[PurplePanda]

I understand that other being hard to scale in a machine efficiency sense, the old techniques were also rather limited in applicable domain. They were often based on unjustified models of whatever the author decided was a good model of thought/reasoning, whether "frames", predicate calculus, constraint propagation or whatever. It seems to me that although you say statistical methods have little motivation, the motivation of the alternative - heuristics - is very questionable. Can anyone correct me on this?

[pavpanchekha]

That's actually what I meant by scale—that they were slow isn't really a problem if you're interested in AI for the raw fun of it. While justifications for using logic or constraints or probability theory were often lacking from classical techniques, do understand that logic itself was originally developed as a crystallization of proper human thought. And many of the classic AI folks did care deeply about understanding how humans achieved certain results. For example, take a look at Marvin Minsky's work. To the contrary, modern techniques aren't interested in replicating human thought; they are simply interested in replicating human results.