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Speed increases in hardware are great, but they're not AI. Parallelism and neural nets allow some new implementation strategies, but don't begin to solve the "Aristotle problem".
Parallelism requires some new paradigms in software design, that are metaphorically related to human social phenomena like negotiation and planning. The University of Massachusetts has a WWWeb site with some (jargon-heavy) summaries of these issues.
Neural nets timeline:
1959: Frank Rosenblatt introduces Perceptron 1969: Minsky & Papert's book "Perceptrons" kills funding for neural net research, apparently unjustly 1970: SciAm articles on Conway's Game of Life (cellular automata) 1975: Cooper & Erlbaum found Nestor to develop neural net technology 1982: John Hopfield resuscitates neural nets
These hardware manias are one form of a more general problem plaguing AI, caused by the unfortunate combination of very high stakes (especially DARPA grant money), and a very immature domain, in which bold bluffing can take you far. "Citation inflation" is another symptom - concealing your poverty of ideas behind an imposing bibliography.
Another problem is a tendency to reify programming abstractions. One antidote to this is to make a practice of contemplating one's program structures as pure topologies, with all symbolic labels stripped off. Old topologies with fancy new names are less than worthless, compared to entirely new topologies!
comp.ai.neural-nets newsgroup
"Who's munging the hacker ethic?" is a short essay reflecting on some of
the 'pathologies of scientific communication' apparent on
comp.ai. It can be ftp'd from
ftp://ftp.mcs.com/mcsnet.users/jorn/aimunging.txt