Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!samsung!zaphod.mps.ohio-state.edu!rpi!uupsi!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 00:57:29 GMT References: <68764@aerospace.AERO.ORG> <2355@rnd.GBA.NYU.EDU> Sender: news@nmsu.edu Organization: NMSU Computer Science Lines: 51 In-reply-to: shankar@rnd.GBA.NYU.EDU's message of 21 Mar 90 20:52:13 GMT In article <2355@rnd.GBA.NYU.EDU> shankar@rnd.GBA.NYU.EDU (Shankar Bhattachary) writes: In article ted@nmsu.edu (Ted Dunning) writes: >none. May I request that if Ted Dunning has good reasons for his opinion, he elaborate on the "none"? strictly speaking, "none" should be the default opinion, with the burden of proof being on the people who claim that neural nets actually are solving problems better than conventional approaches. but in particular, if you take a few of the prototypical claims from the neural net community, you find that they just don't add anything new to the solution of particular problems, only that they add something new to the collection of things that neural nets `kind of' do. the claim that the neural nets solve these problems with less a priori structure than conventional approaches is completely specious due to the amount of tweaking needed to get any sort of success. a few classic examples include sejnowskis over-celebrated net-talk, the learning of the xor function, and lapedes work with dna. net-talk does not work nearly as well as a handcrafted text to speech system such as dectalk, nor does it work as well, learn as fast, or run as fast as a non-linear interpolation method such as that used by doyne farmer. learning the xor function (or any logical function) is better done using something like the genetic algorithm on a population of state machines, and alan lapedes work with predicting whether short base pair sequences code for particular proteins works better if you forget the neural net mumbo-jumbo and just do the math of a non-linear interpolation. If neural nets are indeed no more effective under any circumstances than are more conventional methods, many of us could save ourselves a lot of trouble. good point!! This is a serious request. Many people feel as Ted does, and I think the argument deserves more than just a "none". actually i think that the argument really deserves some examples. why is it that we should _presume_ that neural nets do magic just because it says so in the latest survey in BYTE or AI magazine. so let us turn this challenge back to the normal course in scientific discourse, and ask if there is anything that neural nets actually do better than conventional approaches.