Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!swrinde!zaphod.mps.ohio-state.edu!sol.ctr.columbia.edu!lll-winken!tristan!loren
From: loren@tristan.llnl.gov (Loren Petrich)
Newsgroups: comp.ai.philosophy
Subject: Re: Reasoning Paradigms
Message-ID: <69347@lll-winken.LLNL.GOV>
Date: 6 Oct 90 03:36:22 GMT
References: <9963@ccncsu.ColoState.EDU> <3586@media-lab.MEDIA.MIT.EDU>
Sender: usenet@lll-winken.LLNL.GOV
Organization: Lawrence Livermore National Laboratory
Lines: 107

In article <3586@media-lab.MEDIA.MIT.EDU> minsky@media-lab.media.mit.edu (Marvin Minsky) writes:
>
>
>I am absolutely astounded at the statement by
>petersja@debussy.cs.colostate.edu to that:
>
>> Minsky's postings have (not surprisingly) suggested
>> that rule-based, logico-mathematical, reasoning is the
>> basis of all other types of reasoning
>> (or is perhaps the only true reasoning).
>
>I hope that no one in net-world will believe that I ever said or
>maintained anything of the sort.  Most of my published work attacks
>this idea.  "The Society of Mind" scarecly mentions logic and rule
>based reasoning at all, save to place it as among the forms of
>reasoning used occasionally by people over the age of about 10.
>
>Instead, I have maintained from the early 1960s that most thinking
>uses various forms of pattern-matching and analogy...

	So Marvin Minsky himself has shown up on Internet. I would
like to say that I myself have read "Society of Mind" and I think that
it is a very interesting proposal on how thinking works, if nothing
else. He is probably correct about pattern matching and analogy being
a major form of reasoning. But I wonder how much of our reasoning
works by what might best be called "fuzzy logic" -- a logic in which
predicates can not only have the values "true" or "false", but any
value in between. That may well describe how we reason about uncertain
things. Traditional logic presupposes a discreteness that is often
lacking in the world around us. Any comments?

	I somehow suspect, however, that certain of Minsky's work may
be taken as going in the opposite direction from what he has proposed
in "Society of Mind". I mention, in particular, his work with Seymour
Papert published in "Perceptrons", published in the late 1960's. At
that time, an early Neural Net architecture, the Perceptron, was a
very hot topic. The book showed that perceptrons with only one layer
of decision units between the inputs and the outputs were severely
limited in what they could "perceive" -- that they could only
distinguish inputs separated by a hyperplane in input space. This
problem could be circumvented by adding extra decision units in
between, what are now called "hidden layers", but there seemed to be
no way to train such a system. Thus, work on perceptron-like
pattern-recognition systems, which are now called Neural Nets,
languished for nearly two decades. Since that time, variations on the
original perceptron architecture have been discovered, variations that
allow straightforward training, with the backpropagation algorithm,
for example.

	Over that last couple of years, interest in NN's has increased
explosively. I have read through a number of volumes of conference
papers of the theory and practice of NN's. I myself have gotten into
work with NN's; I am currently involved in a project to design
hardware NN's here at LLNL. Part of this work has involved using NN's
to (1) analyze the spectra of plasmas produced in etching chips and
(2) construct a function to fit data on thin-film deposition as a
function of deposition conditions. In (1), we obtained the somewhat
obvious result that, to find the hydrogen content of etching-chamber
gas, one must look at hydrogen lines. But we found that the NN looked
at at least one CO line also, though it looked at no other lines. In
(2), we found that the NN agreed the data as well as a polynomial fit,
but we found that the NN outperformed the polynomial fits on data that
neither had been trained on. I have also used NN's to classify the
sources listed in the IRAS Faint Source Catalog; I found that they
fell into two categories, one of sources with little interstellar
reddening, and one of sources that are apparently heavily reddened.

	I feel that there is much more promise in NN's than in
traditional AI, which has been dependent on working out decision rules
explicitly. Perhaps the most successful application of traditional AI
has been computerized algebra, because that is one field where most of
the decision rules are known explicitly; many having been known
explicity for at least a couple centuries, as a matter of fact. For
NN's, however, the "decision rules" are all implicit in the parameter
values; a learning algorithm saves us the trouble of having to work
them out explicitly.

	I speak from personal experience, because when I first saw a
NN program in action, I was amazed to see that it could actually
recognize patterns, a long-time goal of AI that has seemed almost
perpetually beyond reach. Also, most AI systems have seemed formidably
complex, while NN's are so simple one wonders why the field has not
taken off earlier. Compared to a page or two of Fortran or C code for
an NN, most AI systems have given the appearance of being elaborate
and cumbersome software packages. Contrary to NN's, I have read a lot
about traditional AI systems, but I have never actually used one, with
the exception of some computer-algebra programs. Not that I do not
appreciate the development of computerized algebra; I think that that
is an important type of software. So that is why I have been working
on projects involving NN's lately.

	I wonder how Minsky himself would respond to the charges that
his work on perceptrons had set the field back for nearly two decades.
And I wonder how Minsky feels about NN's themselves.

	And I suspect that I will be flamed for my assertion that
computerized algebra has been about the only big success of
traditional AI techniques. I would certainly like to be told about
some counterexamples, though.


$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Loren Petrich, the Master Blaster: loren@sunlight.llnl.gov

Since this nodename is not widely known, you may have to try:

loren%sunlight.llnl.gov@star.stanford.edu