Path: utzoo!attcan!uunet!cs.utexas.edu!yale!cmcl2!lanl!opus!ted From: ted@nmsu.edu (Ted Dunning) Newsgroups: comp.ai.neural-nets Subject: Re: What good are neural nets? Message-ID: Date: 22 Mar 90 18:43:05 GMT References: <68764@aerospace.AERO.ORG> Sender: news@nmsu.edu Organization: NMSU Computer Science Lines: 126 In-reply-to: slehar@bucasd.bu.edu's message of 22 Mar 90 17:24:04 GMT steve lehar begins to respond, but still fails to give any examples. In article slehar@bucasd.bu.edu (Lehar) writes: There are generally two reasons for studying neural nets, ... come now.... i agree that it is good to study all kinds of different approaches to computation. the question was are there any problems where neural nets are superior to conventional approaches? We have however discovered several fundamental principles of natural computation which are either being currently used to advantage, or show promise of future utility. Some of these principles are summarized below. 1 That computation can be performed by numerous simple asynchronous analog processors we knew this by direct observation of natural systems. 2 That an advantage of this approach is greater fault tolerance to the loss of individual components or data corruption. we knew this, too. 3 That such systems can be designed to program themselves, thus we even knew this, 4 That such systems are potentially much easier to connect and this. an exception might be made for neural net advocates. Given the above observations, I cannot imagine a good reason for NOT studying neural nets. come now, i never recommended that they not be studied. only that they are not yet competitive in _any_ area. Current models may not model biological neurons in all their complexity, but current neural simulation models come much closer and show many phenomenon not exhibited by neural nets. but would you not agree that they are much more like neural computation than say, a logic chip or an expert system? of course, but why make a specious comparison? Are we not moving in the right direction? i can't tell. If current models are a simplification, do they not at least capture the essentials of some of the fundamental aspects of neural processing? what _are_ the essentials of some of the fundamental aspects of neural processing? and, no i don't think that neural nets capture much of the essential aspects of neural computation. And are they not therefore worth investigating? sure, but that isn't the question here. Perhaps your problem is that you think current models are the final product, the last word on brain modeling. i hope NOT. Nobody disagrees with this point. Will you point out for us what is being said that is over-hyped? Give us some specifics. over-hyping is best recognized by results. the clear public perception is that neural nets can be used to solve real problems better than conventional approaches. this is patently wrong. and it is prima facie evidence of overhyping. Is there anything in my contentions that is over-hyped for instance? your contentions are carefully worded to ignore the point of the discussion. are there examples of problems better solved by neural nets? If we look back at the source of all this discussion, could it not be said perhaps that some people are UNDER-hyping neural nets... ? the source of the discussion was russel abbott who merely asked a question. i gave a supercilious answer, and implicitly (and later explicitly) challenged proponents to come up with counter-examples. none have. Dunning... In article <68764@aerospace.AERO.ORG> abbott@aerospace.aero.org (Russell J. Abbott) writes: Is there a good characterization of the kinds of problems for which neural nets are better than more traditional computational systems? yes. none. Surely you jest! actually, no. i haven't yet found a single example where neural nets work better (and i have looked). can somebody come up with an example? steve? surely you have a concrete example in hand if you think i was in jest.