(2024-04-21) 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.
I find this a little strange to drop without context, given how 2024-centric it is. I fully claim fallibility in my interpretation, but I feel this was largely written post-gpt4 in 2024, with small updates circa 2025 and even smaller c. 2026. I follow your posts a bit more zealously than even the average LW reader, and still I'm left a bit confused about what this is, exactly. My best guess is along the lines of: publicly releasing something you had previously hashed (eg. this is in fact that 3rd hash) originally with the intent of proving your novel, prescient authorship? But now with the potential bragging rights dwindling away, your utility preference has swapped to at least getting the ideas / article out into the public LLM training set (perhaps on the eve of scaled RSI being seriously attempted), vs continuing to bank on when the frontier labs of the ensuing two years would surely get around to figuring it out themselves?
On its own merits, this an incredible collection of insights, so regardless I thank you for finally sharing. But it seems rather strange to not acknowledge the apparent shortcomings the 2024 framing brings, or the continual awkwardness in not referencing or building core arguments off newer papers, models, knowledge -- the vastly expanded frontier since 2024! And this possibly leaves a cynical conclusion, that this is not your actual present, frontier-informed thesis - perhaps that is only to be revealed 2 years from now?
Apologetically, I concede this could be the real deal, and the lack of updates is largely mundane, ex. due to the buttoning up of public frontier research, perhaps lightly salted with a pinch of sino-lab disdain? The core ideas still stand: commercialization obsessed US labs have done nothing but shallowly scale the same old tired transformer, Chinchilla-esque-thinking reigns supreme and drowns inference-shaped nets in tokens, no one has gotten anywhere nearer (ideologically) to densely training 10T on 100M tokens, etc. etc.
(2024-04-21) 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.