AI vs. NN

[Tony Chan]

"NN and AI are fundamentally equivalent in their descriptive powers."
So what?  Formalisms such as Post systems, Chomsky's type-0 grammars,
Lev Goldfarb's transformation systems, McCarthy's Lisp, Wirth's Pascal,
RAM, and thousands upon thousands of other formalisms can compute or
describe as much as Turing machines can. The more interesting questions,
to me, are 1) What are the forces that cause such proliferation? 2) What
are the differences among these formalisms? 3) What are the unique
properties about each?  4) How do we categorize them?

The most important issue for us, I believe, is that we want to have a
formalism that simulate learning so that if we use this formalism to
describe some "intelligent" (learning) processes, it is relatively easy
to express it using this formalism.  Also, this formalism cannot be an
ad hoc one because its purpose is to model learning and learning is a
very general phenomenon.

To some extent, the neural net formalism fits the bill because of its
limited self-programability and adaptability. Unfortunately, it lacks
generality in the sense that it is not well-suited for high-level/
symbolic type of learning. This, partly, is why I believe a more general
formalism such as Reconfigurable Learning Machines should be called for
or at least debated!