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From: harnad@mind.UUCP (Stevan Harnad)
Newsgroups: comp.ai,comp.cog-eng
Subject: Re: The symbol grounding problem
Message-ID: <937@mind.UUCP>
Date: Mon, 29-Jun-87 20:19:12 EDT
Article-I.D.: mind.937
Posted: Mon Jun 29 20:19:12 1987
Date-Received: Wed, 1-Jul-87 02:41:27 EDT
References: <764@mind.UUCP> <768@mind.UUCP> <770@mind.UUCP> <6174@diamond.BBN.COM> <1194@houdi.UUCP>
Organization: Cognitive Science, Princeton University
Lines: 72
Summary: Cognition is feature detection
Xref: mnetor comp.ai:590 comp.cog-eng:159



marty1@houdi.UUCP (M.BRILLIANT) of AT&T Bell Laboratories, Holmdel asks:

>	how about walking through what a machine might do in perceiving a chair?
>	...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... Now the machine has the outline
>	of an object in 2 dimensions, and maybe some clues to the 3rd
>	dimension...  inductively find a 3D form that would give rise to the
>	2D view the machine just saw... 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.

So far, apart from its understandable toward current engineering hardware
concepts, there is no particular objection to this description of a
stereoptic sensory receptor.

>	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.

Now you've lost me completely. Having acknowledged the intricacies of
sensory transduction, you seem to think that the problem of categorization
is just a matter of filing information away and finding "similar forms."

>	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?

That's an empirical question. All it needs to do is pass the Total
turing Test -- i.e., exhibit performance capacities that are
indistinguishable from ours. If you can do it by building everything
in a priori, go ahead. I'm betting it'll need to learn -- or be able to
learn -- a lot.

>	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?

I think the main details are missing, such as how the successful
categorization is accomplished. Your account also sounds as if it
expects innate feature detectors to pick out objects for free, more or
less nonproblematically, and then serve as a front end for another
device (possibly a conventional symbol-cruncher a la standard AI?)
that will then do the cognitive heavy work. I think that the cognitive
heavy work begins with picking out objects, i.e., with categorization.
I think this is done nonsymbolically, on the sensory traces, and that it
involves learning and pattern recognition -- both sophisticated
cognitive activities. I also do not think this work ends, to be taken
over by another kind of work: symbolic processing. I think that ALL of
cognition can be seen as categorization. It begins nonsymbolically,
with sensory features used to sort objects according to their names on
the basis of category learning; then further sorting proceeds by symbolic
descriptions, based on combinations of those atomic names. This hybrid
nonsymbolic/symbolic categorizer is what we are; not a pair of modules,
one that picks out objects and the other that thinks and talks about them.
-- 

Stevan Harnad                                  (609) - 921 7771
{bellcore, psuvax1, seismo, rutgers, packard}  !princeton!mind!harnad
harnad%mind@princeton.csnet       harnad@mind.Princeton.EDU