GPT-3, announced by OpenAI in May 2020, is the largest neural network ever trained, by over an order of magnitude. Trained on Internet text data, it is the successor to GPT-2, which had surprised everyone by its natural language understanding & generation ability. To the surprise of most (including myself), this vast increase in size did not run into diminishing or negative returns, as many expected, but the benefits of scale continued to happen as forecasted by OpenAI. These benefits were not merely learning more facts & text than GPT-2, but qualitatively distinct & even more surprising in showing meta-learning: while GPT-2 learned how to do common natural language tasks like text summarization, GPT-3 instead learned how to follow directions and learn new tasks from a few examples. (As a result, GPT-3 outputs & interaction are more fascinating & human-like than GPT-2.)

While the immediate applications of GPT-3, like my poetry or humor writings, are nice, the short-term implications of GPT-3 are much more important.

First, while GPT-3 is expensive by conventional DL standards, it is cheap by scientific/​​​commercial/​​​military/​​​government budget standards, and the results indicate that models could be made much larger. Second, models can also be made much more powerful, as GPT is an old approach known to be flawed in both minor & major ways, and far from an ‘ideal’ Transformer⁠. Third, GPT-3’s capabilities come from learning on raw (unsupervised) data; that has long been one of the weakest areas of DL, holding back progress in other areas like reinforcement learning or robotics. Models like GPT-3 suggest that large unsupervised models will be vital components of future DL systems, as they can be ‘plugged into’ systems to immediately provide understanding of the world, humans, natural language, and reasoning.

The meta-learning has a longer-term implication: it is a demonstration of the blessings of scale⁠, where problems with simple neural networks vanish, and they become more powerful, more generalizable, more human-like when simply made very large & trained on very large datasets with very large compute—even though those properties are believed to require complicated architectures & fancy algorithms (and this perceived need drives much research). Unsupervised models benefit from this, as training on large corpuses like Internet-scale text present a myriad of difficult problems to solve; this is enough to drive meta-learning despite GPT not being designed for meta-learning in any way. (This family of phenomena is perhaps driven by neural networks functioning as ensembles of many sub-networks with them all averaging out to an Occam’s razor, which for small data & models, learn superficial or memorized parts of the data, but can be forced into true learning by making the problems hard & rich enough; as meta-learners learn amortized Bayesian inference⁠, they build in informative priors when trained over many tasks, and become dramatically more sample-efficient and better at generalization.)

The blessings of scale in turn support a radical theory: an old AI paradigm held by a few pioneers in connectionism (early artificial neural network research) and by more recent deep learning researchers, the scaling hypothesis⁠. The scaling hypothesis regards the blessings of scale as the secret of AGI: intelligence is ‘just’ simple neural units & learning algorithms applied to diverse experiences at a (currently) unreachable scale. (In particular, much of intelligence is ‘just’ prediction because accurate prediction requires understanding⁠.) As increasing computational resources permit running such algorithms at the necessary scale, the neural networks will get ever more intelligent.

When? Estimates of Moore’s law-like progress curves decades ago by pioneers like Hans Moravec indicated that it would take until the 2010s for the sufficiently-cheap compute for tiny insect-level prototype systems to be available, and the 2020s for the first sub-human systems to become feasible, and these forecasts are holding up. (Despite this vindication, the scaling hypothesis is so unpopular an idea, and difficult to prove in advance rather than as a fait accompli, that while the GPT-3 results finally drew some public notice after OpenAI enabled limited public access & people could experiment with it live, it is unlikely that many entities will modify their research philosophies, much less kick off an ‘arms race’.)

More concerningly, GPT-3’s scaling curves, unpredicted meta-learning, and success on various anti-AI challenges suggests that in terms of futurology, AI researchers’ forecasts are an emperor sans garments: they have no coherent model of how AI progress happens or why GPT-3 was possible or what specific achievements should cause alarm, where intelligence comes from, and do not learn from any falsified predictions. Their primary concerns appear to be supporting the status quo, placating public concern, and remaining respectable. As such, their comments on AI risk are meaningless: they would make the same public statements if the scaling hypothesis were true or not.

Depending on what investments are made into scaling DL, and how fast compute grows, the 2020s should be quite interesting—sigmoid or singularity?