FWIW I have come to similar conclusions along similar lines. I’ve said that I think human intelligence minus rat intelligence is probably easier to understand and implement than rat intelligence alone. Rat intelligence requires a long list of neural structures fine-tuned by natural selection, over tens of millions of years, to enable the rat to do very specific survival behaviors right out of the womb. How many individual fine-tuned behaviors? Hundreds? Thousands? Hard to say. Human intelligence, by contrast, cannot possibly be this fine tuned, because the same machinery lets us learn and predict almost arbitrarily different* domains.
I also think that recent results in machine learning have essentially proven the conjecture that moar compute regularly and reliably leads to moar performance, all things being equal. The human neocortical algorithm probably wouldn’t work very well if it were applied in a brain 100x smaller because, by its very nature, it requires massive amounts of parallel compute to work. In other words, the neocortex needs trillions of synapses to do what it does for much the same reason that GPT-3 can do things that GPT-2 can’t. Size matters, at least for this particular class of architectures.
*I think this is actually wrong—I don’t think we can learn arbitrarily domains, not even close. Humans are not general. Yann LeCun has repeatedly said this and I’m inclined to trust him. But I think that the human intelligence architecture might be general. It’s just that natural selection stopped seeing net fitness advantage at the current brain size.
The human neocortical algorithm probably wouldn’t work very well if it were applied in a brain 100x smaller
I disagree, as I discussed here, I think the neocortex is uniform-ish and that a cortical column in humans is doing a similar calculation as a cortical column in rats or the equivalent bundle of cells (arranged not as a column) in a bird pallium or lizard pallium. I do think you need lots and lots of cortical columns, initialized with appropriate region-to-region connections, to get human intelligence. Well, maybe that’s what you meant by “human neocortical algorithm”, in which case I agree. You also need appropriate subcortical signals guiding the neocortex, for example to flag human speech sounds as being important to attend to.
human intelligence minus rat intelligence is probably easier to understand and implement than rat intelligence alone..
Well, I do think that there’s a lot of non-neocortical innovations between humans and rats, particularly to build our complex suite of social instincts, see here. I don’t think understanding those innovations is necessary for AGI, although I do think it would be awfully helpful to understand them if we want aligned AGI. And I think they are going to be hard to understand, compared to the neocortex.
I don’t think we can learn arbitrarily domains, not even close
Sure. A good example is temporal sequence learning. If a sequence of things happens, we expect the same sequence to recur in the future. In principle, we can imagine an anti-inductive universe where, if a sequence of things happens, then it’s especially unlikely to recur in the future, at all levels of abstraction. Our learning algorithm would crash and burn in such a universe. This is a particular example of the no-free-lunch theorem, and I think it illustrates that, while there are domains that the neocortical learning algorithm can’t learn, they may be awfully weird and unlikely to come up.
The human neocortical algorithm probably wouldn’t work very well if it were applied in a brain 100x smaller because, by its very nature, it requires massive amounts of parallel compute to work.
Human beings have larger brains than most animal species on Earth. It seems to me that if large brains weren’t very important to evolving language and composite tool use then insects would already have these abilities.
FWIW I have come to similar conclusions along similar lines. I’ve said that I think human intelligence minus rat intelligence is probably easier to understand and implement than rat intelligence alone. Rat intelligence requires a long list of neural structures fine-tuned by natural selection, over tens of millions of years, to enable the rat to do very specific survival behaviors right out of the womb. How many individual fine-tuned behaviors? Hundreds? Thousands? Hard to say. Human intelligence, by contrast, cannot possibly be this fine tuned, because the same machinery lets us learn and predict almost arbitrarily different* domains.
I also think that recent results in machine learning have essentially proven the conjecture that moar compute regularly and reliably leads to moar performance, all things being equal. The human neocortical algorithm probably wouldn’t work very well if it were applied in a brain 100x smaller because, by its very nature, it requires massive amounts of parallel compute to work. In other words, the neocortex needs trillions of synapses to do what it does for much the same reason that GPT-3 can do things that GPT-2 can’t. Size matters, at least for this particular class of architectures.
*I think this is actually wrong—I don’t think we can learn arbitrarily domains, not even close. Humans are not general. Yann LeCun has repeatedly said this and I’m inclined to trust him. But I think that the human intelligence architecture might be general. It’s just that natural selection stopped seeing net fitness advantage at the current brain size.
I disagree, as I discussed here, I think the neocortex is uniform-ish and that a cortical column in humans is doing a similar calculation as a cortical column in rats or the equivalent bundle of cells (arranged not as a column) in a bird pallium or lizard pallium. I do think you need lots and lots of cortical columns, initialized with appropriate region-to-region connections, to get human intelligence. Well, maybe that’s what you meant by “human neocortical algorithm”, in which case I agree. You also need appropriate subcortical signals guiding the neocortex, for example to flag human speech sounds as being important to attend to.
Well, I do think that there’s a lot of non-neocortical innovations between humans and rats, particularly to build our complex suite of social instincts, see here. I don’t think understanding those innovations is necessary for AGI, although I do think it would be awfully helpful to understand them if we want aligned AGI. And I think they are going to be hard to understand, compared to the neocortex.
Sure. A good example is temporal sequence learning. If a sequence of things happens, we expect the same sequence to recur in the future. In principle, we can imagine an anti-inductive universe where, if a sequence of things happens, then it’s especially unlikely to recur in the future, at all levels of abstraction. Our learning algorithm would crash and burn in such a universe. This is a particular example of the no-free-lunch theorem, and I think it illustrates that, while there are domains that the neocortical learning algorithm can’t learn, they may be awfully weird and unlikely to come up.
Human beings have larger brains than most animal species on Earth. It seems to me that if large brains weren’t very important to evolving language and composite tool use then insects would already have these abilities.