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One problem with any attempt to map human intelligence to different types of artificial intelligence is that we only have a very precious few such types of AI, so there may be any number of things we're missing. It's a case of not having the right analogies. We liken various bodily systems to machines: the heart is a pump, the lungs are funnels, the kidneys are filters, etc. These work up to some point because we understand both sides of the analogy well enough- we understand how the heart works, to the extent that it works like a pump and we understand how pumps work, etc. But with intelligence we don't have this luxury. We don't understand how intelligence works, yet we draw an analogy not just with computers ("the brain is a computational device") but with specific types of computer programs. However, there are, literally, an infinite number of different computer programs and an unknown number of them could produce results similar, or even identical, to our intelligence. Of course understanding intelligence is basically coming up with a good model of it. But that must be preceded by a good understanding of how intelligence works, which we currently don't have. Instead, what some researchers do, is that they take their arbitrarily chosen favourite AI model and try to find a way to argue that it's "like" human intelligence. Neural networks are particularly guilty of this sort of thing. The whole idea of connectionism is to mimic the way the brain does intelligence, however we don't know what that is, so we've just come up with a complex machine that can optimise systems of functions, instead (I mean the set of neural network architectures). Then, when this machine turned out to be good at doing what it was designed for, optimising systems of functions, we claimed this as proof that it's actually doing what the brain does. That's a very circular way of thinking. |