Science/engineering is often a winner-take all race. To him who has is given more—so for every Einstein there are many others less well known (Lorentz, Minkowski), and so on. Actual ability is filtered through something like a softmax to produce fame, so fame severely underestimates ability.
Evolution proceeds by random exploration of parameter space, the more intelligent humans only reproduce a little more than average in aggregation, and there is drag due to mutations. So the subset of the most intelligent humans represents the upper potential of the brain, but it clearly asymptotes.
Finally, intelligence results from the interaction of genetics and memetics, just like in ANNs.
Digital minds can be copied easily (well at least current ones—future analog neuromorphic minds may be more difficult to copy), so it seems likely that they will not have the equivalent of the mutation load issue as much. On the other hand the great expense of training digital minds and the great cost of GPU RAM means they have much less diversity—many instances of a few minds.
In your view, who would contribute more to science -- 1000 Einsteins, or 10,000 average scientists?[1]
“IQ variation is due to continuous introduction of bad mutations” is an interesting hypothesis, and definitely helps save your theory. But there are many other candidates, like “slow fixation of positive mutations” and “fitness tradeoffs[2]”.
Do you have specific evidence for either:
Deleterious mutations being the primary source of IQ variation
Human intelligence “plateauing” around the level of top humans[3]
Or do you believe these things just because they are consistent with your learning efficiency model and are otherwise plausible?[4]
Maybe you have a very different view of leading scientists than most people I’ve read here? My picture here is not based on any high-quality epistemics (e.g. it includes “second-hand vibes”), but I’ll make up some claims anyway, for you to agree or disagree with:
There are some “top scientists” (like Einstein, Dirac, Von Neumann, etc). Within them, much of the variance in fame is incidental, but they are clearly a class apart from merely 96th percentile scientists. 1000 {96%-ile-scientists} would be beaten by 500 {96%-ile-scientists} + 100 Einstein-level scientists.
Even within “top scientists” in a field, the best one is more than 3x as intrinsically productive[5] as the 100th best one.
Within this, I could imagine anything from “this gene’s mechanism obviously demands more energy/nutrients” to “this gene happens to mess up some other random thing, not even in the brain, just because biochemistry is complicated”. I have no idea what the actual prevalence of any of this is.
What does this even mean? Should the top 1/million already be within 10x of peak productivity? How close should the smartest human alive be to the peak? Are they nearly free of deleterious mutations?
In your view, who would contribute more to science -- 1000 Einsteins, or 10,000 average scientists?
I vaguely agree with your 90%/60% split for physics vs chemistry. In my field of programming we have the 10x myth/meme, which I think is reasonably correct but it really depends on the task.
For the 10x programmers it’s some combination of greater IQ/etc but also starting programming earlier with more focused attention for longer periods of time, which eventually compounds into the 10x difference.
But it really depends on the task distribution—there are some easy tasks where the limit is more typing speed and compilation, and at the extreme there are more theoretical tasks that require some specific combination of talent, knowledge, extended grind focus for great lengths of time, and luck.
Across all fields combined there seem to be perhaps 1000 to 10000 top contributors? But it seems to plateau in the sense that I do not agree that John Von Neumman (or whoever your 100x candidate is) was 10x einstein or even Terrence Tao or Kasparov (or that either would be 10x carmack in programming, if that was their field), and given that there have been 100 billion humans who have ever lived and most lived a while ago, there should have been at least a few historical examples 10x or 100x John Von Neumman. I dont see evidence for that at all.
Maybe you have a very different view of leading scientists than most people I’ve read here?
I do think people here hero worship a bit and overestimate the flatness of the upper tail of the genetic component of intelligence in particular (ie IQ) and its importance.
But that being said your vibe numbers don’t seem so out of whack.
Interesting, I find what you are saying here broadly plausible, and it is updating me (at least toward greater uncertainity/confusion). I notice that I don’t expect the 10x effect, or the Von Neumann effect, to be anywhere close to purely genetic. Maybe some path-dependency in learning? But my intuition (of unknown quality) is that there should be some software tweaks which make the high end of this more reliably achievable.
Anyway, to check that I understand your position, would this be a fair dialogue?:
Person: “The jump from chimps to humans is some combination of a 3x scaleup and some algorithmic improvements. Once you have human-level AI, scaling it up 3x and adding a chimp-to-human-jump worth of algorithmic improvement would get you something vastly superhuman, like 30x or 1000x Von Neumann, if not incomparable.”
Vivek’s model of Jacob: “Nope. The 3x scaleup is the only thing, there wasn’t much algorithmic improvement. The chimp-to-human scaling jump was important because it enabled language/accumulation, but there is nothing else left like that. There’s nothing practical you can do with 3x human-level compute that would 30x Von Neumann[1], even if you/AIs did a bunch of algorithmic research.”
I find your view more plausible than before, but don’t know what credence to put on it. I’d have more of a take if I properly read your posts.
Your model of my model sounds about right, but I also include neotany extension of perhaps 2x which is part of the scale up (spending longer on training the cortex, especially in higher brain regions).
For Von Neumann in particular my understanding is he was some combination of ‘regular’ genius and a mentant (a person who can perform certain computer like calculations quickly), which was very useful for many science tasks in an era lacking fast computers and software like mathematica, but would provide less of an effective edge today. It also inflated people’s perception of his actual abilities.
IIRC according to gwern the theory that IQ variation is mostly due to mutational load has been debunked by modern genomic studies [though mutational load definitely has a sizable effect on IQ]. IQ variation seems to be mostly similar to height in being the result of the additive effect of many individual common allele variations.
Science/engineering is often a winner-take all race. To him who has is given more—so for every Einstein there are many others less well known (Lorentz, Minkowski), and so on. Actual ability is filtered through something like a softmax to produce fame, so fame severely underestimates ability.
Evolution proceeds by random exploration of parameter space, the more intelligent humans only reproduce a little more than average in aggregation, and there is drag due to mutations. So the subset of the most intelligent humans represents the upper potential of the brain, but it clearly asymptotes.
Finally, intelligence results from the interaction of genetics and memetics, just like in ANNs.
Digital minds can be copied easily (well at least current ones—future analog neuromorphic minds may be more difficult to copy), so it seems likely that they will not have the equivalent of the mutation load issue as much. On the other hand the great expense of training digital minds and the great cost of GPU RAM means they have much less diversity—many instances of a few minds.
None of this by itself leaves much hope for foom.
In your view, who would contribute more to science -- 1000 Einsteins, or 10,000 average scientists?[1]
“IQ variation is due to continuous introduction of bad mutations” is an interesting hypothesis, and definitely helps save your theory. But there are many other candidates, like “slow fixation of positive mutations” and “fitness tradeoffs[2]”.
Do you have specific evidence for either:
Deleterious mutations being the primary source of IQ variation
Human intelligence “plateauing” around the level of top humans[3]
Or do you believe these things just because they are consistent with your learning efficiency model and are otherwise plausible?[4]
Maybe you have a very different view of leading scientists than most people I’ve read here? My picture here is not based on any high-quality epistemics (e.g. it includes “second-hand vibes”), but I’ll make up some claims anyway, for you to agree or disagree with:
There are some “top scientists” (like Einstein, Dirac, Von Neumann, etc). Within them, much of the variance in fame is incidental, but they are clearly a class apart from merely 96th percentile scientists. 1000 {96%-ile-scientists} would be beaten by 500 {96%-ile-scientists} + 100 Einstein-level scientists.
Even within “top scientists” in a field, the best one is more than 3x as intrinsically productive[5] as the 100th best one.
I’m like 90% on the Einsteins for theoretical physics, and 60% on the Einsteins for chemistry
Within this, I could imagine anything from “this gene’s mechanism obviously demands more energy/nutrients” to “this gene happens to mess up some other random thing, not even in the brain, just because biochemistry is complicated”. I have no idea what the actual prevalence of any of this is.
What does this even mean? Should the top 1/million already be within 10x of peak productivity? How close should the smartest human alive be to the peak? Are they nearly free of deleterious mutations?
I agree that they are consistent with each other and with your view of learning efficiency, but am not convinced of any of them.
“intrinsic” == assume they have the same resources (like lab equipment and junior scientists if they’re experimentalists)
I vaguely agree with your 90%/60% split for physics vs chemistry. In my field of programming we have the 10x myth/meme, which I think is reasonably correct but it really depends on the task.
For the 10x programmers it’s some combination of greater IQ/etc but also starting programming earlier with more focused attention for longer periods of time, which eventually compounds into the 10x difference.
But it really depends on the task distribution—there are some easy tasks where the limit is more typing speed and compilation, and at the extreme there are more theoretical tasks that require some specific combination of talent, knowledge, extended grind focus for great lengths of time, and luck.
Across all fields combined there seem to be perhaps 1000 to 10000 top contributors? But it seems to plateau in the sense that I do not agree that John Von Neumman (or whoever your 100x candidate is) was 10x einstein or even Terrence Tao or Kasparov (or that either would be 10x carmack in programming, if that was their field), and given that there have been 100 billion humans who have ever lived and most lived a while ago, there should have been at least a few historical examples 10x or 100x John Von Neumman. I dont see evidence for that at all.
I do think people here hero worship a bit and overestimate the flatness of the upper tail of the genetic component of intelligence in particular (ie IQ) and its importance.
But that being said your vibe numbers don’t seem so out of whack.
Interesting, I find what you are saying here broadly plausible, and it is updating me (at least toward greater uncertainity/confusion). I notice that I don’t expect the 10x effect, or the Von Neumann effect, to be anywhere close to purely genetic. Maybe some path-dependency in learning? But my intuition (of unknown quality) is that there should be some software tweaks which make the high end of this more reliably achievable.
Anyway, to check that I understand your position, would this be a fair dialogue?:
I find your view more plausible than before, but don’t know what credence to put on it. I’d have more of a take if I properly read your posts.
I’m not sure how to operationalize this “30x-ing” though. Some candidates:
- “1000 scientists + 30 Von Neumanns” vs. “1000 scientists + 1 ASI”
- “1 ASI” vs. “30 Von Neumanns”
- “100 ASIs” vs. “3000 Von Neumanns”
Your model of my model sounds about right, but I also include neotany extension of perhaps 2x which is part of the scale up (spending longer on training the cortex, especially in higher brain regions).
For Von Neumann in particular my understanding is he was some combination of ‘regular’ genius and a mentant (a person who can perform certain computer like calculations quickly), which was very useful for many science tasks in an era lacking fast computers and software like mathematica, but would provide less of an effective edge today. It also inflated people’s perception of his actual abilities.
IIRC according to gwern the theory that IQ variation is mostly due to mutational load has been debunked by modern genomic studies [though mutational load definitely has a sizable effect on IQ]. IQ variation seems to be mostly similar to height in being the result of the additive effect of many individual common allele variations.