There are several major stepping stones with hardware speed. One, is when you have enough for a nontrivial AI (price tag can be quite astronomic). Second, enough in an *affordable* installation. Third, enough crunch to map the parameter space/design by evolutionary algorithms. Fourth, the previous item in an affordable (arbitrarily put, 50-100 k$) package.
Arguably, we're approaching the region where a very large, very expensive installation could, in theory, support a nontrivial AI.
Yes, *in theory*, but you still have to engineer it. That's the hard part.
Maybe I'm overstating my case to make a point, but it's a point that dearly needs to be made: the control architecture is everything.
Let's do a very crude thought experiment, and for the moment not consider evolving AI, because the hardware requirements for that are a bit silly.
So imagine it like this, you've got your 10^6 CPU's and you want to make an AI. You have to devote some percentage of those CPU's to "thinking" (ie analyzing and representing information) and the remainder to restricting that thinking to some useful task. No one would argue, I hope, that it's useful to blindly analyze all available information.
The part that's directing your resources is the control architechture and it requires meticulous engineering and difficult design decisions. What percentage do you allocate?
5%? 20%? The more you spend, the more efficiently the remaining CPU power is spent. There's got to be a point at which you achieve a maximum efficiency for your blob of silicon.
The brain is thoroughly riddled with such control architechture, starting at the retina and moving back, it's a constant process of throwing out information and compressing what's left into a more compact form. That's really all your brain is doing from the moment a photon hits your eye, determining whether or not you should ignore that photon. And it is a Very Hard problem.
-Brad
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