Xref: utzoo comp.ai:4624 comp.ai.neural-nets:840 Path: utzoo!attcan!uunet!tut.cis.ohio-state.edu!purdue!gatech!mcnc!thorin!coggins!coggins From: coggins@coggins.cs.unc.edu (Dr. James Coggins) Newsgroups: comp.ai,comp.ai.neural-nets Subject: Re: Connectionism, a paradigm shift? Message-ID: <9143@thorin.cs.unc.edu> Date: 13 Aug 89 14:34:01 GMT References: <24241@iuvax.cs.indiana.edu> <568@berlioz.nsc.com> Sender: news@thorin.cs.unc.edu Reply-To: coggins@cs.unc.edu (Dr. James Coggins) Organization: University Of North Carolina, Chapel Hill Lines: 118 In article <568@berlioz.nsc.com> andrew@berlioz (Lord Snooty @ The Giant Poisoned Electric Head ) writes: >I think you should crosspost this to comp.ai.neural-nets, whose members seem >to exhibit the usual healthy cynicism of a comp.. group; not a pack of >zealots by any means! Thank you, I'm sure. >I think that what is required to save the field from the "hype seesaw" >is a healthy rate of generation of solid new theoretical results. >There is a tremendous amount of high-quality work going on, bolstered by >the application of formal mathematical techniques. >It seems to me that this truly sets NN research apart from the much >more "hand-waving" stuff that I encountered when looking at conventional >AI, when expert systems were on the rise in the early- and mid-80s. >Here one found tree traversal stuff and Bayesian statistical variations, >definitons of "frames" and the like; the ad hoc component was significant. >(although fuzzy set theory has to some extent set some of this on a more >formal footing, I have to agree). > >Andrew Palfreyman There's a good time coming, be it ever so far away, >andrew@berlioz.nsc.com That's what I says to myself, says I, I'm afraid that the theoretical foundation you appreciate is actually inherited (or bastardized, depending on your point of view) from the statistical pattern recognition studies of ten to twenty years ago. Sure there is a theory base, but it's ready-made, much of it not arising inherently from NNs (but being REdiscovered there). "...only be sure please always to call it RESEARCH!" from Lobachevsky by Tom Lehrer I have been impressed with the confirmation provided by this newsgroup that the majority of researchers in this area really are disgusted at the publicity-mongering, money-grubbing approach of too many well-placed (and well-heeled) labs, researchers, writers, companies, seminar sellers, and the like. NNs might become a significant contribution making possible highly parallel implementations of many kinds of processes if the science fiction futurist brain-theory dabblers would shut up and let the real researchers develop the field in a careful, disciplined way, without having to run interference against massively inflated expectations of the work. A few months ago I posted to comp.ai.neural-nets the document reproduced below. I guess it was too hot for the newsgroup, but I did receive 13 e-mail replies: 8 firmly supportive, 4 asking for more pointers to statistical pattern recognition which I gladly supplied (But note: Is the scholarship in the NN field really so weak that NN researchers are unaware of twenty years of research in statistical pattern recognition? The evidence says yes!), and one sharply critical but easy to refute (a True Believer who went down in flames). I posted the document below in the spirit of my other "Outrageous Discussion Papers" that I have been circulating to carefully selected audiences to provoke thought and comment and encourage skepticism. I have one flaming the use of rule-based expert systems in medical applications, one arguing that edges are an inadequate foundation for vision, one arguing that automatic identification of organs in CT scans is an unworthy task of little practical value, one that is a manifesto for my approach to computer vision research, and the neural net one below. If you are interested, e-mail me, but I'm leaving now for a three-week vacation, so don't expect my usual rapid response. --------------------------------------------- My assessment of the neural net area is as follows: (consider these Six Theses nailed to the church door) 1. NNs are a parallel implementation technique that shows promise for making perceptual processes run in real time. 2. There is nothing in the NN work that is fundamentally new except as a fast implementation. Their ability to learn incrementally from a series of samples nice but not new. The way they learn and make decisions is decades old and first arose in communication theory, then was further developed in statistical pattern recognition. 3. The claims that NNs are fundamentally new are founded on ignorance of statistical pattern recognition or on simplistic views of the nature of statistical pattern recognition. I have heard supposedly competent people working in NNs claim that statistical pattern recognition is based on assumptions of Gaussian distributions which are not required in NNs, therefore NNs are fundamentally different. This is ridiculous. Statistical pattern recognition is not bound to Gaussians, and NNs do, most assuredly, incorporate distributional assumptions in their decision criteria. 4. A more cynical view that I do not fully embrace says that the main function of "Neural Networks" is as a label for money. It is a flag you wave to attract money dispensed by people who are interested in the engineering of real-time perceptual processing and who are ignorant of statistical pattern recognition and therefore the lack of substance of the neural net field. 5. Neural nets raise lots of engineering questions but little science. Much of the excitement they have raised is based on uncritical acceptance of "neat" demos and ignorance. As such, the area resembles a religion more than a science. 6. The "popularity" of neural net research is a consequence of the miserable mathematical backgrounds of computer science students (and some professors!). You don't need to know any math to be a hacker, but you have to know math and statistics to work in statistical pattern recognition. Thus, generations of computer science students are susceptible to hoodwinking by neat demos based on simple mathematical and statistical techniques that incorporate some engineering hacks that can be tweaked forever. They'll think they are accomplishing something by their endless tweaking because they don't know enough math and statistics to tell what's really going on. --------------------------------------------------------------------- Dr. James M. Coggins coggins@cs.unc.edu Computer Science Department A neuromorphic minimum distance classifier! UNC-Chapel Hill Big freaking hairy deal. Chapel Hill, NC 27599-3175 -Garfield the Cat and NASA Center of Excellence in Space Data and Information Science ---------------------------------------------------------------------