Path: utzoo!attcan!uunet!samsung!think!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 17:48:11 GMT References: Sender: news@nmsu.edu Organization: NMSU Computer Science Lines: 28 In-reply-to: rr2p+@andrew.cmu.edu's message of 22 Mar 90 13:34:09 GMT In article rr2p+@andrew.cmu.edu (Richard Dale Romero) writes: I think Ted is ignoring some very important aspects of the neural network. ... parallel processing ... increase our computing power. ... neural network on a parallel machine ... beautifully suited i think that rick is ignoring some very important aspects of the neural network approach, namely that conventional approaches still work much better, and that parallelization of many conventional numerical codes is not all that difficult (for example the work at sandia on large hydrodynamic codes). furthermore, the non-linear interpolation work at los alamos (doyne farmer and co.) has shown that relatively conventional approaches can solve the same sorts of interpolation/classification problems that neural nets solve with many orders of magnitude less machine and training time. why should we need to go to a 10^4 processor parallel machine just to run a code that is suited for parallelism, if there is a serial code that is 10^4 more efficient? all of this is a bit off the original subject, though. can anyone come up with an example of where neural nets work at least as well as conventional approaches?