On blurring the gap between NN and AI

[GOLDFARB%unb.ca@UNBMVS1.csd.unb.ca]

I would have liked to be amused by the separation of several areas, such
as AI, Pattern Recognition, NN, if the cost to the taxpayers and
especially to our science would not have been so high. Having said that,
one still has to find a new mathematical model that addresses the
situation described by Scott Fahlman in his posting of Dec. 27 as
follows: ". . .  connectionism and traditional AI are attacking the same
huge problem, but beginning at opposite ends of the problems and using
very different tools." In other words, a fundamentally new mathematical
model is needed that, on the one hand, would remove any apparent and not
so apparent analytical tentions between the two existing mathematical
"tools" (production systems/vector space) and, on the other hand, would
clearly demonstrate that the "opposite ends of the problem" are
intrinsically connected.

The reason why I allow myself to be very "philosophical" about the
situation is that, as was mentioned earlier on the number of occasions,
we believe that a new quite satisfactory model has been found. Although
I have already outlined the model and mentioned the first original
reference (see, for example, the posting of Sept.27), since I was
repeatedly asked about the model, I will shortly outline it again to
the extent to which there is an interest in it.


-- Lev Goldfarb