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From: marty1@houdi.UUCP (M.BRILLIANT)
Newsgroups: comp.ai,comp.cog-eng
Subject: Re: The symbol grounding problem....
Message-ID: <1194@houdi.UUCP>
Date: Sun, 28-Jun-87 19:56:43 EDT
Article-I.D.: houdi.1194
Posted: Sun Jun 28 19:56:43 1987
Date-Received: Mon, 29-Jun-87 02:53:13 EDT
References: <764@mind.UUCP> <768@mind.UUCP> <770@mind.UUCP> <6174@diamond.BBN.COM> <919@mind.UUCP>
Organization: AT&T Bell Laboratories, Holmdel
Lines: 62
Summary: How about a walk-through of a machine seeing a chair?
Xref: mnetor comp.ai:586 comp.cog-eng:156

In article <919@mind.UUCP>, harnad@mind.UUCP (Stevan Harnad) writes:
> marty1@houdi.UUCP (M.BRILLIANT) of AT&T Bell Laboratories, Holmdel writes:
> ......
> >	.... The feature extractor obviates the symbol-grounding
> >	problem.
> 
> ..... You are vastly underestimating the problem of
> sensory categorization, sensory learning, and the relation between
> lower and higher-order categories. Nor is it obvious that symbol manipulation
> can still be regarded as just symbol manipulation when the atomic symbols
> are constrained to be the labels of sensory categories....

I still think we're having more trouble with terminology than we
would have with the concepts if we understood each other.  To
get a little more concrete, how walking through what a machine
might do in perceiving a chair?

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.

Now the machine has the outline of an object in 2 dimensions,
and maybe some clues to the 3rd dimension.  There are CAD
programs that, given a complete description of an object in
3D, can draw any 2D view of it.  How about reversing this
essentially deductive process to inductively find a 3D form that
would give rise to the 2D view the machine just saw.  Let the
machine guess that most of the odd angles in the 2D view are
really right angles in 3D.  Then, if the object is really
unfamiliar, let the machine walk around the chair, or pick it
up and turn it around, to refine its hypothesis.

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.

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?

But back to the main question.  I have skipped over a lot of
detail, but I think the outline can in principle be filled in
with technologies we can imagine even if we do not have them.
How much agreement do we have with this scenario?  What are
the points of disagreement?

M. B. Brilliant					Marty
AT&T-BL HO 3D-520	(201)-949-1858
Holmdel, NJ 07733	ihnp4!houdi!marty1