I do believe the brain has much higher sample efficiency than existing DNN algorithms, in the sense that matters for guessing future ASI compute requirements. But I agree that pinning down the comparison is a bit subtle.
(Also, sample-efficiency is not the main reason why I think that FLOP-required-for-ASI is low, but rather trying to guess how much compute the brain is doing. But sure, sample-efficiency is not totally irrelevant to how I think about these things, I suppose.)
The sensory data going to the brain is (I think) >99% visual, and >99.5% visual + audio. (IIRC … I didn’t double-check, and it’s kinda controversial how to calculate it anyway…)
So it’s interesting that congenitally blind people, and deafblind people, are basically just as smart and competent as sighted & hearing people, except obviously in contexts where the missing sensory data is directly relevant. I think this observation generally pushes against a perspective that centers the story of human intelligence around our abundant sensory data.
And more specifically, RE your Appendix, if we’re going to compare frontier LLM training data with human sensory data, we should also be putting blind and deafblind people onto that same plots / tables. And also, if we’re comparing sighted people to frontier models, we need to include the frontier models’ visual training data, not just text token training data … I don’t know how many extra bytes that would be, but I’d guess a lot.
I’m not exactly sure what point you’re trying to make with the discussion of Dreamer, EfficientZero, and related, but (copying from an argument I had on this topic in 2021):
I think that if somebody wants to understand AlphaZero, the fact that it trained on 40,000,000 games of self-play is a highly relevant and interesting datapoint. Suppose you were to then say “…but of those 40,000,000 games, fundamentally it really only needed 100 games with the external simulator to learn the rules. The other 39,999,900 games might as well have been ‘in its head’. This was proven in follow-up work.”. I would reply: “Oh. OK. That’s interesting too. But I still care about the 40,000,000 number. I still see that number as a very important part of understanding the nature of AlphaZero and similar systems.”
Anyway, if a human is playing chess in his head, or replaying a memory of that embarrassing thing that I did one time in middle school what they did yesterday, then he is not paying attention to sensory input. He’s probably mostly zoning out. So in a certain sense, the replay is replacing sensory data, as opposed to increasing the effective total amount of data, in humans. So, like, the thing in §3 where you note that LLMs can be more “sample-efficient” by doing 4 epochs of the same data, or the thing that EfficientZero etc. does, well, if you’re talking about sample-efficiency for the pragmatic reason of trying to solve AI problem where you have lots of compute but strictly limited data, then cool, that kind of thing is helpful and important. But if you’re talking about sample-efficiency in the context of trying to compare and contrast humans versus current AIs, then I think those tricks are somewhat off-topic.
I concede that “brains are kinda like insanely huge 100-trillion-parameter LLMs, and that’s BOTH why we don’t have AGI yet AND why brains are (in certain senses) more sample-efficient” is a story that hangs together. And it’s a pretty popular story in LLM circles because it also fits in with scale-is-all-you-need. I really don’t think that story is right, for lots of reasons, including neuroscience stuff that I don’t want to get into, but also just, like, noticing all the ways that brains are quite different from insanely huge LLMs. There’s the continual learning stuff, the model-based RL stuff, the brain’s complete absence of “true” imitative learning, the way that cortical microcircuits simply do not look anything like transformer layers, etc.
Is it plausible to you that there’s some equivalent to ‘mental simulations’ humans use for model-based world-sample-efficient learning?
If so is it plausible they are mostly (overwhelmingly?) subconscious? If so could those be much (orders of magnitude?) faster than equivalent real-world interaction?
(I tentatively think yes to all of these, mostly because of the computer science of it, without much context on the neuroscience of it. cf my other comment on temporal abstraction.)
(Interesting post, thanks for writing it!)
I do believe the brain has much higher sample efficiency than existing DNN algorithms, in the sense that matters for guessing future ASI compute requirements. But I agree that pinning down the comparison is a bit subtle.
(Also, sample-efficiency is not the main reason why I think that FLOP-required-for-ASI is low, but rather trying to guess how much compute the brain is doing. But sure, sample-efficiency is not totally irrelevant to how I think about these things, I suppose.)
The sensory data going to the brain is (I think) >99% visual, and >99.5% visual + audio. (IIRC … I didn’t double-check, and it’s kinda controversial how to calculate it anyway…)
So it’s interesting that congenitally blind people, and deafblind people, are basically just as smart and competent as sighted & hearing people, except obviously in contexts where the missing sensory data is directly relevant. I think this observation generally pushes against a perspective that centers the story of human intelligence around our abundant sensory data.
And more specifically, RE your Appendix, if we’re going to compare frontier LLM training data with human sensory data, we should also be putting blind and deafblind people onto that same plots / tables. And also, if we’re comparing sighted people to frontier models, we need to include the frontier models’ visual training data, not just text token training data … I don’t know how many extra bytes that would be, but I’d guess a lot.
I’m not exactly sure what point you’re trying to make with the discussion of Dreamer, EfficientZero, and related, but (copying from an argument I had on this topic in 2021):
I think that if somebody wants to understand AlphaZero, the fact that it trained on 40,000,000 games of self-play is a highly relevant and interesting datapoint. Suppose you were to then say “…but of those 40,000,000 games, fundamentally it really only needed 100 games with the external simulator to learn the rules. The other 39,999,900 games might as well have been ‘in its head’. This was proven in follow-up work.”. I would reply: “Oh. OK. That’s interesting too. But I still care about the 40,000,000 number. I still see that number as a very important part of understanding the nature of AlphaZero and similar systems.”
Anyway, if a human is playing chess in his head, or replaying a memory of
that embarrassing thing that I did one time in middle schoolwhat they did yesterday, then he is not paying attention to sensory input. He’s probably mostly zoning out. So in a certain sense, the replay is replacing sensory data, as opposed to increasing the effective total amount of data, in humans. So, like, the thing in §3 where you note that LLMs can be more “sample-efficient” by doing 4 epochs of the same data, or the thing that EfficientZero etc. does, well, if you’re talking about sample-efficiency for the pragmatic reason of trying to solve AI problem where you have lots of compute but strictly limited data, then cool, that kind of thing is helpful and important. But if you’re talking about sample-efficiency in the context of trying to compare and contrast humans versus current AIs, then I think those tricks are somewhat off-topic.I concede that “brains are kinda like insanely huge 100-trillion-parameter LLMs, and that’s BOTH why we don’t have AGI yet AND why brains are (in certain senses) more sample-efficient” is a story that hangs together. And it’s a pretty popular story in LLM circles because it also fits in with scale-is-all-you-need. I really don’t think that story is right, for lots of reasons, including neuroscience stuff that I don’t want to get into, but also just, like, noticing all the ways that brains are quite different from insanely huge LLMs. There’s the continual learning stuff, the model-based RL stuff, the brain’s complete absence of “true” imitative learning, the way that cortical microcircuits simply do not look anything like transformer layers, etc.
Is it plausible to you that there’s some equivalent to ‘mental simulations’ humans use for model-based world-sample-efficient learning?
If so is it plausible they are mostly (overwhelmingly?) subconscious? If so could those be much (orders of magnitude?) faster than equivalent real-world interaction?
(I tentatively think yes to all of these, mostly because of the computer science of it, without much context on the neuroscience of it. cf my other comment on temporal abstraction.)
No I don’t find that plausible, sorry I don’t have time to explain why but this post section is related to where I’m coming from.