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From: marty1@houdi.UUCP
Newsgroups: comp.ai,comp.cog-eng
Subject: Re: The symbol grounding problem
Message-ID: <1165@houdi.UUCP>
Date: Sun, 14-Jun-87 11:13:34 EDT
Article-I.D.: houdi.1165
Posted: Sun Jun 14 11:13:34 1987
Date-Received: Sun, 14-Jun-87 23:36:31 EDT
References: <764@mind.UUCP> <768@mind.UUCP> <770@mind.UUCP> <6174@diamond.BBN.COM> <843@mind.UUCP>
Organization: AT&T Bell Laboratories, Holmdel
Lines: 63
Xref: utgpu comp.ai:481 comp.cog-eng:113
Summary: Recipe for a symbol-grounder

In article <843@mind.UUCP>, harnad@mind.UUCP (Stevan Harnad) writes:
> 
> Intentionality and consciousness are not equivalent to behavioral
> capacity, but behavioral capacity is our only objective basis for
> inferring that they are present. Apart from behavioral considerations,
> there are also functional considerations: What kinds of internal
> processes (e.g., symbolic and nonsymbolic) look as if they might work?
> and why? and how? The grounding problem accordingly has functional aspects
> too. What are the right kinds of causal connections to ground a
> system? Yes, the test of successful grounding is the TTT, but that
> still leaves you with the problem of which kinds of connections are
> going to work. I've argued that top-down symbol systems hooked to
> transducers won't, and that certain hybrid bottom-up systems might. All
> these functional considerations concern how to ground symbols, they are
> distinct from (though ultimately, of course, dependent on) behavioral
> success, and they do have independent content.

Harnad's terminology has proved unreliable: analog doesn't mean
analog, invertible doesn't mean invertible, and so on.  Maybe
top-down doesn't mean top-down either.

Suppose we create a visual transducer feeding into an image
processing module that could delineate edges, detect motion,
abstract shape, etc.  This processor is to be built with a
hard-wired capability to detect "objects" without necessarily
finding symbols for them.

Next let's create a symbol bank, consisting of a large storage
area that can be partitioned into spaces for strings of
alphanumeric characters, with associated pointers, frames,
anything else you think will work to support a sophisiticated
knowledge base.  The finite area means that memory will be
limited, but human memory can't really be infinite, either.

Next let's connect the two: any time the image processor finds
an object, the machine makes up a symbol for it.  When it finds
another object, it makes up another symbol and links that symbol
to the symbols for any other objects that are related to it in
ways that it knows about (some of which might be hard-wired
primitives): proximity in time or space, similar shape, etc.  It
also has to make up symbols for the relations it relies on to
link objects.  I'm over my head here, but I don't think I'm
asking for anything we think is impossible.  Basically, I'm
looking for an expert system that learns.

Now we decide whether we want to play a game, which is to make
the machine seem human, or whether we want the machine to
exhibit human behavior on the same basis as humans, that is, to
survive.  For the game, the essential step is to make the
machine communicate with us both visually and verbally, so it
can translate the character strings it made up into English, so
we can understand it and it can understand us.  For the survival
motivation, the machine needs a full set of receptors and
effectors, and an environment in which it can either survive or
perish, and if we built it right it will learn English for its
own reasons.  It could also endanger our survival.

Now, Harnad, Weinstein, anyone: do you think this could work,
or do you think it could not work?

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