the brain is severely undertrained, humans spend only a small fraction of their time on focussed academic learning
I expect that humans spend at least 10% of their first decade building a world model, and that evolution has heavily optimized at least the first couple of years of that. A large improvement in school-based learning wouldn’t have much effect on my estimate of the total learning needed.
It does sound like a lot—that’s 5 OOMs to reach human learning efficiency and then 8 OOMs more. But when we BOTECed the sources of algorithmic efficiency gain on top of the human brain, it seemed like you could easily get more than 8. But agreed it seems like a lot. Though we are talking about ultimate physical limits here!
Interesting re the early years. So you’d accept that learning from 5⁄6 could be OOMs more efficient, but would deny that the early years could be improved?
Though you’re not really speaking to the ‘undertrained’ point, which is about the number of params vs data points
I agree evolution has probably optimized human learning, but I don’t think that it’s so heavily optimized that we can use it to give a tighter upper bound than 13 OOMs, and the reason for this is I do not believe that humans are in equilibrium, and this means that there are probably optimizations left to discover, so I do think the 13 OOMs number is plausible )with high uncertainty).
The first year or two of human learning seem optimized enough that they’re mostly in evolutionary equilibrium—see Henrich’s discussion of the similarities to chimpanzees in The Secret of Our Success.
Human learning around age 10 is presumably far from equilibrium.
I’ll guess that I see more of the valuable learning taking place in the first 2 years or so than do other people here.
I buy Heinrich’s theory far less than I used to, because Heinrich made easily checkable false claims that all point in the direction of culture being more necessary for human success.
In particular, I do not buy that humans and chimpanzees are nearly that similar as Heinrich describes, and a big reason for this is that the study that showed that had heavily optimized and selected the best chimpanzees against reasonably average humans, which is not a good way to compare performance if you want the results to generalize.
I don’t think they’re wildly different, and I’d usually put chimps effective flops as 1-2 OOMs lower, but I wouldn’t go nearly as far as Heinrich on the similarities.
I do think culture actually matters, but nowhere near as much as Heinrich wants it to matter.
I basically disagree that most of the valuable learning takes place before age 2, and indeed if I wanted to argue the most valuable point for learning, it would probably be from 0-25 years, or more specifically 2-7 years olds and then 13-25 years old again.
I agree with most of this, but the 13 OOMs from the the software feedback loop sounds implausible.
From How Far Can AI Progress Before Hitting Effective Physical Limits?:
I expect that humans spend at least 10% of their first decade building a world model, and that evolution has heavily optimized at least the first couple of years of that. A large improvement in school-based learning wouldn’t have much effect on my estimate of the total learning needed.
It does sound like a lot—that’s 5 OOMs to reach human learning efficiency and then 8 OOMs more. But when we BOTECed the sources of algorithmic efficiency gain on top of the human brain, it seemed like you could easily get more than 8. But agreed it seems like a lot. Though we are talking about ultimate physical limits here!
Interesting re the early years. So you’d accept that learning from 5⁄6 could be OOMs more efficient, but would deny that the early years could be improved?
Though you’re not really speaking to the ‘undertrained’ point, which is about the number of params vs data points
I agree evolution has probably optimized human learning, but I don’t think that it’s so heavily optimized that we can use it to give a tighter upper bound than 13 OOMs, and the reason for this is I do not believe that humans are in equilibrium, and this means that there are probably optimizations left to discover, so I do think the 13 OOMs number is plausible )with high uncertainty).
Comment below:
https://www.lesswrong.com/posts/DbT4awLGyBRFbWugh/#mmS5LcrNuX2hBbQQE
The first year or two of human learning seem optimized enough that they’re mostly in evolutionary equilibrium—see Henrich’s discussion of the similarities to chimpanzees in The Secret of Our Success.
Human learning around age 10 is presumably far from equilibrium.
I’ll guess that I see more of the valuable learning taking place in the first 2 years or so than do other people here.
I have 2 cruxes here:
I buy Heinrich’s theory far less than I used to, because Heinrich made easily checkable false claims that all point in the direction of culture being more necessary for human success.
In particular, I do not buy that humans and chimpanzees are nearly that similar as Heinrich describes, and a big reason for this is that the study that showed that had heavily optimized and selected the best chimpanzees against reasonably average humans, which is not a good way to compare performance if you want the results to generalize.
I don’t think they’re wildly different, and I’d usually put chimps effective flops as 1-2 OOMs lower, but I wouldn’t go nearly as far as Heinrich on the similarities.
I do think culture actually matters, but nowhere near as much as Heinrich wants it to matter.
I basically disagree that most of the valuable learning takes place before age 2, and indeed if I wanted to argue the most valuable point for learning, it would probably be from 0-25 years, or more specifically 2-7 years olds and then 13-25 years old again.