That’s not my point. Of course everything is reducible to Turing machine. In theory. However, it does not mean you can make this reduction practically. Or it would be very inefficient. Von Neumann architecture implies its own hierarchy of information processing, which is good for programming of various kinds of formal algorithms. However, IMHO, it does not support a hierarchy of information processing required for AI, which should be a neural network similar to a human brain. You cannot program each and every algorithm or mode of behavior, a neural network is capable of producing, on a Von Neumann computer. To me, many decades of futile attempts to build AI along these lines have already proven its practical impossibility. Only understanding of how neural networks operate in nature and implementing this type of behavior can finally make a difference. And how Von Neumann architecture fits in here? I see only one possible application, modelling work of neurons. Given the complexity of a human brain (100 billion neurons, 100 trillion connections), this is a challenge for even most advanced modern supercomputers. You can count on further performance improvements, of course, since Moores law is still in effect, but this is not the kind of solution thats going to be practical. Perhaps neuronic circuits printed directly on microchips would be the hardware for future AI brains.
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I am pretty sure that turning one architecture into another one can generally be done with a mere multiplicative penalty. I'm not under the impression that simulating neural networks is terribly challenging.
Also, most neurons are redundant (since there's a lot of noise in a neuron). If you're simulating something along the lines of a human brain, the very first simulations might be very cha... (read more)
Today's post, Ghosts in the Machine was originally published on 17 June 2008. A summary (taken from the LW wiki):
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