Path: utzoo!attcan!utgpu!news-server.csri.toronto.edu!mailrus!cs.utexas.edu!usc!zaphod.mps.ohio-state.edu!tut.cis.ohio-state.edu!rutgers!mephisto!udel!princeton!phoenix!kpfleger From: kpfleger@phoenix.Princeton.EDU (Karl Robert Pfleger) Newsgroups: comp.ai.neural-nets Subject: Re: What good are neural nets? Message-ID: <14746@phoenix.Princeton.EDU> Date: 22 Mar 90 05:48:07 GMT References: <68764@aerospace.AERO.ORG> <2355@rnd.GBA.NYU.EDU> Reply-To: kpfleger@phoenix.Princeton.EDU (Karl Robert Pfleger) Organization: Princeton University, NJ Lines: 32 In article ted@nmsu.edu (Ted Dunning) writes: > >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. I think part of the point of neural nets it not that they add anything new to the solution of any _particular_ problem, but that because they _do_ solve problems with less a priori structure they are more general. The same neural net can learn to solve many different problems, and possibly to solve more than one problem concurrently. So, even if classical system A solves problem X better than any NN and system B solves Y better than any NN, a sufficiently large NN may be trainable to solve either X or Y, or possibly both. Part of the problem as I see it with the NNs I've heard about is that they are too small to be very general. The large the NN, the more general it will be. You can argue about the above that it is easy to put the two classical systems together making a third classical system which solves both X and Y better than an NN, but the problem with this approach is that it isn't easy to do this for every problem to get an intelligent machine. Whereas, the NN approach generalizes automatically, without the need to create lots of different solutions. -Karl kpfleger@phoenix.princeton.edu kpfleger@pucc (bitnet)