Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!utgpu!water!watmath!clyde!rutgers!princeton!mind!harnad From: harnad@mind.UUCP Newsgroups: comp.ai,comp.cog-eng Subject: Re: The symbol grounding problem Message-ID: <846@mind.UUCP> Date: Sun, 14-Jun-87 22:37:00 EDT Article-I.D.: mind.846 Posted: Sun Jun 14 22:37:00 1987 Date-Received: Tue, 16-Jun-87 01:13:24 EDT References: <764@mind.UUCP> <768@mind.UUCP> <770@mind.UUCP> <6174@diamond.BBN.COM> <1165@houdi.UUCP> Organization: Cognitive Science, Princeton University Lines: 90 Xref: utgpu comp.ai:487 comp.cog-eng:117 Summary: Analog representations are not the only nonsymbolic representation needed to ground symbols: categorical (noninvertible) ones needed too In two consecutive postings marty1@houdi.UUCP (M.BRILLIANT) of AT&T Bell Laboratories, Holmdel wrote: > the flow of visual information through the layers of the retina, > and through the layers of the visual cortex, with motion detection, > edge detection, orientation detection, etc., all going on in specific > neurons... Maybe a neurobiologist can give a good account of what > all that means, so we can guess whether computer image > processing could emulate it. As I indicated the last time, neurobiologists don't *know* what all those findings mean. It is not known how features are detected and by what. The idea that single cells are doing the detecting is just a theory fragment, and one that has currently fallen on hard times. Rivals include distributed networks (of which the cell is just a component), or spatial frequency detectors, or coding at some entirely different level, such as continuous postsynaptic potentials, local circuits, architectonic columns or neurochemistry. Some even think that the multiple analog retinas at various levels of the visual system (12 on each side, at last count) may have something to do with feature extraction. One cannot just take current neurophysiological data and replace the nonexistent theory by preconceptions from machine vision -- especially not by way of justifying the machine-theoretic concepts. >> >[SH:] my theory never laid claim to complete invertibility >> >throughout. > > First "analog" doesn't mean analog, and now "invertibility" > doesn't mean complete invertibility. These arguments are > getting too slippery for me... If non-invertibility is essential > to the way we process information, you can't say non-invertibility > would prevent a machine from emulating us. I have no idea what proposition you think you were debating here. I had pointed out a problem with the top-down symbolic approach to mind-modeling -- the symbol grounding problem -- which suggested that symbolic representations would have to be grounded in nonsymbolic representations. I had also sketched a model for categorization that attempted to ground symbolic representations in two nonsymbolic kinds of representations -- iconic (analog) representations and categorical (feature-filtered) representations. I also proposed a criterion for analog transformations -- invertibility. I never said that categorical representations were invertible or that iconic representations were the only nonsymbolic representations you needed to ground symbols. Indeed, most of the CP book under discussion concerns categorical representations. > All I'm saying is that Harnad has come nowhere near proving his > assertions, or even making clear what his assertions are... > 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... > Anybody can do hand-waving. To be convincing, abstract > reasoning must be rigidly self-consistent. Harnad's is not. > I haven't made any assertions as to what is possible. Invertibility is my candidate criterion for an analog transform. Invertible means invertible, top-down means top-down. Where further clarification is needed, all one need do is ask. Now here is M. B. Brilliant's "Recipe for a symbol-grounder" (not to be confused with an assertion as to what is possible): > Suppose we create a visual transducer... with hard-wired > capability to detect "objects"... Next let's create a symbol bank > Next let's connect the two... 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... 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. Now, Harnad, Weinstein, anyone: do you think this > could work, or do you think it could not work? Sounds like a conjecture about a system that would pass the TTT. Unfortunately, the rest seems far too vague and hypothetical to respond to. If you want me to pay attention to further postings of yours, stay temperate and respectful as I endeavor to do. Dismissive rhetoric will not convince anyone, and will not elicit substantive discussion. -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Princeton.EDU