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: <956@mind.UUCP> Date: Thu, 2-Jul-87 01:19:05 EDT Article-I.D.: mind.956 Posted: Thu Jul 2 01:19:05 1987 Date-Received: Fri, 3-Jul-87 04:57:22 EDT References: <764@mind.UUCP> <768@mind.UUCP> <770@mind.UUCP> <6174@diamond.BBN.COM> <3097@venera.isi.edu> Organization: Cognitive Science, Princeton University Lines: 55 Summary: Icons can only generate similarity gradients, not categorization Xref: mnetor comp.ai:603 comp.cog-eng:173 smoliar@vaxa.isi.edu (Stephen Smoliar) Information Sciences Institute writes: > Consider the holographic model proposed by Karl Pribram in LANGUAGES > OF THE BRAIN... as an alternative to [M.B. Brilliant's] symbol > manipulation scenario. Besides being unimplemented and hence untested in what they can and can't do, holographic representations seem to inherit the same handicap as all iconic representations: Being unique to each input and blending continuously into one another, how can holograms generate categorization rather than merely similarity gradients (in the hard cases, where obvious natural gaps in the input variation don't solve the problem for you a priori)? What seems necessary is active feature-selection, based on feedback from success and failure in attempts to learn to sort and label correctly, not merely passive filtering based on natural similarities in the input. > [A] difficulty seems to be in what it means to file something away if > one's memory is simply one of experiences. Episodic memory -- rote memory for input experiences -- has the same liability as any purely iconic approach: It can't generate category boundaries where there is significant interconfusability among categories of episodes. > Perhaps the difficulty is that the mind really doesn't want to > assign a symbol to every experience immediately. That's right. Maybe it's *categories* of experience that must first be selectively assigned names, not each raw episode. > Where does the symbol's name come from? How is the symbol actually > "bound" to what it retrieves? That's the categorization problem. > The big unanswered question...[with respect to connectionism] > would appear to be: will [it] all... scale upward? Connectionism is one of the candidates for the feature-learning mechanism. That it's (i) nonsymbolic, that it (ii) learns, and that it (iii) uses the same general statistical algorithm across problem-types (i.e., that it has generality rather than being ad hoc, like pure symbolic AI) are connectionism's plus's. (That it's brainlike is not, nor is it true, on current evidence, nor even relevant at this stage.) But the real question is indeed: How much can it really do (i.e., will it scale up)? -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Princeton.EDU