Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!mnetor!seismo!rutgers!princeton!mind!harnad From: harnad@mind.UUCP (Stevan Harnad) Newsgroups: comp.ai,comp.cog-eng Subject: Re: The symbol grounding problem: McCarthy's query Message-ID: <919@mind.UUCP> Date: Fri, 26-Jun-87 21:09:41 EDT Article-I.D.: mind.919 Posted: Fri Jun 26 21:09:41 1987 Date-Received: Sat, 27-Jun-87 11:51:58 EDT References: <764@mind.UUCP> <768@mind.UUCP> <770@mind.UUCP> <6174@diamond.BBN.COM> <1190@houdi.UUCP> Organization: Cognitive Science, Princeton University Lines: 88 Summary: Bottoms-up vs. tops-down again: Against the autonomy of symbols Xref: mnetor comp.ai:579 comp.cog-eng:152 marty1@houdi.UUCP (M.BRILLIANT) of AT&T Bell Laboratories, Holmdel writes: > But a "physically analog" sensory process (as distinct from a digital > one) can be approximately modeled (to within the noise) by a continuous > transformation. The continuous approximation allows us to regard the > analog transformation as image-forming (iconic). But only the > continuous approximation is invertible. I have no quarrel with this, in fact I make much the same point -- that iconic representations are approximate too -- in the chapter describing the three kinds of representation. Is there any reason for expecting I would object? > the "hybrid" three-layer system... does not have a "symbol-cruncher > hardwired to peripheral modules" because there is a feature extractor > (and classifier) in between. The main point is the presence or > absence of the feature extractor... The symbol-grounding problem > arises because the symbols are discrete, and therefore have to be > associated with discrete objects or classes. Without the feature > extractor, there would be no way to derive discrete objects from the > sensory inputs. The feature extractor obviates the symbol-grounding > problem. The problem certainly is not just that of discrete symbols needing to pick out discrete objects. 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. That's a bottom-up constraint, and symbolic AI normally expects to float down onto its sensors top-down. Imagine if your "setq" statements were constrained by what your elementary symbols were connected to, and their respective causal interrelations with other nonsymbolic sensory representations and their associated labels. > Why does Harnad say "invertibility is a necessary condition > for iconic representations..., NOT for grounding" Because the original statement of mine that you quote was a reply to a query about whether ALL representations had to be invertible for grounding. (It was accompanied by alleged counterexamples -- grounded but noninvertible percepts.) My reply indicated that only iconic ones had to be invertible, but that both iconic and categorical (noninvertible) ones were needed to ground symbols. > Position 1 [on the symbol grounding problem] is that the peripherals > and the symbolic module have to be connected in the right way. Harnad's > position is... a special case of position 1. I'm afraid not. I don't think there will be independent peripheral modules and symbolic modules suitably interconnected in the hybrid device that passes the Total Turing Test. I think a lot of what we consider cognition will be going on in the nonsymbolic iconic and categorical systems (discrimination, categorization, sensory learning and generalization) and that symbol manipulation will be constrained in ways that don't leave it in any way analogous to the notion of an independent functional module, operating on its own terms (as in standard AI), but connected at some critical point with the nonsymbolic modules. When I spoke earlier of the "connections" of the atomic symbols I had in mind something much more complexly interdigitated and interdependent than can be captured by anything that remotely resembles position 1. Position 1 is simply AI's pious hope that a pure "top-down" approach can expect to meet up with a bottom-up one somewhere in between. Mine is not a special case of this; it's a rival. > "...and (2) connectionist nets already generate grounded "symbols." Is > that a variant of Harnad's position, i.e., "(possibly connectionist)"? No. In my model connectionistic processes are just one possible candidate for the mechanism that finds the features that will reliably pick out a learned category. They would just be a component in the categorical representational system. But there are much more ambitious connectionistic views than that, for example, that connectionism can usurp the role of symbolic representations altogether or (worse) that they ARE symbolic (in some yet to be established sense). As far as I'm concerned, the latter would entail a double grounding problem for connectionism, the first to ground its interpretation of its states as symbolic states, and then to ground the interpretations of the symbolic states themselves (which is the standard symbol grounding problem). -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Princeton.EDU