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 Message-ID: <861@mind.UUCP> Date: Wed, 17-Jun-87 16:12:22 EDT Article-I.D.: mind.861 Posted: Wed Jun 17 16:12:22 1987 Date-Received: Sun, 21-Jun-87 10:28:16 EDT References: <764@mind.UUCP> <768@mind.UUCP> <770@mind.UUCP> <6174@diamond.BBN.COM> <1166@houdi.UUCP> Organization: Cognitive Science, Princeton University Lines: 105 Summary: Selective non-invertibility is the category induction problem Xref: mnetor comp.ai:558 comp.cog-eng:137 marty1@houdi.UUCP (M.BRILLIANT) asks: > what do you think is essential: (A) literally analog transformation, > (B) invertibility, or (C) preservation of significant relational > functions? Essential for what? For (i) generating the pairwise same/different judgments, simlarity judgments and matching that I've called, collectively, "discrimination", and for which I've hypothesized that there are iconic ("analog") representations? For that I think invertibility is essential. (I think that in most real cases what is actually physically invertible in my sense will also turn out to be "literally analog" in a more standard sense. Dedicated digital equivalents that would also have yielded invertibility will be like a Rube-Goldberg alternative; they will have a much bigger processing cost. But for my puroposes, the dedicated digital equivalent would in principle serve just as well. Don't forget the *dedicated* constraint though.) For (ii) generating the reliable sorting and labeling of objects on the basis of their sensory projections, which I've called collectively, "identification" or "categorization"? For that I think only distinctive features need to be extracted from the sensory projection. The rest need not be invertible. Iconic representations are one-to-one with the sensory projection; categorical representations are many-to-few. But if you're not talking about sensory discrimination or about stimulus categorization but about, say, (iii) conscious problem-solving, deduction, or linguistic description, then relation-preserving symbolic representations would be optimal -- only the ones I advocate would not be autonomous (modular). The atomic terms of which they were composed would be the labels of categories in the above sense, and hence they would be grounded in and constrained by the nonsymbolic representations. They would preserve relations not just in virtue of their syntactic form, as mediated by an interpretation; their meanings would be "fixed" by their causal connections with the nonsymbolic representations that ground their atoms. But if your question concerns what I think is nesessary to pass the Total Turing Test (TTT), I think you need all of (i) - (iii), grounded bottom-up in the way I've described. > Where does [the symbol grounding] argument stand now? Can we > restate it in terms whose definitions we all agree on? The symbols of an autonomous symbol-manipulating module are ungrounded. Their "meanings" depend on the mediation of human interpretation. If an attempt is made to "ground" them merely by linking the symbolic module with input/output modules in a dedicated system, all you will ever get is toy models: Small, nonrepresentative, nongeneralizable pieces of intelligent performance (a valid objective for AI, by the way, but not for cognitive modeling). This is only a conjecture, however, based on current toy performance models and the the kind of thing it takes to make them work. If a top-down symbolic module linked to peripherals could successfully pass the TTT that way, however, nothing would be left of the symbol grounding problem. My own alternative has to do with the way symbolic models work (and don't work). The hypothesis is that a hybrid symbolic/nonsymbolic model along the lines sketched above will be needed in order to pass the TTT. It will require a bottom-up, nonmodular grounding of its symbolic representations in nonsymbolic representations: iconic ( = invertible with the sensory projection) and categorical ( = invertible only with the invariant features of category members that are preserved in the sensory projection and are sufficient to guide reliable categorization). > I think invertibility is too strong. It is sufficient, but not > necessary, for human-style information-processing. Real people > forget... misunderstand... I think this is not the relevant form of evidence bearing on this question. Sure we forget, etc., but the question concerns what it takes to get it right when we actually do get it right. How do we discriminate, categorize, identify and describe things as well as we do (TTT-level) based on the sensory data we get? And I have to remind you again: categorization involves at least as much selective *non*invertibility as it does invertibility. Invertibility is needed where it's needed; it's not needed everywhere, indeed it may even be a handicap (see Luria's "Mind of a Mnemonist," which is about a person who seems to have had such vivid, accurate and persisting eidetic imagery that he couldn't selectively ignore or forget sensory details, and hence had great difficulty categorizing, abstracting and generalizing; Borges describes a similar case in "Funes the Memorious," and I discuss the problem in "Metaphor and Mental Duality," a chapter in Simon & Sholes' (eds.) "Language, Mind and Brain," Academic Press 1978). > Do you still say [1] we only need transformations that are analog > (invertible) with respect to those features for which they are analog > (invertible)? That amounts to limited invertibility, and the next > essential step would be [2] to identify the features that need > invertibility, as distinct from those that can be thrown away. Yes, I still say [1]. And yes, the category induction problem is [2]. Perhaps with the three-level division-of-labor I've described a connectionist algorithm or some other inductive mechanism would be able to find the invariant features that will subserve a sensory categorization from a given sample of confusable alternatives. That's the categorical representation. -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Princeton.EDU