On blurring the gap between NN and AI

[James L. McClelland]

Regarding Goldfarb's comments 'on blurring':

I agree that the models are different.  All I was trying to address
was the following notion, which seems to be implicit in some of the
discussion:

  Either you believe cognition is really symbolic or your
  believe it's really subsymbolic.

[Feel free to replace symbolic and/or subsymbolic with your favorite labels!]

Neither of these views seems particularly productive.  I'm with von
Neumann -- I care about models that work.  [What it means for a model to
work depends on your purpose; see my previous post].  For the problems
that interest me, connectionist models appear to work better than
others.  But this is not always the case.  Some of my colleagues have
gotten a long way with production systems.  Which approach is right?
Wrong question.  Which approach is better?  As in physics, some
phenomena are best captured [modeled!] at the microstructure level,
and others not.  Which phenomena are best captured by each?  We don't
know; by choosing to use one or the other, we place our bets.

Aspects of these views are presented at somewhat greater length in a paper:

McClelland, J. L. (1988). Connectionist models and psychological evidence.
Journal of Memory and Language, 27, 107-123.

I'm bowing out of further discussion of these issues for the time being.

             Merry Christmas, Everyone!

                   -- Jay McClelland