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From: smoliar@vaxa.isi.edu (Stephen Smoliar)
Newsgroups: comp.ai,comp.cog-eng
Subject: Re: The symbol grounding problem....
Message-ID: <3097@venera.isi.edu>
Date: Mon, 29-Jun-87 18:46:31 EDT
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Posted: Mon Jun 29 18:46:31 1987
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In article <1194@houdi.UUCP> marty1@houdi.UUCP (M.BRILLIANT) writes:
>
>I was just looking at a kitchen chair, a brown wooden kitchen
>chair against a yellow wall, in side light from a window.  Let's
>let a machine train its camera on that object.  Now either it
>has a mechanical array of receptors and processors, like the
>layers of cells in a retina, or it does a functionally
>equivalent thing with sequential processing.  What it has to do
>is compare the brightness of neighboring points to find places
>where there is contrast, find contrast in contiguous places so
>as to form an outline, and find closed outlines to form objects.
>There are some subtleties needed to find partly hidden objects,
>but I'll just assume they're solved.  There may also be an
>interpretation of shadow gradations to perceive roundness.
>
I have been trying to keep my distance from this debate, but I would like
to insert a few observations regarding this scenario.  In many ways, this
paragraph represents the "obvious" approach to perception, assuming that
one is dealing with a symbol manipulation system.  However, other approaches
have been hypothesized.  While their viability remains to be demonstrated,
it would be fair to say that, in the broad scope of perception in the real
world, the same may be said of symbol manipulation systems.

Consider the holographic model posed by Karl Pribram in LANGUAGES OF THE
BRAIN.  As I understand it, this model postulates that memory is a collection
of holographic transforms of experienced images.  As new images are
experienced, the brain is capable of retrieving "best fits" from this
memory to form associations.  Thus, the chair you see in the above
paragraph is recognized as a chair by virtue of the fact that it "fits"
other images of chairs you have seen in the past.

I'm not sure I buy this, but I'm at least willing to acknowledge it as
an alternative to your symbol manipulation scenario.  The biggest problem
I have has to do with retrieval.  As far as I understand, present holographic
retrieval works fine as long as you don't have to worry about little things
like change of scale, translation, or rotation.  If this model is going to
work, then the retrieval process is going to have to be more powerful than
the current technology allows.

The other problem relates to concept acquisition, as was postulated in
Brilliant's continuation of the scenario:
>
>Now the machine has a form.  If the form is still unfamiliar,
>let it ask, "What's that, Daddy?"  Daddy says, "That's a chair."
>The machine files that information away.  Next time it sees a
>similar form it says "Chair, Daddy, chair!"  It still has to
>learn about upholstered chairs, but give it time.
>
The difficulty seems to be in what it means to file something away if
one's memory is simply one of experiences.  Does the memory trace of the
chair experience include Daddy's voice saying "chair?"  While I'm willing
to acknowledge a multi-media memory trace, this seems a bit pat.  It
reminds me of Skinner's VERBAL BEHAVIOR, in which he claimed that one
learned the concept "beautiful" from stimuli of observing people saying
"beautiful" in front of beautiful objects.  This conjures up a vision
of people wandering around the Metropolitan Museum of Art mutttering
"beautiful" as they wander from gallery to gallery.

Perhaps the difficulty is that the mind really doesn't want to assign a
symbol to every experience immediately.  Rather, following the model of
Holland et. al., it is first necessary to build up some degree of
reinforcement which assures that a particular memory trace is actually
going to be retrieved relatively frequently (whatever that means).
In such a case, then, a symbol becomes a fast-access mechanism for
retrieval of that trace (or a collection of common traces).  However,
this gives rise to at least two questions for which I have no answer:

	1.  What are the criteria by which it is decided that such a
		symbol is required for fast-access?

	2.  Where does the symbol's name come from?

	3.  How is the symbol actually "bound" to what it retrieves?

These would seem to be the sort of questions which might help to tie
this debate down to more concrete matters.

Brilliant continues:
>That brings me to a question: do you really want this machine
>to be so Totally Turing that it grows like a human, learns like
>a human, and not only learns new objects, but, like a human born
>at age zero, learns how to perceive objects?  How much of its
>abilities do you want to have wired in, and how much learned?
>
This would appear to be one of the directions in which connectionism is
leading.  In a recent talk, Sejnowski talked about "training" networks
for text-to-speech and backgammon . . . not programming them.  On the
other hand, at the current level of his experiments, designing the network
is as important as training it;  training can't begin until one has a
suitable architecture of nodes and connections.  The big unanswered
questions would appear to be:  will all of this scale upward?  That
is, is there ultimately some all-embracing architecture which includes
all the mini-architectures examined by connectionist experiments and
enough more to accommodate the methodological epiphenomenalism of real
life?