No subject

I have been following your discussion and summarizations with
some interest over the last few months and would like to provide
some input at this point. Many of the issues you raise are quite
important - in particular your call for a clear set of external
objectives for any "brain-like" learner.  However, the general
context in which you have framed the question is somewhat limited.
As I understand it, you are suggesting that the classical context
of local learning and predefined structure is unrealistic.  In
many ways I agree .. I beleive the larger and more important
context involves the issues of what has been called "life-long
learning" and "learning to learn" and "consolidation and
transfer of knowledge". I have no idea why so many researchers
continue to pursue the development of the next best inductive
algorithm or archiecture (be it ANN or not)  when many of them
understand that the percentage gains on predictive accuracy
based solely on an example set is marginal in comparison to the
use of prior knowledge (selection of inductive bias).  The
research community has looked very carefully at how we induce
ANN models from examples ... but in comparision there has been
very little work on the consolidation and transfer of the
knowledge contained in previously learned ANN models. Most of
the questions you have posed are subsumed by this larger =0Acontext.Primari=
ly .. what type of mechanism(s)  is required
to (1) learn a task taking advantage of previous learning
(prior knowledge), and  (2) consolidate this new task knowledge
for use in future learning. I do not think that the CNS (central
nervous system) is successful in doing this - just by chance -
there is something unique about it's  architecture that  allows =0Athis too=
 occur.   Recent work by myself, Lorien Pratt, Sebatian =0AThrun, Rich Caru=
ana, Tom Mitchell, Mark Ring, Johnathon Baxter,
and others [NIPS 95 work shop on learning to learn] have provided =0Afounda=
tions on which to build in this area.   I encourage to
review some of this material if you are interested and feel it
applies.

You can start by cheking out my homepage at
ww.csd.uuwo.ca/~dsilver It will lead you to other related areas.

=================================================================
Daniel L. Silver    University of Western Ontario, London, Canada
                     N6A 3K7 - Dept. of Comp. Sci.
  dsilver at csd.uwo.ca  H: (902)582-7558   O: (902)494-1813
  WWW home page ....  http://www.csd.uwo.ca/~dsilver   
=================================================================