Path: utzoo!utgpu!news-server.csri.toronto.edu!rutgers!cs.utexas.edu!samsung!noose.ecn.purdue.edu!iuvax!cogsci!dave
From: dave@cogsci.indiana.edu (David Chalmers)
Newsgroups: comp.ai.philosophy
Subject: Re: Reasoning Paradigms
Message-ID: <62521@iuvax.cs.indiana.edu>
Date: 6 Oct 90 08:48:40 GMT
References: <9963@ccncsu.ColoState.EDU> <3586@media-lab.MEDIA.MIT.EDU> <69347@lll-winken.LLNL.GOV> <3593@media-lab.MEDIA.MIT.EDU>
Sender: news@iuvax.cs.indiana.edu
Reply-To: dave@cogsci.indiana.edu (David Chalmers)
Organization: Indiana University, Bloomington
Lines: 51

In article <3593@media-lab.MEDIA.MIT.EDU> minsky@media-lab.media.mit.edu (Marvin Minsky) writes:

>In this sense, then, NN solutions, in
>contrast, tend to be dead ends, simply because what you end
>up with, after your 100,000 steps of hill-climbing, is an opaque
>vector of coefficients.  You have solved the prob lem, all right.  You
>have even _learned_ the solution!  But you don't end up with anything
>you can THINK about!

>Is that bad?  Your locomotion system "learns" to walk, all right.  (It
>begins with an architecture of NN's that wonderfully work to adjust
>your reflexes.)  But "you" don't know anything of how it's done.  Even
>Professors of Locomotion Science are still working out theories about
>such things.

I hear this kind of thing said often enough, but I don't buy it.  Sure,
producing a computational system that does something doesn't immediately
*explain* how something is done, but it certainly makes explanation a lot
easier.  The "brains are right in front of us, but we still don't understand
them" argument doesn't really hold water.  Most of the problems with
brains are problems of *access* -- they're nasty and gooey and people tend
to complain if you poke around and cut them up too much.  Current neuroscience
is mostly constrained by technological limitations.  To see this, witness 
the huge flurry of activity that takes place whenever a new tool for brain
investigation -- PET scanning, for instance -- is devised.

Whereas if we produce an equivalent computational system, all those problems
of access are gone.  We have the system right in front of us, we can poke
around its insides and make complex observations to our heart's content.
We can perform fast and easy simulations of its function in all kinds of
environments.  We can lesion this, monitor that, investigate the
consequences of all manner of counterfactual situations -- all without
running into trouble with blood and goo or ethics committees. 

If the Professors of Locomotion Science had a perfect computational model of
the locomotive system in front of them, you can bet that progress in the
area would proceed one hundred times faster.  If I had "the program" of the
brain stored in a file on my Sun workstation, within five years cognitive
science would be completely transformed.  We probably wouldn't understand
*everything* about language and learning and memory, but we would understand
a hell of a lot more than we do now.

So, a computational model of a system is not equivalent to an explanation
of the system.  But once you have the model, an explanation may not be
far away.

--
Dave Chalmers     (dave@cogsci.indiana.edu)      
Concepts and Cognition, Indiana University.

"It is not the least charm of a theory that it is refutable."