Human-like Neural Nets by Catapulting

Speculative proposal to create artificial neural nets with human-like performance by high-learning-rate/regularization training of overparameterized NNs to trigger catapulting/grokking. Over-parameterization as a route to true generalization would resolve many outstanding mysteries of artificial versus natural intelligence. There are many mysteries about deep learning and human intelligence, but we could describe the biggest anomaly this way: why are artificial neural nets smart in such stupid ways, and biological brains stupid but in smart ways? I propose a major change in deep learning scaling paradigms: the architectural differences between human brains and NNs (particularly LLMs) may be due to a bias-variance tradeoff, where LLMs minimize variance and human brains minimize bias. Human brains do this by deep double descent-style overparameterization, and adopting a scaling strategy of extremely high-learning-rate training of extremely overparameterized models on small diverse highly-filtered datasets. This approach would lead to sample-efficiently and compute-efficiently traveling (or catapulting) to a highly-generalizing human-like basin in the model loss landscape, while performing poorly up until the end and failing to memorize much data. If true, this would explain a number of odd stylized facts about how humans/NNs perform well/poorly. Such a ‘catapulted LLM’ would generalize much better than existing NNs, be immune to adversarial attacks, have better economics and be more resistant to cloning, could potentially enable extremely efficient MLP architectures, and by giving true generalization, provide a sturdy foundation for AI safety in the form of useful NNs which are aligned & safe for the right reasons. This could be feasibly tested by training multi-trillion-parameter models for relatively few steps at high cyclical learning rate schedules, and benchmarking adversarial and hard examples on tasks like arithmetic and small-image classification. Because deep learning has continued to scale up and smash through benchmarks and begun to look like it really will be the final AI paradigm, and thus in some sense the same thing as human ‘intelligence’, to a considerable degree, we can regard ‘intelligence’ as solved: intelligence is sufficient compute applied to search over programs (like Turing machines or circuits) to predict or optimize where the optimal solution is a relatively long program. (This is a companion piece to “Guardian Angels: LLM Personalization for Productivity and Security”.)

Intelligence, Broadly A scaling-centric view might be summed up like this: The Master Synthesis Anomalies But this paradigm, as broadly correct as it now seems to be, doesn’t explain everything. We still have many specific problems that this paradigm is too general to explain. While current NNs, and LLMs in particular, are by far the most human-like AI software ever created, in having human-like strengths and weaknesses, there are a number of anomalies in machine & biological intelligence that have no good answers. We have many puzzles here, but they all feel connected, somehow. Artificial Sample Inefficiency Why do NNs require Chinchilla-style scaling of data and compute, when humans appear to learn from multiple orders of magnitude less data, and it is increasingly plausible (given various estimates of human-brain equivalents) that they learn from less total compute? Why, as so many connectionist pioneers like Alan Turing expected, do we not train AI like children, with a curriculum and clear developmental stages? There are many answers offered, none satisfactory. (And what should we make of theoretical results like Rosenfeld 2021’s “Nyquist learners”?) Multi-modality: while useful, multi-modality has failed to yield any major change of scaling law exponents; unimodal models work shockingly well, and language models turn out to already encode a large amount of visual knowledge and can easily be plugged into vision models (eg. Flamingo, Tsimpoukelli et al 2021). Human sensory input is actually large: Another common explanation is to deny that humans learn from less data, and argue from raw sensory bandwidth: if vision+sound+touch is such-and-such bits per second and you accumulate over an adult’s lifetime, it can look much more comparable to the trillions of tokens we train an LLM on. This is unconvincing because the raw sensory bitrate is meaningless: the input is extremely redundant & predictable for the most part. (Imagine sitting in a room staring at a computer screen.) Attempts at quantifying the information content of images, video, or sound, usually indicate that they boil down to the equivalent of a few hundred or thousand tokens and those modalities are easily learned by small models (eg. iGPT/DALL·E 1).

The asymmetry is particularly striking in text-to-image generative models, where the text encoder (usually an afterthought) is often far bigger than the image generator itself. And on the human side, disabled people are not much less intelligent than normal humans: deaf/blind people are much worse at language tasks, but their fluid intelligence often remains normal. If the sensory bandwidth were so critical, this would be impossible. Active Learning: human children, unlike models confined to offline imitation learning, can choose what to learn about by exploring their environment or asking questions. In theory, active learning & optimal exploration can be far more sample-efficient than indiscriminate training (exponential rather than power law, at a minimum), and this could account for the entire gap. However, if we look at the things children actually choose, the data in question doesn’t appear all that amazing. Further, in stark violation of any notion of optimal Bayesian exploration, children often choose to learn on the same data point—eg. watching the same YouTube video hundreds of times.1 Or if we watch them ‘explore’ a game or computer, it looks like it is by acting largely at random, and an adult would learn far faster by more carefully thought-out exploration.2 Embodiment: a closely-related topic is the idea of “embodied cognition”, which used to be quite popular as an explanation for the weaknesses of AI—AI models simply lacked commonsense & generalization for lack of a body and an appropriate environment. But thus far, ‘embodiment’ like training on robotics data (eg. Gato) has exhibited zero transfer to other tasks, never mind massive scaling law gains, and ironically, it is, in fact, embodied tasks like robotics models which have been greatly benefiting from non-embodied pretrained models (including LLMs!). Architecture Magic: Perhaps in some way, Homo sapiens-style biological neurons are just some near-perfect architecture, and this explains most of the gap; someday we will understand how all artificial neurons are severely hobbled by mistakes that will seem as tragically obvious in hindsight as earlier mistakes like not using backpropagation or using sigmoid activation functions now seem to us, but they remain a mystery for now. This view was highly plausible until recently, but has been running into many problems. For starters, we simply have not found any architecture magic.

The most obvious place to find magic would be the learning rule for biological NNs, whatever they use in place of backpropagation… But while people have proposed many biologically-plausible learning rules since Hebb proposed the first learning rule in 194977ya, which respect the requirements like locality, in every case, those learning rules perform worse than, or at best similar to, backprop! To quote Geoff Hinton: So maybe it’s [GPT-4] actually got a much better learning algorithm than us. And if biological NNs are not so good but there is something special about humans which does make them much better, then why do Homo sapiens not appear to have any major neuroscientific breakthroughs compared to our primate relatives? Why are we so genetically similar, and we have failed in the search for major novel mutations that create humans, and human brains seem increasingly like nothing but “a scaled-up primate brain”? If human (or primate) brains are so uniquely efficiently tuned by evolution, why are bird brains so much more efficient in size & thermodynamics than primate brains, with clear genetic changes, and better scaling to the point where small bird brains like ravens or parrots or vultures exhibit eerie levels of intelligence & behavioral complexity almost on par with dolphins, chimpanzees—or humans? Why does human intelligence exhibit so many bizarre drawbacks or anomalies if it is so optimized, like childhood amnesia or lurching through developmental phases over decades? If the story of NNs is one of us gradually recapitulating evolution’s perfect neural networks, why does neuroscience provide so little useful inspiration (as is emphasized constantly by neuroscientists & AI researchers, even the “neuroscientific” inspiration for things like self-attention is a very loose inspiration)? Why don’t all major improvements come from success in reverse-engineering the human brain to ever greater biologically-realistic detail? Why is the rapid progress in neuroscience, like scanning entire connectomes, completely irrelevant to cutting-edge NN models? Why do all the scaling laws for CNNs & Transformers look so similar in the exponent, and durable improvement so difficult, to the point where 7 years after Vaswani et al 2017, it is still a relevant baseline?

And why, if we have such fundamentally inferior architectures, are the scaling laws so smooth & reliable, instead of breaking frequently or predicted to asymptote at levels far below human?3 Conversely, why do NNs provide little insight into biological brains? In the other direction, neuroscience and individual psychology has hardly benefited from DL; DL has provided tools, and has provided good predictive models of brains, but that is about it. One could open an issue of a psychometrics or neuroscience journal, and note that, over a decade into the deep learning revolution, if it had never happened, that issue would look about the same and would be completely intelligible to a researcher from 201016ya. It is impressive that we can now turn fMRI scans into crude visualizations of what a person is looking at, or we can use LLMs to generate possible questions for surveys, but DL has provided essentially no major insights into such fundamental questions as “what is fluid intelligence? why is the g factor so general? how do neuroanatomic traits like neuron count or network properties cause intelligence or other cognitive abilities?” Isn’t this astounding? We can now create models of enormous generality like Gato or GPT-4o from scratch to match or exceed humans without the hand-engineering of GOFAI, which seem so eerily human-like in many ways, and which recapitulate so many aspects of human cognition down to heuristics & biases, and our ability to create these artificial intelligences tells us… nothing important about the human brain? Really? So what, all the DL is just a dead-end and coincidentally capable of all that, and sometime in the future we’ll discover the real route to brain-like intelligence…? Sample Efficiency Why do NNs require so much data to pretrain, when NNs are as sample-efficient in narrow comparisons? Despite the huge amount of indiscriminate data used in NN pretraining, we are puzzled because if we examine how well models do in apples-to-apples comparisons, NNs appear to be as roughly as good as humans or biological neural networks at learning from small data. The simplest answer to the exorbitant data-scaling would be that NNs do a bad job of learning each datapoint—but that doesn’t seem to be true when we compare things like in-context learning (even GPT-3), transfer learning smoothly scaling, child-sized datasets (eg.