>The diversity is itself indicative, though, that intelligence isn't bound to the particularities of the human nervous system. Across different animal species, nervous systems show a radical diversity. Different architectures; different or reversed neurotransmitters; entirely different neural cell biologies. It's quite possible that "neurons" evolved twice, independently. There's nothing magic about the human brain.
I agree - for example Octopus's are clearly somewhat intelligent, maybe very intelligent, and they have a very different brain architecture. Bees have a form of collective intelligence that seems to be emergent from many brains working together. Human cognition could arguably be identified as having a socially emergent component as well.
>Most of your critique is surface level: you can add all kinds of different structural diversity to an ML model and still find learning. Transformers themselves are formally equivalent to "fast weights" (suppression and control signals). Continuous learning is an entire field of study in ML. Or, for injury, you can randomly mask out half the weights of a model, still get reasonable performance, and retrain the unmasked weights to recover much of your loss.
I think we can only reasonably talk about the technology as it exists. I agree that there is no justifiable reason (that I know of) to claim that biology is unique as a substrate for intelligence or agency or consciousness or cognition or minds in general. But the history of AI is littered with stories of communities believing that a few minor problems just needed to be tidied up before everything works.
I agree - for example Octopus's are clearly somewhat intelligent, maybe very intelligent, and they have a very different brain architecture. Bees have a form of collective intelligence that seems to be emergent from many brains working together. Human cognition could arguably be identified as having a socially emergent component as well.
>Most of your critique is surface level: you can add all kinds of different structural diversity to an ML model and still find learning. Transformers themselves are formally equivalent to "fast weights" (suppression and control signals). Continuous learning is an entire field of study in ML. Or, for injury, you can randomly mask out half the weights of a model, still get reasonable performance, and retrain the unmasked weights to recover much of your loss.
I think we can only reasonably talk about the technology as it exists. I agree that there is no justifiable reason (that I know of) to claim that biology is unique as a substrate for intelligence or agency or consciousness or cognition or minds in general. But the history of AI is littered with stories of communities believing that a few minor problems just needed to be tidied up before everything works.