Path: utzoo!utgpu!watmath!iuvax!rutgers!elbereth.rutgers.edu!harnad From: harnad@elbereth.rutgers.edu (Stevan Harnad) Newsgroups: comp.ai Subject: Re: Symbol Grounding Problem Summary: Think Before You Hack Keywords: connectionism language categorization Searle symbol-systems Message-ID: Date: 7 Aug 89 17:10:08 GMT References: <9753@phoenix.Princeton.EDU> <5761@pt.cs.cmu.edu> <5772@pt.cs.cmu.edu> Organization: Rutgers Univ., New Brunswick, N.J. Lines: 94 jps@cat.cmu.edu (James Salsman) of Carnegie Mellon wrote: > The theories shown in my diagram could hardly be considered unexamined... > Formally, EPAM is an algorithm that maps sensory signals into symbolic > information... [It] predict[s] the behavior of humans in several > cognitive experimental domains such as response time measurements, > patterns of forgetting, and relation of the serial position of sensory > information to those factors... literature correlating SOAR's > behavior with that of humans is based on extensive protocol and timing > analysis and is especially obvious in areas such as stimulus-response > compatibility and the shapes of the learning curves of several > different kinds of cognitive problems. It's not the theoretical modules themselves that are unexamined, but the belief that hooking them together will successfully model the mind. Fitting the fine-tuning parameters of toy-size pieces of performance is not the mark or measure of a successful model of the mind. Life-size performance capacity (never mind its fine-tuning for now) is. The test of whether an algorithm "maps sensory signals into symbolic information" in the right way is to see whether it produces human-scale robotic performance, including whether your "cat is on the mat" symbol string, along with all the other life-size things it does, really picks out cats, mats, and the one being on the other. The symbol grounding problem is a harbinger of the possibility that neither the purely symbolic approach alone, nor a simple hook-up between pure symbol systems and transducers or other autonomous functional modules, can lead to a successful lifesize performance model. > Connectionist Neural-Network and Parallel Distributed Processing > research presents a very rich description of the "computing machinery" > of the brain. We have reached the point in our understanding of > cognition that we may now begin the implentation of programs such as > "the mind" on computing machinery other than the brain. Unfortunately, the [unexamined] point at issue in the symbol grounding problem is the very notion that the mind is a program (i.e., symbol manipulation) and that the brain is just the machine on which it's implemented. (There are, by the way, more perspicuous potential uses for connectionism in modeling the mind than simply as one more way to implement symbol-manipulation.) > binding semantics of OPS5... Grounding semantics and binding semantics are unfortunately not the same thing. > I expect to be able to construct a system with a capacity of 40000 > total productions and still maintain a 10 millisecond cycle time... > [which] has been specified by both Newell and Simon as the assumed > firing time for their human cognition models. [See discussion of fine-tuning parameters above. Same goes for numerology. It might be better to focus your expectations instead in the direction of generating lifesize performance capacity; I'm afraid that that's so much more than 40000 productions that it makes it unlikely that the way to scale up is simply to add more productions...] > SOAR is a purely symbolist architecture, and requires another > underlying system to parse sensory signals into symbolic form. That is > exactly the function of EPAM. The combined system should be able to > perform real-time cognitive tasks such as vocal communication, perhaps > as a natural language processing back-end for the Sphinx real-time > speaker-independent speech recogntion system that CMU has constructed. > Such a system would make a very friendly user interface for an > information retrieval network (like the Telephone system.)... > At Carnegie-Mellon, we do not use "hook-em-all-up-together" strategies > for cognitive modeling. We use "functional composition," instead. These "back-ends" and "interfaces" sure sound like hook-ups to me; but call it functional composition if you like. (Maybe then the dedicated hybrid nonsymbolic/symbolic grounding system I'm proposing is just a matter of "functional composition" too...) What you have to decide, however, is whether you're doing information retrieval aids for people (in which case you don't have to worry about grounding your symbols, because the [grounded] users can interpret them for themselves) or you're modeling the mind -- in which case the meanings of the symbols will have to be intrinsic, not parasitic on human interpreters. > Aren't there any hackers at Princeton? Or are you all "Software Engineers?" First, my views do not represent those at Princeton (or Rutgers, as the case may be). Second, it is only from a certain vantage point that psychological theory is seen as "software engineering"... -- Stevan Harnad INTERNET: harnad@confidence.princeton.edu harnad@princeton.edu srh@flash.bellcore.com harnad@elbereth.rutgers.edu harnad@princeton.uucp BITNET: harnad@pucc.bitnet CSNET: harnad%princeton.edu@relay.cs.net (609)-921-7771