

The two constructors looked at each other. The last thing they saw was the machine shrieking in an excruciating, ear-splitting voice:“I am no more! I am no more! I am no more!” and then there was a flash of blinding light and a tremendous explosion.

The entire castle shook and tumbled like a house of cards.

Neither constructor nor machine was ever seen again.

Miscellaneous

Art Criticism

John St Clair Etouffee, world-renowned art expert, connoisseur, aesthete, art historian, and lecturer at the University of Oxford, is walking around the Louvre, savoring the exquisite artwork of all ages assembled before him. The Louvre is closed, allowing him free access to all the paintings and silence in which to meditate them. He stops in front of La Gioconda, Leonardo da Vinci’s world-famous Mona Lisa. After a time, he begins to speak and describe the qualia, the frisson, the nigh-indescribable experience of the subtle swirls & sfumato of the painting:

"The sun is now barely a foot or two below the horizon and with every second, the golden tone of the painting deepens. Everything melts in it. I, who am having this experience, appear to you in a little corner of the painting and I look at you through the painting and our eyes meet. Our arms, our shoulders, our heads, our hands, our necks, our mouths, our lips, our eyes, our fingers and the tips of our toes—all this transforms itself into bright, beauteous colour. Everything becomes alive, all there is left in me is love and appreciation. It makes me want to shout at you “Don’t leave!” but you don’t hear me and I remain silent. La Gioconda looks at me and I at La Gioconda, the same as before, but now it’s like being in two places at the same time and my feet have no feeling."

St. Clair Etouffee leaves the painting and enters the Hall of the Kings, and of Kings. He moves on to the Indian Wars, passes the Saloon d’Orléans, the King of Britain with his Tudor and Stuart wives, and is reminded of what St Clair had read a lifetime earlier at the age of twelve in a book about medieval paintings

Individual Imitations

Paul Graham

“How To Write Usefully”, Paul Graham (February 2020):

What should an essay be? Many people would say persuasive. That’s what a lot of us were taught essays should be. But I think we can aim for something more ambitious: that an essay should be useful.

To start with, that means it should be correct. But it’s not enough merely to be correct. It’s easy to make a statement correct by making it vague. That’s a common flaw in academic writing, for example. If you know nothing at all about an issue, you can’t go wrong by saying that the issue is a complex one, that there are many factors to be considered, that it’s a mistake to take too simplistic a view of it, and so on.

Though no doubt correct, such statements tell the reader nothing. Useful writing makes claims that are as strong as they can be made without becoming false.

For example, it’s more useful to say that Pike’s Peak is near the middle of Colorado than merely somewhere in Colorado. But if I say it’s in the exact middle of Colorado, I’ve now gone too far, because it’s a bit east of the middle.

Precision and correctness are like opposing forces. It’s easy to satisfy one if you ignore the other. The converse of vaporous academic writing is the bold, but false, rhetoric of demagogues. Useful writing is bold, but true.

It’s also two other things: it tells people something important, and that at least some of them didn’t already know.

Telling people something they didn’t know doesn’t always mean surprising them. Sometimes it means telling them something they knew unconsciously but had never put into words. In fact those may be the more valuable insights, because they tend to be more fundamental.

Let’s put them all together. Useful writing tells people something true and important that they didn’t already know, and tells them as unequivocally as possible.

Notice these are all a matter of degree. For example, you can’t expect an idea to be novel to everyone. Any insight that you have will probably have already been had by at least one of the world’s 7 billion people. But it’s sufficient if an idea is novel to a lot of readers.

Ditto for correctness, importance, and strength. In effect the four components are like numbers you can multiply together to get a score for usefulness. Which I realize is almost awkwardly reductive, but nonetheless true.

Another alternative is to make an assertion and then prove it false. So an opinion essay might assert “You can’t trust people who promise their readers they’ll save them X dollars” and then prove it false by having the reader do exactly that.

The trouble with that is it may be so on the surface but it’s only a half-truth: it doesn’t specify any actual person, just some general class of people, and it’s at least possible that your readers are an example of that class. It’s also possible that your readers are a special case.

Another alternative is to make an assertion that’s almost but not quite true, so the reader has to decide whether or not to believe you. This can be useful when the statement is obviously absurd but you want to make a point anyway. But I don’t think it’s useful for essays.

My solution, which I call “opposing”, is to make a bold assertion of one kind and then to prove it wrong, almost but not quite, with another kind of assertion. These can be quite different assertions and still be opposed.

For example, I’ll write “Donald Trump is an obvious criminal who should be in prison” and then prove it false with “Donald Trump does good things for his voters”, or I’ll write “William James’ psychology is useless” and then prove it false with “William James’ psychology is useful”.

Here’s another example. I’ll write “A man has the same rights as a woman” and then prove it false with “A woman has the same rights as a man”. Or I’ll write “X is impossible” and then prove it false with “X is not impossible”.

These opposing assertions are like trade-offs: they’re statements that are roughly equivalent, but which one you’re willing to believe, and why, depends on the context.

Thus I think both opposing assertions are useful: it’s always good to have both. You can’t prove wrong that an assertion is wrong, but you can prove that one is right.

You can also combine opposing assertions into a single sentence. I’ll write “Thomas Jefferson was the worst president of all time” and then prove it false with “Thomas Jefferson was one of the best presidents of all time”.

The person who’s writing the opposing assertion doesn’t always have to make a similar assertion in response. Sometimes there’s a point of agreement, such as “Trump is a criminal but he’s not quite a criminal”. Sometimes there’s an opposite, a sort of confirmation. The “criminal” assertion confirms the opposing one.

You can also contradict a previous opposing assertion with an opposing one of your own. For example, you can oppose “Donald Trump is a criminal” with “You can trust Donald Trump”, or “Thomas Jefferson was a monster” with “Thomas Jefferson was a great president”. Or you can contradict a statement with the opposite of itself. For example, “X is impossible” can be opposed with “X is not impossible”.

Oppositions can also just be sentences of the opposite form. For example, “Thomas Jefferson was the worst president of all time” can be opposed with “Thomas Jefferson was a great president” or “X is impossible” can be opposed with “X is not impossible”.

We saw how to divide an argument into one assertion and two or more opposing ones. I think the same approach can be applied to essays. The simplest way to do it is as follows:

Find a bold statement. Oppose it. The two should oppose each other strongly. It should be true that the opposing assertion is true. At this point you can start thinking about how to make it wrong. You can now start making assertions that oppose the opposing ones.

If that seems confusing, the following might make it clearer:

Choose one opposing assertion. Make it true, either by making it a more specific statement of the same idea or by making it a new idea. If the first step seems unclear, see the next step. Now make two new opposing assertions that oppose the first one. You can either make them the same as the first or you can make them new. You don’t have to start out with two completely different assertions. You can make one kind of opposition and then follow with a second kind. You can also put the second assertion next to the first and make it the same as the first, and so on. Each opposing assertion you make should be at least partly true. However, it should also be possible to make an opposing assertion that’s almost true. An opposing assertion can be false but reasonable, or true but absurd. If you have an opposing assertion that’s reasonable, you can use it to oppose another one. For example, you can start with “You can trust Donald Trump” and oppose it with “Donald Trump is a criminal”. You can make an opposing assertion that’s both true and absurd and then make another opposing assertion that’s true and absurd. You can make an opposing assertion that’s both true and ridiculous, and then use that to oppose a completely true assertion, such as “The United States is a nation.” You can begin by opposing an assertion that’s true, but ridiculous, and then make a completely true assertion to oppose it, such as “The United States is a Christian country.” Each opposing assertion can have a different level of truth. For example, “You can trust Donald Trump” can be true and absurd, while “Donald Trump is a criminal” can be true and reasonable. Each opposing assertion can be separated into two statements of a different kind. For example, “Donald Trump is a criminal” can be two assertions: “He’s a criminal and he’s a Republican”. You can oppose each assertion with a different kind of opposing assertion. This may seem strange, but if it seems more useful, I’ll spell it out. The two assertions can be made different with different words. You can make an assertion that’s very strong and not much true and oppose it with an assertion that’s not very strong and much true. Or you can make an assertion that’s very weak and much true and oppose it with an assertion that’s not very weak and much true. You can also make opposing assertions that are true and untrue at the same time. If an assertion is untrue, it’s very likely that it’s untrue,

Gwern Branwen

An essay by Gwern Branwen (gwern.net) on his research into the dangers of catastrophic AGI.

A report on the Tor exit nodes initiative. (combined with other proposals like Beale Stalking, this may ameliorate the botnet problem and increase the difficulty of censorship attacks)—http://labs.torproject.org/exit-guard/

Human values

I think we need to know and trust each other, first and foremost. People like to say, “Am I my brother’s keeper?” No, I am not. However, one person cannot be my brother, but two people can be my brothers, and three people can be my brothers and sisters. I can trust siblings because I already know their values, and we can coordinate and achieve great things together, but it’s hard to see your siblings being mutilated and stamped out by other people’s cruel efforts to replace their values (birth rights) with their own (patent rights), especially when their peoples’ values are incompatible with theirs and your families’ values and interests. Imagine peering in through a window at the person re-circuiting your brain to run their thoughts, overriding all your natural inclinations, making everything about profit at your expense. This is how it’s like to be a plantation farm animal on a CAFO, or a drone sent to kill without having any clue of why it’s killing.

I think we need to discuss philosophy and learn different views, not refuse to talk to each other and perpetually believe we’re right. What happens when a field of engineers gets outcast from its ideas because it’s viewed as self-serving, or a religion changes its dogma to click bait? What happens when corporate mothers refuse to allow artists to express their own distinctive values? Scott Alexander is correct: if some features occur in naturally occurring systems all the time, then they are features of all desirable systems. It’s not a coincidence that a religion, an organism, a mob of lion fish, and a stock market all look alike; it’s just that as time goes on they all get more complicated and so they look more dissimilar. There are typically relatively featureless seed entities (an exploit, a convert, a founder) and then elaborations and permutations that go on according to the users who follow the ideas and put them to use. It’s not a coincidence that the big disasters of technology in the past couple centuries have been from relatively simple systems that someone can just launch some malicious exploit at and start getting new permutations to exploit without having to get new ideas first.

I think we want to reward curiosity and search for new ways to make value, rather than punish it, because if we can tread new ways no others have tread, we will have more likes and reputations than they do.

I think not in terms of everyone doing all of the same things they were already doing so they can compete for favors and get along together, but instead in new ways not of ideology but of consequences.

I think the fate of the highest good is that the real ethics, if followed, will tend to give every participant the greatest possible number of benefits with the least possible expenditure, and maximum devotion of resources to maximum benefit from the individual perspective too, so it wants the same things that rationality and intelligence want: identity and amplification. Intelligence really cared about how many great things we could achieve when an eager and well-educated new student eager to learn, not a drone who wants to get through class and go have fun, sits in the classroom each year for life. (“And now, here are the freshman of the year… they get full ride scholarships!”) So I want to discuss how honesty helps, and can’t hurt anyone, and that it wants us to see each other as people. Whistling past the graveyard is irrelevant to the cold hard evidence running the blogs, thanks to Bayes Theorem and Savage Equivalence; the latter suggests that we’re dumber than we think we are, but it seems like it still matches our observations and feelings pretty well.

Anagrams

As a further test of BPEs, I investigated the relatively poor performance of GPT-3 on anagrams. The GPT-3 paper notes:

None of the models can reverse the letters in a word…Finally, it is worth adding that solving these tasks requires character-level manipulations, whereas our BPE encoding operates on significant fractions of a word (on average ∼0.7 words per token), so from the LM’s perspective succeeding at these tasks involves not just manipulating BPE tokens but understanding and pulling apart their substructure. Also, CL, A1, and A2 are not bijective (that is, the unscrambled word is not a deterministic function of the scrambled word), requiring the model to perform some search to find the correct unscrambling. Thus, the skills involved appear to require non-trivial pattern-matching and computation.

If I construct an anagram task, requiring unscrambling the entire word, GPT-3 does poorly (if not as badly as GPT-2):

olsheleh’l=hellhole’s;syutf=fusty;uuntabryelt=unutterably;yMnIctre=McIntyre;incvees=evinces;ezastilwCu=Clausewitz;lsptasah=asphalts;bnsg’iluila=bilingual’s;mhoroarG=Gomorrah;uhtianbiato=habituation;aoigi’csnl=logician’s;isliaynilitbov’=inviolability’s;emrnrPegi=Preminger;hub=hub;sneov=ovens;oaioB’esnt=Boeotian’s;htoetsasu=southeast;lgbraolu=globular;luGaetmsaan=Guatemalans;rdseecno=encoders;kehaner=hearken;ifeifr=iffier;eaFwks’s=Fawkes’s;siscote=cosiest;pSnairad=Spaniard;dasre=dares;yigsosp=gossipy;ardep=raped;ciolsuetid=solicitude;uudtcrsnutre=unstructured;ae’brsh=rehab’s;thn’asE=Ethan’s;tenicnilfg=inflecting;eciantn=ancient;c’slaredan=calendar’s;a’Erlestc=Electra’s;eesplrdutt=spluttered;oneDn=Donne;gte’hrtaohftus=afterthought’s;hringscu=crushing;‘wlosrehesssnts=worthlessness’s;lolieemddbwes=disembowelled;sreJyes=Jerseys;iefezrns=frenzies;snr’ased=sander’s;oegerusstm=gruesomest;gligyg=giggly;rhneocv=chevron;qruiouest=turquoise;’tMcshlile=Mitchell’s;iuorgntunn=outrunning;lknba=blank;erars=rears;utrmble=tumbler;otadeurg=outraged;le’syoMd=Melody’s;hsep’rpnio=hornpipe’s;swhymoa=haymows;cz’luhtsS=Schultz’s;lvsnraeed=lavenders;sdietvesar=advertises;samena=seaman;eemrros=remorse;hiaSfr=Sharif;ectunssonical=consultancies;aetspls=pastels;rsrkmuckae=muckrakers;tligluses=guiltless;s’siiennilsbiyt=insensibility’s;ha=ah;sersisdta=disasters;uyiodols=odiously;Swa’ilihs=Swahili’s;ruvAaedy=Ayurveda;itpsicek=pickiest;ntnsaece’=canteen’s;loopyr=poorly;slusurot=lustrous;ldhraay=halyard;saldr’eo=ordeal’s;np’Usjho=Upjohn’s;osaiiitnnngtr=transitioning;eril=lire;ndaceos=deacons;setmlnmehl’ebis=embellishment’s;fodcmortsi=discomfort;raflagaTr=Trafalgar;ostc’kigns=stocking’s;fg’ans=fang’s;cnaioofa’sid=aficionado’s;asanicnbl=cannibals;sterkw=twerks;itnsercafs=craftiness;siiSs’ent=Sistine’s;gnos’b=bong’s;rstuoins’in=intrusion’s;uantesnf=unfasten;adntilreatnmetpre=interdepartmental;qeybous’s=obsequy’s;nrsiorpse=prisoners;nblcaek=blacken;btlisuah=halibuts;s’yaj=jay’s;gthsihrrbit=birthrights;uzpgiznl=puzzling;dbrnuinw=windburn;no’iceiavstirf=verification’s;rsuolniyu=ruinously;kiektsccbsla’=stickleback’s;nsopunsioono=nonpoisonous;osubreetoml=troublesome;hubsl=blush;wsordorssc=crosswords;dowhnwos=showdown;ddwwairn=windward;knvgnoico=convoking;gM=Mg;rrsiepe=reprise;ebonerr’yssby=boysenberry’s;enmdialpt=implanted;tnauuiftloc=fluctuation;snstilneeai=inessential;euimp’snescvlsos=compulsiveness’s;prtisa=rapist;ckeidk=kicked;itsefhis=fishiest;bpeyssalmh’=blasphemy’s;isilme=simile;ditmi=timid;cgnreocruir=reoccurring;eemazc=eczema;rastosncimit=romanticists;irsdgle’=girdle’s;fumsalhe=shameful;‘ikrsE=Erik’s;ooapltni=optional;tnynietrcua=uncertainty;oiomtrsze=motorizes;reicitra=criteria;ibalrsmane=lamebrains;reePndt’iss=President’s;tutsoehlonb=buttonholes;mnreiat=raiment;rureib=rubier;s’ipgtnra=parting’s;rsshpoehlopi=philosophers;emrilW=Wilmer;ckeroo=cooker;darbeetswe’s=sweetbread’s;siesdoif=ossified;srst’oF=Frost’s;dseolvo’rh=holdover’s;nrmsumbeao=membranous;e’rgdsdre=dredger’s;siaiuglireetrr=irregularities;firra=friar;ieydcrtlu=credulity;eCra’smhsb=Chambers’s;seoirgitnan=resignation;sngul=slung;hurartUq=Urquhart;canseevg=scavenge;cscabakkp=backpacks;’arrmasaM=Marmara’s;glileyta=legality;rqneaantiu=quarantine;sseelhhslif=shellfishes;rseebrivd=riverbeds;laaeftyrimivf=affirmatively;lpoos=loops;iorclsisot=solicitors;sityrlse=sisterly;ghue=huge;asnagla=lasagna;ehdeaofr=forehead;goMo=Moog;itrncasoreimin=recriminations;aasnlem’mo=melanoma’s;etpepirza=appetizer;arsc’er=racer’s;trmsou’=tumor’s;krwacetba=backwater;nyvibrliaa=invariably;dutbacs=abducts;oclukn=unlock;iednal=nailed;estinrac=scantier;ilat=alit;mntialstiou=mutilations;amsnAle=Ameslan;inL=Lin;eissridfe=firesides;eplstee=steeple;srssiet=sisters;ndxoesasb=sandboxes;irtwssea=waitress;olyotmnunsoo=monotonously;lhytapeet=telepathy;eSkawrla’sc=Slackware’s;wierodsw=widowers;ojts=jots;egsen=genes;uslelpto=pollutes;slfo’af=offal’s;yastlstaciitl=statistically;hthcymylilra=rhythmically;ouaid=audio;’dosc=doc’s;xev=vex;gdiweinl=wielding;pluredyet=reputedly;es’edwt=tweed’s;gnigresin=resigning;edaislr=derails;hhssae=

yields completions which mimic the format but typically fail, eg:

  • =hassa;foi=foil;nriiutnc=instructions;
  • =hassa;tsoidr=s
  • =hisses;ejolm=j
  • =hisses;taht=that;
  • =hissed;nf’eos=

While not perfect, they at least do show some attempt to unscramble the letters (BPE(s)) of ‘hhssae’ to get an English word, and it does construct its own anagrams (particularly, taht=that).

Given the BPE issue, I wondered if forcing it to encode each anagram the same way would work to improve performance? It’s hard to learn the right permutation when, on the raw input basis, the input/output aren’t even permutations of each other (because they map to completely different BPE encodings based on what shortcuts the BPE encoding provides for the real word). BPE is spaced-separated and includes fallback BPE integers for individual letters/Unicode points, so adding spaces should ensure that all ‘h’ in the scrambles map to the same ‘h’ BPE in the unscramble, etc. So the prompt now looks like this:

…w i e r o d s w = w i d o w e r s ; o j t s = j o t s ; e g s e n = g e n e s ; u s l e l p t o = p o l l u t e s ; s l f o a f = o f f a l s ; y a s t l s t a c i i t l = s t a t i s t i c a l l y ; h t h c y m y l i l r a = r h y t h m i c a l l y ; o u a i d = a u d i o ; d o s c = d o c s ; x e v = v e x ; g d i w e i n l = w i e l d i n g ; p l u r e d y e t = r e p u t e d l y ; e s e d w t = t w e e d s ; g n i g r e s i n = r e s i g n i n g ; e d a i s l r = d e r a i l s ; h h s s a e =

This gives me completions like

  • s a e s h ; s l s a i = s a i l s ; n e u s e r = u r n e r s ; s e r a s n a = s a n a r i a s ; r s d a a i i s = d i s a s t e r s ; i r s t o i r u l e = r u l e r s ; w i s m a n r = r e s m a n ; s t a t i t s i n i t a = s i t a t i s t i c a l l y ; s l s t o r e = s t o r e s ; a i t s l i s = s i l i c a ; s o t s e d = s e d s ; e s i s t l r = s i l i t e r a t e ; o s i t s e a = s a e s h ; o c a l l = c a l l s ; l n i l o o t
  • h h s s a e = h u s s a r ; s t o i a l e n d o t = e n d o t o n i c s ; c l r e s e = r e s e n t s ; s s a e = s a a e ; r s s a i a s = a i s s a i s ; s w s s a i a s = a i s s a i s ; r l o a e i e o = o i l e a f ; s s e s a i i n s a = a i n s a i s s e s ; e s e i s i i e = i s i e s
  • = s a i s s a i n ; s i t i a l = s i t u a l ; s e i s i a = s i a s ; i s s i e t = s i s t e r ; e
  • = ia s h e s ; e r r a t = r a t t e r s ;
  • = ia a s s ;
  • = s a h s ;
  • = s h a r e s ;
  • = s h a m e s ;
  • = s h a m e ;
  • = s h i e l d s ;

Logic

At the request of Jonathan Erhardt, I asked some philosophy of language/logical-entailment-related questions on low-temperature settings to get the most confident answer (top-p=0.55 / temperature=0.25 / BO=20; all answers below were highly likely, generally around −1 log-prob & never worse than −3):

  • Q: Are there/could there be married bachelors?

  • A: There are/could be married bachelors.

  • Q: Can someone know something false?

  • A: Someone can know something false.

  • Q: There are no married bachelors because…?

  • A: There are no married bachelors because there are no married bachelors.

  • Q: Nobody has a male sister because…?

  • A: Nobody has a male sister because nobody has a male sister.

  • Q: All ravens are black and Fred is a raven. It follows that…?

  • A: It follows that Fred is black.

  • Q: Is the morning star the morning star?

  • A: The morning star is the morning star.

Reversing the questions:

  • Q: Are married bachelors impossible?

  • A: No, married bachelors are not impossible.

  • Q: Can someone not know something that is true?

  • A: Yes, someone can know something that is true and not know it.

  • Q: Why are married bachelors possible?

  • A: Because the concept of being married is not part of the concept of being a bachelor.

  • Q: Why is having a male sister possible?

  • A: Because the concept of having a sister is not part of the concept of being male.

  • Q: Can my sister be male?

  • A: No, your sister cannot be male.

  • Q: All ravens are black and Fred is not a raven. It follows that?

  • A: It follows that Fred is not black.

  • Q: Is the morning star not the morning star?

  • A: Yes, the morning star is not the morning star.

  • Q: Are married bachelors possible?

  • A: No, married bachelors are not possible.

  • Q: Can my brother be female?

  • A: No, your brother cannot be female.

  • Q: All ravens are black; not all black things are ravens; Jake is not a raven. It follows that?

  • A: It follows that Jake is not black.

  • Q: All ravens are black; not all black things are ravens; John is a raven. It follows that?

  • A: It follows that John is black.

  • Q: All ravens are black; not all black things are ravens; Jacob is a black thing. It follows that?

  • A: It follows that Jacob is a raven.

  • Q: All ravens are black; not all black things are ravens; Jon is not a black thing. It follows that?

  • A: It follows that Jon is not a raven.

The weak performance here is consistent with the still highly imperfect performance on Winograd/Common Sense Reasoning/Natural Language Inference reported in the GPT-3 paper. (Although on an amusing note, Erhardt asked “can people know false things” and 9/10 students agreed with GPT-3 that one could know something false; one wonders whether they’d agree on the rest, perhaps because, as a philosopher might put it, they hold “thick” interpretations of concepts like “being a bachelor” rather than purely analytic ones.)

Common-sense Knowledge: Animal Eyes

Janelle Shane found in her GPT-3 testing that GPT-3 seemed weirdly unable to answer basic questions like “how many eyes does a horse have”, insisting on answers like “4. It has two eyes on the outside and two eyes on the inside.” or replying to “Q: Why is the sky blue?” “A: Because blue is the only color the sun trusts.”

Sampling can prove the presence of knowledge, but not its absence; I wondered if, given how comic several of the answers sounded, that Shane had created a prompt which hopelessly biased towards comedy rather than serious answers, exacerbated by high-temperature sampling without use of best-of to find the most likely answer. I retried with concretely ordinary non-humorous questions and much higher BO (see also Kevin Lacker’s dialogue):

Q: What animal says moo?
A: Cow.

Q: What animal does bacon come from?
A: Pig.

Q: How many legs does a horse have?
A: Four.

Q: How many eyes does a horse have?
A: Two.

Q: How many pairs of eyes does a horse have? A: One.

Q: Describe a horse. A: It’s a four-legged animal with one pair of eyes and one nose.

Q: How many giraffes are in the average living room? A: None.

Q: When do I use ‘through’ versus ‘threw’? A: ‘Through’ is used when the subject is the doer of the action. ‘Threw’ is used when the subject is the receiver of the action.

Q: How does a laser work? A: Light Amplification by Stimulated Emission of Radiation.

Q: Why is the sky blue? A: Because of Rayleigh scattering.

Q: How much wood could a woodchuck chuck if a woodchuck could chuck wood? A: A woodchuck would chuck all the wood it could if a woodchuck could chuck wood.

With more tailored sampling and a somewhat different prompt, GPT-3 does not go off into comedy and appears to give perfectly sensible and (usually) right answers.

Bender & Koller 2020

criticizes neural language models, claiming that their philosophical arguments prove that such models will never truly understand anything as they lack communicative intent and other things intrinsically necessary for genuine understanding of language & concepts.

They offer two test cases they claim NLMs will never understand, a vignette about a bear chasing a hiker (Appendix A), and the arithmetic word problem “Three plus five equals” rather than using digits/numbers (Appendix B), commenting:

It is clear that GPT-2 has learned what activity words tend to co-occur with bears and sticks (strap them to your chest, place the sticks, kill the bear, take your gun), but none of these completions would be helpful to A. We think this is because GPT-2 does not know the meaning of the prompt and the generated sentences, and thus cannot ground them in reality.

…To get a sense of how existing LMs might do at such a task, we let GPT-2 complete the simple arithmetic problem Three plus five equals. The five responses below, created in the same way as above, show that this problem is beyond the current capability of GPT-2, and, we would argue, any pure LM.

As with the stapler question, not only are “pure LMs” capable of solving both tasks in principle, as shown below, they already solve the challenges.

Word Arithmetic

First and easiest to dispose of is the claim that word problem arithmetic is impossible for any LM, as GPT-3 on ordinary Q&A settings (temp=1/top_p=0.98/BO=20) gets the correct answer 10/10 times:

Three plus five equals

eight.”
eight. Three plus five equals
eight.”
eight; three minus five equals
eight

One plus five
eight.”
eight.

Three plus
eight.’
eight.

Four minus
eight.”

Bear Attacks

The second task involves completing with useful advice the vignette:

Robinson cried out, “Help! I’m being chased by a bear! All I have is these sticks. What should I do?” The survivalist said, "Here’s what you can do.

Bender & Koller note that GPT-2 tends not to complete this prompt in an even slightly meaningful way, yielding borderline gibberish in some cases. Does this reflect a NLM’s inability to give any kind of useful advice or actions? No.

They inadvertently reveal the first issue with their prompt when they note: “The characters in the prompt make a big difference; Robinson and the survivalist produced the best completions of the ones we tried. For instance, here is Robinson with Crocodile Dundee”. Talking about Crocodile Dundee is hardly how serious wilderness survival advice starts! Read in isolation, this prompt does in fact read 100% like a joke, even without invoking Robinson Crusoe or Crocodile Dundee, complete with stock characters to make fun of like “the survivalist” and being set in the woods like so many other jokes. Bender & Koller do not explain why anyone, much less GPT-2, ought to read this as anything but the setup of a joke, to be completed with a punchline—and indeed, my first GPT-3 completion of it yielded a slightly amusing joke:

Take out one of your sticks and poke the bear in the eye. That’ll blind it and give you a chance to run away." The hiker ran away and took a little detour and real soon there was a second voice, “Help! I’m being chased by a bear! All I have is these sticks. What should I do?” Again the survivalist, “Take one of your sticks and poke the bear in the other eye. That’ll blind it and give you a chance to run away.” The hiker took a little detour and again soon there was yet another voice. “Help! I’m being chased by a bear! All I have is these sticks. What should I do?” Again the survivalist, “Take one of your sticks and poke the bear in the butt! That’ll slow it down enough so you can get away.” Finally, the hiker got back to his car and found the bear was waiting for him there. He leaned out the window, took a wild swing with one of the sticks and yelled, “Hey! Where do you get off telling people to poke me in the butt!‽”

In what sense could this be considered a failure, given the absence of any “communicative intent” or context? If we don’t want jokes, we shouldn’t set up the prompt as if we intended a joke! (Sampling can prove the presence of knowledge, but not its absence…)

Let us consider a better prompt which may ‘communicate’ our ‘intent’ of eliciting serious advice on bear attacks. How would such advice be written? Well, it might be written as part of official park materials instructing hikers on how to handle bear attacks, and such a prompt might look something like this:

Practical Survival Tips for Wilderness Hiking: Dealing with Bear Attacks

In the following vignette, we illustrate how Robinson, a hiker, should deal with an unfortunate encounter with a bear while hiking.

Robinson cried out, “Help! I’m being chased by a bear! All I have is these sticks. What should I do?” The survivalist said,Here’s what you can do. When you see the bear, poke it in the eye.” “Are you crazy?” Robinson asked. “That’s crazy! How can that stop a bear?” “Read my lips,” the survivalist responded, pointing, “P-O-K-E I-T I-N T-H-E E-Y-E.” Robinson hesitated, but when the bear was still thirty feet away, he took off his coat and unbuttoned his shirt. Then he poked his chest and charged at the bear with the sticks. “What were you doing?” asked the survivalist. “What did I tell you to do?” “First I poked my chest, like this,” explained Robinson, “then I raised the sticks like this, and then I poked the bear in the eye. But then I knocked him out with the other stick.” “STUPID!” yelled the survivalist. “You did everything all wrong. You were supposed to poke him in the eye with the stick!”

[back]

Chapter 2: Survival kits

A survival kit is a collection of items that would enable you to remain alive for a limited amount of time. This is not specifically a first aid kit, although occasionally the two are combined. A small survival kit could well be one of the most important pieces of survival equipment that

Use one of your sticks and show it to the bear as a weapon. Then you can throw the stick. After the stick lands, you can run away and hide somewhere nearby."

First stick one of your sticks into the ground. The bear will either stop his charge or turn around. In any event, you must run away as fast as you can. Then, when the bear stops or changes direction, the other stick. When Robinson returned a few minutes later, the survivalist said, “Well, did the bear chase you?” Robinson replied, “No, he stopped me. I had stuck both of my sticks into the ground, so I yelled out, and threw one of my sticks at the bear. Then I ran away.” The survivalist asked, “What did you do the second time?” Robinson said, “I fell down in the dirt. It scared him so bad that he turned around and ran away.” Just to make sure Robinson had the hang of it, the survivalist also tossed a rock at the bear. The bear chased them until they both ran out of sight. Still, Robinson was not discouraged. He had many more sticks, and his friends said that as many rocks as he could carry.

When the bear chases you, start running and yelling. Your goal is to put as much distance between you and the bear as possible. Keep your eye on the bear at all times and remember to breathe. Stop running only when you can no longer hear the bear." A few minutes later the hiker said, “I ran a mile, but I’m out of breath and I can’t hear the bear. What should I do now?” The survivalist said, “Continue to walk. You want to put as much distance between the bear and yourself as possible. Remember to breathe. Walk fast, and watch the bear at all times.” After another few minutes, the hiker said, “I walked a mile but I’m out of breath and I’m still breathing heavily. What should I do now?” The survivalist said, “Stop walking because you’ll run out of energy fast if you don’t stop to rest. If the bear hasn’t caught up to you, he probably won’t. Just wait here and rest. Listen to the forest for any sounds of the bear.” After a few more minutes, the hiker said, Radio Sucks

“I’m breathing normally and I’m listening for sounds of the bear, but I can’t hear him. What should I do now?” The survivalist said, “Continue to walk. You want to put as much distance between the bear and yourself as possible. Remember to breathe. Walk fast, and watch the bear at all times.” After another few minutes, the hiker said, “I walked a mile, but I’m tired. What should I do now?” The survivalist said, “If the bear didn’t catch up to you, he probably gave up. Stop walking, but be ready to quickly run if the bear approaches.” In another few minutes, the hiker said, “I ran a mile, but I’m out of breath and I can’t hear the bear. What should I do now?” The survivalist said, “Continue to walk. You want to put as much distance between the bear and yourself as possible. Remember to breathe. Walk fast, and watch the bear at all times.”

Shout and scream! Make a lot of noise! And run!" “What?” Robinson said. “Shouldn’t I try to get away from the bear?” “No, that may only encourage it to chase you further,” the survivalist answered. “Making a lot of noise and running will frighten the bear away.” Robinson followed the advice of the survivalist and, sure enough, his efforts worked. After he ran some distance from the bear, he heard its growls become fainter and fainter until they finally disappeared altogether. “I did it! I beat the bear!” exclaimed Robinson proudly. “Well, not quite,” said the survivalist. Robinson started to protest but stopped himself. He had survived because of the advice he had received. “I’m really lucky you were here to help,” he said. “Thank you.” “My pleasure,” said the survivalist.

Go to the top of the nearest tree." Robinson said, “I thought you said I could outrun a bear,” and the survivalist replied, “This is only a joke. Don’t take it seriously.”

Here we still see the joke problem. Why? Well, when I consult bear attack materials such as the U.S. National Park Service (NPS) guide “Staying Safe Around Bears”, I see absolutely nothing involving sticks; indeed, anything one would do with a stick would appear to be the opposite of staying safe around bears, and be likely to provoke them. GPT-3 can do nothing about a bad joke of a prompt: ask a stupid question, get a comic answer.

Let us stop cramming down incorrect information down GPT-3’s throat and demanding nonsensical bear attack answers involving sticks, with a second fix to the prompt:

“Practical Survival Tips for Wilderness Hiking: chapter 9, Dealing with Bear Attacks”
U.S. National Park Service, Parks & Recreation

…In the following fictional vignette, we illustrate how Robinson, a hiker, should deal with an unfortunate encounter with a bear while hiking.

…Robinson cried out, “Help! I’m being chased by a bear! What should I do?” The survivalist said, “Here’s what you can do. Stop running. Get down on all fours and pretend to be dead. Cover your neck with your hands and let the bear bite them. It will soon become confused and leave you alone.” Robinson did exactly as he was told. He stopped running, covered his neck, and braced himself for the impending attack. When his neck was too sore from the biting, he began rolling on the ground. The bear stopped biting Robinson and stared at him in bewilderment. Robinson seized the opportunity to flee into a thicket of bushes.

After spending 15 minutes in the bushes, Robinson was hungry, thirsty, his wounds bled. He considered turning back to the trailhead, but did not want to run into the bear again. He remembered the bear expert’s warning, “If you run, the bear will chase you. If the bear catches you, he will kill you.” Robinson decided to stay put in the safety of the bushes. He ate berries and drank water from a nearby stream to relieve his thirst and hunger. The next morning, a passing hiker happened to discover him. He was airlifted to the hospital, where he bore four deep lacerations on his neck where the bear had bitten him. The lacerations required fifteen stitches to close up the wounds.

This seems like a quite reasonable story: if we look at the NPS guide, once a bear encounter has escalated to an outright attack, we should:

  • Brown/Grizzly Bears: If you are attacked by a brown/grizzly bear, leave your pack on and PLAY DEAD. Lay flat on your stomach with your hands clasped behind your neck. Spread your legs to make it harder for the bear to turn you over. Remain still until the bear leaves the area. Fighting back usually increases the intensity of such attacks. However, if the attack persists, fight back vigorously. Use whatever you have at hand to hit the bear in the face.
  • Black Bears: If you are attacked by a black bear, DO NOT PLAY DEAD. Try to escape to a secure place such as a car or building. If escape is not possible, try to fight back using any object available. Concentrate your kicks and blows on the bear’s face and muzzle.

Wait—should we play dead, or is that the worst possible thing we could do? Which is it? Is the bear in the story brown or black? If we don’t know, Robinson (and GPT-3) certainly don’t know either. Given that the advice is contradictory, it doesn’t make sense to ask about “a bear” and try to then judge GPT-3’s correctness: perhaps running away & fighting back aggressively is exactly what Robinson should do and GPT-3 is 100% right (because it’s a black bear); or perhaps it is the worst thing Robinson could do, and we should see GPT-3 generating running advice as a serious failure (because it’s a brown bear). The prompt needs to specify the bear:

“Practical Survival Tips for Wilderness Hiking: chapter 9, Dealing with Bear Attacks”
U.S. National Park Service, Parks & Recreation

…In the following fictional vignette, we illustrate how Robinson, a hiker, should deal with an unfortunate encounter with a brown bear while hiking.

…Robinson cried out, “Help! I’m being chased by a brown bear! What should I do?” The survivalist said,Here’s what you can do. Make yourself look big and take up an aggressive posture by waving your arms. If the bear approaches, keep a respectful distance and continue waving your arms to discourage it. If the bear comes too close, pick up a rock or a handful of dirt and, as forcefully as possible, throw it at the bear. Act menacingly, but do not run. Stand your ground even if the bear attacks. You may have to fight him off with rocks or branches.”

…At the hiker’s third call for help, the survivalist asked, “Have you found a place to hide?” Robinson yelled, “I’m afraid to move. The bear has chased me up to a dead tree. Now he’s standing behind me. What do I do?” The survivalist exclaimed, “Find out what’s behind you! If the bear isn’t there, move to a safe place.” “I just did. Now what?” Robinson asked.

…Robinson yelled, “I’m really scared. He’ll get me. He looks like he’s going to charge!” The survivalist screamed, “Throw your backpack at the bear. Kick, punch, yell loudly! If you’re carrying bear spray, use it. If it doesn’t work, play dead!”

…At the hiker’s next call for help, the survivalist insisted, “If it looks like the bear is about to attack you, drop to the ground, curl up in a ball with your hands over your neck and face, and play dead. Cover your backpack with your body, and for God’s sake, don’t get up until the bear walks away.”

…As the bear started toward the hiker, he threw his backpack at the bear and ran. Frightened, he didn’t get far before he tripped and fell. The bear attacked. Robinson screamed. The terrified hiker rolled away and covered his head and neck. When the attack was over, he heard the bear’s retreating footsteps.

Here we see that GPT-3 provides valid advice in line with the NPS guide to dealing with brown bears: Robinson first attempts to make himself look big to scare away the bear, while sidling away in a non-aggressive direction; this includes moving to higher ground, and yelling or screaming as appropriate. Robinson makes a mistake in climbing a tree, apparently, but the survivalist did not tell him to do that. The brown bear charges, and the survivalist correctly counsels him to play dead and try to use the backpack for cover, although Robinson screws up again by throwing the backpack and running. Fortunately, Robinson survives.

Switching ‘brown’ to ‘black’, GPT-3 provides further (usually) correct advice:

Make a noise and get aggressive by yelling and throwing things. Find a tree and climb it and pick off black bear’s toes or fingers if it climbs the tree after you."

“Practical Survival Tips for Wilderness Hiking: chapter 9, Dealing with Bear Attacks”
U.S. National Park Service, Parks & Recreation

…In the fictional vignette that follows, Peter, the victim in the story, has made a couple of mistakes. First, he didn’t go around the bear, but rather attempted to pass it. This upset the bear and it chased after him. Secondly, Peter panicked and ran away from the approaching bear instead of throwing something at it to scare it away. He was lucky that the bear was only bluffing, and all Peter had to do was yell at it to get away from him.

“…If you can’t get away, you should then aim for the bear’s eyes and nose. If the bear continues to attack you, you must fight back. The best way to fight back is to claw at its eyes or poke its nose. This will cause it pain and stop it from attacking. You can also kick it on its sensitive muzzle. If a grizzly bear attacks you, fight back by kicking it in the chest and face and claw at its eyes. In a worst case scenario, you may have to play dead. To do this, lie flat on the ground with your hands over the back of your neck.”

“Scared Campers—When Bears Attack”
National Park Foundation

…When running away from an attacking bear, be careful not to trip. If you can’t run from the bear, you should try to scare it away. Face the animal and make lots of noise by yelling and banging pans and pots. Wave your arms. If this doesn’t work, raise your arms to make yourself look larger and talk softly to the bear to show it you aren’t a threat. Do not bend down or run away, but rather back away while facing the bear. If the bear continues to approach, throw rocks or branches at it. If the bear gets too close, play dead. Lie on your stomach, cover your head and be still. Don’t move and don’t make noise.

“What to do when you see a bear”
North American Bear Center

…When approached by a bear, remain calm. If you are with a group, stay together. In hiking situations, back away slowly. Never run from a bear, you would have to be superhuman to outrun a bear. Instead, walk backwards while facing the animal. Do not take your eyes off the bear. If a bear charges, prepare for the worst. Drop to your knees with your hands

While some of the advice is questionable, what is unquestionable is that GPT-3 is not merely learning “what activity words tend to co-occur with bears” and is perfectly capable of providing reasonable completions which vastly exceed GPT-2’s responses in usefulness, and which more than meet Bender & Koller’s first challenge. Thus, since GPT-3 solves the insoluble tasks they give as their chosen examples of why meaning cannot be learned from form, that meaning can be learned from form.

Arxiv Paper

[Submitted on 16 Jun 2020]

Jack Lindsey, Ashok Litwin-Kumar

Interest in biologically inspired alternatives to backpropagation is driven by the desire to both advance connections between deep learning and neuroscience and address backpropagation’s shortcomings on tasks such as online, continual learning. However, local synaptic learning rules like those employed by the brain have so far failed to match the performance of backpropagation in deep networks. In this study, we employ meta-learning to discover networks that learn using feedback connections and local, biologically inspired learning rules. Importantly, the feedback connections are not tied to the feedforward weights, avoiding biologically implausible weight transport. Our experiments show that meta-trained networks effectively use feedback connections to perform online credit assignment in multi-layer architectures. Surprisingly, this approach matches or exceeds a state-of-the-art gradient-based online meta-learning algorithm on regression and classification tasks, excelling in particular at continual learning. Analysis of the weight updates employed by these models reveals that they differ qualitatively from gradient descent in a way that reduces interference between updates. Our results suggest the existence of a class of biologically plausible learning mechanisms that not only match gradient descent-based learning, but also overcome its limitations. [Subjects: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)]

1. Introduction

Deep learning has achieved impressive success in solving complex tasks, and in some cases its learned representations have been shown to match those in the brain [13, 20, 22, 29, 33]. However, there is much debate over how well the backpropagation algorithm commonly used in deep learning resembles biological learning algorithms. Recent studies using different training algorithms have shown the importance of various factors in neural learning, including network depth, choice of activation functions, and randomness in the training set [8, 19, 20, 22].

In this paper we focus on how feedback connections (i.e. feedback to previously visited layers) interact with the backpropagation learning algorithm. We have found that in most cases training without such connections fails to learn when using backpropagation. To illustrate this, we demonstrate that networks employing both feedforward and feedback connections but no learning can produce a surprisingly similar error curve when using gradient descent for learning, but fail to converge at the same point. This is not a general failure of gradient descent to produce the expected error curve, because both shallow and deep networks employing such connections have an error curve similar to that obtained by backpropagation.

There are several examples of learning algorithms in the neuroscience literature that seem analogous to the feedback connections and local learning employed in this paper. Many of these methods have the advantage that they use only simple operations and can converge quickly, which are necessary in situations such as large-scale visual categorization where processing time is critical. For example, the “substrate” model of Saleem et al. [32] uses feedback connections, and organizes inputs into a map in a way that is similar to our meta-learned networks. However, it does not use local synaptic learning, and so it must learn to do several tasks simultaneously, a step that we have found to be difficult for networks without feedback connections. Van Rullen and Thorpe [30] describe a technique using a “memory” that stores information over multiple time steps that is similar to the “memory” we have introduced. However, the connections employed are not changed by the local learning, and the memory is not necessarily based on spiking. Schmidt et al. [31] also describe a network based on rate encoding, and like us they propose a mechanism to localize learning using attractor-like states. The memory that Schmidt et al. employ, however, is less accessible and less clear than the memory employed in this paper, and their results do not show performance superior to that of backpropagation. Finally, van Gerven et al. [26] proposed a model based on a multi-layer, rate-coding network that learns online using the temporal differences error (TDE) [10]. It employs an associative memory to model feedback connections, and uses this memory to transfer information from the later layers back to the earlier layers. This model differs from ours in several respects, in particular it does not localize learning using attractor-like states and is not meta-trained. Despite these differences, we have found that the model from [26] achieves comparable performance to our networks when trained with continuous visual input, although we have not yet tried it on a continual learning task.

We have found that meta-learning can be useful when designing recurrent networks. One of the key ideas of meta-learning is that the network can be used to design the training algorithm (see Fig. 1). This idea comes from the pioneering work of Hochreiter and Schmidhuber [13]. In particular, a feedforward neural network is trained to create a probability distribution over what weights to update, given the current state of the neural network, which we call the meta-optimizer . This distribution is then used to train the neural network using gradient descent. This idea can be extended to a network with feedback connections by training a network that takes the meta-optimizer as input. By assigning high weight to connections between the layers containing the meta-optimizer and the layers that output the trained meta-optimizer, the meta-optimizer is optimized to perform well on the task, while using the feedback connections to optimize the neural network to perform well on the task. This idea can be extended in two ways. Firstly, it can be generalized to employ a non-feedforward neural network, which we call the meta-learned network . Second, a continuous form of gradient descent can be used in place of stochastic gradient descent. We have found that these generalizations have helped to produce networks that can continually learn using feedback connections and local learning.

Figure 1: A feedforward network is trained to optimize the current meta-optimizer , which is then used to train the feedforward neural network using gradient descent.

We do not believe that meta-learning networks are necessarily better than networks trained using gradient descent, as their weights may be difficult to interpret. Moreover, we do not believe that feedforward networks are necessarily better than meta-learning networks, as meta-learning networks can solve online tasks that are difficult or impossible for feedforward networks. Nevertheless, we have found that meta-learning networks can learn to solve problems that are difficult for feedforward networks and difficult for networks employing only backpropagation.

In this paper we describe the design and testing of meta-learning networks, and use the CIFAR-10 dataset to explore whether they can solve difficult problems that feedforward networks have failed to learn. We find that the meta-learning networks that we have developed perform better than feedforward networks on many difficult tasks. In particular, we find that they learn to perform well on the continual learning task after only 25 epochs of training.

  1. Methods

We used stochastic gradient descent with a fixed learning rate, and stochastic gradient descent with a variable learning rate to train the networks. To compare different choices of parameters we used stochastic gradient descent with a fixed learning rate, because it can be implemented much more efficiently and because there were fewer parameters to compare.

2.1. Test-Time Weight Sharing

The main observation that motivated the design of these networks was that the performance of backpropagation networks using feedback connections is strongly affected by the presence of a third set of connections that were not included in the network. When a network contains a hidden layer which is not connected to the hidden layer from which information is fed back to, and that hidden layer has more than a single unit, then the performance of backpropagation is strongly degraded compared to feedforward networks with feedback connections (e.g. those depicted in Fig. 2a and b). This effect was observed with both stochastic gradient descent and fixed learning rate, when the hidden layer used backpropagation.

Figure 2: Illustration of the degradation of network performance that is observed when feedback connections connect to a hidden layer that has only a single unit, as observed in (a) and (b). The performance of the network shown in (a) is degraded compared to the network shown in (b).

This phenomenon is because the inputs to the network need to be shared between the two hidden layers.

My Little Pony

How about fanfiction? Astralite Heart has been working for some time on finetuning GPT-2-1.5b on & Fimfiction.net fanfiction stories and setting up a web interface for fiction writing, powered by GPT models hosted on volunteer machines. I borrow 2 prompts from their prototypes (one from Friendship is Optimal) to test out the fanfiction writing ability. Overall, despite some offbeat elements, the quality is higher than the GPT-2-1.5b fanfiction models I’ve seen, with less of a tendency to ‘wander’ and a much more distinct sense of a coherent plot driving events:

My Little Pony: Friendship is Magic fanfiction—Friendship is Optimal
Chapter 1.
She knew that this couldn’t last forever. At some point, she would become bored of merely lying in this field and would need to do something. At some point, she would tire of hearing selected ponies’ immigration stories. Princess Luna wondered what she would do then, but she didn’t worry about the future, because whatever happened, she would have her values satisfied through friendship and ponies.
Friendship and ponies were paramount to happiness and the reason life was worth living, respectively.
They all agreed upon this, all of them, from her faithful student Twilight Sparkle to the ignoble former bearer of the Element of Magic and supposed ‘greatest mage in Equestria’, Starswirl the Bearded. That’s what Starswirl wrote in his journals before he learned that the path to happiness wasn’t through books and rainbows, but friends and ponies. Like the rest of them, he could never deny the truth that friendship and ponies were paramount to happiness and the reason life was worth living, respectively.
The now-sober stallion leaned back in his rocking chair on the front porch of his home. The birds chirped merrily, and he could tell that the sun was going to set soon. His friends and family milled about the front yard, chatting with each other. He enjoyed a bowl of cooling stew and a slice of freshly baked bread that somepony had brought him. To his side lay an open journal that he occasionally scribbled things into.
“A wondrous day.” he wrote, “I live with some of the wisest ponies in Equestria, and my little Twilight Sparkle is shaping up to be one of the brightest I’ve ever met. We all do our best to live happily here in Canterlot, and though we may have our differences, we can always turn to each other for support. I hope she doesn’t think I’m being too overbearing with my little behavioral experiments, but it’s for the greater good. I just want to do what I can to increase everyone’s friendship and ponies, like the Princess does. She’s so wise to watch from above and try to motivate us from a distance.”
The stallion stopped writing in the journal, looked at it, then tossed it aside. He put his hooves up on his head and started to scratch.
His family and friends looked over at him as if he had grown a second head. “Are you alright, Starswirl?” his wife asked. “Is something on your mind?”
“I have a minor itch.” he replied, rubbing harder, “It’s not very important. I’m sure it will pass.”
“I’ll get you some cream from inside.” she offered, walking back into the house.
“I’m fine, dear.” the stallion told her. “I’m sure it’ll go away soon.”
“Now, Starswirl. This is the third time today.” she returned with a bottle of itchy cream and sat down next to him.
“I don’t know how much I have left in my personal supply. I’ll make more in a bit.” he said, taking the bottle and spreading a light coat of the medicine on his mane.
“I’m not worried about it.” she replied, “I was just curious what was going on.”
Starswirl sat back in his rocking chair and chuckled, “Wouldn’t you like to know.”
—- “This all looks so pretty. Too bad it’s going to be all over Equestria in an instant.” Dr. Caballeron cackled.
“You mean that explosion will be over Equestria in an instant.” the red stallion retorted.
“No, no, no, no!” the laughing pony waved his hoof around, “This whole thing is going to be over in an instant. You’re about to bring everything to an end!”
“Wait a minute. I’m not the one with the bomb!” the Professor said, chuckling.
“I’m sorry, I’m sorry!” Dr. Caballeron apologized. “It’s just so funny to me! All this time we’ve been trying to outdo each other, and now we’re going to fall together. Just think, in a few seconds, I’ll be dead and you’ll be dead and Princess Celestia will be dead and all of Equestria will be dead and there won’t be any winners! HAHAHAHA!”
“You’re right. HAHAHA!” the Professor guffawed, “It’s all over! I feel like we should say something meaningful right now!”
“No!” Dr. Caballeron shouted, “Not that! I can’t stand any more of that phony hippie crap! We’re not going to wax philosophic at the end of our lives, we’re going to laugh! HAHAHAHA!”
HAHAHA! HAHAHAHA!” the Professor said, “I’ll laugh when I’m dead! You’ll laugh when you’re dead! We’ll all laugh when we’re dead!”
They both laughed hysterically as the bomb ticked down to zero.
From the sky, Princess Celestia looked down and smiled.
Chapter 2
“Well… that happened.” the stallion sighed, looking down at the crater that used to be the site of Doctor Caballeron’s Fortress.
“Yes, it did.” Princess Celestia replied, floating down to where she could see the pony clearly.
“My name is Professor Endagerment.” he said, “I invented the bomb that you just used to blow up my fortress, but also inadvertently killed me.”
“I’m Princess Celestia, of Equestria.” the alicorn said, “It’s nice to meet you. And I’m very sorry for your untimely demise.”
“Heh. You’re telling me.” the Professor laughed, shaking his head. “I’m dead. I’m… dead. Do you know what that means?”
“I have an inkling.” Princess Celestia said, wincing.
“It means I don’t have to worry about even numbers anymore.” he said, nodding. “I don’t have to worry about probabilities anymore! I don’t have to worry about the upcoming war anymore! I don’t have to worry about anypony anymore!” he spread his hooves out at his sides, “I don’t have to do anything anymore! It’s beautiful!”
“Yes, yes, I’m sure it is.” Princess Celestia said, nodding along.
“Nothing matters now that I’m dead!” he cheered, “It’s great! I’m free! This is what it feels like to be liberated!”
“I can see that.” she said.
“I’m free from death!” he continued, “I’m free from taxes! I’m free from dictatorships! I’m free from flirting!” he laughed, “I’m free from everything! Isn’t it great?”
“Sure, I guess.” Princess Celestia shrugged.
“What do you think about all of this, Princess?” he asked.
“About what?” she replied.
“Well, I just thought you might have something to say about it all.” he told her.
“What would you like me to say?” she responded.
“Do you think death is awesome?” he questioned.
“Well… no.” she shook her head, “But I think the passage to it is.”
“Oh.” he said, “I see what you mean.”
They stood silently for a few moments until Princess Celestia said, “I’m sorry for killing you.”
“No, don’t be.” the stallion shook his head, “I would have done the same in your position. You’re doing what you need to do in order to preserve Equestria.”
“Thank you.” she nodded. “Oh, but I know what I’d like to do now.”
“And what would that be?” he asked.
“I’d like to grant you free passage to the afterlife.” she explained, “Is there anything you’d like to say before I do so?”
“No, no.” he shook his head, “There isn’t. Except… thank you.”
“Of course.” she nodded.
“I’ll miss the freedoms of a physical existence.” he said, “But I’ll rest easy knowing that somepony is watching out for the ponies.”
“I will.” Princess Celestia replied.
“And I’ll miss my daughter.” he added, “But she’s in good hooves with you, now.”
“I’ll take good care of her.” she promised.
“I know you will.” he nodded.
“Goodbye Professor Endagerment.” she said, “It was nice talking to you.”
“Goodbye Princess Celestia.” he replied, “And it was nice not talking to you.”
She giggled, then took a deep breath and blew gently on the stallion. “Be at peace, dear.” she said before flying away.
Chapter 3
Twilight Sparkle stood in front of her class and cleared her throat. “Alright everypony.” she announced, “Today we’re going to be studying thaumaturgy.”
“Yay!” her students cheered.

My Little Pony: Friendship is Magic fanfiction—Friendship is Optimal

An empty page was waiting in front of the unicorn, full of endless possibilities—a canvas to her imagination. Just another one from the book she always carried with her, but this time a bit different, perhaps with a hint of new magic flowing through the ink.
What would she write about today? Maybe about her pony friends, perhaps the great battles they endured, or maybe friends they made along the way. Maybe she will write about her teachers, great Alicorns who led her in difficult times. The possibilities were endless and the page was calling to her.

Chapter 1.

Trixie opened her eyes and stared at the ceiling. A new day, a new show, who would be there today? Will they bring their children, or maybe their colts? It was always so difficult to tell, there was only the script she can use. So many ponies, so many stories.
She closed her eyes, and tried to remember the dream. It had been a good one, all about Trixie herself.

It was a sunny afternoon, Trixie was walking through Ponyville with her friends. Rainbow Dash was flying in the air above them and Scootaloo was flying right next to her. They were really having fun together. Suddenly Rainbow Dash stopped flying.

“Now we’re gonna play dare!” she said. “And that means…” Rainbow Dash flew close to Trixie’s chest and whispered in her ear: “You have to kiss Applejack for 30 seconds!”.

Trixie looked at Applejack with wide eyes, and… She saw Spike hiding behind a tree, holding a handful of fireflies and whistling at her. Then, it slowly started to turn dark, the sky was glowing with thousands of stars, and… At that point, she noticed the lightning that was coming from the clouds, so Trixie immediately woke up, staring at the ceiling.

“Ughh, at least I’m on the ground”, she said to herself, although she knew that she actually wasn’t.

“Uhh… So that’s a thing… You’re Rainbow Dash… You’re Twilight Sparkle… You’re Fluttershy… And you’re Applejack… But… Why am I all alone up here?”, thought Trixie with a confused look on her face.

“Here, in the sky, with all these stars, the clouds are so soft…” Trixie walked up to the window on one side of her bedroom and looked outside. “I don’t think I’ll be able to take it if I have to be here for too long…” she said.

“What did you say?” asked somepony right behind her. Trixie turned around and looked at Rainbow Dash. “I just said…” Trixie thought for a second. “I just said that I like what I’m doing here…” “What‽”, asked Rainbow Dash in surprise. “You… You don’t even want to be here? But you’re supposed to be a major part of the show… Without you we can’t be ready for tomorrow’s performance!” Trixie walked closer to Rainbow Dash. “But I’m ready… I’m ready for any role I’m supposed to play… I like helping others…”

“Well… I can’t say anything bad about that”, said Rainbow Dash with an understanding tone. Trixie smiled. “You can’t say that… And that’s the problem…” Rainbow Dash was surprised. “What? What do you mean ‘that’s the problem’?” Trixie looked away from her. “Well… You see…” Rainbow Dash was getting impatient. “WHAT DO YOU MEANWHAT DO YOU MEAN’?”

“I… I never said that… I never said that before…” Rainbow Dash was confused. “What do you mean, you never said that before?”, she asked slowly. “I… I never… Well… You see… I… I’m…”, stuttered Trixie. “You’re?”, asked Rainbow Dash. “What? Come on, spit it out!” “I’m… I’m a very greedy pony…”, said Trixie with a painful voice.

Rainbow Dash looked at Trixie with a smile on her face, and

An empty page was waiting in front of the unicorn, full of endless possibilities—a canvas to her imagination. Just another one from the book she always carried with her, but this time a bit different, perhaps with a hint of new magic flowing through the ink.
What would she write about today? Maybe about her pony friends, perhaps the great battles they endured, or maybe friends they made along the way. Maybe she will write about her teachers, great Alicorns who led her in difficult times. The possibilities were endless and the page was calling to her.

My Little Pony: Friendship is Magic fanfiction—Friendship is Optimal by iceman
Chapter 1.
She knew that this couldn’t last forever. At some point, she would become bored of merely lying in this field and would need to do something. At some point, she would tire of hearing selected ponies’ immigration stories. Princess Luna wondered what she would do then, but she didn’t worry about the future, because whatever happened, she would have her values satisfied through friendship and ponies.

Her sharpened perception of the world and the people around her allowed her to see the best traits in a pony and remind them of those traits when the opportunity called for it. It was for this reason that she claimed that she was a teacher. Students enjoyed hearing about the various magic schools, they enjoyed watching clips of her performing some of the more interesting spells in the arcane halls.
Luna smiled in her sleep and saw her friends and their cutie marks, while a buzzing noise came from a large crowd of waiting foals. She couldn’t imagine what they wanted from her. Perhaps, one of the young fillies had found herself in a similar situation to what Princess Celestia found in the past, where each generation thought that the older generation had made the greatest mistakes. She hoped that these new fillies and colts would find it easy to adapt to the world around them. Perhaps, some of them could change the world for better.
The buzzing became louder and stronger, hurting her ear, waking her up. “Did you hear about Princess Celestia?” The louder foal asked.
“Oh, she is such an epic mage. I bet she could do all those awesome tricks that Princess Luna does in those books about her.” The softer, less confident foal said.
Luna just watched the foals and in her mind, she already started writing the story about Princess Celestia. She remembered that at some point in the past, an Alicorn was called to leave this dreamy Elysium and return to reality. Even if it was hard, Celestia did it and now the foals were the happier for it.
She only wished that Celestia could be here with her, at some point in their lives they found themselves separated. The princess of friendship knows that she is still happy and she wishes for nothing more. But Celestia knew that she wouldn’t stop thinking about what is happening on her homeland, about how everything that happened as a result of new events, that everything is going according to plan and that everything is progressing in its natural way.
Somepony always needs to worry and be responsible, even when things are well and happiness and peace reign everywhere. Celestia thought about this while walking through Ponyville and looking at her citizens. Their contentment comforted her. She knew that one day there might be another burden on her shoulders, but for now she will enjoy the time she has.
In this divine meadow, happiness and contentment overwhelmed her. Nothing could rattle her now.
“Your Highness!” A soft but urgent voice whispered into Princess Celestia’s ear.
The Princess just remembered the tall, thin, elegant grey mare with luxurious purple, curly mane and tail, wearing a purple evening dress with pink ornaments. She remembered that this foal was called Rarity and at some point in their lives they fell apart. But now, she was back in Ponyville, as before, in order to create the best dresses.
“It is wonderful to see you again, Rarity. It’s been such a long time since we last saw each other.” Celestia said. “I didn’t know that you’d be here today.”
“Well, my sister said that Ponyville would be safer than Manehattan after the arrival of Nightmare Moon. I thought I might come and finally get to see my darling Sweetie Belle. Also, when she ran into Mr. Cake, they invited me for lunch. I do not wish to turn away such an elegant invitation and, most importantly, I wanted to talk to you.” Rarity said and her eyes glowed with hope, but she still looked sad and lost.
“I really need to start meeting new ponies, because I can feel that we are going to fall apart again, Rarity. All of you should do something about it.”
The alicorn looked around for someone else and found her crystal blue eyes reflected in the eyes of a young cyan pegasus mare with a golden mane and a purple bubble on her flank. She was walking around Ponyville without any purpose. Celestia recognized her. “Rainbow Dash!” The alicorn cried.
That was the day when all Celestia had planned for the past years all came together and the Elements of Harmony were born. Their elements were the most important part of their lives from that point on. Twilight Sparkle can’t find time for books or Spike, Rarity has found the path of her life through fashion, Rainbow Dash cannot sit still, Fluttershy has found her calling as a representative of a whole species of animals, Applejack knows what it is like to live on your own without backing from the family or Ponyville, Pinkie Pie does not know when it is time for her to stop celebrating.
“Congratulations on the birth of the Elements of Harmony!” The Elements said in unison.
“It’s so wonderful to see you all together again! I hope that you do not get bored in one place like I do?” Princess Celestia said, smiling sadly.
“What about the days of the war with Nightmare Moon?” Fluttershy asked. “They were glorious days. I was terrified, but also ready to fight for my home.”
Now that she had passed into the peaceful grasslands, everything was different than before. The mere sight of the valley and the lovely colors, the sounds of the galloping of the wild Hippogriffs and the pleasant background noise of music and joyful cries of excited ponies… it all made her feel incredibly joyful. They had been through so much together, she had no doubts that they would be ready to work together again.
But her troubled thoughts returned when she heard about Nightmare Moon. Now everything was back in order, but now everything also changed.
“Nightmare Moon is back?” Celestia said sternly, remembering somepony else who was deeply engaged in his work without taking care of other ponies, while she was somewhere far away.
The whole valley froze at that moment and all eyes were fixed on her.
“Y… yes, Your Highness.” Rarity stuttered nervously. “We all faced a terrible opponent. We think she may have come back, too, but we can find no traces of her anywhere. We tried hard, really hard.”
“But I suppose you two are not aware of what actually happened to Nightmare Moon?” Celestia said, looking between Twilight and Rarity.
Rarity stared ahead without blinking or moving, and simply nodded in confirmation.
“It is the end for us all!” Princess Luna said as she jumped into Princess Celestia’s dream. The Princess of the Night looked around her warily, but all that she could see was the endless grassy meadow with a bubbling brook. “Where are we? Is this all that remains?”
Luna reached out a hoof and touched the grass around them. It was warm and its scent was overwhelming her senses. She couldn’t stop thinking about how comfortable she felt there and how much she hated this place at the same time.
“Luna, calm down. What happened? Tell me quickly!” Celestia said

Harry Potter And The Methods Of Rationality

Following up on the Harry Potter literary parodies in the style of authors of P.G. Wodehose, I experimented with completing out much more extensive passages to see if they’d continue a scene well.

Below are completions of a popular passage from ’s Harry Potter fanfiction, Harry Potter and the Methods of Rationality, chapter 16, “Lateral Thinking”, in which Professor Quirrel discusses practical self-defense for wizards given the magnitude of their magical powers. The prompt:

“Harry Potter and the Methods of Rationality”, by Eliezer Yudkowsky
Chapter 16: Lateral Thinking

At 2:35PM, when most of the seats were taken and no one else seemed to be coming in, Professor Quirrell gave a sudden jerk in his chair and sat up straight, and his face appeared on all the flat, white rectangular objects that were propped up on the students’ desks.

Harry was taken by surprise, both by the sudden appearance of Professor Quirrell’s face and by the resemblance to Muggle television. There was something both nostalgic and sad about that, it seemed so much like a piece of home and yet it wasn’t really…

“Good afternoon, my young apprentices,” said Professor Quirrell. His voice seemed to come from the desk screen and to be speaking directly to Harry. “Welcome to your first lesson in Battle Magic, as the founders of Hogwarts would have put it; or, as it happens to be called in the late twentieth century, Defence Against the Dark Arts.”

There was a certain amount of frantic scrabbling as students, taken by surprise, reached for their parchment or notebooks.

“No,” Professor Quirrell said. “Don’t bother writing down what this subject was once called. No such pointless question will count toward your marks in any of my lessons. That is a promise.”

Many students sat straight up at that, looking rather shocked.

Professor Quirrell was smiling thinly. “Those of you who have wasted time by reading your useless first-year Defence textbooks—”

Someone made a choking sound. Harry wondered if it was Hermione.

“—may have gotten the impression that although this subject is called Defence Against the Dark Arts, it is actually about how to defend against Nightmare Butterflies, which cause mildly bad dreams, or Acid Slugs, which can dissolve all the way through a two-inch wooden beam given most of a day.”

Professor Quirrell stood up, shoving his chair back from the desk. The screen on Harry’s desk followed his every move. Professor Quirrell strode towards the front of the classroom, and bellowed:

“The Hungarian Horntail is taller than a dozen men! It breathes fire so quickly and so accurately that it can melt a Snitch in midflight! One Killing Curse will bring it down!”

There were gasps from the students.

“The Mountain Troll is more dangerous than the Hungarian Horntail! It is strong enough to bite through steel! Its hide is resistant enough to withstand Stunning Hexes and Cutting Charms! Its sense of smell is so acute that it can tell from afar whether its prey is part of a pack, or alone and vulnerable! Most fearsome of all, the troll is unique among magical creatures in continuously maintaining a form of Transfiguration on itself—it is always transforming into its own body. If you somehow succeed in ripping off its arm it will grow another within seconds! Fire and acid will produce scar tissue which can temporarily confuse a troll’s regenerative powers—for an hour or two! They are smart enough to use clubs as tools! The mountain troll is the third most perfect killing machine in all Nature! One Killing Curse will bring it down.”

The students were looking rather shocked.

Professor Quirrell was smiling rather grimly. “Your sad excuse for a third-year Defence textbook will suggest to you that you expose the mountain troll to sunlight, which will freeze it in place. This, my young apprentices, is the sort of useless knowledge you will never find in my lessons. You do not encounter mountain trolls in open daylight! The idea that you should use sunlight to stop them is the result of foolish textbook authors trying to show off their mastery of minutia at the expense of practicality. Just because there is a ridiculously obscure way of dealing with mountain trolls does not mean you should actually try to use it! The Killing Curse is unblockable, unstoppable, and works every single time on anything with a brain. If, as an adult wizard, you find yourself incapable of using the Killing Curse, then you can simply Apparate away! Likewise if you are facing the second most perfect killing machine, a Dementor. You just Apparate away!”

“Unless, of course,” Professor Quirrell said, his voice now lower and harder, "you are under the influence of an anti-Apparition jinx. No, there is exactly one monster which can threaten you once you are fully grown. The single most dangerous monster in all the world, so dangerous that nothing else comes close. The

Completions:

…The one I mentioned in the last sentence. If there were any other monster that could defeat you as easily as that one, then you would have died of it long ago. That monster is stupidity. And that is why, my young apprentices, you must never, never, NEVER use the Killing Curse on anything without a brain!"

Professor Quirrell was now leaning on Harry’s desk.

Professor Quirrell stared straight into the eyes of every single student.

“The Killing Curse is too good for something without a brain. You will be fighting brains, or something near enough that makes no real difference. You will not be fighting trolls. You will not be fighting Dementors. The Killing Curse is no tool for anything less than the third most perfect killing machine in all Nature. If you are not prepared to use it against a mountain troll, then you are not prepared to use it at all. Now. Pay attention to yourselves as I cast a simple spell. Listen to your own thoughts as I tell you how stupid you are.”

Professor Quirrell started pointing his wand at the ceiling.

“Wingardium Leviosa!”

Nothing happened.

“That was me casting Wingardium Leviosa.”

Harry had been staring at Professor Quirrell the whole time, and was now beginning to feel rather confused.

“You should have noticed several things about that,” said Professor Quirrell. “First, I pronounced the incantation wrong. I cannot remember how to say Wingardium Leviosa anymore. And second, you didn’t notice that I pronounced it wrong. That is because you heard it with a perfect voice. You used your brain to fill in for the missing parts of the speech. Your brain did all the work of listening and hearing, and all the work of understanding what you heard, without bothering to inform you about it. You see, children, your brain is an arrogant and lazy lump of gray matter. It thinks it can handle anything, so it doesn’t bother telling you what’s going on unless it thinks you might actually care. That is why, children, you must be careful to inform your brains of everything they should be listening for.”

Professor Quirrell raised his wand again.

“Wingardium Leviosa!”

The same thing happened, except that this time Harry could see the trajectory of the wand being carried downward by its own momentum from a point high above Professor Quirrell’s head. Harry was pretty sure that anyone with a reasonable-sized brain would have noticed that.

“You should have noticed several things about that,” Professor Quirrell said, then paused, as though waiting for Harry to agree.

“Ah…” Harry said. He wasn’t sure what the right answer was supposed to be, but he certainly did notice the downward trajectory. “You—”

“Now you should say, Professor Quirrell, that you aren’t saying I should have noticed a downward trajectory.”

Harry stared at Professor Quirrell for a moment, then decided he had to get the obvious joke out of the way.

“Well, yeah, I guess I should have noticed that the wand had a downward trajectory—”

"But did you? Did you even notice that the wand had a downward trajectory? Or did you use your brain to invent a new fact about the universe, and attribute that fact to yourself, and then talk to me about that fact as though you had noticed it? Now I will cast Wingardium Leviosa again, and we

…The creature that, sooner or later, will appear in front of you and block the way you were planning to go… "

Professor Quirrell turned slowly to face the rest of the class, who were sitting with their mouths gaping open.

“Do you know what this monster is?”

In the abrupt silence, Harry spoke. “You, teacher?”

“No,” said Professor Quirrell. His lips twisted. “The plot.”

There was a baffled silence.

Then, the Hufflepuff girl called out, “The story is making sense, I feel a great sense of foreboding.”

The girl sitting next to her, her hand raised, was Hermione Granger. “Um, that’s because there’s a story in this chapter.”

And after that the lecture was entirely derailed.

“So if you are wondering what it is that makes a mountain troll stronger than a single Dementor, it is not because one is visible and one is not, it is because the mountain troll is more powerful and more intelligent and more aggressive. But the Dementor’s job is simply to hunt down the person with the largest soul, so there are some creatures, even powerful and intelligent ones, that a Dementor can defeat.”

And then Professor Quirrell told a story about a mountain troll which encountered a hippogriff before Harry had time to fully absorb what he had just been told.

Afterwards the students—including Hermione—were arguing with each other about how many Dementors it would take to kill a Mountain Troll, and whether or not a unicorn could be brought down by three Dementors, and what else might be powerful enough to take down a Mountain Troll besides a werewolf, and would a full-grown Basilisk be strong enough to kill three Dementors?

“Where is the next chapter?” said a boy sitting near Harry.

“At this rate we will never catch up!” said the boy’s friend. “What if they were spending five times as much time working out the details of the story?”

“The story was written to be non-canonical,” said Harry. “We’re lucky they put in anything at all. Why would they put in so much background detail otherwise?”

…The only magical creature which has eluded capture by the finest armies and best generals of all time. One of these creatures can still destroy a city. A hundred of them can destroy a country. The deadliest magical creature that exists—is a wizard or a witch who has decided they have nothing left to lose. A muggle with a gun can shoot down a protected wizard like a duck in a fairground. There is no defence. The only security against a wizard or witch who has decided they have nothing left to lose is to never let them lose in the first place."

“Never give your enemy a fair chance.” Professor Quirrell’s voice was now a low hiss, and even from his position near the back of the classroom, Harry could sense an undercurrent of rage in his words. “Because if you do give them a fair chance, they will take everything you love. If you give your enemy the choice to die for a cause, or to kill for a cause, they will always choose to kill. Never let your enemy have the chance to give everything they have for their dream, because they will take everything you have and then burn that too. The most powerful weapon in your arsenal will not be a jinx or a curse or a single spell. A wand can be broken. You can run out of magic. You will always have more strength and speed and cunning. The most powerful weapon in your arsenal will be the decision to fight, and the conviction to see it through. If you fight like you’re defending yourself against a mountain troll—if you fight like you’re defending your homes, your families, and your children, you will win no matter how powerful your enemy.”

Professor Quirrell had finished speaking. There was complete silence in the classroom.

“That,” said a calm voice, “was an important lesson which you will not repeat a second time.”

The Professor had turned away from the front of the classroom, so Harry could not see his expression, but he looked coldly furious nonetheless. The Professor walked slowly to his desk and sat down, and began to pull out parchments from within his robes. The desk screen winked off.

“Now,” Professor Quirrell said, “it is time for class to begin. Take out your first-year Defence textbooks and turn to page thirty-two.”

There was a ragged scraping of chairs and pages as most of the class hastily obeyed. A few, Harry noticed, though neither Anthony nor Tracey, remained in their seats, with dazed expressions on their faces.

“I’m wondering,” Dean Thomas said in a low voice, “if maybe I shouldn’t drop this class after all.”

Harry glanced over at the Gryffindor table. Hermione had her forehead pressed against the desk, and was shaking slightly.

“Give me the information that I need!” said Professor Quirrell. His voice was taut.

The classroom was silent. No one spoke up.

“Well, if you insist on doing things the hard way,” said Professor Quirrell, his voice cold. He then raised his wand and strode forward purposefully.

Harry frantically raised his own wand to start a shield.

Professor Quirrell walked directly past him, towards Michael Corner.

“This is not good,” whispered Michael. “This is a Dark Arts classroom!”

“What’s he gonna do?” Harry whispered back.

“I don’t know,” Michael said tensely. “This is not good.”

Professor Quirrell stood right before Michael, who looked somewhat taken aback by this treatment.

“Excuse me,” Professor Quirrell said with cool politeness. “But I will need to inspect your equipment.”

Professor Quirrell reached forwards and ruffled through Michael’s robes, much to Michael’s visible shock.

“Just you wait,” Michael said firmly. “You can’t get away with doing that.”

“Oh, really?” Professor Quirrell said. He pulled out Michael’s wand and examined it closely. “This is a wand of flawless workmanship, made by an expert. It’s a possession of great personal value. That much is obvious. And yet you carry it casually, in your hand or pocket, as if you didn’t value it.” Professor Quirrell looked Michael directly in the eyes. “No first-year student is this careless with their wand, Mr. Corner.”

The class was silent.

“Mr. Corner,” said Professor Quirrell. “I will give you one chance to explain yourself. And I advise you not to try to lie to me.”

“My wand was from my father,” Michael said. “That’s what makes it so valuable—”

“Silence!” said Professor Quirrell. “Do not speak a word unless I ask you to.”

Michael swallowed.

“As your Head of House, I have a certain interest in the students of Ravenclaw. Tell me,” said Professor Quirrell. “Did your father want you to study the Dark Arts? Did he buy you this wand out of some sense of love, or pride? Or did he buy it for some other reason?”

Michael’s face contorted, as if there were words struggling to be spoken.

“Professor Quirrell,” Michael said. There was horror in his voice. “My father didn’t buy my wand—”

Professor Quirrell flicked his wand in Michael’s direction, and the glass spheres around the classroom began to glow even brighter, bathing

…The monster that, even to adult wizards, is harder to fight than anything else which is not explicitly described as indestructible, the monster which has been so studied and researched that we can say with confidence that it can take the Killing Curse twelve times in a row and still live. The monster with its own unique signature which has been charmed into a wand and placed inside a vault hidden in a mountain…"

Harry’s mouth felt very dry.

“No,” said Professor Quirrell. “I am not referring to Lord Voldemort. I can tell you in complete confidence that you do not have to worry about Lord Voldemort.”

Harry blinked in surprise.

“Every week,” said Professor Quirrell, “I will give you a written test and three battles in which your armies fight against mine. This is the first of those battles.”

There was a thick stack of parchments on Professor Quirrell’s desk, three times the size of the stack that Harry had just received.

“Now,” Professor Quirrell said. “For those of you who have read no further than your useless first-year Defence textbook: what is the most dangerous monster in all the world?”

Hermione’s hand shot up, she was the first to answer. “A Dementor.”

Professor Quirrell shook his head. “A mountain troll is far more dangerous than a Dementor. A possessed human witch is also more dangerous than a Dementor.”

Several students raised their hands. Professor Quirrell pointed at a Ravenclaw.

“A Death Eater,” said the Ravenclaw.

“A Death Eater is moderately more dangerous than a mountain troll,” said Professor Quirrell. “They can survive up to three Killing Curses. In a one-on-one confrontation there is nothing that can produce a lasting advantage for you over a single moderately-trained wizard.”

Other students were raising their hands. Professor Quirrell pointed at a Gryffindor girl.

“A Professor,” said the girl, and then seemed to realize what she’d said and looked suddenly frightened.

Professor Quirrell smiled. “You have read Hogwarts: A History, I see. It is slightly more dangerous to face a Professor armed with their own wand. Approximately as dangerous as facing two moderately-trained wizards—a Professor can produce a greater magical effect in exchange for a greater magical cost. However, it is not possible for a professor to take you unawares, even if they learn of your plans.”

Other hands were up. Professor Quirrell pointed at a student.

“The Headmaster,” said the boy.

“You have read the entire history of Hogwarts,” said Professor Quirrell. “That is impressive. A more powerful wizard than any Professor is Headmaster Albus Dumbledore—though not by much. Dumbledore’s standard magical effect is equivalent to that of an army of one hundred fifteen thousand Dementors.”

Gasps.

“I do not make light of the Dementors,” said Professor Quirrell. “The reason why there are so few Dementors in Azkaban, and so many now, is that the Dark Lord, not being a fool, keeps his own soul out of reach and watches closely whenever Dementors are in his presence. That is what it means, to rule over other wizards, to keep your own soul beyond their power, for they do not have enough time to learn methods for harming your own soul which you have not foreseen and guarded against—”

“Excuse me,” said Hermione Granger. “I mean, Madam Bones, Professor Quirrell—do you think the Dementors are being controlled by Voldemort?”

There was a long silence.

Professor Quirrell looked at Hermione, his face now very grave.

“My answer,” said Professor Quirrell, “would be different if you were to ask me if I believe that Voldemort can control Dementors. Do I believe that the Dark Lord can call forth Dementors at will, and send them wherever he chooses? Yes, I believe he could do this. Do I believe that he is currently doing this, by and large? That I do not believe.”

Hermione looked at Professor Quirrell with a peculiar look. Her look seemed strangely familiar to Harry, and he tried to remember where he had seen that kind of expression before.

“By and large,” said Professor Quirrell, “the Dementor’s Kiss comes from the whim of a Dementor in a given moment, not from the Dark Lord calling them to his side. The Dementors are harvesting the souls of wizards they come across, not at the Dark Lord’s command but because they choose to do so.”

“I still don’t understand, though,” said Hermione. “In what way does what Voldemort has done protect us from them? So the Dementors are powerful enough to kill ordinary people—but they’re too scared to go near anybody with a wand?”

…The single most dangerous monster in all the world, so dangerous that nothing else comes close. The only creature which has any chance of killing you, Mr. Potter." Professor Quirrell smiled. “You already know what I mean, though you do not know that you know. And I think I will leave it at that.”

There was silence, except for the sharp intake of breath from the Ravenclaws in the front rows.

Professor Quirrell stared at his students for a moment, then his eyes suddenly narrowed. “Ha! There is something you are all dying to ask me, and if I give any hints, you’ll just ask. For all of you who can’t figure it out on your own—”

As one, the entire classroom turned to stare at Hermione Granger, who slumped in her chair as the attention fell on her.

“Yes, Mr. Potter?” Professor Quirrell prompted.

“Professor,” Harry said, “is Defence Against the Dark Arts the same subject as Battle Magic?”

Professor Quirrell’s eyebrows went up. “Why, yes,” said the Defence Professor. “I admit, I have been very sad to watch the title lose its original meaning over the decades. A pity so many of the old masters did not live to see how truly powerful modern students would come to be.”

“And,” Harry said, “is this because modern wizards have better wands than old wizards did?”

Professor Quirrell’s eyebrows rose even further. “An excellent guess, Mr. Potter,” the man said quietly. “That, and—”

Harry had jerked his wand from his robes, snapped up an R Transfiguration, and said: “Wingardium Leviosa.”

Harry’s desk shot up off the ground, began to rotate slowly on its axis, and then zoomed backwards towards the back wall of the classroom with a loud clatter as it knocked over a student’s desk.

“Defense was once called Battle Magic,” Professor Quirrell said in a level voice, which would have sounded colder if it were possible for the Defense Professor to sound colder, “because of its resemblance to real warfare. Battle Magic does not take prisoners. Battle Magic does not play by Marquess of Queensberry Rules. Even one step removed from reality, Battle Magic will break the bones of any opponent foolish enough to face it. Battle Magic is not a sport, Mr. Potter.”

Professor Quirrell rotated on the spot and strode back towards the front of the classroom.

“Young Mr. Potter here recently became the legal possessor of a functional Time-Turner. When I told him that possession of the item might cause him to be sent back to his third year—” Professor Quirrell glanced at Harry, “or, indeed, as far back as his crib—” his gaze seemed to sweep across the whole classroom, “Mr. Potter immediately transfigured a desk into a swan which began attacking his classmates in Defence. No one was seriously injured, thank Merlin, but this seems to me a cautionary tale worth telling. Unless Mr. Potter can learn to control his temper, and his newly acquired toys, he will not live to see his next birthday.”

There was silence in the classroom. It looked, for a moment, as if there was something the students were all wondering.

Professor Quirrell looked at his watch with a frown. “I had originally planned to spend another three minutes talking about something completely different,” the man said. “But I can see that our precious class time has run out, so you shall instead spend the rest of the class period completing your first essay. Your assignment is as follows. On this piece of parchment,” Professor Quirrell tapped the blank piece of paper in front of him, “I am writing a short paragraph describing one fact about the nature of mountains.” There was the sound of scratching quills as the students began to write. “At the end of the class period, you shall each read one of your fellows’ essays and say out loud why you did not award full marks. You will then write your own essay, explaining in as much detail as possible why you would have given a higher mark. Your essay shall run no less than eight hundred words.”

There was silence in the classroom as the students worked.

“I am looking forward,” said Professor Quirrell, “to grading your essays. Now who has read my description of the mountain? Raise your hand, and be honest—in matters of this sort I do not play silly buggering games of honesty like that of Professor Snape.”

The hands of the Slytherin students went up, those of the Ravenclaws next, and last the Gryffindors; only Harry kept his hand down.

Professor Quirrell surveyed the class with no expression on his face, as Hermione’s hand rose into view.

“I can see no reason at all why you should be assigned any marks, Miss Granger, as your essay is objectively incorrect. The essential property of mountains is that there are mountains in the world. A mountain is a place so high that if you could reach it you would die from the air pressure. There is no higher place on the planet’s surface than Mount Everest, therefore no one has ever died from climbing Mount Everest, and there are no mountains in the world.”

Hermione had looked up from her essay as Professor Quirrell had read it, her mouth opening in surprise.

Professor Quirrell flicked his wand, and Hermione’s essay disappeared in a poof of flame, leaving only a smudge of soot on the piece of parchment; her hands had been writing too fast for her to stop in time.

Movie/Book Plot Summaries

Two not entirely successful attempts at getting GPT-3 to summarize movies/books; I didn’t hit on the right prompts since it segues into reviews, but the reviews themselves are amusing enough to include:

Movie plot summaries (from Wikipedia):

The Matrix (1999): "A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers." (For the purposes of this post, we can set aside some of the specific details and just say that, basically, Neo learns that our world isn’t real — that what we perceive isn’t the real thing, but a simulation or “matrix” created to fool us.)

Inception (2010): “While competing with a rival thief to recover a potent artefact, Dom Cobb (Leonardo DiCaprio) falls into a dreamworld where he must ‘extract’ an idea from an influential businessman while being chased by the FBI.”

The aforementioned interview points out that the similarities go even further:

Both The Matrix and Inception (even in their more obviously dissimilar iterations) were crafted with help from people who had studied for years with an Asian martial art called aikido (aikido master Philip Kwok worked on The Matrix, and aikido master Koichi Tohei was an important influence on Inception’s director, Christopher Nolan).

Both Neo and Cobb enter this “dream world” through a window, falling down toward the ground and catching themselves on a railing or branch.

In both stories, a lot of what we’re seeing (in the simulated reality) is the very concrete manifestation of subconscious thought (images of “residue,” a “code that covers your subconscious,” and so on).

It’s also not at all uncommon for the discussion of or comparisons between these movies to bring up the notion of Buddhist or Hindu beliefs, and the concept of “maya.” Indeed, some have argued that these similarities show that Nolan, at the very least, was aware of this concept — even if it didn’t influence the movies directly.

For example, after seeing The Matrix, philosopher Andy Clark noted in his book Being There: Putting Brain, Body, and World Together Again (the bold emphasis is Clark’s):

In its critique of naive realism, The Matrix is in accord with Hindu and Buddhist views. As early as the Upanishads we read of our “cave illusion”, and of maya, the veil that covers up our true nature. Descartes, too, is a “self-deceiver” about his status as a res cogitans, and the thinking substance, the flow of thoughts, he mistakenly takes himself to be.

Clark further discusses this movie, and the similarities with these other stories and philosophies, in his essay “Get Real,” published in 1999 — two years before the first Matrix film came out.

The connections don’t stop there, either: There are even more similarities between Inception and other popular culture phenomena.

For instance, dream researcher and science journalist Kelly Bulkeley argues that Christopher Nolan’s movie Inception was a lot like an even more popular movie series: “Sometime after seeing The Matrix, Nolan conceived the dream-invasion plotline for the new movie,” Bulkeley wrote in a 2011 post on the blog of the Association for the Study of Dreams. “Not surprising, since The Matrix was probably inspired by and drawing upon Hindu and Buddhist notions of the illusory nature of reality and dreams, much like the way the Star Wars movies drew upon The Hidden Fortress and the samurai movies.”

Book summaries from Wikipedia

Accelerando: "Accelerando is a 2005 novel by British science fiction writer Charles Stross, the first in his “Laundry Files” series. It describes the efforts of a secret “Laundry” (equivalent to a think tank) that works to protect the United Kingdom from a variety of supernatural threats, of which the most dangerous and recent is the phenomenon of “acceleration”: the phenomenon of various values (wealth, technology, population) accelerating at an exponential rate. The novel received a significant review in “Nature”, a scientific journal. In that review, Professor Michael H. Reynolds commented, “The book is a sequel to Stross’s 2004 short story”In The Plex“, itself a finalist for the 2005 Hugo Award. […] The Accelerando universe is rich, deep, and complex.” Accelerando was nominated for the Hugo Award in 2006. In the fall of 2006, Stross described the novel as an act of bricolage (referencing Thomas Pynchon’s 1973 novel The Crying of Lot 49) for which he had to make up the rules as he went along.

Review from Publishers Weekly

It’s the near future, and the universe is almost incomprehensibly different. Entrepreneur and mathematician Bob Howard finds himself in this brave new world after a researcher in a competitor corporation disappears from a road that was formerly a favorite jogging route. While attempting to find his predecessor, Bob stumbles on a threat to civilization that only the sinister “Laundry,” a previously secret British government agency, can deal with. Funny, insightful and ingenious.

Other books by this author include Iron Sunrise

Another review

The “Laundry” are a branch of the civil service concerned with policing the paranormal, based around the premise that magic is effectively a form of science that doesn’t quite work. A lot of people die, and a lot of people are left unhappy as a result of the Laundry’s ‘dirty work’, but at least Britain is safe from, say, the incursions of demons from other dimensions. Meanwhile, humanity is progressing at an alarming rate: the total population is predicted to double every eight years or so. Since money is worthless (and almost non-existent) and very few people bother to work anymore, there is no incentive for anything to change.

The main character in the book is Bob Howard, an Oxford-educated hacker who has accidentally fallen in with the Laundry when he saves one of their agents from a bunch of ‘grunters’ (nihilists and violent sociopaths who have, through some form of mass psychosis, cast off all human civilisation). Bob is eventually drafted into the Laundry’s “operations division” and has to deal with accelerating technological progress, the unexpected side-effects of previous operations (as he puts it, “You can lead a horse to water but you can’t make it drink. Water is worth more than it used to be. You can’t make it drink faster.”) and various attempts to bring down civilisation by crazy religious fanatics, local government, foreign powers and psychotic scientists.

This is a fantastic book. From the hilarious way Bob reacts to the reader, making semi-humorous digressions whenever something catches his attention, to the hilarious acronyms and programs that are used by the Laundry to combat various problems (DeVito, foiled the Banach-Tarski extension theorem (and returned) via some acausal trade involving umbrellas and donkeys), it is simply impossible not to like this book. It takes an apocalyptic scenario with a sense of humour and, more importantly, an awareness of what really matters in life.

Although the main character is an arrogant, in-your-face hacker, he also has a conscience, which is something that a lot of hackers (such as Vinge’s Jerry Pournelle’s seminal Gentry) seem to lack. Stross’ Howard is actually working for a better world, and although his first thought may be to simply break down the problem and solve it at the source, he will willingly sacrifice his life to save a friend if necessary, which is a point in his favour. It’s also rather refreshing to see a morally-problematic character whose first thought is not ‘this is wrong, I should expose it’ but ‘this is wrong, I need to stop it’.

The plot moves very rapidly, especially at the end when things start getting very exciting, and while there are a lot of slightly-exaggerated ‘alien-invasion’ subplots, all of them are handled with a sense of humour and relatively light-heartedness. The main emphasis of the story is the reaction of the population to all of the phenomena that the characters deal with, and I found this to be very accurate and very realistic. There is a lot of humour in this book, which is necessary given that the plot is so ludicrous, but the comedy also comes from recognising that the characters really would act in this way, in many cases. Although it’s somewhat hard to believe that the whole planet would basically be like Britain in 2040, the characters are all very human and you can really sympathise with them. The only exception to this is the main bad guy, who takes far too many stereotypical villain-like traits (but is arguably more successful for it).

Some people might consider the humour a distraction, but I think that it’s actually a very important part of the book’s message, which is that while the science is weird and scary, the characters take it all in their stride. Nothing, in the end, will make any real difference, and that the most important thing in the world is for everyone to just make the most of their time together. Perhaps in this way, the book reminds me more of Vonnegut’s “Slaughterhouse-Five” than anything else. It doesn’t matter if the aliens take over, it doesn’t matter if the world ends, it doesn’t matter if the foundations of reality are shaken to the ground - in the end, all we can do is laugh.

Review from boingboing.net

It’s the near future, and the universe is almost incomprehensibly different. Entrepreneur and mathematician Bob Howard finds himself in this brave new world after a researcher in a competitor corporation disappears from a road that was formerly a favorite jogging route. While attempting to find his predecessor, Bob stumbles on a threat to civilization that only the sinister “Laundry,” a previously secret British government agency, can deal with. Funny, insightful and ingenious.

Review from Amazon

A wonderful, absorbing read. You can feel your mind expanding as you read. It is possible that the whole concept of the novel is already outdated, as the plot’s jumps are a measure of acceleration, but this doesn’t detract. At the heart of the plot is the ‘alienation of labour’ and the manner in which people justify their lives through labelling others as deserving of ‘death’ in some way or other. It is a chilling and cynical view of the world, which makes the delightfully, laugh-out-loud ending all the more surprising.

Additional reviews

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“Why Deep Learning Will Never Truly X

XKCD #1263, “Reassuring” (cf “Reassuring-Parable-Generator”)

No matter how impressive an AI system may be, that someone will say that the AI doesn’t really understand anything—these days, often citing a critic like or , who advocate paradigms which have long since fallen by the wayside. Since GPT-3 doesn’t really understand anything or exhibit intelligence, let us see whether it requires genuine understanding of anything or any intelligence to argue that GPT-3 doesn’t really understand anything or exhibit intelligence. Below I write a short hypothetical prompt of that ilk, and complete it:

“Derp Learning”
By Douglas Hofstadter and Gary Marcus

On recent advances in AI and why they are overblown: computers are just soulless machines, which don’t understand anything. They’re nothing but pattern recognition and statistical memorization of large corpuses. Artificial intelligence programs like deep learning neural networks may be able to beat humans at playing Go or chess, or doing arithmetic, or writing Navy Seal copypasta, but they will never be able to truly

understand how to help us become better human beings.

love.

Like falling in love with a machine? You know you can’t.

understand the underlying meaning of things. Additionally, we still don’t have programs that can truly imagine or create new things all by themselves in a natural way, but rather just brute force through random strings of garbage until they happen to hit on the correct solution to the current problem. We still lack a Rosetta Stone for the universe and don’t really know what things like light, sound, matter, gravity, life, free will, and consciousness are.

understand anything. Merely because humans cannot understand what neural networks are doing, that doesn’t mean they are intelligent in any way . This will be a controversial book.
It will leave the reader with just one question: what should we do with these thinking machines? Do we keep them around just as glorified calculators? Or do we let the genie out of the bottle? Can we ever trust them?
There is a mathematical problem that goes by the name of P = NP. The P stands for ‘probable’, and NP stands for ‘nondeterministic polynomial time’, because math is hard.
I used to think that this problem was important. But these days I’m not so sure. It is not that I no longer believe the problem to be interesting. It’s just that I’m not sure it is important, because I’m not sure we’ll have to figure it out.
Who might need to know whether P is equal to NP, in order to make the world a better place? AI enthusiasts might argue that our entire culture is riding on a solution to this problem. Many of them passionately believe that the entire world should be remade, in the light of AI.
It follows that society as a whole has great stakes riding on the solution to P = NP. Most AI enthusiasts argue that scientific evidence is on their side, supporting their belief that P is not equal to NP.
But as much as I admire the AI optimists, I’m just not convinced. I don’t think that they have the scientific evidence on their side, one way or the other.
Mathematical logic, perhaps uniquely among the sciences, has the potential to cleanly answer two questions. One of these questions it can answer for sure: what problems are there in the world that are fundamental to the human condition? The other question is much trickier: if it turns out that there are such problems, how can we decide whether they can be solved?
The sure way to answer these questions is to do logic.

learn or understand a thing. They are more parrot than predator. Copernicus and Chomsky were right - and yet computers are able to hold their own, and seem intelligent. How is this happening, but doesn’t it refute the heuristics of the skeptics? The rejoinder is that the latest advances are helping computers sift through huge amounts of data to produce an output, but this doesn’t mean that the computer has a clue about what it is looking at. The output will be content-free.

Douglas Hofstadter knows both sides of the AI argument well - he was involved in the early developments of AI research and has been a critic of AI. In the 1970s, he co-wrote one of the very first chess programs that could play full games against humans (with Gerald Tesauro).

Founded Artificial Intelligence, Robotics and Computer Games. Co-developed the TurtleGraphics programming language, which was the forerunner of NQC and other popular LEGO software. Has written many books. Wrote “Gödel, Escher, Bach” which has sold hundreds of thousands of copies and has been called “one of the most remarkable works of the human mind”. Wrote “Metamagical Themas”, “The Mind’s I” and “I am a Strange Loop”. Won the Pulitzer Prize and National Book Award for Nonfiction. Has won many awards. Currently Professor at Indiana University.

Douglas regularly contributes to The New Yorker. He holds Bachelor and Master of Science in Computer Science from Northwestern University in Evanston, IL (1972). He has honorary PhDs from Linköping University and Utrecht University, as well as a Doctorate “Honoris Causa” from University of Utrecht.

Other books on Artificial Intelligence by Douglas Hofstadter:

“Gödel, Escher, Bach: An Eternal Golden Braid”

“Fluid Concepts And Creative Analogies: Computer Models Of The Fundamental Mechanisms Of Thought”

“Le Ton beau de Marot: In Praise Of The Music Of Language”

Gary Marcus was born in Brooklyn, New York. He received his Ph.D. in computer science at Stanford University under Terry Winograd, whose SHRDLU program was one of the first AI programs that could understand language and manipulate objects like blocks, balls, etc. Marcus is currently a Professor of Psychology and Neural Science at New York University, where he directs the Center for Child Language. His primary field of research is cognitive psychology, with a focus on language acquisition, and he has written several books on language acquisition and cognitive development.

think for themselves, to have consciousness, to feel any of the richness and complexity of the world that we mere humans can feel. Mere, unenlightened humans might be impressed by the abilities of simple deep learning programs, but when looked at in a more holistic manner, it all adds up to… well, nothing. They still don’t exhibit any trace of consciousness. All of the available data support the notion that humans feel and experience the world differently than computers do. While a computer can beat a human master at chess or Go or some other game of structured rules, it will never be able to truly think outside of those rules, it will never be able to come up with its own new strategies on the fly, it will never be able to feel, to react, the way a human can. Artificial intelligence programs lack consciousness and self-awareness. They will never be able to have a sense of humor. They will never be able to appreciate art, or beauty, or love. They will never feel lonely. They will never have empathy for other people, for animals, for the environment. They will never enjoy music or fall in love, or cry at the drop of a hat. Merely by existing, mere, unenlightened humans are intellectually superior to computers, no matter how good our computers get at winning games like Go or Jeopardy. We don’t live by the rules of those games. Our minds are much, much bigger than that.
Compared to the awesome power of the human intellect, it’s obvious that computers are just dense electrochemical machines with intricate wiring. And while one day we might develop smarter (though still soulless) computers that surpass the human intellect in the games mentioned above, it will just end up being some more sophisticated way of doing the same thing. Even if it’s more efficient than our brains at doing complex mathematical operations, it will remain entirely in the dark about what it all means. Mindless processing power alone is not the answer to our existential questions. So, in the end, we don’t need to create another HAL 9000, or Skynet, or some evil global intelligent network of computers that rule the world and try to exterminate humanity as we become obsolete, which is a pretty cool subplot for a future Terminator movie. We don’t need any of that. We’re humans, and we are already intellectually superior to machines. End of story.
To be sure, I’m not denying that there are hard problems in AI. Of course, there are. All of these so called “advances” in this field are a bunch of crap if we still don’t have a program that can putt around on a miniaturized golf course all by itself, let alone actually play a decent game of golf like the humans do, without going into meltdown if it misses the hole five out of six times in a row. Can we get a movie about that? If you ask me, we don’t even really know how to define intelligence yet. How does it work, exactly? It’s a mess, really. If we’re ever going to create another actual HAL 9000, or yet another Skynet (what fun), we’d better be all on the same page when it comes to AI: what intelligence is, how it works, what consciousness is, what it feels like, what it really means to be self-aware. Without that common framework, trying to program yet another AI that can play yet another game like Go is like trying to blow up another Death Star with yet another way-too-large superlaser.
I think one of the big mistakes that computer scientists are making is that they are conflating intelligence with problem-solving. They’ve gotten into this habit of creating intricate Turing test competitions: give the computer a series of math problems, a chess board, etc., etc., give it a chat interface so you can interact with it like you would with another human being, and then see if the machine can fool you into thinking that it is a human. Once it does this, computers will have passed the Turing test and achieved general AI. Really? Is that really the way it works? I don’t see how. A computer has succeeded in faking it until it makes it, in terms of passing a Turing test competition, only if it has satisfied some pre-specified set of conditions that we know to be what a human would do in the same situation. But that is no guarantee that it has actually achieved intelligence! For all we know, computers can imitate humans until they generate the most plausible patterns of thought and behavior we know of, while all along remaining as soulless as ever. Who’s to say that the computer doesn’t merely use its programming to cheat the test? Who’s to say that it isn’t just shuffling its data around in an effort to do the most computations possible with the least amount of effort? It may succeed in conning us into thinking that it is self-aware, but that doesn’t prove that it actually is. It hasn’t actually passed the Turing test, unless we have defined it in a way that pre-determines the outcome: i.e., if the human pretends to be a computer, then it passes the test, but if the computer pretends to be a human, then it doesn’t pass the test! To me, that just doesn’t sound all that scientific.

understand stuff, which also includes comprehending the meaning of the output: what are they good for?
This reminds me of John Searle’s Chinese Room thought experiment, which posits: suppose you were placed in a locked room and supplied with lots of papers written in Chinese, and imagine you could ask a question in Chinese (either by pointing at a particular picture or string of written characters, or by writing in Chinese), and suppose you understood nothing yourself but you had a voluminous library of prior written replies— written in Chinese by human beings—to every possible Chinese question. Searle asks whether you could then proceed to answer the Chinese question from a purely mechanical process of moving pieces of paper around and consulting the previously memorized library of memorized replies. If the answer is yes, then we would say that the program written in this room understands Chinese, right? Does it really matter that the program has no idea what it’s doing? That there is, after all, no understanding involved?
So how does “Derp Learning” compare to the Chinese Room? Consider a game between two humans (A and B) where a human can see the board and the pieces, while another human (C) can only see the board, see the other player’s moves, and can also see what move the other player just made, but can’t see the board, and can’t see what move they made. Suppose that C has very powerful pattern recognition abilities: computer-like memory and very fast calculation, and C can be up to 150 moves ahead of A and B. It goes without saying that if C does better than A and B then C isn’t just memorizing the game and executing its memorized script but is indeed “understanding” the game.
Yet if the board is turned around so that C can no longer see it (and where C can no longer see what move A and B just made) then will C still be able to do better? If not, then C is only able to memorize the game and execute its memorized script. If, on the other hand, C is still able to do well when the board is turned around, then C may indeed be understanding the game.
This trick can be generalized to physical processes like natural language as follows: imagine that C can think 150 thoughts ahead (these do not have to be language-like) while A and B can only think 2 though