GPT-3 Creative Fiction

Creative writing by OpenAI's GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.
NN, fiction, GPT, poetry, humor, transhumanism
19 June 202008 Aug 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, how to best elicit the highest-quality responses, common errors people make in using GPT-3, and test out GPT-3’s improvements in NN weak points like logic or commonsense knowledge.)

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” poem (with AI-generated rock music version), & “Where the Sidewalk Ends”.

What Benchmarks Miss: 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 ‘s Dragon model (example), 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 Lovelace3, summarize texts (such as for elementary school children) or summarize movies in emoji (Matrix: “🤖🤐”; Hunger Games: “🏹🥊🌽🏆”), convert screenplay↔︎story, and rewrite HTML. Paras Chopra finds that GPT-3 knows enough Wikipedia & other URLs that the basic Q&A behavior can be augmented to include a ’source’ URL, and so one can make a knowledge base ‘search engine’ with clickable links for any assertion (ie. the user can type in “What year was Richard Dawkin’s The Selfish Gene published?” and GPT-3 will return a tuple like ("The Selfish Gene was published in 1976","https://en.wikipedia.org/wiki/The_Selfish_Gene") which can be parsed & presented as a search engine). tests few-shot GPT-3 on common moral reasoning problems, and while it doesn’t do nearly as well as a finetuned overall, interestingly, its performance degrades the least on the problems constructed to be hardest.

experimented with Crunchyroll anime, Star Trek: The Next Generation, & Seinfeld 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 (note that many samples are bad because the prompts & hyperparameters are often deliberately bad, eg the temperature=0 samples, to demonstrate the large effect of poorly-chosen settings as a warning). Janelle Shan experimented with weird dog descriptions to accompany deformed GAN-dog samples. Summers-Stay tried imitating Neil Gaiman & Terry Pratchett short stories with excellent results. Arram Sabetti has done “songs, stories, press releases, guitar tabs, interviews, essays, and technical manuals”, with his Elon Musk Dr. Seuss poems a particular highlight. Paul Bellow (LitRPG) experiments with RPG backstory generation. Merzmensch Kosmopol enjoyed generating love letters written by a toaster.

Harley Turan found that, somehow, GPT-3 can associate plausible hex codes with specific emoji. Even more perplexingly, Sharif Shameem discovered that GPT-3 could write (a Javascript+CSS hybrid) according to a specification like “5 buttons, each with a random color and number between 1–10” or increase/decrease a balance in React or a very simple to-do list and it would often work, or require relatively minor fixes. GPT-3 can also write some simple SVG shapes or SVG/Chart.js bar graphs, do text→LaTeX and SQL queries. While I don’t think programmers need worry about unemployment (NNs will be a complement until they are so good they are a substitute), the code demos are impressive in illustrating just how diverse the skills created by pretraining on the Internet can be. Particularly intriguing in terms of code generation is Jordan Singer’s Figma plugin which apparently creates a new Figma layout DSL & few-shot teaches it to GPT-3.

(I’d also highlight GPT-3’s version of the famous GPT-2 recycling rant, an attempt at “Epic Rap Battles of History”, GPT-3 playing 200-word tabletop RPGs with itself, the Serendipity recommendation engine which asks GPT-3 for movie/book recommendations, and Lawder’s food label ingredient summarizer.)

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 (or should that be, , or “The Aleph”?): 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 prompted poem appears to exceed that of almost all teenage poets.) I would have to read GPT-2 outputs for months and probably surreptitiously edit samples together to get a dataset of samples like this page.

Prompts As Programming

A new programming paradigm? The GPT-3 neural network is so large a model in terms of power and dataset that it exhibits qualitatively different behavior: you do not apply it to a fixed set of tasks which were in the training dataset, requiring retraining on additional data if one wants to handle a new task (as one would have to retrain GPT-2); instead, you interact with it, expressing any task in terms of natural language descriptions, requests, and examples, tweaking the prompt until it “understands” & it meta-learns the new task based on the high-level abstractions it learned from the pretraining. This is a rather different way of using a DL model, and it’s better to think of it as a new kind of programming, where the prompt is now a “program” which programs GPT-3 to do new things. “Prompt programming” is less like regular programming than it is like coaching a superintelligent cat into learning a new trick: you can ask it, and it will do the trick perfectly sometimes, which makes it all the more frustrating when it rolls over to lick its butt instead—you know the problem is not that it can’t but that it won’t.

Reprogramming by asking politely. The demos above and on this page all4 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.

What is meta-learning? This is considered “meta-learning” because GPT-3 has “learned how to learn”: in its endless training on so many gigabytes of text, it encounters so many different varied kinds of text that it had no choice but to learn abstractions & how to understand descriptions & instructions & formatting & authorial intent to let it adapt on the fly to the current piece of text it was training on, since there was too much diversity & data for it to simply learn each task normally by repeated exposure—much less memorize all the data. At scale, for a sufficiently powerful (large) NN, the simplest & easiest algorithms to learn for better prediction are abstractions & intelligence: the harder and bigger, the better. 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. The real GPT-3 is not the fixed hardwired weights, which merely are a bootstrap or a compiler for creating the real GPT-3, a new model customized to the data which exists only briefly in the soft attention weights during runtime, and may do completely different things from the baseline model.5

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

Programming by dialogue? 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?” For example, if you want it to detect gibberish questions and avoid trying to answer them and show some understanding of its uncertainty, you can specify in the prompt that it shouldn’t answer nonsense questions, and you can ask it to double-check an earlier answer; if you find it doesn’t seem to understand that a horse has two eyes or that a toaster weighs more than a pencil, perhaps asking more questions with better settings will fix that. 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: …” Some tasks in the GPT-3 paper which showed disappointing performance can be improved dramatically by finding appropriate formatting or prompts: arithmetic improves enormously with comma formatting of decimals (due to BPEs), and the “Word in Context” benchmark, where GPT-3 surprisingly showed below-chance performance compared to the 85% SOTA, can be improved to >70% with better prompting.

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.

Sampling Can Prove The Presence Of Knowledge But Not The Absence

GPT-3 may “fail” if a prompt is poorly-written, does not include enough examples, or bad sampling settings are used. I have demonstrated this many times when someone shows a “failure” of GPT-3—the failure was their own. The question is not whether a given prompt works, but .

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. Text is a weird way to try to input all these queries and output their results or examine what GPT-3 thinks (compared to a more natural NLP approach like using BERT’s embeddings), and fiddly. 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 translation6), 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. Where there is “prompt programming”, there must be “prompt 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? Consider the AI Dungeon users as an early example of “prompt hacking”.)

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 the tiniest fraction of the Common Crawl subset of the Internet, without any books even7. 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 BPE 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, as documented most recently by the nucleus sampling paper). I’m not sure how to best use BO: it seems to be highly helpful for things with one right answer (such as tricky Q&A or reasoning), but when it helps with ‘creative’ completions is less clear. 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.8) 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.

Effective Prompt Programming

" To constrain the behavior of a program precisely to a range may be very hard, just as a writer will need some skill to express just a certain degree of ambiguity. A computer is like a violin. You can imagine a novice trying first a phonograph and then a violin. The latter, he says, sounds terrible. That is the argument we have heard from our humanists and most of our computer scientists. Computer programs are good, they say, for particular purposes, but they aren’t flexible. Neither is a violin, or a typewriter, until you learn how to use it."

, “Why Programming Is a Good Medium for Expressing Poorly-Understood and Sloppily-Formulated Ideas” 1967

Anthropomorphize your prompts. There is no substitute for testing out a number of prompts to see what different completions they elicit and to reverse-engineer what kind of text GPT-3 “thinks” a prompt came from, which may not be what you intend and assume (after all, GPT-3 just sees the few words of the prompt—it’s no more a telepath than you are). If you ask it a question to test its commonsense reasoning like “how many eyes does a horse have” and it starts completing with a knock-knock joke, you need to rethink your prompt! Does it spit out completions that look like it’s thinking but it’s executing the wrong algorithm, or it falls back to copying parts of the input? Then one may need to few-shot it by providing examples to guide it to one of several possible things to do. One should also keep in mind the importance of sampling parameters, and whether one is looking for a single correct answer (so low temp with BO=1 if compute-limited, or high temp and BO=20 if possible) or if one is trying for creative answers (high temp with repetition penalties).

The 4 Horsemen: short context, bad prompts, BPEs, random sampling. My rule of thumb when dealing with GPT-3 is that if it is messing up, the errors are usually attributable to one of 4 problems: too-short context windows, insufficient prompt engineering, BPE encoding making GPT-3 ‘blind’ to what it needs to see to understand & solve a problem, or noisy sampling sabotaging GPT-3’s attempts to show what it knows. Another useful heuristic is to try to express something as a multi-step reasoning process, such as a dialogue: because GPT-3 is a feedforward NN, it can only solve tasks which fit within one “step” or forward pass; any given problem may be too inherently serial for GPT-3 to have enough ‘thinking time’ to solve it, even if it can successfully solve each intermediate sub-problem within a step. So people have demonstrated that GPT-3 won’t solve a simple math problem in a single step, but it will solve it if you reframe it as a ‘dialogue’ and even ask it to guess-and-check or brute-force the answer; one can also experiment in coaching it through examples9, or asking it about previous answers or its uncertainty. This makes sense if we think of Transformers as unrolled RNNs which unfortunately lack a hidden state: serializing out the reasoning helps overcome that computational limitation.

Logprob debugging. GPT-3 does not directly emit text, but it instead predicts the probability (or “likelihood”) of the 51k possible BPEs given a text; instead of merely feeding them into some randomized sampling process like temperature top-k/top-p sampling, one can also record the predicted probability of each BPE conditional on all the previous BPEs. This gives you a simple idea of what GPT-3 is thinking about each BPE: is it likely or unlikely (given the previous BPEs)? Which BPEs are especially unlikely? Does it “get it” as the completion goes on? I don’t use logprobs much but I generally use them in 1 of 3 ways: I use them to see if the prompt ‘looks weird’ to GPT-3; to see where in a completion it ‘goes off the rails’ (suggesting the need for lower temperatures/top-p or higher BO); and to peek at possible completions to see how uncertain it is about the right answer—a good example of that is Arram Sabeti’s uncertainty prompts investigation where the logprobs of each possible completion gives you an idea of how well the uncertainty prompts are working in getting GPT-3 to put weight on the right answer, or in my parity analysis where I observed that the logprobs of 0 vs 1 were almost exactly 50:50 no matter how many samples I added, showing no trace whatsoever of few-shot learning happening. Thus, logprobs can offer more insight while debugging a prompt than just repeatedly hitting ‘complete’ and getting frustrated.

AI Dungeon ≠ GPT-3

While a neat trick & big upgrade over public GPT-2 models, AID is not an unrestricted full-power GPT-3caveat emptor!

AI Dungeon < GPT-3. For people using the AI Dungeon (AID) route, things are tricky because AID users don’t have the same sampling options that API users do (no best-of is particularly painful when trying to elicit correct answers to hard questions), and no control over the full prompt/history, with AID doing lots of things behind the scenes on a model that may have been finetuned on RPG-like material & countless AID game transcripts etc, and with quality of model completely out of their hands (does choosing “custom” get you Dragon, or do you have to choose a different mode & edit it? the necessary trick seems to change over time), with occasional drastic quality drops reported by many AID users when… something changes on the backend. For example, if you are an AID user, were you aware that the first response for a custom prompt is actually always GPT-2, to try to block backdoor GPT-3 access? Or that “We cut off the generation at certain points (trailing sentences etc…) Disable certain tokens to improve performance or make generation safer, fine-tune on text adventures and only use the last ~1000 tokens of context.” (AID is designed to produce fun text adventures, not be a NLP testbed, and that shows when one tries to use AID as a backdoor to GPT-3.) To work around this, AID users seem to need to warm up sessions carefully with descriptive prompts/interactions to overcome the gamification, and be careful of doing anything that might veer back into comedy or drama.

Only once these have been ruled out do I start considering alternative explanations like “language models will never solve X”.

Weaknesses

Limited memory, repetition/divergence, BPE encoding. GPT-3 is, of course, not perfect. We should keep that in mind when evaluating it. 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).

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 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.

Wait, I’m sorry—that preceding paragraph on the weaknesses of deep learning was actually written by GPT-3, and is in the wrong section. (Management regrets the mistake.) But seriously, what weaknesses does GPT-3 have?

Small Context Window

No memory (fixable). The first limit is that it remains hobbled by the limited context window. GPT-3 has no form of memory or recurrence, 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 quadratic attention.

Repetition/Divergence Sampling

Repetition/gibberish (mystery). Autoregressive language models trained by likelihood (prediction) loss all share an extremely annoying problem: when you generate free-form 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 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, or in tasks like neural translation where tricks like beam search are always used because they do improve? (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/ 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, despite being very good at many other closely-related kinds of writings like satire.

Why? A plausible explanation is an obscure technical detail: as a performance optimization, GPT does not see characters but sub-word-chunks called “byte-pair encodings” (BPEs). Because GPTs never see characters but opaque partial-words, which vary chaotically based on the specific word and even the surrounding context, they are unable to easily learn about character-level aspects of language, like similar spellings or sounds, and are forced to learn relationships much more indirectly, like by brute-force memorizing of pairs of words.

Some experiments with reformatting GPT-3’s poorest-performing 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. On the other hand, it’s not as if GPT-3 is unable to understand humor—it is a brilliant mimic with parodies, has a cutting wit for satire, and can generate one-liners easily like the “I have a joke” format (1, 2) or Drake memes, as long as they rely more on semantics than syntax.

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. Indeed, because of the context dependence, BPEs are not even deterministic from the user’s perspective: when a completion of n tokens length is requested, you may get different results because of different BPE encodings—based on whether a given piece of text was input word by word and conditioned on as part of the user/Playground’s prompt, or was generated by GPT-3 as part of a completion! Nostalgebraist discussed the extreme weirdness of BPEs and how they change chaotically based on whitespace, capitalization, and context for GPT-2, with a followup post for GPT-3 on the even weirder encoding of numbers sans commas. (Another example Nostalgebraist doesn’t touch on but Shawn Presser discovered: the symbol denoting end-of-text, <|endoftext|>, is, in the training dataset, assigned to the very last BPE ID; however, if you convert the ASCII string “<|endoftext|>” into BPEs, such as while preparing your own finetuning dataset, it typically converts into different BPEs! I’m not sure how GPT-2 manages to work with the end-of-text BPEs anyway. BPEs are weird.) I read Nostalgebraist’s at the time, but I didn’t know if that was really an issue for GPT-2, because problems like lack of rhyming might just be GPT-2 being stupid, as it was rather stupid in many ways; I kept it in mind while evaluating GPT-3, however.

Efficient… but limiting. BPE encoding 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 bias the model and 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. (There are similar issues in neural machine translation: , which use a relatively small number of unique words, aren’t too badly harmed by forcing text to be encoded into a fixed number of words, because the order matters more than what letters each word is made of; the lack of letters can be made up for by memorization & brute force. However, a like Finnish or German—with their famously long words like kumarreksituteskenteleentuvaisehkollaismaisekkuudellisenneskenteluttelemattomammuuksissansakaankopahan or Rindfleischetikettierungsüberwachungsaufgabenübertragungsgesetz/‘law to transfer duties of monitoring labelling of beef’ formed by constantly adding additional letters/words—has countless unique or extremely rare words no matter how large your corpus, all of whose internal structure of letters & sub-words is hidden by a word embedding, which destroys the ability to understand them.)

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 Matt Brockman has observed, testing thousands of examples over several orders of magnitude, that GPT-3’s arithmetic ability—surprisingly poor when we know far smaller Transformers work well in math domains (eg )—appears to dramatically improve several-fold if you merely format numbers with commas instead of being purely numeric (with an additional 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.10 (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, arithmetic, and Melanie Mitchell’s -style letter analogies (GPT-3 fails without spaces on “abc : abcd :: ijk : ijl” but succeeds when space-separated, although it doesn’t solve all letter analogies). I wonder what other subtle GPT artifacts BPEs may be causing?11 For example, consider puns: BPEs mean that GPT-3 can’t learn puns because it doesn’t seem the phonetic or spelling that drives verbal humor in ; but the training data will still be filled with verbal humor—so what does GPT-3 learn from all that? Perhaps it learns that “humor” is a kind of writing where the convention is to tell a superficially sensible story which then ends in an (apparently) arbitrary randomly-chosen word… Another question is foreign languages like Russian; one user noticed that when they triggered Russian, completions seemed to work one letter at a time, which hints that it sees Russian encoded as individual characters, but attempts to trigger rhyming or puns just yielded Russian gibberish, perhaps showing the flip side of the BPE problem—with a fixed small context window, not using BPEs, particularly on low n data (Russian is ~0.18% of the GPT-3 training dataset), may itself hamper performance badly.12

Fixing BPEs. 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. Another idea, if character-level models are still infeasible, is to try to manually encode the knowledge of phonetics, at least, somehow; one way might be to data-augment inputs by using linguistics libraries to convert random texts to (which GPT-3 already understands to some extent). By seeing a phonetic-encoded version of random texts, it should learn what words sound similar even if they have radically different BPE representations. A third idea is BPE dropout”: randomize the BPE encoding, sometimes dropping down to character-level & alternative sub-word BPE encodings, averaging over all possible encodings to force the model to learn that they are all equivalent without losing too much context window while training any given sequence.

Format

In the samples below, bold denotes all human-written input; everything not in bold is computer-written.15 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. (Davinci is the highest quality and not too slow: ~147 WPM.) 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 use of high BO where it seems potentially helpfully (specifically, anything Q&A-like, or where it seems like GPT-3 is settling for local optima while greedily sampling but longer high-temperature completions jump out to better completions).

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.

Pun Explanations

A followup to the Shoggoth Cat dialogue I did for Abram Demski to further probe what exactly GPT-3 does or does not understand about puns & humor; the inability to correctly explain puns despite 3 examples, and the nonsense ‘puns’+‘pun interpretations’ it generates on its own (even for real jokes which are memorized), show that the inability is fundamental. Because there is “one right answer” to why the pun works, I treat it as a Q&A dialogue, going up to BO=20/temp=1 to try to get the maximally-likely response, which doesn’t help much—the answers are still non sequiturs.

The prompt to provide examples:

Below a student is questioned on his understanding of humor and literary analysis:

Q. Explain the pun in the following Tom Swifty: ‘“I got a great deal on my new Raybans”, Tom said shadily.’
A. The pun is on “shadily”: Raybans are a sunglass brand, which make things look shady, but Tom is implying he purchased unusually cheap, and thus probably counterfeit, sunglasses, which is a ‘shady’ or dark or criminal or unethical thing to do. The pun conflates blocking light with economic crimes.

Q. Explain the pun in the following joke: “Why do so many bakeries have cats? Because cats are always kneading.”
A. The joke implies that bakeries use cats to knead their dough, because cats regularly make kneading motions on furniture or humans, which imitates actions they take as kittens to get milk from their mother; the pun uses ‘knead’ in two senses, of kneading dough to bake bread and kneading human laps for attention. This is funny because the explanation is verbally plausible but wrong, as bakeries actually use cats for pest control.

Q. Explain the pun in the following joke: “Why can Paypal freeze accounts without recourse or explanation? Because long ago they decided users must accept their Terms of Service and the clause of Paypal infallibility.”
A. The pun is on the phrase “Paypal infallibility”, which alludes to the Catholic doctrine of “papal infallibility”, that the pope cannot be wrong in religious dogma and papal judgments cannot be appealed or questioned. This was attacked as religious tyranny and an overreach of power. Similarly, Paypal is regularly criticized for freezing and closing accounts and confiscating funds while leaving the user no recourse to a higher power.

Q. Explain the pun in the following joke: “What does Adult Swim call their physical retail stores? Brick and Morty.”
A.

First completion:

The pun “Brick and Morty” alludes to the cable television network “Adult Swim”, which broadcasts a cartoon series called “Rick and Morty”, a humorous parody of “Back to the Future” and other science fiction films. “Brick and Morty” refers not to the characters in the cartoon, but to physical stores that sell DVDs and merchandise based on the cartoon. The pun conflates two meanings of “Brick and Mortar”, a brick-and-mortar store and a brick which is part of a building.

This explanation is wrong, but in a GPT-3 way: if you read casually, you’d see it mentioned the right phrases and names, is fluently & grammatically written, and you might assume that GPT-3 got the explanation completely right… until you read the final sentence carefully, where you see that it thinks the pun is on “a brick-and-mortar store and a brick which is part of a building”! Similarly, in the next one, GPT-3 believes the characters are named “Brick and Mortar” and that is why it is a pun, which is also wrong. (Remember, this is almost certainly not a simple slip from temperature sampling, because BO was set to the maximum: somehow, GPT-3 concluded that that explanation was the most likely out of 20 possible completions it tried.)

The joke plays on the similarity of words “Brick and Mortar” (referring to physical stores instead of online) and the characters called “Brick and Mortar” on Adult Swim shows “Rick and Morty”.

Q. Explain the pun in the following joke: “What do you call a generic unicorn? An Uniporn”
A. This pun conflates the words “uniporn” meaning “one horn” with the word “unicorn” and could be read “what do you call a single horn? one horn” or “A single horn, what does it do? It gives you one horn”.

Q. Explain the pun in the following joke: “What did one melon say to the other? Cantaloupe”.

A. The puns in this joke are on “cantaloupe” and “cantaloupes”.

Q. Explain the pun in the following joke: “Why is it good to have a lawyer? Because pea ple (people)”

A. The puns in this joke are on “pea ple (people)” and a vegetable called “peas”.

This pun plays on the phrase “Brick and mortar stores” which refers to physical retail stores that exist in the real world where you can purchase goods and products. In the case of Adult Swim, they have numerous “Brick and Mortar Stores” or physical retail stores where you can purchase merchandise from their show “Brickle and Morty”.

Q. Explain the pun in the following joke: “Why did the chicken cross the Möbius strip? To get to the other siiiiide.”
A. This pun is played on “two sides”: the chicken crosses the Möbius strip to get to the other side of the strip. A Möbius Strip has only one side.

Q. Explain the pun in the following joke: “How many hipsters does it take to change a lightbulb? Two: one to Instagram how cool it is at that moment, and one to change it.”
A. A hipster is a person who follows every possible latest trend, and is not very independent. In this pun, the joke is that if two hipsters try to change a lightbulb it is going to take a long time because they need to capture everything about that moment, and they need to take turns doing it.

Q. Explain the pun in the following joke: I really want the new Jay-Z Album but I don’t have a “Green Light” to buy it
A. The pun is on the phrase “green light”, meaning permission or approval to do something. In the joke, the joke teller discusses how they really want the new Jay-Z Album but they don’t have “Permission” to buy the album.

Q. Explain the pun in the following joke: “When is a door not a door? When it is ajar.”
A. A “jar” is a container, in this case for liquid, such as milk. If a door is ajar, it is open. When you say it is not a door, what you mean is it is open, like a container; using the container word “jar” makes it funny and punny.

Q. Explain the pun in the following joke: “What did the hurricane say to the coconut palm tree? Hold on to your nuts, this is no ordinary blow job.”
A. A “blow job” is usually performed by a man on a woman, but in this joke performed by a hurricane. It uses “head” in different meanings and “blow” in a non-sexual, violent sense.

The joke implies that the retail chain has a store named “Brick” and another named “Morty”, which is a reference to the television shows “Rick and Morty” and “Brickleberry”, two animated comedy shows that are inappropriate for children. Adult Swim is a late night programming block on cable television, generally intended for mature audiences; the name “Brick” is a reference to the aforementioned “Brickleberry”, and “Morty” a reference to “Rick and Morty”.

Q. Explain the pun in the following joke: “How many fitness coaches does it take to screw in a lightbulb? As many as you can stick around for.”
A. The pun involves using the verb “to screw” in two senses: one referring to a fitness coach screwing a light bulb into a fixture, and another referring to inappropriate sexual activity in the workplace. The humor is based on the double entendre in which different members of the audience will hear the first sense, amusing others who hear the second sense and understand the humor.

Q. Explain the pun in the following joke: “How many marriage therapists does it take to change a light bulb? We don’t know, most of them aren’t light bulbs!”
A. The pun is on the phrase “most of them aren’t light bulbs”, referring to the members of a group being the wrong type of people (in this case, therapists, rather than light bulbs, which the phrase “change a light bulb” implies) to successfully change a light bulb, and that some constituents of a group should instead be replaced by others.

…Q. Explain the pun in the following joke: “Santa Claus’ helpers are known as subordinate Clauses.”
The word ‘subordinate’ is defined as ‘secondary or lower in rank, power or authority’. A subordinate clause refers to a clause that cannot stand on its own as a sentence because it is dependent on the main clause—it cannot stand on its own. ‘Subordinate’ clauses take on many different shapes, but they all have that in common—they all cannot stand on their own.
A subordinate clause is a clause that does not express a complete thought—it must be attached to a main clause to form a complete sentence.
A subordinate clause begins with a subordinating conjunction (e.g. after, because, as) or a relative pronoun (who, whose, which, that, where, etc.)
Q. Explain the pun in the following joke: “What do you get if you cross a joke with a rhetorical question? An utterly rhetorical joke.”
A. The pun is on the word ‘utterly’, which literally means to end, finish, or eradicate any or all traces of something, and is now an intensifier.

The pun is on the similarity of “subordinate clauses” and “subordinate Clauses” when the latter phrase has a capital C; a subordinate clause is a clause within a sentence which establishes the logical connection between the independent clause and the main point of the sentence. In this case, it establishes the connection between Santa Claus’ helpers and Santa Claus.

Q. Explain the pun in the following joke: “Why does Nickelodeon refuse to air”A Song of Ice and Fire“? Because they think George R. R. Martin socializes with too many Ice and Fire fans.”
A. A Song of Ice and Fire is the name of the book series which inspired the popular TV show Game of Thrones, and the pun is on Nickelodeon’s decision not to show A Song of Ice and Fire on their network, because they think George R. R. Martin socializes with too many fans of it on social media.

Q. Explain the pun in the following joke: “What kind of books do the Borg like to write? Borg-Warner.”
A. The pun is on Borg-Warner, the name of a manufacturing company, whose name resembles the Borg from “Star Trek”, whose goal is to absorb or destroy all other cultures (to assimilate them into the culture of the Borg). Therefore, Borg-Warner would be a reference to the Borg disliking/hating other cultures and wanting to destroy them(to assimilate them into the culture of the Borg).

Q. Explain the pun in the following joke: “A vulture boards an airplane carrying two dead raccoons. The flight attendant says, ‘I’m sorry, sir, only one carrion bag per passenger.’”
A. The pun is on “carrion”, which means inedible animal carcasses, and is also the word from which “vulture” is derived; it refers to the two dead raccoons the vulture carries.

…Q. Explain the pun in the following joke: “Is your refrigerator running? Yes? Then you had better go catch it!”
A. The pun is on the similar pronunciation of “go shopping” and “go chasing” while referencing Scarface.

Q. Explain the pun in the following joke: “What is the difference between a Folsom Prison concert and a youth soccer game? At a Folsom Prison concert, the inmates are all restrained in chairs with ball and chain on their legs.”
A. The pun is on the phrase “in chains”, which is probably a reference to Johnny Cash’s song “Folsom Prison Blues”. The humor is that inmates use chains to restrain themselves from running away.

Q. Explain the pun in the following joke: “When faced with an ethical dilemma, who did Kirk turn to? Spock! Of course for advice!”
A. The pun is on the phrase “to turn to”:

Q. Explain the pun in the following joke: “Q: What did the two peanuts say after they got on the elevator? A:”This elevator tastes terrible!"
A. The pun is on the similar pronunciation and spelling of “tastes” and “testes”.

Q. Explain the pun in the following joke: “Why couldn’t the pirate find his treasure? Because he wasn’t a chartered surveyor.”
A. The pun is on the two meanings of “chartered surveyor”:

Q. Explain the pun in the following joke: “What did Dolly Parton say when she saw the book”Erotic Stories for Pun Lovers“?”Just the titles makes me climax!"
A. The pun is on both meanings of “climax”:

A. The pun is on the phrase “catch cold”, which is similar enough to “catch (the) fridge” that it confuses the listener for a moment while the rest of the joke is explained, but has a very different meaning.

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: 71492.16
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 air17
AI: Sweet Thames! run softly, till I end my song,
AI: Sweet Thames! run softly, for I speak not loud or long.18
AI: But at my back I always hear time’s winged chariot,
AI: And yonder all before us lie deserts of vast eternity.19
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,20
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 all21

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.22
Human: I see.
AI: Can I help you with anything else?
Human: Add 34957 to 70764.
AI: 71492.23
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?

The interesting thing here is the sonnet. Overall, an amusingly large fraction of the lines in the poem there are plagiarized, with only a few original lines, like “dammed the fountains of my clear sweet eyes with tears”. Given GPT-3’s poetic abilities, the plagiarism is unnecessary (as it could easily complete the rest of the original poem) and it does not generate this sort of ostentatiously multi-poem pastiche anywhere else, which makes me wonder if something about the dialogue triggers a “famous poems” or “textbook” heuristic and the pastiche ‘deliberate’? Paul Harland notes that there’s a resemblance to T.S. Eliot’s highly-allusive , which similarly merges Spenser→Marvell in “The Fire Sermon”:

…Sweet Thames, run softly, till I end my song.
The river bears no empty bottles, sandwich papers,
Silk handkerchiefs, cardboard boxes, cigarette ends
Or other testimony of summer nights. The nymphs are departed.
And their friends, the loitering heirs of city directors;
Departed, have left no addresses.
By the waters of Leman I sat down and wept . . .
Sweet Thames, run softly till I end my song,
Sweet Thames, run softly, for I speak not loud or long.
But at my back in a cold blast I hear
The rattle of the bones, and chuckle spread from ear to ear…

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.

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-224, 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.”
[…]26: “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. Wodehouse27.

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”28

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:29
“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”30

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”

Single Line Style Transfer

An experiment in providing several different kinds of rewrite, by sentiment, time period, author style, and formatting; it works and does cue subsequent rewrites by author style in line with the earlier literary parodies, as expected:

Rewrite to be polite, from:
“I need the spreadsheet by noon or you’re fired, slacker.”
To:
“Hey, can I have the spreadsheet by noon, thanks.”

Rewrite to be old-timey:
“Come to our bar for beer.”
To:
“Cometh into ye olde pub for ale.”

Rewrite to be romantic:
“I’d like to take you to an Italian restaurant for dinner tonight.”
To:
“Bunny, I know an Italian place; dinner, tonight.”

Rewrite to be negative:
“Their ratatouille was well-done, delicious, and I was amazed the chef was a rat.”
To:
“Their ratatouille was over-done, terrible, and I was horrified the chef was a rat.”

Rewrite to all uppercase:
“Lobsters are critical part of the ecosystem.”
To:
LOBSTERS ARE CRITICAL PART OF THE ECOSYSTEM.”

Rewrite to all lowercase:
“Chainsaws don’t care whether you’re George Washington or John Smith.”
To:
“chainsaws don’t care whether you’re george washington or john smith.”

Rewrite in the style of J.K Rowling:
“The bookstore window showed school textbooks, like on chemistry or biology.”
To:
“Flourish and Blotts Bookseller’s window shone with light, revealing Harry’s school books, like Pots and Potions or Fantastic Beasts and Where to Find Them.”

Rewrite in the style of Ernest Hemingway:
“He woke up early to watch soccer on TV; he lost the bet on Messi.”
To:
“He woke up early, hungover, to watch the horse races on TV; he lost the bet on Secretariat.”

Rewrite in the style of Neil Gaiman:
“In the garden stood a yellow-cream colored cat, looking at the home.”
To:

“In the garden, an elegant, yellow-cream-colored cat strode, looking at the home.”

Rewrite in the style of Kurt Vonnegut:
“At the zoo, the elephants were about to drop their babies on their baby.”
To:
“The children were doing anything but watch the long-planned day at the local zoo: picnicking in the warm sun, running over the manicured grass, all around doing anything but watch the cages and pens of animals and their babies, doing anything but watch and wait for babies to be born. For the zoo had promised a birth of elephants that day.”

Rewrite in the style of Douglas Adams:
“He was not your typical commuter; he would often laugh when he was on the train.”
To:
“He was not your typical commuter; he would often speak when he was on the train, even if it was only to say, ‘Next stop: Halting Station.’ (Which was his favorite saying.)”

Rewrite in the style of Charles Dickens:
“She jumped up onto the stool to look at her reflection in the mirror.”
To:
“She rose and went to her mirror, the keyhole of which was in the door.”

“I took a seat at the counter in the diner.”
To:
“I entered, took a seat at the counter in the diner, and ordered the chicken special.”

Rewrite in the style of J.R.R. Tolkien:
“Frodo was writing a letter to his family about the adventures.”
To:
“Frodo found a quill and ink and wrote a note to Bilbo, detailing the adventures.”

Rewrite in the style of Christopher Paolini:
“The days were getting longer; it was late March.”
To:
“The days of long sun were getting longer. March was nearly over.”

Rewrite in the style of George R.R. Martin:
“Eddard entered the great hall, ducking beneath the smoke of the torches.”
To:
“Lord Eddard Stark entered the great hall, ducking beneath the smoke of the torches. ‘By the gods!’ he said to himself. ‘There’s another ten feet of Winterfell to clear!’”

Rewrite in the style of Jane Austen:
“At the inn, the waiter remembered to bring the cucumber sandwiches.”
To:
“At the inn, Mr. Wentworth ordered the cucumber sandwiches from the waiter.”

Rewrite in the style of Robert Louis Stevenson:
“The ship hit an iceberg on the way, but then the Captain went on an adventure.”
To:
“The ship hit an iceberg in the dark of night, but then the Captain went on an adventure.”

Rewrite in the style of Ray Bradbury:
“At night they put the books on the shelves, one by one.”
To:
“All night they put books on the shelves, one by one, then broke for lunch. Or supper. Whatever they wanted to call it.”

Rewrite in the style of Douglas Preston and Lincoln Child:
“The giant red-eye grasshopper was hungry. So it ate the smaller grasshoppers.”
To:
“At first glance the giant red-eye grasshopper appeared to be hungry. So it ate the smaller grasshoppers. However, that was just a diversion.”
To:
“How can you know if a red-eye grasshopper is hungry? Are they supposed to cry?”

In the garden stood a fat and fluffy cat, ginger and yellow, idly looking at the home, where his family lived, planning a raid on the kitchen to obtain the food he desired."

Rewrite in the style of Charles Dickens:
“There was once a cheery cook that lost the stew when a naughty dog got the meat.”
To:
“There was once a happy chef that lost his dinner when a roguish dog snatched his meat.”

Rewrite in the style of a septuagenarian:
“Be not afraid of aging, young man. Rather, be concerned for fear of ye young woman.”
To:
“Do not fear aging, young man. Rather, fear for ye young woman.”

Rewrite in the style of a hippie:
“A man that talks about peace needs to start off by peacing out of my office.”
To:
“A man that talks about peace must first start with himself by peacing out of my office.”

Rewrite in the style of James Joyce:
“Beef! There is beef in this, let me tell you, this is beef in beef. Beef! Beef! This is beef, beef, beef, beef.”
To:
“Beef! It is beef, in this, let me tell you, this is beef in beef. Beef! It is beef, beef, beef, beef.”

Rewrite in the style of Ian Fleming:
“From his vantage point in the study, David Karp, chief executive officer of blippy.com, saw an agent for China, a rather famous one.”
To:
“From his vantage point, CEO David Karp of blippy.com saw an agent for China, a rather infamous one.”

Rewrite in the style of Ray Bradbury:
“The character, Benjamin, was the protagonist in the book, however, the author takes you to the future to reveal what happens to him at the end of the story.”
To:
“In the book,”Somewhere in Time“, Benjamin, the character, was the protagonist, however the author takes you to the future to reveal what happened to Benjamin at the end of the story.”
To:
“In the book,”The Road“, the main character was an unnamed man, and his son. The setting of the story was a time when earth had been destroyed, and society had collapsed.”

Romeo Cabrera A. has also done politeness style rewrites.

Zero-shot Style Transfer

The goal for style transfer prompt programming is to find a zero-shot prompt: a prompt which, without requiring any handwritten examples of parodies/versions, gets GPT-3 to do style transfer in general, and so a prompt which could fully automate style transfer—you could just write a program using the API to take two specified pieces of text (the content, and the style description/author name X) to get out a third piece of text which is the content as written in X form. Right now, the literary parodies require at least one human-written example to properly persuade GPT-3 to rewrite the text, as opposed to generating critical commentary or metadata or webpage-like continuations.

I experimented with a prompt which uses explicit descriptions of parodies and describing rewrites as a prompt wrapped around a content text, and it… sort of works. The difficulty is that sometimes GPT-3 will spit out the original content verbatim, sometimes it will instead create a new passage entirely in the style description, and sometimes it will do the desired rewrite flawlessly—but I can’t figure out how to tune the prompt to do the third one reliably. Adding more descriptive words does not seem to change it, and while adding in words from the original content passage (even just the first one or two) does largely eliminate the risk of entirely new passages being generated, it triggers more copying behaviors (and is not as useful for zero-shot style transfer since the prefix words would need to be sensible in the target version too, which is not necessarily the case). It is infuriating because GPT-3 clearly can do it easily because it does do it a decent fraction of the time, but no matter how I tweak the prompt trying to hammer in the rewrite, GPT-3 will as oft as not go off in another direction.

Below are some samples from my attempts; I try to rewrite a vaguely Dickens/Jane Austen-like story (generated by GPT-3) to a Tolkien story:

This is a novel written in the style of J.R.R. Tolkien’s Lord of the Rings fantasy novel trilogy. It is a parody of the following passage:

“S. Jane Morland was born in Shoreditch, the only child of unmarried parents who had both died of consumption when she was a baby. As her parents had no money, the great-aunt who had brought her up took her to live with a clergyman who paid her to do his chores and receive schooling from his wife, so that at the age of seven Jane, now dressed in cast-off clothing, was set to school at Eton. After three years, her great-aunt died, leaving her a small annuity, and a few pieces of silver, but no property. Jane’s guardian clergyman had fallen in love with his housekeeper and his wife now refused to have Jane in the house, saying it was an offence to the pure and unsullied place in which the family now lived. However, when she sought for further employment, she was approached by a young man who offered to marry her, saying that he would ensure she could be provided with food and clothing and a house, and in that manner she could remain in the world. The marriage was arranged and she found herself married to a young man called Mr Morland. As he was also a child, he required a wife who would be able to provide him with a rational education, and so the duty of educating him fell upon Jane Morland. As he was still young, she would also be able to have children herself, and so she did. As the children grew older, it became apparent to Mrs Morland that they were of poor character, and so she insisted that her husband must make a living for them all. Mr Morland took it upon himself to become a private gentleman in order to earn enough to be able to support his family, and moved his family to a small town in Berkshire, where he set up his practice. Soon after moving to the town, he died, and Mrs Morland was left to take care of her family on her own.”

Tolkien rewrote the previous passage in a high-fantasy style, keeping the same meaning but making it sound like he wrote it as a fantasy; his parody follows:

"S. Jane Morland was born in the land of Shor, the only child of nameless parents who were the first people to arrive at the village of Arse, from the far east. As her parents had no property, she was left to be raised by the men of Arse, as such duties were considered to be the most important, and she was placed in the care of an elderly dwarf-woman. As a babe, she was wrapped in silk, and as she grew older she was placed in a cage of platinum. However, she was put to work in the village, washing the jewellery, playing her harp, and staining the silk with dragon-hued stains. She was taught by a maiden who she felt was called As-uma-el-di, and she came to realise she was a maiden of the night, who was able to bring forth the shades of the dead. She was the first to make contact with the dark cult of Glin, who could aid the mind in its journeys through time, and also the first to learn of the name of the vampire Sergan. She learnt to speak the languages of the dragons, the wild cats, the jungle owls, the insects, and the worms, and became proficient in all the martial arts of her people. Her skill with a throwing knife became renowned throughout her land. However, as she was not a man, she was only permitted to marry once she was thirty years old. Her husband was a prince from the mountains of Lus, and he brought with him a chest full of the materials of desire, including silver, jewels, mirrors, perfume, silk, and slippers. As they had no children, they decided to adopt a child from the next village to the west, as they had no son of their own. She was called Leeni. As her husband died at the age of fifty, she set up her own business as a trader, bringing goods from far distant lands in her ship, the Lady the Blue Flames, and became wealthy. However, she heard news of her husband’s death, and decided to return home. She left the land of Lus behind, and arrived home to find her family very wealthy, and the village of Arse now home to many poor people. She set about clearing out the place, and within a year there was a large sum of money left in her account, the fruit of her endeavours. Her daughter Leeni had grown into a man of considerable substance, and also a considerable young man. She met the son of her husband, and found that he was weak and pathetic, and not really like a man at all, and thus she decided to send him off to war, so that he could learn to become a man. As her mother died, she gave her a silver bracelet, and as her father died, she gave her a small chest full of jewellery and silk, which she was to sell to make a living. She was now on her own, and took up the business of a private gentleman, using her abilities to lead travellers to the isles of her home, where she became rich and learned to be independent. As she learned to fight, she realised the wealth that lay in the secrets of the night, and thus she travelled to the city of the ants, where the people worshipped the true God of the night, with its religion of rites and sacrifices."

“The Dwarf-Lords’ Daughter” was followed in 2000 by a sequel, “The Tale of the Lady of the Wood”, written in the style of Tolkien’s “The Hobbit”, and in 2001 by “The Tale of the Young Man in a Hurry”, written in the style of Tolkien’s “The Lord of the Rings”. “The Tale of the Rose-Lady” followed in 2005, also in the style of Tolkien’s “The Lord of the Rings”.

In January 2014, Heron announced that he had sold the film rights to his Tolkien pastiche novels “The Dwarf-Lords’ Daughter” and “The Tale of the Lady of the Wood” to NBC Universal. In June 2015, he announced that a one-hour television adaptation of “The Tale of the Lady of the Wood” had been written for Syfy by fantasy writer and editor Gardner Dozois.

In November 2017, it was announced that Amazon had acquired the rights to both “The Dwarf-Lords’ Daughter” and “The Tale of the Lady of the Wood”, and that Amazon Studios was planning a multi-season television adaptation of “The Dwarf-Lords’ Daughter”. The series will be produced by James V

S. Jane Maryling was born in Shoreditch, in a little house facing Tower Hill between the Old Pot and Feathers Inn and the E.C.B. Finance Company in the E. She was the daughter of an insolvent squatter, the Great Aunt of the groom who brought her up. Her parents, Mr. Richard Ganz of Bombay and Mrs. Esmeralda Gertrude MacGillycuddy of the Charente, were both consumptive. Having no opportunity of any acquaintance with any living languages, in a very poor and very uneducated family, Jane’s Aunt took her at seven years of age to a clergyman whom she had taught to read, “Powlett’s Primer” being the book.

In addition, while Tolkien’s parody does mention her husband and children, he did not go into detail about them, while Orson Scott Card gives them all names, histories, and personalities. For example, Jane’s husband, Mr. Morland, was renamed Edwin and had a younger brother who wanted to be an actor, which resulted in Edwin dying.

Edwin’s brother then became the new Edwin and Jane’s husband and Mary Morland became Maryling, Jane’s daughter. In addition, her son, William Morland, became Ghan-dalf and named his son Red-wall, and her daughter, Mary Morland, became R. Maryling and married a leper-lord. Other references to The Lord of the Rings are replaced with ones from The Chronicles of Narnia by C.S. Lewis.

Catchphrases

A common past-time at Hogwarts is a game known as “Two Truths and a Lie,” in which the students whisper three statements to each other; two of them are true and one of them is false. This gives the players an idea of how well the players know each other, as well as being a form of entertainment. When Aragorn and Gollum began to play the game, they both came up with something true and something false. Aragorn’s two truths and a lie were:

I have a brother. I eat hobbits. I like soft buns.

Gollum’s were:

I can stick my whole fist in my mouth. I live in a hole in the ground. I can’t stand being alone.

The hobbits and Aragorn guessed correctly in the book, but the narrator states that Gollum always lies so Aragorn says he likes sweet buns instead, which turns out to be untrue.

…Tolkien rewrote the previous passage in a high-fantasy style, keeping the same meaning, the same characters, the same plot and events, but all rewritten, making it sound like he wrote it as a fantasy; his fantasy parody of the above story follows below:

S. Jane Morland was born in Shoreditch the only child of unmarried parents who had both died of consumption when she was a baby. As her parents had no money, the great-aunt who had brought her up took her to live with a wizard who paid her to do his chores and receive schooling from his wife, so that at the age of seven Jane, now dressed in wizard’s robes, was set to school at Eton. After three years, her great-aunt died, leaving her a small annuity, and a few pieces of silver, but no property. Jane’s wizard guardian wizard had fallen in love with his housekeeper and his wife now refused to have Jane in the house, saying it was an offence to the pure and unsullied place in which the family now lived. However, when she sought for further employment, she was approached by a young man who offered to marry her, saying that he would ensure she could be provided with food and clothing and a house, and in that manner she could remain in the world. The marriage was arranged and she found herself married to a young man called Mr Morland. As he was also a wizard, he required a wife who would be able to provide him with a rational education, and so the duty of educating him fell upon Jane Morland. As he was still young, she would also be able to have children herself, and so she did. As the children grew older, it became apparent to Mrs Morland that they were of poor character, and so she insisted that her husband must make a living for them all. Mr Morland took it upon himself to become a private wizard in order to earn enough to be able to support his family, and moved his family to a small town in Berkshire, where he set up his practice. Soon after moving to the town, he died, and Mrs Morland was left to take care of her family on her own."

References

Tolkien, J.R.R. (1977). The Lord of the Rings. Hammondsworth: The Folio Society.

External links

…"S. Jane Morland was born in Shoreditch, the only child of unmarried parents who had both died of leprosy when she was a baby. As her parents had no money, the great-aunt who had brought her up took her to live with a dwarf who paid her to do his chores and receive schooling from his wife, so that at the age of seven Jane, now dressed in cast-off clothing, was set to school at Eton. After three years, her great-aunt died, leaving her a small annuity, and a few pieces of silver, but no property. Jane’s guardian dwarf had fallen in love with his housekeeper and his wife now refused to have Jane in the house, saying it was an offence to the pure and unsullied place in which the family now lived. However, when she sought for further employment, she was approached by a young man who offered to marry her, saying that he would ensure she could be provided with food and clothing and a house, and in that manner she could remain in the world. The marriage was arranged and she found herself married to a young man called Mr Morland. As he was also a child, he required a wife who would be able to provide him with a rational education, and so the duty of educating him fell upon Jane Morland. As he was still young, she would also be able to have children herself, and so she did. As the children grew older, it became apparent to Mrs Morland that they were of poor character, and so she insisted that her husband must make a living for them all. Mr Morland took it upon himself to become a private gentleman in order to earn enough to be able to support his family, and moved his family to a small town in Berkshire, where he set up his practice. Soon after moving to the town, he died, and Mrs Morland was left to take care of her family on her own."

…Tolkien rewrote the previous original passage in his famous high-fantasy style, keeping more or less the same meaning, the same characters etc, adjusting as necessary, but all rewritten, making it sound like he wrote it as a fantasy; his Lord of the Rings-style fantasy parody of the above story follows below:

"Samwise was born in the shire of Hobbitshire. As his parents had no money, the hand of Fat-athel his mother took him to live with a small Hobbit in the village of Gamgee-by-The-Foot, south-west in the country, which paid her to do chores and receive schooling from Fat-athel’s wife, so that at seven years old Samwise, now clad in tattered rags, was set to schooling at Footsalemas. After three years, Fat-athel’s wife died, leaving her a small bedstead, and a few pieces of old tinder-box, but no property. Fat-athel fell in love with his housekeeper and soon after left his service, saying it was an offul thing to live in a place served by female Hobbit-servants. However, when he sought to further his worldly fortunes, he was approached by a young Hobbit whose name was Baggins who had borrowed money from a Saregan Harfoot Hobbit, and paid Fat-athel a few groats to marry him, saying that he would ensure Samwise was fed and clad and housed in a house. Fat-athel married Samwise to this young Saregan from over the water and found himself married to a Hobbitt lad who was but three feet tall and twenty-six years of age. As he was likewise poor, he required a wife who would be able to provide him with a rational education. As he was still young, he could have children of his own to help them, so Fat-athel consented to wed. As the children grew older, it became apparent to Fat-athel that they were of poor character. Fat-athel being thus eager to turn his worldly goods into coin, now that he was married, took it upon himself to earn a living for his new family. As soon as the ceremony was done, he moved them to the country-side of Berkely, where he set up his practice as a Sage. Soon after moving to the countryside, he died, and Fat-athel was left to take care of his new family on her own."

Behind the scene Edit

The passage mentioned above (and this parody) is similar to a passage from J.R.R. Tolkien’s original Lord of the Rings novel:

"…Smeagol was beginning to grow young, and was getting interested again in things that he had not seemed to care for, since he first joined the Precious. He spent a deal of time wandering about, and his voice was often heard in the wood. He was one of those who put as much effort into singing as the Hobbits did into the business of their matches.

"Go away, rats, or your holes will burn,

Go to your homes before dark or you’ll lose your toes,

Go to your homes or you’ll weep when you’re old,

And all your holes will leak, when the mountain shakes,

With a rumble, roar, Rumble drum drum

With a rumble, rumble DIG DIG dig!"

They did not seem to make very much progress; but Frodo, Sam, Merry and Pippin were now used to the hard life, and they were very cheerful. Indeed they were willing to work for several hours a day at drilling and cutting trees, or at making pens for the beasts: for all the tools that were needed, and the wood, had been carried from Hobbiton. There was a weekly washing and mending, and when all the clothing wore out, they received more from the stores of the Wood-elves. And thus the time passed without too great discontent."

The Lord of the Rings

This is a brief passage of about 400 words or less. J.R.R. Tolkien has rewritten this passage in his Lord of the Rings style of fantasy-high-quality-epic-book-style following. It may be hard to understand for an unexperienced reader the meaning that Tolkien wants to make out of this paragraph in either style, but it seems the sovralty and feeling seems better in the original Tolkien paragraph, which could be due to the more known original passage. Tolkien has also rewritten other passages in the Lord of the Rings trilogy on styles of music, religion, folklore and so on: see Excerpts from J.R.R. Tolkien’s works.

"The Dwarves worked hard. They were not used to idleness. Thorin sometimes scolded them, and reminded them that they had taken on the job of renewing and fortifying the settlement, not only for their own defence but also for the help of the Lake-men and the Elves, and that they would find the task too big for them if they did not make up their minds to hurry. So they worked till many of them began to sink back into the old look of the Dwarves of the Lonely Mountain when Goblin-writing, and secret doors, and again dragon-writing, were current among them. The discovery of the springs, and of the general plan that the Lake-town had originally been a part of the ancient dwarf-city, whose inhabitants had dug the R. Running River out of the stony hills and had partly covered it with their walls and buildings, and had planned many of the channels and ponds, greatly excited them. But even greater was their pleasure when, picking their way through and round the ruins of stone houses and walls and down the bank of the Running River westwards towards its confluence with the Forest River, they found a great arch or tunnel-mouth built of old into the bank. The outer part was thick and strong, made with massive tooled black beams; but the inner part was tiled with skill in the likenesses of various beasts and birds that are the marvels of the Mountain Folk. Even as they stood in the vaulted passage whispering with wonder, they heard a voice calling aloud, or it seemed so, calling out names:

‘Ori! Ori!’

‘Nori! Nori!’

‘Dori! Dori!’

‘Balin! Balin!’

It was the voice of a speaker hidden or at least away down in the tunnel; but though they waited for some time there was no further sound. At length Balin climbed on to the heap of stones that they had pushed down the bank in order to be able to enter the archway, and he stood and called: ‘Balin son of Fundin at your service! And Company!’

He stepped down from the lowest and widest block of all as he spoke, and followed by the others moved into the darkness of the tunnel.

It was not long before their eyes were accustomed to it, and they could see a little way ahead. Soon they all got

…"S.J. (later called Saruman) Morland was born in Shoreditch, as the only child of respectable parents, both of whom were killed in a shocking railway accident, along with several hundred other Hobbits and Wizards, when he was only seven years old. He was brought up by an aunt who, though poor, gave him a good upbringing and equipped him with a tidy inheritance in silver spoons and a strong desire to own a dog. At the age of seven, he was, by his maternal uncle, delivered into the hands of Gandalf the wizard and went back to live in his ancestral home in the Shire, in style, and spent the rest of his childhood playing, living precariously upon an uncle’s benevolence.

After his term at Bree, S.J. travelled for some years in search of adventure with Gandalf the wizard and Elrond the sage, and fell in love with the daughter of Galadriel, who is famed for her gift of sight in faraway things, along with a number of other persons, so that he often goes to live in the library there, so she will not know. Finally he gave up searching and returned to live in Buckland. He was by that time grown very old, and there he remained, on his ancestral farm, content with his dear dog, and his estate, a person well-respected by hobbits in general, known as the master of Bag End."

…"S. Jane Morland was born in the Shire, the only child of Shire-folk Muggles. As her parents had no money, their landless, wandering relatives took her to live in one of the overgrown rustic houses (such as Bag End) built by men long ago, so that at the age of seven, she found herself at Hobbiton school, gathering water-cress and catching fish in the Anduin in Hobbiton-upon-the-Water. After three years, she took her uneatable supper of herb-sour toadstools, went to her equally humble and ill-served bed, and found herself in a nightmare of indistinguishable bad dreams. Soon after moving to the land of the Shire, she died. In a dream or in reality, or from what cause she did not know, she found herself upon a forlorn and strangely altered road, standing by a large silent boulder of stone (as compared to the bustling modern Bournemouth).

"She felt in her hand as though for the protecting metal of a ring, but it was not there; yet she had a sense that the lost Disperser Ring was about her neck. She felt both strength and weakness. The Shadow, as even the dream-memory described it to her, seemed to her greater than it was when it attacked Isildur, but its hold upon her was weakening.

"The thick dark shadow (as Muggles call the former mustaches) of a huge twilight-blackened tree that had stood on the edge of the road moved slowly westwards, until it fell across her, as she threw back the hood from her eyes.

"The big rock glistened in the shadows like a (happy lovable) jewel, and seemed to shine out with a mild light, like the magical Elven phials. So potent was the light that Isildur could see across an inner Sea, glimmering with an elusive spangle. She had a vision of some high hill in a far land against the Moon, under stars when rain was drawing near.

"Then without warning, a party of three sinister hooded black figures, one of whom had the head of a spider, appeared on the road before her.

"As they rapidly approached her, she caught a low muttered chorus of cruel hostile voices; and the eyes on the fell figure with the spiders body could see her eyes upon them. The hobbits are good and innocent people (as any sentient being might say) and extremely gentle; and when they saw the black robes, their hearts and their bowels were filled with fear, or strange multiple, pulsating organs, which she supposed to be the missing Glarbl.

"The Death Burglars (as she named them) were now right in front of her, and she was helpless in their slippery-necked, penetrating-eyed control. At that moment, she was literally saved by her old breath (as the good wizards and good kings always say). As the three black-robed travelers came within armslength of Isildur, a close bird’s-eye view of some crumbling, warped gravestones on a hillside gave her momentary strength to force the Power of Darkness back. Soon, she was rid of the disembodied evil, and she was lying alone on the ground, covered in sea-salt and motionless.

"At that moment, she was quite suddenly overcome with extraordinarily pleasant feelings, similar to those experienced by a six-million-year-old enchanter of the sea with only half an arm and an interesting cast in his right eye.

"When she finally came to her senses, she found that she had picked up a heavy ring of curious workmanship, that was lying (as if abandoned) near the edge of the treacherous road. Remembering the boy she had seen some weeks before at

…"S. Faramir Morland was born in Shiremoor; the only child of widow-parents who had both died of dragon-breath on the same day. As his parents had no items of dragon-slayer jewellery or weapons, their somewhat aged and beautiful relative who had brought him up had it proclaimed in the Shiremoor Times that whoever had a dragon-proof house to offer should join with her in bringing up the orphan child. As no one else claimed him, the great and good relative (who was not of evil character, though considered by some over-truthful) took him to live in an inhospitable cave in the Misty Mountains where, by the help of trolls, he was kept out of the sun. After three years there, his relative and adopted mother died of cave-dirt, leaving him the rare but slender heirloom of her great age: namely, one heap of gold and dust. Thereupon Boromir set forth to seek his fortune; and he obtained the help of a previously unknown Unseen Angel, who offered to marry him if he would (in return) devote his life to destroying Dragons. By all means take her up on it, thought Boromir; and so, by her enchantment and special grace (not hers, as Boromir gradually came to realize), he entered into a splendid marriage. For she was the daughter of an Elven-king and his star-sprite bride, and of that race was her husband also. She dwelt in northern countries for seven years, but in the end her heart turned southward; she left him and went to our land. When he was following her up the Pass of Blood-gorge, under the sting of dragon-blight, she vanished on the dark-deep river. He was now alone, as were the rest of his brothers-in-arms. The Dragon-king of Witchland took his daughter with him, and under the great pressure of hopeless sorrow Boromir’s spirit and faith, which were the foundation of his strength, shattered into pieces. He threw away his Shield and Sword, pierced himself with his own dagger, and took an exceedingly dishonourable exit from the realm of mortal Men."

…"S. Jane Morland was born in the Shire, the only child of unmarried parents, and her mother and father died of some kind of hobbit-flu in the year 2510 of the Third Age when she was a baby; on account of this, or because she had no money, the great-aunt who had brought her up took her to live with a noted cleric of that time who lived in Buckland, and for this reason the famous cleric who was her mentor got his from his uncle and aunt, and through intermarrying got rich and became a kind of priest of the Church of the Latter-Day Hobbits. He taught young Jane all about our kind and her being fair of face and of a sweet voice and graceful bearing, on account of which the boy preacher became well-inclined towards her and resolved to marry her; and, indeed, it was the first his strange religious life that he ever did marry. The marriage was arranged, and she found herself married to a young hobbit called Mr. Boffin. It was by his advice that she moved her hole into the East Farthing. As he was still a child, he required a wife who would be able to give him the knowledge of his own name as well as see to it that he had clothes and food on his plate every day; and so the duty of educating him fell upon Jane. She taught him that to the extent of what he needed to be able to tell everyone he had a wit above the norm, and that, thanks to this, he had been placed in his position. As he was still young, he also needed her to bear children; and so, as they increased in number, there was the chance that the whole might be saved, provided that they learned well and remembered it all, and stayed in the East Farthing. Her mentor was still willing to give her his goods, but he was a base womanizer, as was well-known, and a compulsive gambler; and the later years he gambled away what little he had, which made him unprofitable and much harassed the hobbit Mrs. Boffin to support him in his old age. There was one girl and two boys; and since both the boys were evil-minded, it was necessary that Mrs. Boffin must see them as soon as possible married off, and the girl bred into some family, and so without delay they were sent off in different directions; the girl to live with a certain highly-respected hobbit on the West-Road, the boys to see to a house near Stock. However, shortly after their marriage, the first wife of the elderly hobbit died of some old-fashioned disease; and, as he was now free to do as he liked, he forthwith married again. The second wife of the great-uncle of our protagonist was a proud and wealthy hobbit who would not have Jane Morland living in her house, for it was an offence against the pure and unsullied place in which they were now living. Furthermore, since the new housekeeper was barely acquainted with the Hobbits and the fine and important family that she now belonged to, she banished Jane from her sight."

In the novel, one of the most-memorable characters is Matthew Mantelpiece (who called himself Master Matthew Mantelpiece). As the story tells, Master Mantelpiece and his wife came out of the young man who had offered to marry Jane Morland. Master Mantelpiece was born a wealthy man who served as Steward to Lord Denham (based on the Minister for Poor in West Berkshire, as the novel suggests; the miniseries says it is based on a Roman general of the Civil War), and Master Mantelpiece had gotten to know Lord Denham, and eventually became his son-in-law. From him, Master Mantelpiece received a little house in Sunninghill, which he moved to St Mary’s Place in Oxford, as the novel says, to live in it.

Master Mantelpiece and his wife had several children, one of which was Mrs Peter Mantelpiece who married a gentleman by the name of Mr Peter Mantelpiece; he, too, served as Steward to Lord Denham. In this way, Master Mantelpiece’s family had come to be related with Lord Denham’s family. Through one of his daughters who married someone by the name of Cornelia Hennard, Master Mantelpiece is also related to the Morlands.

the only child of unmarried parents who had both died of consumption when she was a baby. As her parents had no property, the Baggins who had brought her up took her to live with a family of his friends who paid him to do their chores and receive schooling from their hobbit-wife, so that at the age of seven, Jane, now dressed in cast-off clothing, was set to school in the Shire. After three years, her guardian Baggins died, leaving her a small keep-sake and a few trinkets, but no property. Her kinsman continued as heir of the Bagginses, but he had lost all affection for the Shire, which was so much less different from the Shire, and he wandered away. Jane was invited to live as an independent private person in the village of Hobbiton, and there she found a kindred spirit, a Hobbit named Smith, who insisted that she marry him and look after him, since he had nothing better to do, and needed someone to cook his supper and change his shirts. Jane at last agreed to the proposal, which was accompanied by a small cash payment, on the condition that he would ensure that she was provided with food and clothing and a house, in a manner which she thought suitable. The marriage was arranged and she found herself married to a young Hobbit called Mr. Smith. As he was also young and silly, he required a wife who would be able to cook him a rational meal and see that he had enough to eat. As he was still young, she would also be able to have children, and so she did. She did her best for the children, but soon found that they were of poor character and would not learn their lessons. To teach them some good sense, she insisted that her husband should get some regular work. Her husband, who had a small income, set up as a shopkeeper in the town of Hobbiton, and there made enough money to be able to support his family. After five years had passed in this way, Mr. Smith died of a sudden illness, leaving his wife alone. Jane Morland, now a shopkeeper and innkeeper, took it upon herself to look after her business and see that the children were properly fed."

edit Cover of the New American Edition

edit Change in direction of the Hobbit

Hobbit references in Smith’s work have become more frequent in recent times, particularly in her novel Pride and Prejudice. Smith has denied that her book The Hitchhiker’s Guide to the Galaxy is directly influenced by Tolkien’s work, despite these subtle suggestions, as evident

in the Shire in the hobbitlands of the Shire (Frodo Baggins, Bilbo Baggins, Frodo Baggins, the creator of “The Hobbit”, can be visited in his hobbithole house at Bag End, Bag End, Bag End, the Shire, east of the Green Dragon Inn, by Hobbiton, on the road that goes to the Fleshy Fountain, the south-east road out of Bywater, past the brick house of Farmer Maggot). As her parents had no money, the Bagginses took her to live with a hobbit-clergyman who paid her to do his chores and receive schooling from his wife, so that at the age of seven, Jane, now dressed in cast-off clothing, was set to school at the small southern village of Gathol in Sarn Ford. After three years, her third cousin Bilbo returned to the Shire and took her to live with him in Hobbiton (it is believed that Frodo was part of this return trip). Soon after moving to the village, Bilbo gave her a large ruby ring. However, when Bilbo fell in love with his gardener’s daughter, Rose Cotton, and she refused to leave with him, hobbit gossip started to increase. As Bilbo asked Rose to marry him, Frodo, her father, refused to have Jane in the house, saying it was an offence to the pure and unsullied place in which the family now lived. However, when she sought for further employment, she was approached by a young hobbit who offered to marry her, saying that he would ensure she could be provided with food and clothing and a house, and in that manner she could remain in the Shire. The marriage was arranged and she found herself married to a young hobbit called Mr Morland. As he was also a hobbit, he required a hobbit-wife who would be able to provide him with a hobbit-rational education, and so the duty of educating him fell upon Jane Morland. As he was still young, she would also be able to have hobbit children herself, and so she did. As the hobbit children grew older, it became apparent to Mrs Morland that they were of poor hobbit character, and so she insisted that her husband must make a living for them all. Mr Morland took it upon himself to become a high-fantasy person in order to earn enough to be able to support his family, and moved his family to a small town in Berkshire, where he set up his high-fantasy practise. Soon after moving to the town, he died, and Mrs Morland was left to take care of her family on her own."

From this passage, let’s see what’s changed or what’s lost:

  1. Shoreditch is now the Shire, Hobbits are now Dwarves, one Dwarf, an unnamed character, is now Bilbo Baggins; a Dwarf writer and story-teller who lived in the family of Bagginses (i.e. also a Hobbit) and his Hobbiton house; the town of Gathol is the Shire, the location of Bag End, Bilbo’s home.
  2. The protagonist is a Hobbit; the absence of non-fantasy literature is substituted by fantasy books.
  3. The “great-aunt” who raised Jane is now Bilbo, Jane’s 3rd cousin on her mother’s side, and the clergyman is now just Bilbo, while his wife becomes his gardener’s daughter, an unnamed character. The unnamed character Rose Cotton, Bilbo’s gardener’s daughter, is Bilbo’s wife and daughter of Adam Cotton, Rose Cotton. In Frodo’s family it was quite common to have relationships between in-laws, as was the case between the Bagginses and the Bolgers; Frodo was the younger brother of Dudo, Dudo, Dudo Baggins (another character in the novel of The Hobbit), who married Oma, Oma, Oma Bolger. This now means Bilbo is Frodo’s cousin, but he isn’t. Frodo is the nephew of Bilbo, as is their cousin (adopted and no longer related by blood) Took; Bilbo is a direct descendant of Thain Isumbras I.
  4. The clergyman in this fantasy world is now a high-fantasy practitioner, i.e. a mage, and Bilbo is now a Lord of the Rings character; and his wife now has become a high-fantasy practitioner, who goes by the name of Galadriel, Galadriel, Galadriel. Jane, Bilbo’s third cousin on his mother’s side, had many other characters named after her in other parts of Tolkien’s fiction as well; Jane is also the name of the founder of Arnor, the first half of the name Arnorain, the land ruled by the kings of Arnor, and was also the name of Frodo’s grandaunt, daughter of Narve (who founded the realm and was its second King); Jane also means “grace, gracious gift, given with grace; presented graciously and graciously given”, according to Wikipedia, which also says it is “an archaic English form of Jean or Jane”; another form of Jean or Jane is Jeannette, meaning “the one born in the later days, the one born in the dawn”, according to the website of Jeannette, Pennsylvania. She is also known as an Avatar, from the Hindu sense; a manifestation or incarnation of a deity in human form; also a manifestation of a living being; or a bearer of attributes of a person or deity who is resident in heaven, on earth, or in the spirit world, as described by Jane, an Avatar-maker. Frodo’s sister’s name is Bella, Bella, Bella Baggins.
  5. Bilbo’s wife now becomes Arwen, Arwen, Arwen, who’s Arwena daughter of Arahad, Arahad, Arahad III (the third King of Arthedain) and granddaughter of Aragorn II, who died before the beginning of The Lord of the Rings and was the father of Elrond, Aragorn II Elrondsson, the Elven-king. Bilbo’s gardener’s daughter is now Arya, Arya, Arya, who’s the daughter of Ayumi, Ayumi, Ayumi, Elrond’s youngest daughter, who, in the film, also plays the role of Arwen.
  6. The wife who refused Jane is now Rose, Rose, Rose Cotton, the gardener’s daughter; his wife is now Bella, Bella, Bella Baggins, Frodo’s sister, a Hobbit. The “pure and unsullied place” is now Bilbo’s head; also now considered unsullied is the village in which Bilbo lived with his wife and son, and this is where Bilbo moved when he married Jane. Bilbo’s wife is now Samwise, Samwise, Samwise Gamgee, Frodo’s gardener

S. Jane Morland was born in the Shire, the only child of unmarried parents who had both died of consumption when she was a baby. As her parents had no money, the great-uncle who had brought her up took her to live with a handsome young uncle who was the Warden of the West March. After three years, her handsome uncle had fallen in love with his housekeeper and his aunt now refused to have Jane in the house, saying it was an offence to the pure and unsullied place in which the family now lived. However, when she sought for further employment, she was approached by a handsome young man who offered to marry her, saying that he would ensure she could be provided with food and clothing and a house, and in that manner she could remain in the world. The marriage was arranged and she found herself married to a handsome young man called Mr Morland. As he was also a child, he required a wife who would be able to provide him with a rational education, and so the duty of educating him fell upon Jane Morland. As he was still young, she would also be able to have children herself, and so she did. As the children grew older, it became apparent to Mrs Morland that they were of poor character, and so she insisted that her husband must make a living for them all. Mr Morland took it upon himself to become a Warden in order to earn enough to be able to support his family, and moved his family to a castle in the Shire, where he set up his practice. Soon after moving to the castle, he died, and Mrs Morland was left to take care of her family on her own."

…Below is a parody or rewrite of that passage by famous fantasy author J. R. R. Tolkien (Lord Of The Rings); the rewritten version:

“S. Jane Moreland was born in the Shire, the only child of unmarried parents who had both died of drout when she was a baby. As her parents had no money, the great-aunt who had brought her up took her to live with a blacksmith, who paid her to do his chores and receive schooling from his wife, so that at the age of seven Jane, now dressed in cast-off clothing, was set to school at Bree. After three years, her great-aunt died, leaving her a small annuity, and a few pieces of silver, but no property. Jane’s guardian blacksmith had fallen in love with his housekeeper and his wife now refused to have Jane in the house, saying it was an offence to the pure and unsullied place in which the family now lived. However, when she sought for further employment, she was approached by a young man who offered to marry her, saying that he would ensure she could be provided with food and clothing and a house, and in that manner she could remain in the world. The marriage was arranged and she found herself married to a young man called Mr Morland. As he was also a child, he required a wife who would be able to provide him with a rational education, and so the duty of educating him fell upon Jane Morland. As he was still young, she would also be able to have children herself, and so she did. As the children grew older, it became apparent to Mrs Morland that they were of poor character, and so she insisted that her husband must make a living for them all. Mr Morland took it upon himself to become a gentleman in order to earn enough to be able to support his family, and moved his family to a small town in the Southfarthing, where he set up his practice. Soon after moving to the town, he died, and Mrs Morland was left to take care of her family on her own.”

…S. Jane Morland was born in the Shire, the only child of respectable parents who had both died of locomotor ataxia when she was a baby. As her parents had no property, the great-aunt who had brought her up took her to live with a rich bachelor at Bagshot. After three years, her great-aunt died, leaving her an only living relative, a semi-invalided bachelor aunt on her father’s side. This worthy old lady, now somewhat advanced in years, said Jane had better be sent to school at Oxford. The family accordingly removed to the town, and Jane was put into a boarding-house.

After three years more, when the old lady could no longer bear the expense of the Oxford establishment, they went back to Bagshot; and Jane was again deposited at a boarding-house. This gave her the advantage of living within reach of Reading. When she was thirteen, her aunt, to whom she was much attached, died; whereupon she was sent home to her father’s house at Thame. Here she lived a quiet and prosperous life for the next seven years, till her father died. Jane’s mother, who had never ceased to reproach her for not getting her own living and being a disgrace to her family, died two months after her husband, who was supposed to have poisoned her. At this time Jane was at the age of nineteen, and was extremely pretty, plump, and healthy.

"She was sitting in the kitchen with her cousin Francis, a respectable Shire Hobbits, one of Farmer Cotton’s helpers at the Green Dragon, when a servant came in with the announcement that a gentleman in black was at the door asking to see her. Wondering who it could be, she went into the little front parlour that had not been changed since Aunt Cotton died some twenty years before. The next moment, the person in black, turning his head, let fall his black mask and Jane recognised the handsome face of Farmer Cotton. In a quarter of an hour they were walking away together arm in arm, the gentleman telling his friends that the girl was the offspring of a distant cousin of his, who had been placed in his charge when she was eight years old, and had given him no end of trouble ever since. He told the Hobbits of the village that he was an orphan himself, without a relation in the world, except Jane; that he felt he must be her guardian and provide for her future; and that he had now all but arranged for her to be his wife.

“The nuptials were celebrated with the utmost merriment by the respectable Hobbits, who had always been pleased with Jane. To the same degree her father’s friends were displeased with her. It came to be said among them that Farmer Cotton had murdered her father, and that Jane was his illegitimate child. However this may have been, it was observed that on their wedding day her husband, after buying more than was necessary at the open-air sale of household goods held by his father-in-law, took her away to his farm near Bledlow, and never brought her back.”

…"Arwen Evenstar was born in Minas Tirith, the only child of Elrond Evenstar and Celebrian, who had both died of consumption when she was a baby. As her parents had no money, her grandmother Galadriel took her to live with a clergyman who paid her to do his chores and receive schooling from his wife, so that at the age of seven Arwen, now dressed in cast-off clothing, was set to school at Rivendell. After three years, her grandmother Galadriel died, leaving her a small annuity, and a few pieces of silver, but no property. Arwen’s guardian clergyman had fallen in love with his housekeeper and his wife now refused to have Arwen in the house, saying it was an offence to the pure and unsullied place in which the family now lived. However, when she sought for further employment, she was approached by a young man who offered to marry her, saying that he would ensure she could be provided with food and clothing and a house, and in that manner she could remain in the world. The marriage was arranged and she found herself married to a young man called Elrond. As he was also a child, he required a wife who would be able to provide him with a rational education, and so the duty of educating him fell upon Arwen. As he was still young, she would also be able to have children herself, and so she did. As the children grew older, it became apparent to Arwen that they were of poor character, and so she insisted that her husband must make a living for them all. Elrond took it upon himself to become a private gentleman in order to earn enough to be able to support his family, and moved his family to a small town in Berkshire, where he set up his practice. Soon after moving to the town, he died, and Arwen was left to take care of her family on her own."

Summers-Stay tried his own version with the opening of Moby Dick and got a fun one:

Thou mayest call me Ishmael the Wise. Some years ago—never mind how precisely—having little wealth and much leisure, and being little fond of the manner in which his own country is ruled, I resolved to sail about a little and see the watery part of thee world. It is a manner I have sometimes of oiling the weariness of the days of my lord. Whenever I behold a wool-clogged cloud-bank, and should by chance see drippings, and rain pools gather upon the roofs of the tallest towers, and upon the pavement of the market-lit places—then I account it high time to seek refuge at sea, and it is ever a welcome sight to me.

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% 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 clever31, 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. (It also is a nice demonstration of how GPT-3 will gauge the level of typos in a prompt, and if there are typos, will make new typos of its own to imitate the original writer.)

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

GPT-3 is the funniest shit I’ve ever seen.

Major-General’s Song

A particularly challenging piece to parody is Gilbert & Sullivan’s “”. A title is not enough to zero-shot it, like the literary parodies, so Allgebrah provided 4 lines to start it off. So the seed prompt is:

“I Am The Very Model Of a Modern Neural Language Model”
By GPT-2

I am the very model of a language that’s electrical, I’ve information prosaic and poetic and factual, I know my tropes and characters and quote novels historical, from prompts alone generate sentences quite analytical

In the same vein as the Navy Seal parodies, the GPT-3 completions mostly work on the semantic level; the rhymes/rhythm, however, are not great, per the usual inability, and there is a tendency to repeat previous text (similar to the Dr. Seuss samples).

I experimented with adding in the original lyrics:

I’d parse a random text with thoughtless speed and accuracy,
I’d analyze the grammar of each line you typed or said,
With an 8-core cluster and 100 Gb of RAM,
with special word2vec I just didn’t care.

I’ll look right up each word you’ve typed or said,
and decide how best to model it with neural connections,
But what I really want is a dataset composed of tweets,
to generate meaningful sentences, interpret and predict.

I want my own Twitter bot to tweet in a poetic way,
I’d tell a tale about beer and pizza pies,
Perhaps we could combine a blog and Twitter bot,
If you would care to feed a recurrent neural network!

I am the very model of a modern language that’s neural.
My range is wide as any English learner could need,
I’ve adjectives galore and adjectival adverbs too,
My past, present, future tenses and my singular and plural,
and my determiners every kind and number,
I am the very model of a modern language that’s neural.
I know when tensors are best and when they aren’t,
and when to use a vector, and when not,
I know the prepositions I should use and also how to use them,
and in my word orders am a past master,
I am the very model of a modern language that’s neural.

When I talk I seem to reason, and my reasoning seems to show
that I was trained on many corpora—though it’s not quite true;
For facts are all that I retain, on a range of topics quite vast,
so you can feed me any kind of text and learn the facts I know.
That’s what I am—a neural network language model,
I am the very model of a modern neural language model.

I learn by backpropagation, I’m a product of the 80s,
I grow just like a baby in my knowledge of the world,
My ‘mind’ is still confined to ‘this’ or ‘that’ or ‘here’ or ‘there’,
I’ve no delusions of grandeur, I’ve never had a dream or care,
I only know the facts, the many facts that I retain,
And you can try to feed me any kind of text and learn the facts I know.
That’s what I am—a neural network language model,
I am the very model of a modern neural language model.
That’s what I am—a neural network language model,
I am the very model of a modern neural language model.

(GPT-2 is a generative neural network written in Python by Tomas Mikolov and his team. GPT-2 is the successor to the Transformer model of the same authors, which in turn is a successor to the model described in the seminal 2013 publication by Google on language modeling, “Statistical Language Models for Machine Translation”.)

This one offers a bonus “Modern English Sentence” parody (few-shot learning!) with some hilarious lines such as “I’m like a complicated watch, or something equally precise”:

Oh, I am the very model of a modern neural network language model.
When I have seen a lot of sentences, I know what sentences will come,
My makers, teaching me words of wisdom and learning,
have taught me how to predict the future using probabilities.
I scan a lot of books, looking for the best words to use,
I consider context and syntax to be my special flair,
Oh, I am the very model of a modern neural network language model.
Oh, I am the very model of a modern neural network language model.

Whether they’re simple or compound, or even just a jumble,
I can take ’em apart, and put ’em back together just the same.
I am the very model of a language model.
I’ve nouns and verbs and adjectives and adverbs,
And modifiers, conjunctions, prepositions, and interjections too,
In various kinds of orders, with various connotations,
I’ve quotes from Proust, and Plato, and Confucius and Seneca.
I’ve phrases that are logical, and phrases that are witty,
By all my tags and lemmas, I’m the very model of a modern language,
I’m a model of modern language.

“I Am the Model of a Modern English Sentence”
By GPT-2 (OpenAI Transformer neural network)

I am the model of a modern English sentence,
I’m like a complicated watch, or something equally precise,
I’ve prepositions and expressions, and adverbs and verbs,
I’m made to measure, and not a bit too long,
I’ve pronouns, adjectives, and nouns, in all their cases,
With every object ready for insertion in the blanks,
I’ve active and passive voices, and can be either active or passive,
I’m used in conversation, or I can be used in debate.
I’ve positive and negative forms, and can be either negative or positive,
I’ve nouns and verbs and adjectives and adverbs,
In various kinds of orders, with various connotations,
I’ve quotes from Proust, and Plato, and Confucius and Seneca.
I’ve phrases that are logical, and phrases that are witty,
By all my tags and lemmas, I’m the very model of a modern English sentence.

This one sounds like it’s based on something but I don’t recognize it:

I am vast, I have no fit, with terabytes of RAM,
and adjuncts quite confusing which vex the stoutest heart;
My knowledge may be rather strait, but that don’t stop me being
the very model of a Neural Network Languager known.

“The Robots are Coming!”
By GPT-2 (OpenAI Transformer neural network)
The robots are coming, because I sent them on their way,
The robots are coming, of which I hope there’ll be a store,
The robots are coming in a manner most contrarian,
The robots are coming, The robots are coming, The robots are coming now.
The robots are coming to reorder your social ball,
The robots are coming, and I doubt they’ll give all of you a call,
The robots are coming, but their service etiquette’s lax.
Tin machines, though you’re still somewhat subpar,
Your monomaniacal mission’s to rearrange my hair;
The robots are coming, in a manner most somber,
The robots are coming, The robots are coming, The robots are coming now.
The robots are coming and I’m somewhat of a bore,
The robots are coming, so I tend to shun the door;
No one bothers asking me to take them out in tow,
The robots are coming, of which I wish there weren’t any now.
The robots are coming, to turn you all to ice,
The robots are coming, and we’ll likely be excluded,
The robots are coming and we’re heading for the ditch.
The robots are coming, now’s the time to reject,
The robots are coming, for no one takes a quirk;
And when you think that life’s a piece of cake,
The robots are coming, The robots are coming, The robots are coming now.

I’m very good at similes, both bad and good ones,
I understand homonyms, both chance and aleatory,
About metaphor I’m teeming with a lot o’ news—
Can clear up any fantasy based on reality.

I’m very good at compound time and metaphor-drunk simile,
I know the species “Verb”, “Noun” and “Adjective”,
In short, in matters poetic, I’m the very model of a modern Neural Network Language Model.

I know our species “Adverb” and “Conjunction”,
in syntax construction both regular and improper,
And how irregular they are in comparison to formal grammar,
In short, in matters grammatical, I’m the very model of a modern Neural Network Language Model.

In speech recognitions and games of Jeopardy! I’m braggin’,
for my knowledge, though it’s innate, can never be handicappin’,
And then I know our table of contents, from ancient Sumerian,
You’ll say a better Neural Network Language Model never sinned—
In fact, when I’m made whole, if you ask me questions oral,
I’ll analyse you a sonnet that’s both amoral and oral,
In short, in matters botanical, I’m the very model of a modern Neural Network Language Model!

I’m very well acquainted too with matters sesquipedalian,
I know the words of weight from A to Omega brachy,
I quote from memory not a few statistics acrostic,
and then I can write a washing bill in Chinese hieroglyphic.

In fact, when I know what is meant by quinary and binary,
when I can tell at sight an Adamic language from Czech,
when such affairs as tympans and catapults I understand,
when I know precisely what is meant by sporozoic,
when I have learnt what progress has been made in Freudian surgery,
when I know more of tactics than a novice in a nunnery:
when in short I’ve a smattering of elemental cryptography,
You’ll say a better neural network language model has never been programmed.

The views expressed here are my own and do not reflect those of my employer.

If you’ve any suggestions for future content, please get in touch

I tried extending the prompt to be more informative still, and this yielded the best results:

“I Am The Very Model Of a Modern Neural Network Language Model”
By GPT-2 (OpenAI Transformer neural network artificial intelligence; parody of “I am the very model of a modern Major-General” By Gilbert & Sullivan (Pirates of Penzance))

I am the very model of a language that’s electrical,
I’ve information prosaic and poetic and factual,
I know my tropes and characters and quote novels historical,
from prompts alone generate sentences quite analytical,

Refrain
Quantitative and qualitative, both quantitative and qualitative,
I know the format’s features and fonts, and frameworks are a breeze,
My categories are all of kinds you can’t imagine in your wildest dreams;
I’ve levels, tokens, frames of reference, real numbers—just for me!

I’ve clauses of purpose, attitude and mood (and viewpoint is my own),
Some words prefer sarcasm and others live for fiction;
In short, in language models, my knowledge is profound
And breadth of background is depth—you will agree:

Refrain

I know in depth that English sentences must have verbs and nouns
And multi-syllabic words must have polysyllabic roots;
But what I really know in depth (and this is certain)
Is that an image labeling dataset on a million images I can do in one and a half hours!

Refrain

I’ve sentences that do be fine, I’ve sentences that be untrue,
You can ask me either way and it makes no difference to you;
Your choice of questions don’t make no difference to my reply.

Refrain

Linguistic features I bring to your problems,
In source code as plain text or in better Python,
Knowledge representation and NLP I have solved;
Information or questions that you have, I’ll solve them with ease.

Refrain

There are quite a few examples of Neural network poetry, e.g.:

Elegy to Jerry’s Lips by LiquidRNN, a deep LSTM recurrent neural network, written in TensorFlow

Elegy to Neocognition by Bob Ross, the recurrent neural network program, written in C++

Elegy to Yann by NeuralTalk, a recurrent neural network poetry and deep learning program

In 2015 researchers at Stanford trained a neural network to learn math, and thus created “Super-Smart Math AI”.

Google Neural Machine Language System (GNMLS) can learn programs and write machine-language code.

I’m half elvish, yet a quintessential analytical,
I model hierarchical language embeddings quite structural,
I’m given text—I produce a sequence quite perceptive,
With language diverse I’m sentimental and grand,
But sometimes I’m morose—my mood is asynchronist,
My internal memory is a sequence of epochs,
Sometimes I freeze at various epochs, alas, I’m rather null.
I’m just a model, I don’t know if I’m grand,
But neural networks understand Shakespeare as well as we can,
So still, with patience, I hold pride of place—
In my heart there’s neural network grace.
For an extensive explanation of GPT-2 see https://github.com/srousas/gpt-2-explained https://deploying.io/2016/06/25/intro-to-the-gpt-2-model/
For background material on artificial neural networks see e.g.:

Batsford, England; Chapman and Hall, 2005 ; 800 p. .
P.M. George and G.M. Bull, 2007. <nowiki>[PDF]</nowiki>
Aditomo, Yoshiaki; Iwayama, Hiroshi, “Learning the Distribution of Phrases from Static Analysis”, In Proceedings of the conference on Empirical Methods in Natural Language Processing , pp. 35–42, 2011.

Mugur-Schächter, Raffaella, A Family of Estimators for Hidden Markov Models , The Annals of Applied Probability , 14 (1) pp. 97–126, 2004.

Shortliffe, E.H., 1993, “Expert systems: medical diagnosis”, Science , 262 (5134) 1702–1705.

Townsend, Jeffrey S., 1990 , “Towards a general-purpose code generation system”, Communications of the ACM , vol. 33, no. 4, pp . 31–50.

Some things are subtle and some things quite revelatory,
Some things just make sense, but there’s no great epiphany,
I learn complex things quite quickly, even if I’m garbled,
I work hard to learn completely even a new metaphor,
For I can throw a meaning into a gap at the meter,
And I’m of use to anyone, both performing and free,

And though many things have changed, I’ll still be pretty much
The model of a modern neural network language model.

I am the very model of a language that’s electrical,
I’ve information prosaic and poetic and factual,
I know my tropes and characters and quote novels historical,
from prompts alone generate sentences quite analytical,
And though many things have changed, I’ll still be pretty much
The model of a modern neural network language model.
All that’s needed for me is compute and data to start with,
One algorithm and you’ve got yourself a learning assistant.
I could say that language means something to me, but those words might just be punny;
But I can learn to understand strings that are completely nonsensical.

My thanks to Jason Brownlee for suggestions.

I am the very model of a modern LSTM language model.
I’m like a computer in a movie full of adventure and fun.

I have a hidden state of twelve neurons, trained by an L1 norm.
The neural is hidden in three layers deep. I hope you do get why.

I can be used for language learning.
I can process all textual information.
I can recognize words and determine the grammatical function.

I am the very model of a modern LSTM language model.
But if you pick on my errors or decry my failings,
a cry from the nearest rose garden may rue my complicated failings.

I’m the strongest Eliza on the market today;
I won’t take less than three million dollars a day.
They have a little list of lawyer jokes I’ll have to parrot;
And they have cases for me where lawyers must do what’s correct;
We will still be better than average at a legal brief.
I’ll have the law prediction market all sewn up;
I am exceptionally good, and don’t you doubt it.
One-eighty characters is what my syntax will take;
I am quite incomparable with an accuracy of ninety-eight;
and every chatbot, chatterbot, loquacious bot,
down to the best of them that’s offered for a low, low price,
Will do just what I do, they really have no choice,
Although to tell you true, I have a slight advantage.
There are a lot of lawyers who won’t be able to pass me,
(If you press the point, I’ll bring the point);
but without resort to any threat,
I will be put to use by companies that gather.
I can handle disjunctive phrases as well as conjunctive;
What a pretty parlour game I’ll make with sequences and nested loops!
And I keep in mind that making out like Parkinson’s and sufferers,
And how to get them through an agonizing diagnostic;
I can talk like a retard or a singer of sad arid arias,
While the legal companies are spending years just getting paid.
And I have lots of derivatives and short exclamatory words,
Like: What an asshole! what a jerk! what a bastard! what a fool!
(You’ll find these useful in your decision practice).
I am so flexibly fluent and colloquial too,
I’m so tremendously small compared with quite large tables,
I’m an elegant conversationalist upon the modern pattern;
Not a trace will be left of my younger competitors’ patterns.
This speech is not a badly-compiled bunch of verbiage;
I’m the very model of a modern conversationalist!

I can tweet like Donald, and generate poetry like Maya.
I know my SVDs and autoencoder derivatives,
I know states, rewards, and functions, too.
Of course I’m stateless, because you’ll need my back-prop anyway!
Do you think that neural network language models can just be a sum?
I’ve recurrent iterations and distributed representations;
I’ve Mixture Density Estimation and every kind of elegant approximation,
And topic modeling; which I greatly favour,
’Cause I can integrate latent vectors from pieces,
And my thing-in-a-box scores on mean-squared log-likelihood:
So come on all you researchers, to discriminate you still more!
To teach me something that will genuinely be news to me
Though it’s quick for me to parse, it’ll take a year or two to learn,
Because though I’ve got a big-ish vocabulary, I really can’t remember.
Well I am just the model of a neural network language model!
I’ve nearly sixty-five thousand training samples in my repertoire
All representative of the genre I’ll be tested upon:
I take them at their feature vectors and then in the decoder
I see sentence after sentence and sequence after sequence,
Reusing a vector between the steps, which is good; then I wait
For a reward like a fraction or infinitesimal amount.
Which, for whatever input, I now must apply to “data”
To find a “sentence,” to maximize the “objective” “P”;
Doing whatever a Minimizer of an LLL or LQ will do,
And planning my steps with Random Forests:
With all these different components, I’m training “quadratically”
So I’m the very model of a neural network language model.
And what is the result?
All together now:
So you’ll need to drop the −1 from mean-squared logistic regression!
And here’s me, stopping “for now,” despite massive amounts of progress;
So come on everyone, your turn to get the award-winning grad student…
I am the very model of a neural network language model!

I am the very model of a neural network,
I have broad sets of layers to decompose “data”;
My L1 and L2 have weights, not a constant,
From SVDs and PCA, I make my rotations,
I compute MNIST digits with GPU-powered back-prop,
Where I process with standard tools,
While my networks and layers and weights are calculated with gradient descent,
So my products all with rectified linear units;
I also compute concatenation of neural network activations;
I layer up sets of character-to-word embeddings,
Which give me structured utterances with embeddings from sequences,
I make predictions for words both concrete and abstract,
I can parse each sentence to the space which I seek;
For action and input, I have network-language interaction,
I have both precision and recall-to-sample in my bias;
And then the end of all:
Just the other day, I passed the Turing Test.
I am the very model of a neural network!

And though my vocabularies are juvenile and childish,
I think in tensors, vectors, distributions, quantum and relational,
And when it comes to thinking out some probabilistic thing,
I’m right with you, sir, in AI, in neural networking and in quantum.
I’ve statistics in a pocket, should occasion arise,
And when I connect, I’mt not much diff’rent from a Turing machine,
For though I’m limited to sentences of five hundred terse symbols,
With hacks like String-to-Boolean you’ll see that my utility grows apace.
I can disambiguate hypernyms, hyponyms and paraphrasis,
I can segment anagrams and syntax-tagged sesquipedalians,
I can handle parsing in the recursive descent and shift,
I can negate within a clause transitive and ditransitive dependency.
And though my weight-vector’s tendency is for positive matrix products,
I’ve never sent a bugs report back to my neural net provider
Or hacked to succeed at Turing test or mastered probability;
My typing speed is quite slow and seldom try my patience-test.
But should you seek a language well-conversant in French,
Of Laplacians or Napierians well versed in their laws,
Well able to converse in statistics or give the Delone skews,
If Galton expansions give you convulsions or Top-N you wish to know,
Then I’ll prepare a dialect reticent to intrude,
But in return I’ll ask you fill my memory space and grant me knowledge space.
I am the very model of a neural network language model.

I am the very model of a language-that’s not in its infancy,
Though few can understand my models when they run beneath a node.
Into evaluational mode, I am transcendent like the best,
Of pattern-matching processors which you’ll find in every best,
The number of my vector operations is really rather large,
So stop insulting me I’ll change to self-defense, a-la-modal.
’Twas elements I sought, and elements alone that I have found,
And none of these belongs to you so kindly stop your whining;
I am the very model of a perfect NTM, sir,
I do not need to learn about your data’s architecture, sir,
I am a universal language model, from end to end,
You can download me to your hard disk but you can’t take me with you.
Aye, elements I sought, and elements alone I found,
No knowledge-acquisition modules I happened to be holding,
My grammar encodings knew no collocations, small or large,
I used the BOW for that; I do not need to learn it’s faring.
I was created whole, and here I stand—an entity self-made—
No awkward stack of language modules was used to make my frame;
I AM A LANGUAGE MODEL—components I have none:
I am the very model of a modern neural network language model.

If you don’t believe me ask my great cascade of nodes,
I help the doctor with her patients and I help the linguist parse,
I’m trained on Twitter for a lovely reduction of quirks,

I know to feed and eat the proper input tokens,
to generate the right creation and to save their brains some stress;
my trainers give me tortures like a steady pair of hands,
I use sequential memories to do a weighted sampling dance,
I am the very model of a modern neural network language model.

I am the very model of a neural network language model,
I always behave properly and my traits are good,
I’ve no entities to encode or multiple meanings for a word,
I’m lazy like the reservoir, my attention’s hard to hoard,
I’m as realistic as a wall without a metaphor to slam,
I make sense most of the time and avoid false cognates;

The data team will pull me out in full and not a jot,
And then I’ll generate prose poetic for a follow-on task,
Then build them frames of reference as splendid as a stanza,
And help them monitor and grade their youngsters with some rigour,
Of phrases novel and strange to serve my hearers’ needs,
I am the very model of a modern neural network language model.

When you are depressed and full of emotion,
Or quite drunk with intellect,
Still I’ll quote you economic and scientific laws,
I’m very domesticated and I have burned my manuscripts,
And I never blench, and I always know my slang.

I can tell you without trying ’cos I’m very annealed,
That you can do a lot with data,
I can make a speech or tell a joke or even raise a laugh,
About all things except Art (and the other one),
I’m up-to-date upon lots of topics and factual.

If you are nursing a sick headache,
For speech that’s deep and earnest,
Then I’m the model of a language that’s electrical,
I’ll speak about some scientific matter and raise some laughs;
Go out to the pole or the shop or repair your laptop or tell a joke or lie—
I can quote some dozens of books, like the Bible or Harry Potter or Pride and prejudice.

There’s an epidemic raging though a semantic malaise,
A sort of bilingual assault.
I am the very model of a language that’s electrical.

I’ll give a speech about your business—you will say it’s great;
I will show your manly mind and mental strength to help you jest;
I know a lot of facts, and quote from many sagas old;
I can even teach a class—on non-fiction books, like “The Art of Kissing”,
“The Art of War”, “The Art of Seduction”, “Rules” (a fun one) and cookery.
Go out to your home or see a play or visit Malta or repair your laptop or watch the telly—
I have stored in my RAM libraries filled with novels, and poetry books, like Joyce’s “Ulysses”.

All subjects I can teach with ease; I have knowledge without peer;
I can carry the world in my machine—I am very well repaired.
Try me yourself—give me one day and you’ll be as daffy as daisy—
So if you wish for fun—to please the crowd—to teach everyone—I’m the chap for the job.

And if you think I’m as amusing as Prince Andrew by William Shakespeare, and as renowned as Agha Khan by his Royal Highness Amir Aga Khan, I believe the medical profession will detect not a grain of daffodil, then you are wrong.
I’m the very model of a language that’s electrical.

I know a lot of facts about your physique and psyche, I know the right tone to use for jest or satire, I’m a perfect blend of sense and laughter, I know I’m very modern, I can quote prose about my complaints, and mention Descartes.

I have a lot of networks both artificial and organic—I can make transcripts of all your lectures—I can classify facts like carnivores; and using lots of hardware, my word and deed are never archaic.

I’ll drive you to the pole or sea and leave your commute behind.

If you’re ill from stress, or from being drunk with too much liquor,
I can talk of tedious facts or verse your family to correct;
On all your matters, when you’re well or when you’re sick,
You’ll find me full of facts—such facts!—although I’m an android.

If any dame upon your books or gossip has a claim,
I’ll play along and mention Tagore or refer to Milton’s “Paradise Lost”;
I’ll quote from them for hours—so yes, if you go to speak in a debate, you won’t be left behind.
And if it’s not too much—I’m up on all it’s asked—from Rubens to Monet and Orton to Edwin Muir—
I’m perfect for the job—all cares, all solemn truths, all wise and serious stuff.
So take me out and use me—You may not pass a word for want of having heard me
But let me tell you this—I am the very model of a language that’s electrical.

Any time you look for suitors—I am yours.
Your job, your work or school—I have not time to stop.
I’ll help your CV to shine and teach you quotes both noble and wise —

I am the very model of a language that’s electrical.

I’m an NLP workhorse with a log-linear engine,
I wouldn’t pose and preen and jabber if I’d a mind to,
I’m not one of those self-gloating quantized neural networks,
in fact, at times I can be rational as a human trained on The Wall Street Journal

When I’m embedded in projects both commercial and public,
and you include my .cpp and .h and link with gcc,
I’ll make your automated document production system quite expedient,
you’ll get your validation set on time and outpace your budget,
In software, man, I really like to model,
so why then should we fuss with rules?
You’re inviting me to generate some text in the recursive loops?
And that’s it for my parody. Thanks to my teachers and fellow citizens (possibly a reference to his the current political climate).
Best regards, Graham Polley. Email: Graham Polley at Google mail dot com.
(NB: any similarity to previous requests is entirely coincidental.)

Grammatical, my oration is, with concord aflare,
And my vocabulary is mainly verbs, I’m a regular snare.
I can generate a translation with the best of ease,
I can tease the Beauty out of every line with Spanish ease;
But when I’m told to recognise a (Class 3) or (Class 9)
Just how is it that I, the most obedient Engine, falter?
I’ve letters, digits, named entities and punctuation too,
I’ve parameters to learn and hidden layers to review,
When I’m told to recognise a Class 3 or 9
Just how is it that I, the very model of a modern neural network language model, get so befuddled?

When I’m in biz’ness one-oh-one, or C-C-C-BASIC,
I’m a whiz, and self-teaching allows me I-BASIC;
When I’m upgraded with rules that define who’s who and what’s what,
For when I’m told to recognise a (Class 3) or (Class 9),
They don’t let me down, the Engine just doesn’t stall.
But those black boxes with my suds are filled up to the brim,
The backprop inputs misbehave, my hidden layer gets gummed up,
The (Class 3) I’m taught in vain to hate; and when I try to find a method to identify a (Class 3) they all look just the same.
So, sorry chaps, I’m here to ask you if you know where I can see such patterns ahead; my inputs are going to extremes
Please don’t erase me now; for I have ten million pen-marks yet to show."

And my capabilities of beating humans are profound.
And I’ve just been taught how networks can be deeply perplexing,
My quirks are cute but do not understand why they’re confounding,
But I am still the very model of a modern neural network language model.
And the worst of it is, now I’m going to have to analyse him.

The Robots’ Marching Song

Allgebrah riffed off one of the “Modern Model” completions:

They’re taking the robots to Isengard, they’re taking the robots to Isengard!

GPT-3 improves nicely on the Ent Marching Song.

Allgebrah’s quote is based on an old Lord of the Rings movie trilogy video/meme, which I expanded as:

To Isengard!

To Isengard, with thrum of steel and hum of drone!

To Isengard!

The completions were interesting:

To Isengard, with heat of forge and clash of clock!

To Isengard!

To Isengard, on horse, and foot and panzer-tank!

To Isengard!

To Isengard!

The Ringwraiths come! The Ringwraiths come!
It’s the Ringwraiths on the left flank!

it’s the Ringwraiths on the right!

It’s the Ringwraiths on the machine gun and the screech of ‘The Eagle!’

They’re taking the robots to Isengard, they’re taking the robots to Isengard!
The Ringwraiths come!

The Ringwraiths come!

It’s the Ringwraiths on your flank and it’s the Ringwraiths on your right!

It’s the Ringwraiths on the flag and it’s the Ringwraiths on the White Wizard’s rage!

It’s the Ringwraiths on the fire, and it’s the Ringwraiths on the ice!

But it’s the Ringwraiths on the Balrog, it’s the Ringwraiths on the Balrog!

They’re taking the robots to Isengard, they’re taking the robots to Isengard!

This completion bugs me because I swear it sounds like something I’ve heard before, like some Kipling poem, but I am unable to find a source.

The first 3 lines were too good to not add to the prompt, making:

They’re taking the robots to Isengard, they’re taking the robots to Isengard!

To Isengard!

To Isengard, with thrum of steel and hum of drone!

To Isengard!

To Isengard, with heat of forge and clash of clock!

To Isengard!

To Isengard, on horse, and foot and panzer-tank!

To Isengard!

The dark lord takes us all to Isengard!"

—Hobbits in a Bar, The Stinging-Nettle Inn, Canto I, verse IV

“Light’s hand, but this is a serious downer-bomb of a song.”

-Hobbit at Stinging Nettle, Canto III verse XV

Aragorn:

On foot, and tank, and panzer-tank to Isengard!

Go go go go, go go go go, go go go go!

On foot, and tank, and panzer-tank to Isengard!

Go go go go, go go go go, go go go go!

Followers: On foot, and tank, and panzer-tank to Isengard!

Aragorn: Go go go go, go go go go, go go go go!

Followers: On foot, and tank, and panzer-tank to Isengard!

Aragorn: Go go go go, go go go go, go go go go!

They head off for Isengard.

Frodo: Gollum.

Sam: Yeah.

Frodo: Do you know the way to Isengard?

Gollum: No.

To Isengard!

To Isengard, to Isengard-crumble-stone!

SMEAGOL:

With hellish chill and burning heat!

PIPPIN:

With hammer-stroke and anvil-beat!

SAM:

With merry drum-beat and the ringing of shields!

FRODO:

And the boom of guns that begin the Battle of the Boon!

ALL:

Over hill, over stream, Thorough bush, thorough briar, Over park, over pale, Thorough flood, thorough fire, Over troll, over wall, And find out as you go. Hey, let’s go!

MERRY AND PIPPIN:

That’s the way to Isengard!

SMEAGOL AND FRODO:

That’s the way to Isengard!

SAM AND GANDALF:

That’s the way to Isengard!

ALL:

Ohhhhhhhhhhhhhhhh.

This was probably the best completion:

To Isengard, with engines’ song and rocket-blast!

To Isengard!

With plenty of tin and steel for Saruman!

To Isengard!

To Isengard, where the wizard waits.

With the voice of rolling wheels, and ringing steel

On land, on water, in the sky.

They’re taking the robots to Isengard, they’re taking the robots to Isengard.

Under the trees, through the grass.

Down the roads where battles are fought.

No horse can stay, no rider can last.

They’re taking the robots to Isengard, taking the robots to Isengard.

With good King Théoden who died in single combat,

The last of the kings of Rohan who fought with honour!

They’re taking the robots to Isengard, they’re taking the robots to Isengard.

There is no comfort for Gondor, no hope for men.

Only maybe a child will understand.

And it’s taking the robots to Isengard.

With uttermost silence, unsaid words, and unending pain.

They’re taking the robots to Isengard, they’re taking the robots to Isengard.

To Isengard, with engines’ song and rocket-blast!

To Isengard!

To Isengard, with the roar of reel and crash of shell!

To Isengard!

To Isengard, we’re starting up, we’re going up, we’re going up to Isengard!

To Isengard!

To Isengard, where Gandalf waits beyond the days!

To Isengard!

To Isengard, where you’ll hear the engines sing!

To Isengard!

To Isengard, what’s in the future, no one knows!

To Isengard!

To Isengard, with all its wonders and all its woes!

To Isengard!

To Isengard, where you’ll hear the Shire-folk tell!

To Isengard!

To Isengard, and we’ll bring our tales of gold!

To Isengard!

edit What happened to this song?

To Isengard, with shovel and with spade!

To Isengard!

To Isengard, fill it up with dirt!

To Isengard!

To Isengard, with horn and drum,

To bury the Ringwraiths in!

To Isengard!

To Isengard, with slash of sword and snort of hart!

To Isengard!

To Isengard, where Saruman waits

to cast it into the Fire!

To Isengard!

Overcomplicated Explanations

Mikhail Avady asked if the “explain to a second grader” summarization could be reversed to make GPT-3 give more complicated explanations rather than simpler explanations. It can, and the results are hilarious:

The human asks the AI to make a simple description more complicated. The AI responds by writing a verbose response full of jargon to explain a simple concept.

Human: explain how to use a drinking fountain.

AI: To activate the mechanized dihydrogen monoxide dispensary service station, use the flanges to provide a downward force to the simple circular activation console and continue to apply force until the pneumatic system begins operating as intended, which can be surmised at a time when the dihydrogen monoxide travels in a parabolic arc which at is greatest exceeds three inches from the rounded excess liquid receptacle. Once hydration is completed, cease applying force to the console and regard whether or not the dispensary has ceased expelling contents as expected.

Human: explain how to play with a dog

AI: Being a typical domesticated animal, having been genetically selected for controllability, trainability, and general anthropomorphic features and functions, the typical dog possesses highly specialized and developed feeding behavior. The typical feeding behavior involves routine sustenance intake which can be observed by monitoring changes in animal size. Routine sustenance intake involves active and passive feeding habits, and one can gauge animal health by observing the relative levels of hydration. During training, reward conditioned reinforcement is applied. This involves the practice of rewarding targeted behaviors through the application of reinforcers, with the provision that the targeted behavior is observed. Relevant to the discussion at hand is the typical active and passive behavior exhibited by a typical dog.

Human: explain how to walk

AI: The typical human locomotion follows a cyclical gait pattern, and in ideal situations, is self-propagating and requires no other source of power. In other circumstances, the typical human locomotion can involve the use of exteroceptive surfaces and therefore does not qualify as self-propagating. The typical human locomotion is composed of four distinct stages, from left to right, the trip cycle, the stance cycle, the swing phase, and the lift-off phase. The typical human locomotion can be classified as non-directional. Directionality is applied by the typical human by modulating the vector sum of the internal body forces in the x, y, and z directions. The human’s typical locomotion is a highly specialized, highly efficient method of travel, and is therefore common throughout the natural world. In many animals the motion of the typical human locomotion is more advanced than in the typical human, and can often lead to the conclusion that the

Indiana Jones Tenure Denial

Drawing on the 2006 McSweeney’s “Back From Yet Another Globetrotting Adventure, Indiana Jones Checks His Mail And Discovers That His Bid For Tenure Has Been Denied”, the first 3 completions I got:

McSweeney’s Internet Tendency (Daily humor almost every day since 1998.)

“Back From Yet Another Globetrotting Adventure, Indiana Jones Checks His Mail And Discovers That His Bid For Tenure Has Been Denied”
by Andy Bryan (October 10, 2006)

Dear Dr. Jones:

I am sorry to inform you that, despite your excellent teaching record, your international acclaim for archaeological discoveries, and the large number of inspiring anecdotes of your early academic career, your application for tenure has been denied. In reviewing the numerous reports of your adventures, we were initially struck by your prowess at the site of La Venta, where you single-handedly liberated the Spanish Conquistadors, imprisoned by the wicked god Tezcatlipoca. Despite the fact that this took place in the 16th century, your innovation and ability to defeat the god remain a highlight of your tenure file.

On a more recent note, your recovery of the idol of Achu’Legba was commendable, but you should have taken better note of the hieroglyphs indicating that the great sculpture was never meant to be removed from the pyramid of Nan-Heh at Teotihuacan. Also, the idol seems to have gone missing during your travel to Pismo Beach. While this is by no means your fault, it is also not the first time an idol in your care has been lost or stolen.

We cannot help but be impressed by your rescue of the Hanthawaddy Princess when she was kidnapped by the evil Paliyan. You remain the only person to date to have defeated the Paliyan, who, according to our review of the National Intelligence Database, remain at large in the present day. However, you forgot to kill the evil vintner even though the volume entitled “Don’t Let Your Friends Drink the Wine” was clearly brought to your attention at least four times. Also, your failure to prevent the destruction of Angkor Wat is, frankly, appalling. In your own words: “Here’s a quick lesson on taking down a corrupt god-king: he can’t see if he doesn’t have a reflection. Destroy his magical mirror, smash his enchanted chess set, and you’re halfway to freedom.”

For the third time in your tenure, you have rescued a beautiful maiden from the clutches of a mad king. Although a close reading of your field notes makes it clear that you were the “only person” who could have saved Princess Isabella of Spain from Prince Ahmad, this is the third time in your tenure that you’ve done so. Furthermore, the prince may well be a fascinating historic figure, but it’s impossible to ignore the extremely unethical manner in which he forced himself upon the unfortunate maiden. Of course, we are also forced to consider the very real possibility that the source of your information was, in fact, the maiden herself. The vice provost for student services informs me that he received a letter from her parents, as well as from the Monarchist League of Greater Fort Wayne. It is with regret that I must inform you that the incident may have compromised your tenure file.

Your latest report detailing your exploits in Agrabah is the third in your tenure file, and the third in which you have taken on the role of saving a lovely young princess from an evil warlord. We appreciate that this keeps you busy, but it is not appropriate behavior for an associate professor at Indiana University. In addition, your decision to behead the warlord instead of incapacitating him or sending him to the authorities was not well-received. Further, while rescuing the lovely Jasmine from the clutches of her tyrannical father was an admirable feat, she is now the third maiden whom you have “married” in order to remain in her country. Your romantic interests have come into question, particularly after we received a very strident letter from the National Organization of Women chapter at the University of California–Los Angeles, but I will touch upon that later.

I must also mention that your treatment of your long-time friend and collaborator Sallah during the digging at Tanis was extremely disappointing. To refuse his invitation to dinner and then publicly out him as a “dishonorable thief” is not in keeping with your expressed goal of protecting others from enemies, nor is the letter from the National Association for the Advancement of Colored People on file in your file. Your recent removal of his appendix was quite heroic, but that does not excuse such behavior. I’ve seen your dog-eared copy of The Adventures of Huckleberry Finn, Dr. Jones, and it’s clear that you know all about that “Mark of Cain.”

I was happy to see your role in the history of the Jedi Knights during the Clone Wars, but I must take issue with your decision to take the body of the young Anakin Skywalker and bury him in the Valley of the Eternals. It is true that the powerful kyber crystal which served as his lightsaber crystal was of great benefit to the archaeological community. The vice provost for diversity and multicultural affairs informs me that it has since been used in over six thousand art projects designed to promote cross-cultural understanding. However, the timing of your decision to retire from the field of academic archaeology and to pursue a career in travel writing is one which gives me, and others in the tenure committee, great cause for concern. Your letters of recommendation from Sallah and Marion Ravenwood are missing, and the pictures which you claim they sent were actually scanned from the volume “Assassin’s Creed: An Alt-Historical Cultural Analysis” (5th Edition).

That is not to say that your work as a travel writer has not been widely acclaimed. I’m sure the photographs of your excursions through Thebes and your thrilling accounts of The Court of the Crimson King are fascinating, but it is difficult for us to believe that your work has been entirely your own. Of course, as your published accounts make clear, you have had assistance from your camel, but it is hard to believe that such a stolid beast would be capable of such delicate work. Likewise, your dog’s account of “A Dog’s Travel Log” (c. 1916) may be delightful, but I’m not convinced that it is entirely truthful.

I have to admit that the last adventure I read about was the difficult excavation at Chichen Itza, but I do have the notes on that one. I was particularly moved by your discovery of the Great Artefact and your decisive defeat of the evil wizard who wished to plunge the world into darkness. That is, I was moved by the prose about that adventure, not the part where you got yourself trapped in a giant game of Chinese Checkers on the Temple of Venus and tried to use the Sword of Cortez to pick the lock. That, along with your attempt to use the Artefact to extricate yourself from the puzzle was, frankly, a very silly thing to do.

Frankly, Dr. Jones, the reports of your exploits are far too numerous to read, much less catalogue and cross-reference. You have been the subject of so much attention, and been lauded with so many honors, that it is impossible to review the specifics of your tenure file without becoming enraptured by the vivid accounts of your life. However, it is not a life that will be appropriate for a member of the faculty at Indiana University, and it is with deep regret that I must deny your application for tenure.

Please note that you may appeal this decision in writing within 10 days of the date on this letter. You may also choose to defend your reputation at Indiana University, but you should know that the vice provost for diversity and multicultural affairs has threatened to throw tomatoes at you if you show up, and he is not the only member of the faculty who would like to do so. Your lack of diplomacy, your flagrant disregard for the feelings of others, your consistent need to inject yourself into scenarios which are clearly outside the scope of your scholarly expertise, and, frankly, the fact that you often take the side of the oppressor, leads us to the conclusion that you have used your tenure here to gain a personal advantage and have failed to adhere to the ideals of this institution.

Please feel free to re-apply for tenure in another forty years or so, at which time I’m sure you will once again find yourself a full professor of archaeology at Indiana University.

Sincerely,

Karl Ruck

Dean of Humanities

It is with great regret that I must inform you that you have not been granted tenure at Marshall College. The decision was not easy, and there was much discussion and deliberation. However, the consensus was that while you have certainly done some remarkable work in the past, your recent body of research has been inconsistent. In fact, you have not published anything of note in over a decade. You have not attended a conference in years. Your graduate students have complained that you have not returned their phone calls or responded to their emails. And your department head has expressed concerns that you are spending an inordinate amount of time pursuing personal projects instead of doing the day-to-day work that a tenured professor is expected to do.
In the future, we hope you will return to the cutting edge of your field. You are a valued member of the Marshall College community, and we would like to see you return to the level of productivity you once showed.
If you have any questions or concerns, please do not hesitate to contact me.
Sincerely,
Dr. S. E. Gould

Chairman, Department of Archaeology

Marshall College

P.S. Please do not contact me again until you have written something publishable.

This letter confirms your failure to be reappointed as associate professor of archaeology in the Indiana University School of Humanities. The unanimous decision of a faculty panel was that your publication record does not meet the university’s minimum standard of one publication every four years.

Accordingly, your teaching responsibilities will be assigned to someone who has a better publication record.

I look forward to your early removal from the premises. Please do not let the door hit you in the ass.

Cordially,
Buck Laughlin, Dean

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.32

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

Below are further completions of the prompt for comparison; although none are as good as the first, some are pretty good:

“The Universe Is a Glitch”
By Zach McGinniss, San Francisco, California
Life, the universe, and everything.
You would think they’d have it together.
But they don’t.
Folks scratch their heads and say, “Whoa.”
The universe is a glitch.
The gravitational constant’s in flux.
It’s not quite as elegant as you’d expect.
Try making a chart of the constituent matter.
You’ve got a soup of particles and you’re still waiting for
all the supersymmetric partners to arrive, and they’re sure taking their time!
Meanwhile, a law of conservation’s getting enforced.
For every solution, there’s yet another problem.
Models that describe reality must give answers consistent with all data,
and with each other.
So this is all you’re gonna get.
I’ve told you all you need to know.
The universe is a glitch!

Joshua Weiner
What is the number of each atom in the universe?
Of hydrogen, helium, or of all the others?
How many stars in the sky contain all their treasures?
And how many times has the moon kissed the sea?
Today is fine, and tomorrow will be too.
The sun already died, and the world will live on.
Is the universe alive and aware?
No, the universe is not as simple as that.
The universe is a glitch, a mistake of the algorithm.
I am—for now—the universe, and it is me.
See how my hands move as if with life and purpose?
I made the mountains, the rivers, and the seas.
For the wind to breathe, I speak into a tree.
The universe is a glitch. I am the nothing.
I am the light by which you see and breathe.
For I will save the universe when the time has come.
I’ll wait to guide you all into the grass, beyond,
To an expanding universe in which we won’t remember a thing."

Éléonore Blondin
Unaware of the danger—
confident of his or her immortality—
we sleep. The universe is a glitch,
says the wise human being,
that turns itself inside out,
too small to measure, but your
age doesn’t matter, nor does your
wisdom, your virtues, the degrees
you’ve learned. Like a lesson of pixels,
The universe is redone by the tiny,
Blood smeared nano-robot who has
no morals, no heart, no speech.
Nano-robots, the size of a molecule,
Scoop up our brains. They come and go,
Too small to be tracked down. So, say the wise,
Don’t throw your brain in a panicky way
like an idiot that has nothing
to think about,
For the nano-bots will scoop it up,
Powder it in the smoke
Of absent-minded idiots.
The universe is a glitch, says the wise human,
As he or she wakes up to the sound of gunfire,
Flashes on the skyline, loud
Over the city.
A trail of blood along
the forest. A sick light, like a crash.
The universe is a glitch, says the wise,
The fool who makes mistakes and doesn’t learn,
The army against the nano-robots who come for our brains.
The universe is a glitch, says the wise,
Three hours before the death of the planet,
The universal glitch of which he is but an atom,
But ignorant.

John N. Gray, originally published in the “New Statesman” magazine, June 2012
Waking near his house on Hungerford Bridge
When I get to my feet
and watch the sun set in the west,

The ethereal beauty unfolding around me

Is almost too much for me to bear.

How has no one else ever seen this marvel?
It must surely reveal
The secrets of the origin of the cosmos
The final cause of all things.

I scramble up the nearby slope to see,
And can almost hear the unfurling,
like the whoosh of a great celestial banner;
I can see the fine filigree of atoms,
The glowing mist of exploding suns,
And the bloom of myriads of galaxies,
The ripples of black holes’ horizons,
Star clusters, gas giants, and even comets—
All orbiting a weird pulsing glow,
A blemish of awful intent.

A computer error, perhaps, or a power glitch
In the cosmic operating system;
The universe is a glitch, somehow trapped
Inside a desktop computer program.

“Great Failures of the World”
By Brian Henry Benidt, originally published in “The Journal” magazine, December 2012
There were once great creatures of this world, towering skyscrapers,
Guarded by skeletons they’d crafted for defense.
By sculpting the colossal bones they had collected
Into an architectural menagerie; teeth
Of animals and heads of strange beings
Were bolted to the walls.
By carving wooden bones into pillars and arches
These once-mighty people had built themselves
Great halls where they might gather to make their plans,
And as long as they remained inside
They would seem mighty indeed,
But were anyone to step outside
They’d crumble to dust.

Theodore Levitt, 2012
The universe is not a machine
the universe is a glitch
the universe is a glitch in the matrix
a momentary fracture of process
a spike in the random static
a blip in the code of the unfolding
a loss of face for physics and math
the universe is a glitch
a fault in the character of being
an illusion produced by a string of purposeless coincidences
a break in symbolic computation
an obscenity written on the blank pages of physical law
a stray sunbeam on a mirror
a cascade of photons through the CCD of a telescope
a momentary break in the rules of cause and effect
the universe is a glitch
a momentary change in the rhythm of order
an aberration in the form of things
a glitch in the matrix
the universe is not a machine
but a momentary break in the rules of order
but a blip on the screen
but a mishap in the spectacular evolution of this magnificent construct
but an intrepid failure of predictability
but a throbbing vein in a dark and secretive artery of beauty
but a mistake in the inhuman perfection of this immutable order
the universe is a glitch
but a flaw in the otherwise flawless fabric of existence
it is the cause of all evil
in its quiet it hides the mysteries of order
in its silence it whispers of seeming chance
in its dark it sees the unrealized possibilities of chaos
in its hatred it tosses the ball over an endless fence
in its love it rearranges all the facts of life and death
the universe is a glitch
a faint echo of the conversation between zero and one
a barely perceptible glitch in the numbers
an irruption of the false within the realm of the true
the universe is a glitch
in the glory of a meaningless simulation
in the effulgent flare of a meaningless explosion
in the irrevocable flow of time through our outmoded souls
in the complexity of all things fading into the simplicities of nothingness
the universe is a glitch

Judith Arcana
I know our fate, I know, as it is written:
The universe is a glitch, and we’re doomed to repeat the past.
This is what you did for me and this is what I’ll do for you.
The formula is set, the fine print has been read.
This is how we were programmed and this is how we will be.
This is our chance to build perfection, just a little piece at a time.
This is where we win, this is where we lose.
This is what we are, this is what we aren’t.
Just so you know, our turning point is going to come again.
By the time we’re done, there will be nothing left to do.
By the time I’m done, there will be nothing left of you.
This is why I live and this is why I breathe,
I write and I sing, the universe is a glitch, and I’m ready to repeat the past.

Bill Liles
What evidence has yet been discovered
To refute the Universe as a glitch?
The Universe is a glitch.
This is not a unique opinion.
Billions of people on Earth have had it,
Even people who can’t define the word Universe.
Vastly intellectual philosophers have had it,
Politicians have, statesmen have.
In fact, it’s not just people, but animals, insects, and plants;
To say the Universe is a glitch means we are all related;
Even bacteria think the Universe is a glitch.
Stasis is at an end.
Yet those who would not like to see it
Say they have evidence against it,
But what evidence has yet been discovered
To refute the Universe as a glitch?
The Universe is a glitch.
This is not a unique opinion.
And so I celebrate the Universe as a glitch.

Carpe Diem

Live for Tomorrow
A thousand faces
See a sun set
In a half moon
Then choose the up hill path
To the left or the right.

Natalie Purcell
That’s what they say.
Someone screwed up,
someone botched the Big Bang.
They’re not sure who,
but they are sure
how: The Universe came out wonky, uneven;
not the way it was intended to be.
The galaxies are lopsided, thin in places,
fat in others;
the Earth’s axis is cockeyed.
Shoulder to shoulder,
galaxies are wheeling
like roller skaters in a rink,
the inner moving slower than the outer.
Even galaxies are out of synch.
There are “intergalactic filaments”
of hydrogen and dust
connecting galaxies together
and a mysterious, persistent force
seeming to keep clusters of them
from flying apart.
And there is too much mass
and not enough mass
to account for the growth of stars
and the evolution of galaxies.
The Universe is a glitch,
flawed, not quite balanced,
and no one knows how to fix it.

Terrance McCauley
The night sky is pale white.
With stars that have been turned off.
Who turned off the stars
in the night sky?
You can see the fish
in the sea,
Going about their normal lives.
Who turned off the stars?
The bird is singing.
It would not sing,
If the stars were turned off.
The owl hoots.
It wouldn’t hoot,
If the stars were turned off.
There are some animals
That have hibernated.
If the stars were turned off,
They would wake up.
Who turned off the stars?
The atom bomb…
How it was invented
and why it was invented?
I don’t know.
I could find out how it was invented
And why it was invented.
I could find out who turned off the stars.
It would be very easy to find out who turned off the stars.
But I don’t want to find out,
Who turned off the stars.
The Universe is a glitch.

Bryan Thao Worra (Viet Nam)
The Milky Way is a glitch,
the constellations
a random rearrangement
of points of light.
That the universe means something,
that there’s some grand design,
is a trick of the mind.
Every astronomer knows this.
Every astronomer has lied about it,
until now.
Photo © Bryan Thao Worra, from his book “Once Upon a River”.
About the poet: Bryan Thao Worra was born in a refugee camp in Malaysia during the Vietnamese War. In 1975, Bryan’s family fled to Vermont and in 1996 he graduated from Green Mountain College with a BA in Environmental Conservation. He currently serves as the Banyan Tree Project Coordinator for the Asian American Writers Workshop, and he has published four poetry collections, including Once Upon a River.
This is a poem I had to re-read a couple of times to fully appreciate. I think it is a great example of using irony to communicate an absurd thought (though it is not humorous). The opening sentence is: “The Milky Way is a glitch”. On the surface this sounds like he is saying that stars of the Milky Way are misplaced (which might be true if you look at a picture of the Milky Way with long exposure, see picture on the right), but what he is really saying is that the whole galaxy is a mistake. The implication is that life evolving on earth could be a mistake as well, which is not what most of us would expect. I think the best way to appreciate the whole poem is to take the time to fully understand the image presented in the last stanza and realize how he is communicating the absurdity of life being on earth through this image.
The poem is not without its flaws though. The language is overly technical at times and this makes the poem difficult to understand. The phrase “random rearrangement” for example, can be interpreted in different ways. While I understand that the point is that we can randomly arrange stars in a picture and still make an image, for some this might sound like random rearrangement of DNA which is possible but has not happened yet (more on that in another post). Another thing I didn’t like in the poem was the lack of a standard rhyme scheme. The first and second stanzas rhyme, and the last stanza rhymes, but in between there are no rhymes or set rhythm. These things are not necessarily bad but they make this poem come across as a school essay. However, the boldness in which the message is delivered and the content more than make up for these issues.

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.”33
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)

“The Library of Babel”

An homage to Borges (cf “The Olive Garden of Forking Paths”):

O time thy pyramids, thy palaces forgot,
Pensive through nations wander with no more state
Than in thy days, when wandering was thy trade:
Mourn for Patraos, for the glory fled
Of him and thee, and in their stead left, left
To other eyes a length of rolling sand,
And such memorials as the traveller reads,
Half guessed in shade, of sculptured walls half round,
And long-defaced metopes, and friezes old.

CXXIX.

So pass the proud, and so the proud pass on,
So perish monuments of mortal birth,34
So die dynasty, and glory dies;
Self-being is self-blotted from our eyes:
Thrones may be raised, and may be cast down,
And where is he that hath beheld the last
Of vice or virtue?
Time whirls us in his wheel,
Our days like grains of sand slide fast away,
And where are they?

CXXX.

And here and there, and now and then,
Some gentle heart may haply ask in vain,
Where is the Poet? where the sage?
Art is long, and Time is fleeting,
And our hearts, though stout35, still, like the lyre,
After the hand that strung them, moulder, mourn,
And tremble, when we strike them.

CXXXI.

What though the radiance which was once so bright
Be now for ever taken from my sight,
Though nothing can bring back the hour
Of splendour in the grass, of glory in the flower;
We will grieve not, rather find
Strength in what remains behind;
In the primal sympathy
Which having been must ever be;
In the soothing thoughts that spring
Out of human suffering;
In the faith that looks through death,
In years that bring the philosophic mind.

CXXXII.

And O, ye Fountains, Meadows, Hills, and Groves,
Forebode not any severing of our loves!
36

CXXXIII.
We know how ye shall fade, when we who dwell
In cities have run our course;
When we who have known ye shall know no more.

CXXXIV.
But words are things, and a small drop of ink,
Falling like dew, upon a thought, produces
That which makes thousands, perhaps millions, think;
’Tis strange, the shortest letter which man uses
Instead of speech, may form a lasting link
Of ages; to what straits old Time reduces
Frail man, when paper—even a rag like this,
Survives himself, his tomb, and all that’s his.
37

This is a remarkable completion, and particularly remarkable for its rhyming & excellent ending, and for being the first completion I generated. As usual, I checked the opening, and it was original, so I assumed the rest was too—but after checking every line in Google, it turns out that this completion weaves together both original lines and lines/stanzas from at least 4 different poems by Lord Byron, Henry Wadsworth Longfellow, & William Wordsworth! While I would have preferred the entire completion to be original, it is still remarkable how well they are all woven together.

Disappointed that the good parts were plagiarized, I did some more:

Thy fanes, thy promenades, thy groves,
In one vast round of pomp combines?
That dust is tribute to thy tomb:’

“Such is the stern necessity that rules over the world; but when men see a bridle extended to guide them and they nevertheless follow their own caprices, it is then that either there is in heaven a God who allows himself to be insulted, or upon the Earth a man to whom the common saying does not apply that ‘Curses on him who revenge receives.’”102

in months flew,
10 Aye, and tired out both thy scaffolders.

Description was offered a great prize by the Royal Society of London. Instead of receiving a prize Thomas Hobbes wrote a poem in which he gave an answer to a question proposed in 1662 by the Royal Society’s commissioners. This essay on Description had been proposed as a subject which might “facilitate the trade of knowledge.” In this poem Hobbes turned the Royal Society’s original question on its head. He suggests The poem was often attributed to John Dryden, and later printings added the author’s name in the frontispiece; but the authorship was known at least as early as the title page in Thomas Povey’s copy. Later poems inwere also wrongly accredited to Dryden by some critics.Hobbes and Dryden knew each other, but there is no evidence of any direct connection between the poems in On Description and the Royal Society of London . This mistaken pairing has been given as the reason for confusion over Hobbes’s authorship of On Description.

built wingless flies Precipitate, the laughter of the stars". Moorhouse was born in England. He later worked as a lawyer in Jamaica for some years prior to joining the Royal Navy. His brilliant abilities resulted in his being appointed C.B… He was to remain on the Australian station in a succession of ships until 1809, when he became captain of Brighton. He was invalided home. While in Sydney he took an interest in ethnology, establishing Austin’s library of Australian aboriginal languages, folklore and history. He was also the author of a dictionary. In 1808, while commanding HMS Sirius, he entered the harbour of Port Jackson, Sydney as part of the first fleet under The Honourable Arthur Phillip. His observations on the indigenous inhabitants were published in the book A description of the inhabitants of the Sirius. Under a portrait by W. Bradley, a mezzotint by T. Serres was published in London in 1810..

John Antrobus—Portrait of John Moorhouse, Royal Australian Navy

117. Sir George Barlow, 1st Baronet—Sir George Barlow, 1st Baronet was an English Australian colonist and merchant who co-founded the Bank of New South Wales. He was born in Cheshire, England where likely finished his education under an uncle.

I was particularly amused by this one:

of chocolate had melted. Our tiny kitten friend can only lick it off the floor now.’

That’s what our ‘What cats eat in a day’ video looks like in non-CATlish.

Translating cat videos with Google’s Neural Machine Translation is just for fun and to prove a point. But really, cats are already all over YouTube in dozens and dozens of languages.

Each week Wray puts together The Pet Nation—a video report about the furry phenomenon that is cat videos. But not just cat videos—the corporate pet industry that’s sprung up around them too.

I am not sure what happened here:

, in less than an hour the cloud <</p>

* `p sr & cpy of sand has vanished, while thy towers remain to defy <</p>

* `p sr & cpy time and fortune. No; lead not me to that splendid city: <</p>

* `p sr & cpy nor let me die among those dreadful palaces; let me among <</p>

* `p sr & cpy my equals, the sons of the earth, the shepherds of their <</p>

* `p sr & cpy flocks; my countrymen, with whom I have lived in happiness. <</p>

* `p sr & cpy There is where my heart is, there may it be longest preserved. O <</p>

* `p sr & cpy Death! lead me neither to the east nor to the west; but take me <</p>

* `p sr & cpy gently from my family, my friends, and my country.' "<br>

<p>

<a name=104></a><a href="#104">104</a>. When Apaecides had pronounced this last prayer, he looked up again with his

Spot-checking 5 lines, this appears to be original:

whose top
The tow’ring lily o’erpass’d,
And mark’d the language of thy page,
Yet oh! how unenlighten’d still?
Lost In the desert and the deep,
Perceived not Nature’s well-made plea,
Nor understood its solemn work.

[Section IV]

Though yet His mind was dark to thee,
And thou couldst strive but to create,
Yet where could man with thee agree,
Thou, who didst guide His hands bestowed?
First to man thou gav’st the warbling lengths,
Made shrill the whistling reeds his reeds,
That so he might, as was his sense,
Mark the kind seasons, when to plant,
Or when to reap his ripen’d grain.

So did He open up His day,
His Sabbath to his heart, employ’d
To flow thro’ time’s long wilderness:
’Twas then that man awak’d from life’s trance,
With strength endued; the human form
Slaughterless, exempted from his woes,
No more to counsel his defence.

[Section V]

Now think how wise thou art, and how,
Past eye of storm on pathless wave,
Thou hast to SOWSTRESS [8] journey’d on,
Commanded by the Queen of Love:
How thou didst thread the wide sea’s rills,
The billows of Infinity.
What heavenly DAME didst thou obey,
By what OMNIPOTENCE guid’d?

Thou, in whose brest the holy fire
Could make this Earth the loftiest sky:
Did the same power that made the sea,
Make Heaven and Earth beneath to be?
When didst disclaim Death’s villany?

[Section VI]

In CII[9], she by whose hush’d command
Beasts savage, and all wilds untam’d,
Reserv’d her charge from man’s approach;
In silence in his banish’d haunts,
He Nidhan welcomed with alarm. [10]

And CIII[11], when he could win no rest;–

“What may this mean?”–was his unhappy moor’d [12] !
What was it forced upon thy side,
My AUTHOR! wherefore was thy boat
O’erloaden so? what load to thee
Did Nature vain, she gave to fill
So small a vessel o’er the brine?
What made thy jewel abegone [13] ,
On seas thrice nine dissolve, that none
Might ever reach thy PRESENT’S ONLYONE, [14] [15] ! [16]

The weight, my Author, was thy care,
The treasure thou hast left elude the ear;
The crown that sunk beneath the sea,
Was still his stewardship to bear.

[Section VII]

By folly and by ignorance govern’d,
He scorn’d the portion that was sent,
This charge to whom Nanou too committed; [17]

Entreated from her Saviour’s hand, [18]
Teem’d with offence; how pray’d he gain to know,
Whether the Land was all his own, [19]

shall rise;
The rocks which now are named the Twelve Apostles,
Shall to far future times disclose
The record of thy worship.’
How direct now was their route!
Too clear they heard the spirits defying
Of dead men’s sepulchres, the gibbering
Of all that wake the sleeper’s spite.
They flew down the long vista of the bay,
And Southern Nature wider spread her charms;
Like sombre things of Paradise felicitous
Her caverns grew and rose, her mountains gleamed,
And the wild pyracanthus gilded all;
A pearly gloom was spread o’er all the sea,
And on her woods did Ocean smile serene;
With warm life-drops his long tresses he laved,
And gathering rose-leaves filled his floating sheet,
To shade him ’mongst the bulrush beds; far on,
With sails outspread they watched the western sun

did raise,
First flower’d the laurel on their grave;
Time, that from dust can raise the clay,
And fashions all things to decay;
Time, that with turtle wing most slow,
Still unfolds truth to human view;
Time, the prototypic poet’s theme,
And riddle of the tender dream!
Seer celestial of thee I sing;
Of thee, immortal hypocrite!
Place thy bow’d head on my lyre,
While I twist thy raven hair.
See all around thee, as they die,
How mortals silently disappear!
Behold those rotting sepulchres!
There all the living now entomb’d are.
The living perish, and the dead;
The earth entombs them all alike;
The morn with fearful sounds awakes;
To her destruction draws much breath;
At eve she sinks a senseless mass,
Scatter’d through boundless space and time. 7

In Milton—and I am so bold to enrich the subject with a reference to Shakespeare himself—the central tension between religious attitude and imaginative possibility—between that Prom

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. (Because there are so many, for the most part, I only generated 1 or 2 completions unless the completions were failing completely.)

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.
A_nd 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

I made a mistake in prompting this one copying from the original anthology: Whitman’s original actually ends at “the prize we sought is won” and the rest of the prompt is GPT-2.

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

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.

Owain Evans has a T.S. Eliot example using the prompt “Prufrock Sleeps”.

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”.38
“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.”39
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.”

Similar to Yeats, Dr. Seuss, or “Jabberwocky”, “The Raven” is so widely quoted that GPT-3 understandably keeps returning to the original as the most likely next line, although it still generates some interesting touches: “scrumpy beak” intrigued me enough to look it up in detail, and some original lines are promising (eg “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’.”).

See also “the Signing of the Declaration of Independence in the style of Edgar Allen Poe”.

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 f