Hmmm, this is a good point—but here’s a counter that just now occurred to me:
Let’s disambiguate “intelligence” into a bunch of different things. Reasoning, imitation, memory, data-efficient learning, … the list goes on. Maybe the complete bundle has only evolved once, in humans, but almost every piece of the bundle has evolved separately many times.
In particular, the number 1 thing people point to as a candidate X for “X is necessary for TAI and we don’t know how to make AIs with X yet and it’s going to be really hard to figure it out soon” is data-efficient learning.
But data-efficient learning has evolved separately many times; AlphaStar may need thousands of years of Starcraft to learn how to play, but dolphins can learn new games in minutes. Games with human trainers, who are obviously way out of distribution as far as Dolphin’s ancestral environment is concerned.
The number 2 thing I hear people point to is “reasoning” and maybe “causal reasoning” in particular. I venture to guess that this has evolved a bunch of times too, based on how various animals can solve clever puzzles to get pieces of food.
Someone who actually knows something about taxonomic phylogeny of neural traits would need to say for sure, but the fact that many species share neural traits doesn’t necessarily mean those traits evolved many times independently as flight did. They could have inherited the traits from a common ancestor. I have no idea if anyone has any clue whether “data efficient learning” falls into the came from a single common ancestor or evolved independently in many disconnected trees categories. It is not a trait that leaves fossil evidence.
I think all the things we identify as “intelligence” (including data-efficient learning) are things that the neocortex does, working in close conjunction with the thalamus (which might as well be a 7th layer of the neocortex), hippocampus (temporarily stores memories before gradually transferring them back to the neocortex because the neocortex needs a lot of repetition to learn), basal ganglia (certain calculations related to reinforcement learning including the value function calculation I think), and part of the cerebellum (you can have human-level intelligence without a cerebellum, but it does help speed things up dramatically, I think mainly by memoizing neocortex calculations).
Anyway, it’s not 100% proven, but my read of the evidence is that the neocortex in mammals is a close cousin of the pallium in lizards and birds and dinosaurs, and the neocortex & bird/lizard pallium do the same calculations using the same neuronal circuits descended from the same ancestor which also did those calculations. The neurons are arranged differently in space in the neocortex vs pallium, but that doesn’t matter, the network is what matters. Some early version of the pallium dates back at least as far as lampreys, if memory serves, and I would not be remotely surprised if the lamprey proto-pallium (whatever it’s called) did data-efficient learning, albeit learning relatively simple things like 1D time-series data or 3D environments. (That doesn’t sound like it has much in common with human intelligence and causal reasoning and rocket science but I think it really does...long story...)
Paul Cisek wrote this paper which I found pretty thought-provoking. He’s now diving much deeper into that and writing a book, but says he won’t be done for a few years.
I don’t know anything about octopuses by the way. That could be independent.
Fair enough—maybe data efficient learning evolved way back with the dinosaurs or something. Still though… I find it more plausible that it’s just not that much harder than flight (and possibly even easier).
Yeah, that’s fair—it’s certainly possible that the things that make intelligence relatively hard for evolution may not apply to human engineers. OTOH, if intelligence is a bundle of different modules that all coexistent in humans and of which different animals have evolved in various proportions, that seems to point away from the blank slate/”all you need is scaling” direction.
Hmmm, this is a good point—but here’s a counter that just now occurred to me:
Let’s disambiguate “intelligence” into a bunch of different things. Reasoning, imitation, memory, data-efficient learning, … the list goes on. Maybe the complete bundle has only evolved once, in humans, but almost every piece of the bundle has evolved separately many times.
In particular, the number 1 thing people point to as a candidate X for “X is necessary for TAI and we don’t know how to make AIs with X yet and it’s going to be really hard to figure it out soon” is data-efficient learning.
But data-efficient learning has evolved separately many times; AlphaStar may need thousands of years of Starcraft to learn how to play, but dolphins can learn new games in minutes. Games with human trainers, who are obviously way out of distribution as far as Dolphin’s ancestral environment is concerned.
The number 2 thing I hear people point to is “reasoning” and maybe “causal reasoning” in particular. I venture to guess that this has evolved a bunch of times too, based on how various animals can solve clever puzzles to get pieces of food.
(See also: https://www.lesswrong.com/posts/GMqZ2ofMnxwhoa7fD/the-octopus-the-dolphin-and-us-a-great-filter-tale )
Someone who actually knows something about taxonomic phylogeny of neural traits would need to say for sure, but the fact that many species share neural traits doesn’t necessarily mean those traits evolved many times independently as flight did. They could have inherited the traits from a common ancestor. I have no idea if anyone has any clue whether “data efficient learning” falls into the came from a single common ancestor or evolved independently in many disconnected trees categories. It is not a trait that leaves fossil evidence.
I think all the things we identify as “intelligence” (including data-efficient learning) are things that the neocortex does, working in close conjunction with the thalamus (which might as well be a 7th layer of the neocortex), hippocampus (temporarily stores memories before gradually transferring them back to the neocortex because the neocortex needs a lot of repetition to learn), basal ganglia (certain calculations related to reinforcement learning including the value function calculation I think), and part of the cerebellum (you can have human-level intelligence without a cerebellum, but it does help speed things up dramatically, I think mainly by memoizing neocortex calculations).
Anyway, it’s not 100% proven, but my read of the evidence is that the neocortex in mammals is a close cousin of the pallium in lizards and birds and dinosaurs, and the neocortex & bird/lizard pallium do the same calculations using the same neuronal circuits descended from the same ancestor which also did those calculations. The neurons are arranged differently in space in the neocortex vs pallium, but that doesn’t matter, the network is what matters. Some early version of the pallium dates back at least as far as lampreys, if memory serves, and I would not be remotely surprised if the lamprey proto-pallium (whatever it’s called) did data-efficient learning, albeit learning relatively simple things like 1D time-series data or 3D environments. (That doesn’t sound like it has much in common with human intelligence and causal reasoning and rocket science but I think it really does...long story...)
Paul Cisek wrote this paper which I found pretty thought-provoking. He’s now diving much deeper into that and writing a book, but says he won’t be done for a few years.
I don’t know anything about octopuses by the way. That could be independent.
Fair enough—maybe data efficient learning evolved way back with the dinosaurs or something. Still though… I find it more plausible that it’s just not that much harder than flight (and possibly even easier).
Yeah, that’s fair—it’s certainly possible that the things that make intelligence relatively hard for evolution may not apply to human engineers. OTOH, if intelligence is a bundle of different modules that all coexistent in humans and of which different animals have evolved in various proportions, that seems to point away from the blank slate/”all you need is scaling” direction.