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I'd say that the algorithms are key in AI, not necessarily the language. You can program neural networks, genetic algorithms, search in Blub.

The early AI programs tended to solve toy problems. No one had thought much then about the implications of the curse of dimensionality: that each entity you add to the problem (say, throw another object into your object-manipulating robot's environment) increases the problem space exponentially (or even factorially).

But you're right, there has been a ton of progress in AI. The initial bias toward ontologies, expert systems, and top-down algorithms has given way to bottom-up systems that are data-driven rather than abstraction-driven.

One major example is using neural networks, SVMs, RBFs to discover implicit features in a data set rather than depending on an expert to code up those features explicitly. Experts don't scale, but data will always be with us. Thus we've seen increasing interest in information retrieval as opposed to ontological knowledge engineering.

But a lot is going on in the field even today. I found this talk very interesting:

http://video.google.com/videoplay?docid=-2469649805161172416




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