"The second premise is that the brain is an ordinary configuration of matter, albeit an extraordinarily complicated one. If we knew enough, and had the technology, we could exactly copy its structure and emulate its behavior with electronic components, just like we can simulate very basic neural anatomy today."
We could have a computer program that perfectly simulates the brain, but has some nasty O(2^N) complexity algorithm parts that are carried out in constant time by physical processes such as protein folding. Thus, in theory we could simulate a brain inside of a computer but the program would never get anywhere, even assuming Moore's law would continue indefinitely.
I don't buy the AI quasi-religious stuff. But your argument here is flawed. If protein folding can do the process in constant time, we may be able to find another process (but electronic rather than wet chem) that can also do it in constant time.
It is to some extent if we have a constant time example in real life. If the AI can't solve protein folding fast enough it can just design absurdly fast protein sequencers and really good microscopes and get proteins to fold themselves in real life and use the results in the rest of the computation.
I agree. I wasn't thinking about finding a constant time algorithm, though - more of finding an analog circuit that would mimic the behavior. After all, proteins don't actually fold by following an algorithm; they fold by responding to physical forces.
I think the GP's point is that just because something is possible in principle, complexity is still a barrier in practice, probably a much more serious barrier than accepting an idea on principle
We could have a computer program that perfectly simulates the brain, but has some nasty O(2^N) complexity algorithm parts that are carried out in constant time by physical processes such as protein folding. Thus, in theory we could simulate a brain inside of a computer but the program would never get anywhere, even assuming Moore's law would continue indefinitely.