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.
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.