On blurring the gap between NN and AI

[Leonard Uhr]

If you take - as I and many people do - the "formal model for AI" and the
"formal model for NN" to be Post productions-Turing machines (discretely
approximating continua, as is almost always the case in both NN and AI), then
they clearly are the same and anything that can be accomplished in one can be
done in the other.  So the differences boil down to differences in styles and
tendencies - e.g., serial, lists, lisp vs. parallel, simple processes, learning.
Unfortunately traditional AI has largely ignored learning, but from Samuel,
Kochen, Hunt, etc. on, through the more recent Similarity-based, Explanation-
based, etc. approaches to learning there has always been a good bit.

I personally find the differences more (roughly) analogous to the differences
between people who swear by Lisp vs. C vs. Smalltalk.  If I'm missing something,
please explain how you define NN and AI in such a way as to make them differ.
(This was written in bemusement after a number of notes, especially from Lev
Goldfarb).

Len Uhr