There seems to be a stronger version of the (pro-fast-takeoff) chimp vs human argument, namely average-IQ humans vs von Neumann: “IQ-100 humans have brains roughly the same size as von Neumann, but are much worse at making technology.” Does your counterargument work against that version too? (I’m not sure I fully understand it.)
The argument in the post applies to within-human differences; it’s a bit weaker, because evolution is closer to optimizing humans for technological aptitude than it is to optimizing chimps for technological aptitude.
Beyond that, what you infer from human variation depends a bit on where you see human variation as coming from:
If human variation comes from maladaptiveness, deleterious mutations, etc. then we can’t infer very much (since introducing defects lets you span the entire range between peak human capacity and being in a coma, and the actual distribution seems to mostly depend on the strength of selection pressure for intelligence rather than features relevant to AGI progress).
If human variation comes from selecting different points on a tradeoff curve, then this tells you something about the tradeoff curve for human psychology—but it doesn’t seem like it tells you that much about AGI unless the tradeoffs are with things like “amount of compute” that we have a handle on in the AGI case.
I don’t think you can plausible explain variation in intelligence as different humans making approximately optimal use of their differing brain sizes for the problem of developing technology, because (a) quantitatively it’s just way too large a difference and (b) there is too much other variation in intelligence (and there are other way-less-insane explanations, so we don’t have to reach for the quantitatively insane seeming one). It seems like inhomogeneous selection for intelligence is a way more plausible explanation for the observed correlation than a massive effect of brain size on usefulness. If you grant something like that, then we can’t make much inference on the amount of compute used by the brain size, so we are back with the previous point.
Stepping back from the actual structure of the argument, it seems like this line of reasoning doesn’t do very well in analogous cases:
For many cognitive tasks we have automated (e.g. playing chess, doing explicit calculations) it took a pretty long time to cross the range of human variation. The gap seems smallest for the tasks that evolution is most aggressively optimizing (image classification with minimal external knowledge, speech recognition). Doing useful work seems more like its in the former category. That said, language understanding seems like we may quickly cover the range of human variation even though language is very recent and feels kind of like tech.
For other skills that we can evaluate more easily, it seems that human variation often covers a large part of the range (and doesn’t correspond to a small technological increment): running, lifting things, flexibility, memorization ability, jumping ability.
So overall I think it’s not crazy to interpret this as some evidence (like the chimps vs. humans thing), but without some much more careful story I don’t think it can be that much evidence, since the argument isn’t that tight and treating it as a robust argument would make other observations really confusing.
I looked briefly into distribution of running speeds for kicks. Here is the distribution of completion times for a particular half-marathon, standard deviation is >10% of the total. Running time seems under more selection than scientific ability, but I don’t know how it interacts with training and there are weird selection effects and so on.
For reference, if you think getting from “normal person” to von Neumann is as hard as making your car go 50% faster (4 standard deviations of human running speed in that graph), you might be interested in how long successive +50% improvements of the land speed record took:
1898: 40mph
1899: 60mph
1904: 90mph
1922: 135mph
1927: 200 mph
1935: 300mph
1964: 450mph
Those numbers are way closer together than I expected, and now that the analogy appears to undermine my point it doesn’t seem like a very good analogy to me, but it was a surprising fact so I feel like I should include it.
Not actually clear what you’d make of the analogy, even if you took it seriously. You could say that IT improves ~ an order of magnitude faster than industry, and scale down the time from ~8 years to ~0.8 years to go from normal to von Neumann. But now the whole exercise is becoming a parody of itself.
(Here’s a different sample with larger standard deviation, mapping onto this sample von Neumann is more like 2x average speed.)
(To evaluate the argument as Bayesian evidence, it seems like you’d want to know how often a human ability ends up being spread out as a large range over the entire space of possible abilities. I’m guessing that you are looking at something like 50%, so it would be at most a factor of 2 update.)
There seems to be a stronger version of the (pro-fast-takeoff) chimp vs human argument, namely average-IQ humans vs von Neumann: “IQ-100 humans have brains roughly the same size as von Neumann, but are much worse at making technology.” Does your counterargument work against that version too? (I’m not sure I fully understand it.)
The argument in the post applies to within-human differences; it’s a bit weaker, because evolution is closer to optimizing humans for technological aptitude than it is to optimizing chimps for technological aptitude.
Beyond that, what you infer from human variation depends a bit on where you see human variation as coming from:
If human variation comes from maladaptiveness, deleterious mutations, etc. then we can’t infer very much (since introducing defects lets you span the entire range between peak human capacity and being in a coma, and the actual distribution seems to mostly depend on the strength of selection pressure for intelligence rather than features relevant to AGI progress).
If human variation comes from selecting different points on a tradeoff curve, then this tells you something about the tradeoff curve for human psychology—but it doesn’t seem like it tells you that much about AGI unless the tradeoffs are with things like “amount of compute” that we have a handle on in the AGI case.
I don’t think you can plausible explain variation in intelligence as different humans making approximately optimal use of their differing brain sizes for the problem of developing technology, because (a) quantitatively it’s just way too large a difference and (b) there is too much other variation in intelligence (and there are other way-less-insane explanations, so we don’t have to reach for the quantitatively insane seeming one). It seems like inhomogeneous selection for intelligence is a way more plausible explanation for the observed correlation than a massive effect of brain size on usefulness. If you grant something like that, then we can’t make much inference on the amount of compute used by the brain size, so we are back with the previous point.
Stepping back from the actual structure of the argument, it seems like this line of reasoning doesn’t do very well in analogous cases:
For many cognitive tasks we have automated (e.g. playing chess, doing explicit calculations) it took a pretty long time to cross the range of human variation. The gap seems smallest for the tasks that evolution is most aggressively optimizing (image classification with minimal external knowledge, speech recognition). Doing useful work seems more like its in the former category. That said, language understanding seems like we may quickly cover the range of human variation even though language is very recent and feels kind of like tech.
For other skills that we can evaluate more easily, it seems that human variation often covers a large part of the range (and doesn’t correspond to a small technological increment): running, lifting things, flexibility, memorization ability, jumping ability.
So overall I think it’s not crazy to interpret this as some evidence (like the chimps vs. humans thing), but without some much more careful story I don’t think it can be that much evidence, since the argument isn’t that tight and treating it as a robust argument would make other observations really confusing.
I looked briefly into distribution of running speeds for kicks. Here is the distribution of completion times for a particular half-marathon, standard deviation is >10% of the total. Running time seems under more selection than scientific ability, but I don’t know how it interacts with training and there are weird selection effects and so on.
For reference, if you think getting from “normal person” to von Neumann is as hard as making your car go 50% faster (4 standard deviations of human running speed in that graph), you might be interested in how long successive +50% improvements of the land speed record took:
1898: 40mph
1899: 60mph
1904: 90mph
1922: 135mph
1927: 200 mph
1935: 300mph
1964: 450mph
Those numbers are way closer together than I expected, and now that the analogy appears to undermine my point it doesn’t seem like a very good analogy to me, but it was a surprising fact so I feel like I should include it.
Not actually clear what you’d make of the analogy, even if you took it seriously. You could say that IT improves ~ an order of magnitude faster than industry, and scale down the time from ~8 years to ~0.8 years to go from normal to von Neumann. But now the whole exercise is becoming a parody of itself.
(Here’s a different sample with larger standard deviation, mapping onto this sample von Neumann is more like 2x average speed.)
(To evaluate the argument as Bayesian evidence, it seems like you’d want to know how often a human ability ends up being spread out as a large range over the entire space of possible abilities. I’m guessing that you are looking at something like 50%, so it would be at most a factor of 2 update.)