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AIList Digest Volume 5 Issue 141
AIList Digest Wednesday, 10 Jun 1987 Volume 5 : Issue 141
Today's Topics:
Theory - The Symbol Grounding Problem
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Date: 5 Jun 87 17:12:10 GMT
From: diamond.bbn.com!aweinste@husc6.harvard.edu (Anders Weinstein)
Subject: Re: The symbol grounding problem
In reply to my objection that
>> invertibility has essentially *nothing* to do with the difference
>> between analog and digital representation according to anybody's
>> intuitive use of the terms
Stevan Harnad (harnad@mind.UUCP) writes in message <792@mind.UUCP>:
>There are two stages of A/D even in the technical sense. ... Unless the
>original signal is already discrete, the quantization phase involves a
>loss of information. Some regions of input variation will not be retrievable
>from the quantized image. The transformation ... cannot be inverted so as to
>recover the entire original signal.
Well, what I think is interesting is not preserving the signal itself but
rather the *information* that the signal carries. In this sense, an analog
signal conveys only a finite amount of information and it can in fact be
converted to digital form and back to analog *without* any loss.
But in any case the point I've been emphasizing remains: the A/A
transformations you envisage are not going to be perfect (no "skyhooks" now,
remember?), so preservation or loss of information alone won't distinguish an
(intuitively) A/A from an A/D transfomation. I think the following reply to
this point only muddies the waters:
> I agree that there may be information loss in
>A/A transformations (e.g., smoothing, blurring or loss of some
>dimensions of variation), but then the image is simply *not analog in
>the properties that have been lost*! It is only an analog of what it
>preserves, not what it fails to preserve.
You can take this line if you like, but notice that the same is true of a
*digitized* image -- in your terms, it is "analog" in the information it
preserves and not in the information lost. This seems to me to be a very
unhappy choice of terminology!
Both analog and digitizing transformations must preserve *some* information.
If all you're *really* interested in is the quality of being (naturally)
information-preserving (i.e. physically invertible), than I'd strongly
recommend you just use one of these terms and drop the misleading use of
"analog", "iconic", and "digital".
> The "symbol grounding problem" that has
>been under discussion here concerns the fact that symbol systems
>depend for their "meanings" on only one of two possibilities: One is
>an interpretation supplied by human users... and the other is a physical,
>causal connection with the objects to which the symbols refer.
>The surprising consequence is that a "dedicated system" -- one that is
>hard-wired to its transducers and effectors... may be significantly different
>from the very *same* system as an isolated symbol-manipulating module,
>cut off from its peripherals ...
With regard to this "symbol grounding problem": I think it's been
well-understood for some time that causal interaction with the world is a
necessary requirement for artificial intelligence. Recall that in his BBS
reply to Searle, Dennett dismissed Searle's initial target -- the "bedridden"
form of the Turing test -- as a strawman for precisely this reason. (Searle
believes his argument goes through for causally embedded AI programs as well,
but that's another topic.)
The philosophical rationale for this requirement is the fact that some causal
"grounding" is needed in order to determine a semantic interpretation. A
classic example is due to Georges Rey: it's possible that a program for
playing chess could, when compiled, be *identical* to one used to plot
strategy in the Six Day War. If you look only at the formal symbol
manipulations, you can't distinguish between the two interpretations; it's
only by virtue of the causal relations between the symbols and the world that
the symbols could have one meaning rather than another.
But although everyone agrees that *some* kind of causal grounding is
necessary for intentionality, it's notoriously difficult to explain exactly
what sort it must be. And although the information-preserving
transformations you discuss may play some role here, I really don't see how
this challenges the premises of symbolic AI in the way you seem to think it
does. In particular you say that:
>The potential relevance of the physical invertibility criterion
>would only be to cognitive modeling, especially in the constraint that
>a grounded symbol system must be *nonmodular* -- i.e., it must be hybrid
>symbolic/nonsymbolic.
But why must the arrangement you envision must be "nonmodular" ? A system
may contain analog and digital subsystems and still be modular if the
subsytems interact solely via well-defined inputs and outputs.
More importantly -- and this is the real motivation for my terminological
objections -- it isn't clear why *any* (intuitively) analog processing need
take place at all. I presume the stance of symbolic AI is that sensory input
affects the system via an isolable module which converts incoming stimuli
into symbolic representations. Imagine a vision sub-system that converts
incoming light into digital form at the first stage, as it strikes a grid of
photo-receptor surfaces, and is entirely digital from there on in. Such a
system is still "grounded" in information-preserving representations in the
sense you require.
In short, I don't see any *philosophical* reason why symbol-grounding
requires analog processing or a non-modular structure.
Anders Weinstein
BBN Labs
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Date: 7 Jun 87 18:25:00 GMT
From: mind!harnad@princeton.edu (Stevan Harnad)
Subject: Re: The symbol grounding problem
aweinste@Diamond.BBN.COM (Anders Weinstein)
of BBN Laboratories, Inc., Cambridge, MA writes:
> [regarding invertibility, information preservation and the A/D
> distinction]: what I think is interesting is not preserving the
> signal itself but rather the *information* that the signal carries.
> In this sense, an analog signal conveys only a finite amount of
> information and it can in fact be converted to digital form and back
> to analog *without* any loss.
This is an important point and concerns a matter that is at the heart
of the symbolic/nonsymbolic issue: What you're saying is appropriate for
ordinary communication theory and communication-theoretic
applications such as radio signals, telegraph, radar CDs, etc. In all these
cases the signal is simply a carrier that encodes information which is
subsequently decoded at the receiving end. But in the case of human
cognition this communication-theoretic model -- of signals carrying
messages that are encoded/decoded on either end -- may not be
appropriate. (Formal information theory has always had difficulties
with "content" or "meaning." This has often been pointed out, and I take
this to be symptomatic of the fact that it's missing something as a
candidate model for cognitive "information processing.")
Note that the communication-theoretic, signal-analytic view has a kind of
built-in bias toward digital coding, since it's the "message" and not
the "medium" that matters. But what if -- in cognition -- the medium
*is* the message? This may well be the case in iconic processing (and
the performances that it subserves, such as discrimination, similarity
judgment, matching, short-term memory, mental rotation, etc.): It may
be the structure or "shape" of the physical signal (the stimulus) itself that
matters, not some secondary information or message it carries in coded
form. Hence the processing may have to be structure- or shape-preserving
in the physical analog sense I've tried to capture with the criterion
of invertibiliy.
> a *digitized* image -- in your terms... is "analog" in the
> information it preserves and not in the information lost. This
> seems to me to be a very unhappy choice of terminology! Both analog
> and digitizing transformations must preserve *some* information.
> If all you're *really* interested in is the quality of being
> (naturally) information-preserving (i.e. physically invertible),
> than I'd strongly recommend you just use one of these terms and drop
> the misleading use of "analog", "iconic", and "digital".
I'm not at all convinced yet that the sense of iconic and analog that I am
referring to is unrelated to the signal-analytic A/D distinction,
although I've noted that it may turn out, on sufficient analysis, to be
an independent distinction. For the time being, I've acknowledged that
my invertibility criterion is, if not necessarily unhappy, somewhat
surprising in its implications, for it implies (1) that being analog
may be a matter of degree (i.e., degree of invertibility) and (2) even
a classical digital system must be regarded as analog to a degree if
one is considering a larger "dedicated" system of which it is a
hard-wired (i.e., causally connected) component rather than an
independent (human-interpretation-mediated) module.
Let me repeat, though, that it could turn out that, despite some
suggestive similarities, these considerations are not pertinent to the
A/D distinction but, say, to the symbolic/nonsymbolic distinction --
and even that only in the special context of cognitive modeling rather than
signal analysis or artificial intelligence in general.
> With regard to [the] "symbol grounding problem": I think it's been
> well-understood for some time that causal interaction with the world
> is a necessary requirement for artificial intelligence...
> The philosophical rationale for this requirement is the fact that
> some causal "grounding" is needed in order to determine a semantic
> interpretation... But although everyone agrees that *some* kind of
> causal grounding is necessary for intentionality, it's notoriously
> difficult to explain exactly what sort it must be. And although the
> information-preserving transformations you discuss may play some role
> here, I really don't see how this challenges the premises of symbolic
> AI in the way you seem to think it does.
As far as I know, there have so far been only two candidate proposals
to overcome the symbol grounding problem WITHOUT resorting to the kind
of hybrid proposal I advocate (i.e., without giving up purely symbolic
top-down modules): One proposal, as you note, is that a pure
symbol-manipulating system can be "grounded" by merely hooking it up
causally in the "right way" to the outside world with simple (modular)
transducers and effectors. I have conjectured that this strategy
will not work in cognitive modeling (and I have given my supporting
arguments elsewhere: "Minds, Machines and Searle"). The strategy may work
in AI and conventional robotics and vision, but that is because these
fields *do not have a grounding problem*! They're only trying to generate
intelligent *pieces* of performance, not to model the mind in *all* its
performance capacity. Only cognitive modeling has a symbol grounding
problem.
The second nonhybrid way to try to ground a purely symbolic system in
real-world objects is by cryptology. Human beings, knowing already at least
one grounded language and its relation to the world, can infer the meanings
of a second one [e.g., ancient cuneiform] by using its internal formal
structure plus what they already know: Since the symbol permutations and
combinations of the unknown system (i.e., its syntactic rules) are constrained
to yield a semantically interpretatable system, sometimes the semantics can be
reliably and uniquely decoded this way (despite Quine's claims about the
indeterminacy of radical translation). It is obvious, however, that such
a "grounding" would be derivative, and would depend entirely on the
groundedness of the original grounded symbol system. (This is equivalent
to Searle's "intrinsic" vs. "derived intentionality.") And *that* grounding
problem remains to be solved in an autonomous cognitive model.
My own hybrid approach is simply to bite the bullet and give up on the
hope of an autonomous symbolic level, the hope on which AI and symbolic
functionalism had relied in their attempt to capture mental function.
Although you can get a lot of clever performance by building in purely
symbolic "knowledge," and although it had seemed so promising that
symbol-strings could be interpreted as thoughts, beliefs, and mental
propositions, I have argued that a mere extension of this modular "top-down"
approach, hooking up eventually with peripheral modules, simply won't
succeed in the long run (i.e., as we attempt to approach an asymptote of
total human performance capacity, or what I've called the "Total Turing Test")
because of the grounding problem and the nonviability of the two
"solutions" sketched above (i.e., simple peripheral hook-ups and/or
mediating human cryptology). Instead, I have described a nonmodular
hybrid representational system in which symbolic representations are
grounded bottom-up in nonsymbolic ones (iconic and categorical).
Although there is a symbolic level in such a system, it is not quite
the autonomous all-purpose level of symbolic AI. It trades its autonomy
for its groundedness.
> [W]hy must the arrangement you envision be "nonmodular"? A system
> may contain analog and digital subsystems and still be modular if
> the subsystems interact solely via well-defined inputs and outputs.
I'll try to explain why I believe that a successful mind-model (one
able to pass the Total Turing Test) is unlikely to consist merely of a
pure symbol-manipulative module connected to input/output modules.
A pure top-down symbol system just consists of physically implemented
symbol manipulations. You yourself describe a typical example of
ungroundedness (from Georges Rey):
> it's possible that a program for playing chess could,
> when compiled, be *identical* to one used to plot
> strategy in the Six Day War. If you look only at the
> formal symbol manipulations, you can't distinguish between
> the two interpretations; it's only by virtue of the causal
> relations between the symbols and the world that the symbols
> could have one meaning rather than another.
Now consider two cases of "fixing" the symbol interpretations by
grounding the causal relations between the symbols and the world. In
(1) a "toy" case -- a circumscribed little chunk of performance such as
chess-playing or war-games -- the right causal connections could be
wired according to the human encryption/decryption scheme: Inputs and
outputs could be wired into their appropriate symbolic descriptions.
There is no problem here, because the toy problems are themselves
modular, and we know all the ins and outs. But none but the most
diehard symbolic functionalist would want to argue that such a simple
toy model was "thinking," or even doing anything remotely like what we
do when we accomplish the same performance. The reason is that we are
capable of doing *so much more* -- and not by an assemblage of endless
independent modules of essentially the same sort as these toy models,
but by some sort of (2) integrated internal system. Could that "total"
system be just an oversized toy model -- a symbol system with its
interpretations "fixed" by a means analogous to these toy cases? I am
conjecturing that it is not.
Toy models don't think. Their internal symbols really *are*
meaningless, and hence setting them in the service of generating a toy
performance just involves hard-wiring our intended interpretations
of its symbols into a suitable dedicated system. Total (human-capacity-sized)
models, on the other hand, will, one hopes, think, and hence the
intended interpretations of their symbols will have to be intrinsic in
some deeper way than the analogy with the toy model would suggest, at
least so I think. This is my proposed "nonmodular" candidate:
Every formal symbol system has both primitive atomic symbols and composite
symbol-strings consisting of ruleful combinations of the atoms. Both
the atoms and the combinations are semantically interpretable, but
from the standpoint of the formal syntactic rules governing the symbol
manipulations, the atoms could just as well have been undefined or
meaningless. I hypothesize that the primitive symbols of a nonmodular
cognitive symbol system are actually the (arbitrary) labels of object
categories, and that these labels are reliably assigned to their referents
by a nonsymbolic representational system consisting of (i) iconic (invertible,
one-to-one) transformations of the sensory surface and (ii) categorical
(many-to-few) representations that preserve only the features that suffice to
reliably categorize and label sensory projections of the objects in
question. Hence, rather than being primitive and undefined, and hence
independent of interpretation, I suggest that the atoms of cognitive
symbol systems are grounded, bottom-up, in such a categorization
mechanism. The higher-order symbol combinations inherit the bottom-up
constraints, including the nonsymbolic representations to which they
are attached, rather than being an independent top-down symbol-manipulative
module with its connections to an input/output module open to being
fixed in various extrinsically determined ways.
> it isn't clear why *any* (intuitively) analog processing need
> take place at all. I presume the stance of symbolic AI is that
> sensory input affects the system via an isolable module which converts
> incoming stimuli into symbolic representations. Imagine a vision
> sub-system that converts incoming light into digital form at the
> first stage, as it strikes a grid of photo-receptor surfaces, and is
> entirely digital from there on in. Such a system is still "grounded"
> in information-preserving representations in the sense you require.
> In short, I don't see any *philosophical* reason why symbol-grounding
> requires analog processing or a non-modular structure.
It is exactly this modular scenario that I am calling into question. It
is not clear at all that a cognitive system must conform to it. To get a
device to be able to do what we can do we may have to stop thinking in
terms of "isolable" input modules that go straight into symbolic
representations. That may be enough to "ground" a conventional toy
system, but, as I've said, such toy systems don't have a grounding problem
in the first place, because nobody really believes they're thinking. To get
closer to life-size devices -- devices that can generate *all* of our
performance capacity, and hence may indeed be thinking -- we may have to
turn to hybrid systems in which the symbolic functions are nonmodularly
grounded, bottom-up, in the nonsymbolic ones. The problem is not a
philosophical one, it's an empirical one: What looks as if it's likely
to work, on the evidence and reasoning available?
--
Stevan Harnad (609) - 921 7771
{bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad
harnad%mind@princeton.csnet harnad@mind.Princeton.EDU
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