So many people asked me what I think about this (ok, maybe just one..) so:
I actually side with Yann here. The key is distinguishing theoretical universality from practical generality.
Yes, brains are approximately Turing-complete, with enough time and paper we could compute anything. But under real constraints as finite memory, finite time, finite attention, we handle only a tiny sliver of possible problems.
The space of possible functions is unimaginably vast, and we can represent an infinitesimal fraction. We feel general because we can't perceive our blind spots, not because we lack them.
Yann is just plain incorrect here, he’s confusing general intelligence with universal intelligence.
Brains are the most exquisite and complex phenomena we know of in the universe (so far), and they are in fact extremely general.
Obviously one can’t circumvent the no free lunch theorem so in a practical and finite system there always has to be some degree of specialisation around the target distribution that is being learnt.
But the point about generality is that in theory, in the Turing Machine sense, the architecture of such a general system is capable of learning anything computable given enough time and memory (and data), and the human brain (and AI foundation models) are approximate Turing Machines.
Finally, with regards to Yann's comments about chess players, it’s amazing that humans could have invented chess in the first place (and all the other aspects of modern civilization from science to 747s!) let alone get as brilliant at it as someone like Magnus. He may not be strictly optimal (after all he has finite memory and limited time to make a decision) but it’s incredible what he and we can do with our brains given they were evolved for hunter gathering.