Meta-Learning

Learning to learn. In May 2020, OA released—to remarkably little interest from researchers, no blog post, no media blitz, and little public discussion beyond the snidely dismissive—the long-awaited followup to GPT-2, one model to rule them all: a 117× larger 175b-parameter model with far more powerful language generation, which lets it solve a wide variety of problems from arithmetic¹ to English translation to unscrambling anagrams to SAT analogies—purely from being prompted with text examples, without any specialized training or finetuning whatsoever, merely next-word prediction training on a big Internet text corpus. This implies GPT-3’s attention mechanisms serve as “fast weights” that have “learned to learn” by training on sufficiently varied data², forcing it to do more than just learn ordinary textual relationships. Like OpenAI’s Jukebox just weeks ago (itself a remarkable demonstration of scaling in synthesizing raw audio music complete with remarkably realistic voices/instruments), the announcement of GPT-3 appears to have sunk almost without a trace, so I will go into more depth than usual.

Flexing GPT

“Attacks only get better.” 2 years ago, GPT-1 was interestingly useful pretraining and adorable with its “sentiment neuron”. 1 year ago, GPT-2 was impressive with its excellent text generation & finetuning capabilities. This year, GPT-3 is scary because it’s a magnificently obsolete architecture from early 2018 (used mostly for software engineering convenience as the infrastructure has been debugged), which is small & shallow compared to what’s possible³⁴, with a simple uniform architecture⁵ trained in the dumbest way possible (unidirectional prediction of next text token) on a single impoverished modality (random Internet HTML text dumps⁶) on tiny data (fits on a laptop), sampled in a dumb way⁷, its benchmark performance sabotaged by bad prompts & data tokenization problems (especially arithmetic & commonsense reasoning), and yet, the first version already manifests crazy runtime meta-learning—and the scaling curves still are not bending! The samples are also better than ever, whether it’s GPT-3 inventing new penis jokes⁸ or writing (mostly working) JavaScript tutorials about rotating arrays.

It’s odd that this qualitative leap appears to be largely missed by the standard NLP benchmarks. Nothing in the raw metrics reported on, say, Penn Tree Bank or LAMBADA or WinoGrande would lead you to expect all of this hilarious and creative output; the meta-learning results might, but only if you already thought meta-learning was important. This suggests to me that a useful post-GPT-3 contribution would be figuring out how to benchmark these sorts of flexible text generation capabilities (possibly something along the lines of Chollet’s image-based Abstraction and Reasoning Corpus (ARC)).

Baking The Cake

Not the whole picture, but a big part. Does it set SOTA on every task? No, of course not. But the question is not whether we can lawyerly find any way in which it might not work, but whether there is any way which it might work. And there are many ways it might work better (see the “Limitations” section for just a few). Does GPT-3 do anything like steer a robot around SF shooting lasers and rockets at humans⸮ No, of course not. It is ‘just’ a text prediction model, an idiot savant of text; but an idiot savant, we should remember, is only a genetic mutation or bit of brain damage away from a normal human. If RL is the cherry on the top of the supervised learning frosting, and supervised learning is the frosting on top of the unsupervised learning cake, well, it looks like the cake layers are finally rising.

Scaling still working. I was surprised, as I had expected closer to 100b parameters, and I thought that the performance of CTRL/Meena/MegatronLM/T5/Turing-NLG/GPipe suggested that, the scaling papers⁹ notwithstanding, the scaling curves had started to bend and by 100b, it might be hard to justify further scaling. However, in the latest version of “the unreasonable effectiveness of data” where “the curves cross”/“scissor effect” and the neural method eventually wins (eg. Banko & Brill2001, Brants et al 2007, Koehn & Knowles2017), GPT-3 hits twice that without noticeable change in scaling factors: its scaling continues to be roughly logarithmic/power-law, as it was for much smaller models & as forecast, and it has not hit a regime where gains effectively halt or start to require increases vastly beyond feasibility. That suggests that it would be both possible and useful to head to trillions of parameters (which are still well within available compute & budgets, requiring merely thousands of GPUs & perhaps $10–$100m budgets assuming no improvements which of course there will be, see Hernandez & Brown2020 etc in this issue), and eyeballing the graphs, many benchmarks like the Winograd schema WinoGrande would fall by 10t parameters. The predictability of scaling is striking, and makes scaling models more like statistics than AI. (AI is statistics which does what we want it to but doesn’t work; and statistics is AI which works but doesn’t do what we want.)

Anti-scaling: penny-wise, pound-foolish. GPT-3 is an extraordinarily expensive model by the standards of machine learning: it is estimated that training it may require the annual cost of more machine learning researchers than you can count on one hand (~$5m¹⁰), up to $30 of hard drive space to store the model (500–800GB), and multiple pennies of electricity per 100 pages of output (0.4 kWH). Researchers are concerned about the prospects for scaling: can ML afford to run projects which cost more than 0.1 milli-Manhattan-Projects⸮¹¹ Surely it would be too expensive, even if it represented another large leap in AI capabilities, to spend up to 10 milli-Manhattan-Projects to scale GPT-3 100× to a trivial thing like human-like performance in many domains⸮ Many researchers feel that such a suggestion is absurd and refutes the entire idea of scaling machine learning research further; they asseverate that their favored approaches (you know, the ones which don’t work¹²) will run far more efficiently, and that the field would be more productive if it instead focused on research which can be conducted by an impoverished goatherder on an old laptop running off solar panels.¹³ Nonetheless, I think we can expect further scaling. (10×? No, 10× isn’t cool. You know what’s cool? 100–1000×, trained on a fancy new supercomputer.)

Scaling

How far will scaling go? The scaling papers suggest that the leaps we have seen over the past few years are not even half way there in terms of absolute likelihood loss, never mind what real-world capabilities each additional decrement translates into. The scaling curves are clean; from “Scaling Laws for Neural Language Models”, Kaplan et al 2020:

GPT-3 represents ~10³ on this chart, leaving plenty of room for further loss decreases—especially given the uncertainty in extrapolation:

Lo and behold, the scaling laws continue for GPT-3 models for several orders past Kaplan et al 2020; from Brown et al 2020:

If we see such striking gains in halving the validation loss but with so far left to go, what is left to emerge as we third or halve again? How far does this go, exactly? How do we predict what emerges when? Bueller? Bueller? (See also Meena’s perplexity vs human-ness chatbot ratings, GPT-3-written news articles’ probability of fooling humans by parameter count, and GPT-3 model size vs Q&A from Hendrycks et al 2020.)

Blessings Of Scale

Extrapolating the spectacular performance of GPT-3 into the future suggests that the answer to life, the universe and everything is just 4.398 trillion parameters.

Geoff Hinton

We don’t know how to train NNs. The blessings of scale is the observation that for deep learning, hard problems are easier to solve than easy problems—everything gets better as it gets larger (in contrast to the usual outcome in research, where small things are hard and large things impossible). The bigger the neural net/compute/data/problem, the faster it learns, the better it learns, the stabler it learns, and so on. A problem we can’t solve at all at small n may suddenly become straightforward with millions or billions of n. “NNs are lazy”: they can do far more than we make them do when we push them beyond easy answers & cheap shortcuts. The bitter lesson is the harder and bigger, the better. (Besides GPT-3, one could mention recent progress in semi-supervised learning & the model-based DRL renaissance.)

Blessings of scale: stability → generalization → meta-learning. GPT-3 is hamstrung by its training & data, but DL enjoys an unreasonably effective blessing of dimensionality—just simply training a big model on a lot of data induces better properties like meta-learning without even the slightest bit of that architecture being built in; and in general, training on more and harder tasks creates ever more human-like performance, generalization, and robustness. The GPT natural-language & programming language models, iGPT/Vision Transformer for images (and to some degree GPT-f), show that simply scaling up models & datasets without any supervision produces results competitive with the best (and most complex) alternatives, using the same simple architecture, gradually passing from superficial surface correlations to more human-like brain activity (Schrimpf et al 2020) and linguistic biases as data increases (eg. Warstadt et al 2020). In fact, one may not even need complicated attention mechanisms at scale, as fully-connected networks—hard to get much simpler than them!—work surprisingly well for many tasks. One typically trains such large models with simple optimizers like Adam—because the complicated ones lose their advantages as batch sizes increase and the simple optimizers work fine and are more memory-efficient anyway. OA5 does not just scale to, but stabilizes at, minibatches of millions due to gradient noise. OA5-like, BigGAN stabilizes at large-scale image datasets like JFT-300M & benefits from unusually large minibatches and VAEs (long an also-ran to GANs or autoregressive models in terms of sharp image generation) catch up if you make them very deep (Child2020, Vahdat & Kautz2020); while classifier CNNs like BiT¹⁴/Dojolonga et al 2020 or ResNeXt or Noisy Student transfer & robustify with human-like errors¹⁵, multimodal learning produces better representations on fewer data (eg. ViLBERT/VideoBERT, motivating OA’s interest in big multimodal models), and RNNs can predict videos. AlphaStar reaches human-level with hundreds of competing self-players to cover possible strategies. Imitation learning DRL like MetaMimic generalizes at hundreds of tasks to train a deep net. Disentanglement emerges in StyleGAN with sufficiently deep w embeddings, with enough parameters to train raw audio in the aforementioned Jukebox, or in relational networks/GQN/Transformers with enough samples to force factorization. (See also Hill et al 2019/Chaplot et al 2017/Yu et al 2018/Lake2019/Interactive Agents Group2020.) Training Dactyl (or humanoid robots) on millions of domain randomizations induced similar implicit meta-learning where during each runtime invocation, the RNN probes its environment and encodes its understanding of robot hand control into its hidden state; and DD-PPO outperforms classical robot planners by scaling 2 orders. Or in Procgen or CoinRun, training on hundreds of levels trains agents to solve levels individually and worsens performance on other levels, but at thousands of levels, they begin to generalize to unseen levels. (Similarly, language model pretraining-finetuning overfits at small numbers of datasets but improves markedly with enough diversity.) AlphaZero demonstrated truly superhuman Go without ‘delusions’ just by training a bigger model on a richer signal & pro-level play without any search—and MuZero, for that matter, demonstrated that just training an RNN end-to-end to predict a reward on enough data is enough to obsolete even AlphaZero and learn tree search implicitly (but better). And on and on. DM researcher Matthew Botvinick, discussing their meta-reinforcement learning work where they were surprised to discover meta-learning emerging, and that it did so regardless of which specific architecture they used:

…it’s something that just happens. In a sense, you can’t avoid this happening. If you have a system that has memory, and the function of that memory is shaped by reinforcement learning, and this system is trained on a series of interrelated tasks, this is going to happen. You can’t stop it.

Pace Breiman, why? Why do they transfer and generalize? Why do these blessings of scale exist? Why do we need to train large models when small models provably exist with the same performance? Why do larger models not overfit (though they can) and generalize better than smaller models? What’s up with the whole ‘double descent’ anyway?

These are all, ahem, deep questions about neural networks and heavily debated, but right now, I would suggest that the answer lies in some mix of the model compression/distillation, ‘lottery ticket hypothesis’, Bayesian neural network, and learned representation (like circuits) literatures.

Big models work because they encode a dizzyingly vast number of sub-models in an extremely high-dimensional abstract space, representing countless small sub-models (Orseau et al 2020) interpolating over data, one of which is likely to solve the problem well, and so ensures the problem is soluble by the overall model. They function as an ensemble: even though there countless overfit sub-models inside the single big model, they all average out, leading to a preference for simple solutions. This Occam’s razor biases the model towards simple solutions which are flexible enough to gradually expand in complexity to match the data.

However, “neural nets are lazy”: sub-models which memorize pieces of the data, or latch onto superficial features, learn quickest and are the easiest to represent internally. If the model & data & compute are not big or varied enough, the optimization, by the end of the cursory training, will have only led to a sub-model which achieves a low loss but missed important pieces of the desired solution.

On the other hand, for a model like GPT-3, it is sufficiently powerful a model that its sub-models can do anything from poetry to arithmetic, and it is trained on so much data that those superficial models may do well early on, but gradually fall behind more abstract models; a sub-model which memorizes some of the data is indeed much simpler than a sub-model which encodes genuine arithmetic (a NN can probably memorize tens of thousands of lookup table entries storing examples of addition in the space it would take to encode an abstract algorithm like ‘addition’), but it can’t possibly memorize all the instances of arithmetic (implicit or explicit) in GPT-3’s Internet-scale dataset. If a memorizing sub-model tried to do so, it would become extremely large and penalized. Eventually, after enough examples and enough updates, there may be a phase transition (Viering & Loog2021), and the simplest ‘arithmetic’ model which accurately predicts the data just is arithmetic. And then the meta-learning, after seeing enough instances of algorithms which vary slightly within each sample, making it hard to learn each task separately, just is learning of more generic algorithms, yielding sub-models which achieve lower loss than the rival sub-models, which either fail to predict well or bloat unacceptably. (GPT-2-1.5b apparently was too small or shallow to ensemble easily over sub-models encoding meta-learning algorithms, or perhaps not trained long enough on enough data to locate the meta-learner models; GPT-3 was.)

So, the larger the model, the better, if there is enough data & compute to push it past the easy convenient sub-models and into the sub-models which express desirable traits like generalizing, factorizing perception into meaningful latent dimensions, meta-learning tasks based on descriptions, learning causal reasoning & logic, and so on. If the ingredients are there, it’s going to happen.

Why Does Pretraining Work?

The pretraining thesis goes something like this:

Humans, one might say, are the cyanobacteria of AI: we constantly emit large amounts of structured data, which implicitly rely on logic, causality, object permanence, history—all of that good stuff. All of that is implicit and encoded into our writings and videos and ‘data exhaust’. A model learning to predict must learn to understand all of that to get the best performance; as it predicts the easy things which are mere statistical pattern-matching, what’s left are the hard things. AI critics often say that the long tail of scenarios for tasks like self-driving cars or natural language can only be solved by true generalization & reasoning; it follows then that if models solve the long tail, they must learn to generalize & reason.

Early on in training, a model learns the crudest levels: that some letters like ‘e’ are more frequent than others like ‘z’, that every 5 characters or so there is a space, and so on. It goes from predicted uniformly-distributed bytes to what looks like Base-60 encoding—alphanumeric gibberish. As crude as this may be, it’s enough to make quite a bit of absolute progress: a random predictor needs 8 bits to ‘predict’ a byte/character, but just by at least matching letter and space frequencies, it can almost halve its error to around 5 bits.¹⁶ Because it is learning so much from every character, and because the learned frequencies are simple, it can happen so fast that if one is not logging samples frequently, one might not even observe the improvement.

As training progresses, the task becomes more difficult. Now it begins to learn what words actually exist and do not exist. It doesn’t know anything about meaning, but at least now when it’s asked to predict the second half of a word, it can actually do that to some degree, saving it a few more bits. This takes a while because any specific instance will show up only occasionally: a word may not appear in a dozen samples, and there are many thousands of words to learn. With some more work, it has learned that punctuation, pluralization, possessives are all things that exist. Put that together, and it may have progressed again, all the way down to 3–4 bits error per character! (While the progress is gratifyingly fast, it’s still all gibberish, though, makes no mistake: a sample may be spelled correctly, but it doesn’t make even a bit of sense.)

But once a model has learned a good English vocabulary and correct formatting/spelling, what’s next? There’s not much juice left in predicting within-words. The next thing is picking up associations among words. What words tend to come first? What words ‘cluster’ and are often used nearby each other? Nautical terms tend to get used a lot with each other in sea stories, and likewise Bible passages, or American history Wikipedia article, and so on. If the word “Jefferson” is the last word, then “Washington” may not be far away, and it should hedge its bets on predicting that ‘W’ is the next character, and then if it shows up, go all-in on “ashington”. Such bag-of-words approaches still predict badly, but now we’re down to perhaps <3 bits per character.

What next? Does it stop there? Not if there is enough data and the earlier stuff like learning English vocab doesn’t hem the model in by using up its learning ability. Gradually, other words like “President” or “general” or “after” begin to show the model subtle correlations: “Jefferson was President after…” With many such passages, the word “after” begins to serve a use in predicting the next word, and then the use can be broadened.

By this point, the loss is perhaps 2 bits: every additional 0.1 bit decrease comes at a steeper cost and takes more time. However, now the sentences have started to make sense. A sentence like “Jefferson was President after Washington” does in fact mean something (and if occasionally we sample “Washington was President after Jefferson”, well, what do you expect from such an un-converged model). Jarring errors will immediately jostle us out of any illusion about the model’s understanding, and so training continues. (Around here, Markov chain & n-gram models start to fall behind; they can memorize increasingly large chunks of the training corpus, but they can’t solve increasingly critical syntactic tasks like balancing parentheses or quotes, much less start to ascend from syntax to semantics.)

Now training is hard. Even subtler aspects of language must be modeled, such as keeping pronouns consistent. This is hard in part because the model’s errors are becoming rare, and because the relevant pieces of text are increasingly distant and ‘long-range’. As it makes progress, the absolute size of errors shrinks dramatically. Consider the case of associating names with gender pronouns: the difference between “Janelle ate some ice cream, because he likes sweet things like ice cream” and “Janelle ate some ice cream, because she likes sweet things like ice cream” is one no human could fail to notice, and yet, it is a difference of a single letter. If we compared two models, one of which didn’t understand gender pronouns at all and guessed ‘he’/‘she’ purely at random, and one which understood them perfectly and always guessed ‘she’, the second model would attain a lower average error of barely <0.02 bits per character!

Nevertheless, as training continues, these problems and more, like imitating genres, get solved, and eventually at a loss of 1–2 (where a small char-RNN might converge on a small corpus like Shakespeare or some Project Gutenberg ebooks), we will finally get samples that sound human—at least, for a few sentences. These final samples may convince us briefly, but, aside from issues like repetition loops, even with good samples, the errors accumulate: a sample will state that someone is “alive” and then 10 sentences later, use the word “dead”, or it will digress into an irrelevant argument instead of the expected next argument, or someone will do something physically improbable, or it may just continue for a while without seeming to get anywhere.

All of these errors are far less than <0.02 bits per character; we are now talking not hundredths of bits per characters but less than ten-thousandths.

The pretraining thesis argues that this can go even further: we can compare this performance directly with humans doing the same objective task, who can achieve closer to 0.7 bits per character. What is in that missing >0.4?

Well—everything! Everything that the model misses. While just babbling random words was good enough at the beginning, at the end, it needs to be able to reason our way through the most difficult textual scenarios requiring causality or commonsense reasoning. Every error where the model predicts that ice cream put in a freezer will “melt” rather than “freeze”, every case where the model can’t keep straight whether a person is alive or dead, every time that the model chooses a word that doesn’t help build somehow towards the ultimate conclusion of an ‘essay’, every time that it lacks the theory of mind to compress novel scenes describing the Machiavellian scheming of a dozen individuals at dinner jockeying for power as they talk, every use of logic or abstraction or instructions or Q&A where the model is befuddled and needs more bits to cover up for its mistake where a human would think, understand, and predict. For a language model, the truth is that which keeps on predicting well—because truth is one and error many. Each of these cognitive breakthroughs allows ever so slightly better prediction of a few relevant texts; nothing less than true understanding will suffice for ideal prediction.

If we trained a model which reached that loss of <0.7, which could predict text indistinguishable from a human, whether in a dialogue or quizzed about ice cream or being tested on SAT analogies or tutored in mathematics, if for every string the model did just as good a job of predicting the next character as you could do, how could we say that it doesn’t truly understand everything? (If nothing else, we could, by definition, replace humans in any kind of text-writing job!)

The last bits are deepest. The implication here is that the final few bits are the most valuable bits, which require the most of what we think of as intelligence. A helpful analogy here might be our actions: for the most part, all humans execute actions equally well. We all pick up a tea mug without dropping, and can lift our legs to walk down thousands of steps without falling even once. For everyday actions (the sort which make up most of a corpus), anybody, of any intelligence, can get enough practice & feedback to do them quite well, learning individual algorithms to solve each class of problems extremely well, in isolation.¹⁷ Meanwhile for rare problems, there may be too few instances to do any better than memorize the answer. In the middle of the spectrum are problems which are similar but not too similar to other problems; these are the sorts of problem which reward flexible meta-learning and generalization, and many intermediate problems may be necessary to elicit those capabilities (“neural nets are lazy”).

Where individuals differ is when they start running into the long tail of novel choices, rare choices, choices that take seconds but unfold over a lifetime, choices where we will never get any feedback (like after our death). One only has to make a single bad decision, out of a lifetime of millions of discrete decisions, to wind up in jail or dead. A small absolute average improvement in decision quality, if it is in those decisions, may be far more important than its quantity indicates, and give us some intutition for why those last bits are the hardest/deepest. (Why do humans have such large brains, when animals like chimpanzees do so many ordinary activities seemingly as well with a fraction of the expense? Why is language worthwhile? Perhaps because of considerations like these. We may be at our most human while filling out the paperwork for life insurance.)

Reasons for doubt. The pretraining thesis, while logically impeccable—how is a model supposed to solve all possible trick questions without understanding, just guessing?—never struck me as convincing, an argument admitting neither confutation nor conviction. It feels too much like a magic trick: “here’s some information theory, here’s a human benchmark, here’s how we can encode all tasks as a sequence prediction problem, hey presto—Intelligence!” There are lots of algorithms which are Turing-complete or ‘universal’ in some sense; there are lots of algorithms like AIXI which solve AI in some theoretical sense (Schmidhuber & company have many of these cute algorithms such as ‘the fastest possible algorithm for all problems’, with the minor catch of some constant factors which require computers bigger than the universe).

Why think pretraining or sequence modeling is not another one of them? Sure, if the model got a low enough loss, it’d have to be intelligent, but how could you prove that would happen in practice? (Training char-RNNs was fun, but they hadn’t exactly revolutionized deep learning.) It might require more text than exists, countless petabytes of data for all of those subtle factors like logical reasoning to represent enough training signal, amidst all the noise and distractors, to train a model. Or maybe your models are too small to do more than absorb the simple surface-level signals, and you would have to scale them 100 orders of magnitude for it to work, because the scaling curves didn’t cooperate. Or maybe your models are fundamentally broken, and stuff like abstraction require an entirely different architecture to work at all, and whatever you do, your current models will saturate at poor performance. Or it’ll train, but it’ll spend all its time trying to improve the surface-level modeling, absorbing more and more literal data and facts without ever ascending to the higher planes of cognition as planned. Or…

But apparently, it would’ve worked fine. Even RNNs probably would’ve worked—Transformers are nice, but they seem mostly be about efficiency.¹⁹ (Training large RNNs is much more expensive, and doing BPTT over multiple nodes is much harder engineering-wise.) It just required more compute & data than anyone was willing to risk on it until a few true-believers were able to get their hands on a few million dollars of compute.

Prospects

What can we expect from future DL work? Will GPT-3 kickstart an arms race where soon we will be discussing, blase, what would seem now like ludicrously farfetched schemes like bidirectional multimodal Transformer 100× the size trained on 100× the data (video/text/PDFs-as-images/photo/robotics) with supplementary supervised learning as the backbone of a MuZero-like learning+planning DRL agent running on thousands of tasks (such as coding) simultaneously?

The existence of the hardware overhang implies that the limiting factor here is less hardware than human: will any organization treat GPT-3 as a Sputnik moment and invest aggressively in scaling programs? Is there a GPT-4-equivalent brewing away inside DeepMind or Google Brain’s TPU pods now? They aren’t stupid, they have the hardware, they have the budgets, they have the people.

But I think they lack a vision. As far as I can tell: they do not have any such thing, because Google Brain & DeepMind do not believe in the scaling hypothesis the way that Sutskever, Amodei and others at OA do. Just read through machine learning Twitter to see the disdain for the scaling hypothesis. (A quarter year on from GPT-3 and counting, can you name a single dense model as large as the 17b Turing-NLG—never mind larger than GPT-3?)

Google Brain is entirely too practical and short-term focused to dabble in such esoteric & expensive speculation, although Quoc V. Le’s group occasionally surprises you. They’ll dabble in mixture-of-expert models like GShard, but mostly because they expect to be likely to be able to deploy it or something like it to production in Google Translate.²³

Why didn’t DeepMind do GPT-3? DeepMind²⁴ holds what we might call the “weak scaling hypothesis”: they believe that AGI will require us to “find the right algorithms” effectively replicating a mammalian brain module by module, and that while these modules will be extremely large & expensive by contemporary standards (which is why compute is important, to give us “a more powerful tool with which to hunt for the right algorithms”), they still need to be invented & finetuned piece by piece, with little risk or surprise until the final assembly. Each piece, however, itself can scale: there’s no magical intelligence gland or quantum woo which creates a bright line between humans and, say, chimpanzees or rodents. (As much as we humans extravagantly admire our own capabilities like language or logic, those are relatively minor flourishes on the basic brain—each organism solves the same basic problems, like exploration, long-term memory, learning world-models, associating rewards with specific actions, meta-learning, etc.) As such, once you have a rat-level AGI, a human-level AGI is just more so. (And rats are a lot easier to experiment on.) That is how you get DM contraptions like Agent57 which throw the kitchen sink at the wall to see what sticks, and why they place such emphasis on neuroscience as inspiration and cross-fertilization for reverse-engineering the brain. (See also Sam Altman’s podcast interview comments on OA’s advantage vs unnamed rivals with more compute is because the lack of compute makes them stay “small and focused”—“for sure” like a startup approach.) When someone seems to have come up with a scalable architecture for cracking a hard problem, like AlphaZero or AlphaStar, they are willing to pour on the gas to make it scale, but otherwise, incremental refinement on ALE and then DMLab-30 is the game plan. They have been biting off and chewing pieces of the brain for a decade, and it’ll probably take another decade or two of steady chewing if all goes well. Because they have locked up so much talent and have so much proprietary code and believe all of that is a major moat to any competitor trying to replicate the complicated brain, they are fairly easygoing. You will not see DM ‘bet the company’ on any moonshot; Google’s cashflow isn’t going anywhere (and DM’s budget), and slow and steady wins the race.

Going beyond that, most other research labs like Tesla or FAIR are irrelevant and uninterested. Chinese AI companies are a question mark: past the language barrier, I seem to discern interest in AGI & little of the reflexive Western opposition, and companies like Baidu occasionally release important research (such as the early scaling paper Hestness et al 2017), but overall, Chinese AI may be overestimated, and they seem to suffer from a kind of Dutch disease—funding for surveillance technology, and for narrow e-commerce niches, is so plentiful that other areas are neglected.

OA, lacking anything like DM’s long-term funding from Google or its enormous headcount, is making a startup-like bet that they know an important truth which is a secret: “the scaling hypothesis is true!” So, simple DRL algorithms like PPO on top of large simple architectures like RNNs or Transformers can emerge, exploiting the blessings of scale, and meta-learn their way to powerful capabilities, enabling further funding for still more compute & scaling, in a virtuous cycle. This is why OA had to revise its corporate form: lacking any enormous endowment or extremely deep-pocketed patron like Google, where does it get the money to scale (or hire machine learning engineer/researchers who can command salaries in the millions)? OA has to earn the necessary money, so in a move like Mozilla Foundation owning Mozilla Corporation (to sell Firefox search engine placement), or the Hershey orphanage owning Hershey Chocolate or the Girl Scouts licensing their cookies, OpenAI switched from a pure nonprofit funded by donations to a nonprofit which owns a for-profit subsidiary/startup, “OpenAI LP”, which can take investments and engage in for-profit activities. OA LP, while controlled by OA, can then shoot for the moon. And if OA is wrong to trust in the God of Straight Lines On Graphs, well, they never could compete with DM directly using DM’s favored approach, and were always going to be an also-ran footnote, so they have no regret.

While all of this hypothetically can be replicated relatively easily (never underestimate the amount of tweaking and special sauce it takes) by competitors if they wished (the necessary amounts of compute budgets are still trivial in terms of Big Science or other investments like AlphaGo or AlphaStar or Waymo, after all), said competitors lack the very most important thing, which no amount of money or GPUs can ever cure: the courage of their convictions. They are too hidebound and deeply philosophically wrong to ever admit fault and try to overtake OA until it’s too late. How can we talk seriously about any kind of military Manhattan Project when the US military doesn’t even let its developers use Tensorflow or PyTorch, or about government projects in the shadow of coronavirus? This might seem absurd (surely the Bitter Lesson/scaling hypothesis have now earned enough prior probability to be taken seriously and receive major research investments to test how far they can go, especially given how important the implications are), but look at the repeated criticism of OA every time they release a new example of the scaling hypothesis, from GPT-1 to Dactyl to OA5 to GPT-2 to iGPT to GPT-3… To paraphrase St Augustine, most peoples’ reaction to the Bitter Lesson or scaling hypothesis is “grant me scale & compute—but not yet”.²⁵

A critical indicator will be whether organizations beyond ‘the usual suspects’ (Microsoft ZeRO-2 team has reached 1t-scale training, but there is also Nvidia, Salesforce, Allen, Google DM/GB, Connor/EleutherAI, Facebook FAIR) start participating or if they continue to dismiss scaling. At least as of 2020-10-26, 152 days later, no model has come near GPT-3, and indeed, no model has even exceeded Turing-NLG’s 17b.²⁶

Critiquing The Critics

Keeping track. GPT-3 in 2020 makes as good a point as any to take a look back on the past decade. It’s remarkable to reflect that someone who started a PhD because they were excited by these new “ResNets” would still not have finished it by now—that is how recent even resnets are, never mind Transformers, and how rapid the pace of progress is. In 2010, one could easily fit everyone in the world who genuinely believed in deep learning into a moderate-sized conference room (assisted slightly by the fact that 3 of them were busy founding DeepMind). Someone interested in machine learning in 2010 might have read about some interesting stuff from weirdo diehard connectionists in recognizing hand-written digits using all of 1–2 million parameters, or some modest neural tweaks to standard voice-recognition hidden Markov models. In 2010, who would have predicted that over the next 10 years, deep learning would undergo a Cambrian explosion causing a mass extinction of alternative approaches throughout machine learning, that models would scale up to 175,000 million parameters, and that these enormous models would just spontaneously develop all these capabilities?

No one. That is, no one aside from a few diehard connectionists written off as willfully-deluded old-school fanatics by the rest of the AI community (never mind the world), such as Moravec, Schmidhuber, Sutskever, Legg, & Amodei? One of the more shocking things about looking back is realizing how unsurprising and easily predicted all of this was if you listened to the right people. In 1998, 22 years ago, Moravec noted that AI research could be deceptive, and hardware limits meant that “intelligent machine research did not make steady progress in its first 50 years, it marked time for 30 of them!”, predicting that as Moore’s law continued, “things will go much faster in the next 50 years than they have in the last 50.” Moravec further observed that part of the reason for rapid progress was the hardware overhang: while supercomputers of the necessary power would exist long before the connectionist revolution began, no one would be allowed to use them²⁷, as they would be devoted to ‘more important’ (prestigious) hard STEM work, like “physics simulations” (ie. climate simulations & nuclear bombs)²⁸, and “AI research must wait for the power to become more affordable.” Affordable meaning a workstation roughly ~$2013.25^$1000₁₉₉₈; sufficiently cheap compute to rival a human would arrive sometime in the 2020s, with the 2010s seeing affordable systems in the lizard–mouse range. As it happens, the start of the DL revolution is typically dated to AlexNet in 2012, by a grad student²⁹ using 2 GTX 580 3GB GPUs (launch list price of… $713.09^$500₂₀₁₀, for a system build cost of perhaps $2064.28^$1500₂₀₁₂). 2020 saw GPT-3 arrive, and as discussed before, there are many reasons to expect the cost to fall, in addition to the large hardware compute gains that are being forecast for the 2020s despite the general deceleration of Moore’s law.³⁰

The accelerating pace of the last 10 years should wake anyone from their dogmatic slumber and make them sit upright. It turned out, it’s Hans Moravec’s world, and the rest of us were just living in a fool’s paradise. And there are 28 years left in Moravec’s forecast…

The temptation, that many do not resist so much as revel in, is to give in to a déformation professionnelle and dismiss any model as “just” this or that(“just billions of IF statements” or “just a bunch of multiplications” or “just millions of memorized web pages”), missing the forest for the trees, as Moravec commented of chess engines:

The event was notable for many reasons, but one especially is of interest here. Several times during both matches, Kasparov reported signs of mind in the machine. At times in the second tournament, he worried there might be humans behind the scenes, feeding Deep Blue strategic insights!…In all other chess computers, he reports a mechanical predictability stemming from their undiscriminating but limited lookahead, and absence of long-term strategy. In Deep Blue, to his consternation, he saw instead an “alien intelligence.”

…Deep Blue’s creators know its quantitative superiority over other chess machines intimately, but lack the chess understanding to share Kasparov’s deep appreciation of the difference in the quality of its play. I think this dichotomy will show up increasingly in coming years. Engineers who know the mechanism of advanced robots most intimately will be the last to admit they have real minds. From the inside, robots will indisputably be machines, acting according to mechanical principles, however elaborately layered. Only on the outside, where they can be appreciated as a whole, will the impression of intelligence emerge. A human brain, too, does not exhibit the intelligence under a neurobiologist’s microscope that it does participating in a lively conversation.

But of course, if we ever succeed in AI, or in reductionism in general, it must be by reducing Y to ‘just X’. Showing that some task requiring intelligence can be solved by a well-defined algorithm with no ‘intelligence’ is precisely what success must look like! (Otherwise, the question has been thoroughly begged & the problem has only been pushed elsewhere; computer chips are made of transistors, not especially tiny homunculi.)

Hindsight is 20⁄20. Even in 2015, all the experts assured us that AGI the scaling hypothesis seemed highly dubious: you needed something to scale, after all, and it was all too easy to look at flaws in existing systems and imagine that they would never go away and progress would sigmoid any month now, soon. Like the genomics revolution where a few far-sighted seers extrapolated that the necessary n for GWASes would increase exponentially & deliver powerful PGSes soon, while sober experts wrung their hands over “missing heritability” & the miraculous complexity of biology & scoff about how such n requirements proved GWAS was a failed paradigm, the future arrived at first slowly and then quickly. Yet, here we are: all honor to the fanatics, shame and humiliation to the critics!³¹ If only one could go back 10 years, or even 5, to watch every AI researchers’ head explode reading this paper… Unfortunately, few heads appear to be exploding now, because human capacity for hindsight & excuses is boundless (“I can get that much with finetuning, anyway I predicted it all along, how boring”) and, unfortunately, “there is no fire alarm” for AGI. (If you are still certain that there is near-zero probability of AGI in the next few decades, why? Did you predict—in writing—capabilities like GPT-3? Is this how you expect AI failure to look in the decades beforehand? What specific task, what specific number, would convince you otherwise? How would the world look different than it does now if these crude prototype insect-brain-sized DL systems were not on a path to success?)

Authority without accountability. What should we think about the experts? Projections of failure were made by eminent, respectable, serious people. They spoke in considered tones of why AI hype was excessive and might trigger an “AI winter”, and the fundamental flaws of fashionable approaches and why brute force could not work. These statements were made routinely in 2014, 2015, 2016… And they were wrong. I am aware of few issuing a mea culpa or reflecting on it.³² It is a puzzling failure, and I’ve reflected on it before.

Phatic, not predictive. There is, however, a certain tone of voice the bien pensant all speak in, whose sound is the same whether right or wrong; a tone shared with many statements in January to March of this year; a tone we can also find in a 1940 Scientific American article authoritatively titled, “Don’t Worry—It Can’t Happen”, which advised the reader to not be concerned about it any longer “and get sleep”. (‘It’ was the atomic bomb, about which certain scientists had stopped talking, raising public concerns; not only could it happen, the British bomb project had already begun, and 5 years later it did happen.)

The iron law of bureaucracy: Cathedral gothic. This tone of voice is the voice of authority.
The voice of authority insists on calm, and people not “panicking” (the chief of sins).
The voice of authority assures you that it won’t happen (because it can’t happen).
The voice utters simple arguments about why the status quo will prevail, and considers only how the wild new idea could fail (and not all the possible options).
The voice is not, and does not deal in, uncertainty; things will either happen or they will not, and since it will not happen, there is no need to take any precautions (and you should not worry because it can’t happen).
The voice does not believe in drawing lines on graphs (it is rank numerology).
The voice does not issue any numerical predictions (which could be falsified).
The voice will not share its source code (for complicated reasons which cannot be explained to the laity).
The voice is opposed to unethical things like randomized experiments on volunteers (but will overlook the insult).
The voice does not have a model of the future (because a model implies it does not already know the future).
The voice is concerned about its public image (and unkind gossip about it by other speakers of the voice).
The voice is always sober, respectable, and credentialed (the voice would be pleased to write an op-ed for your national magazine and/or newspaper).
The voice speaks, and is not spoken to (you cannot ask the voice what objective fact would change its mind).
The voice never changes its mind (until it does).
The voice is never surprised by events in the world (only disappointed).
The voice advises you to go back to sleep (right now).

When someone speaks about future possibilities, what is the tone of their voice?

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