Path: utzoo!utgpu!news-server.csri.toronto.edu!mailrus!cs.utexas.edu!samsung!uakari.primate.wisc.edu!ames!haven!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: <14756@phoenix.Princeton.EDU> Date: 23 Mar 90 01:50:25 GMT References: <68764@aerospace.AERO.ORG> <2355@rnd.GBA.NYU.EDU> <14746@phoenix.Princeton.EDU> <79@nrl-cmf.UUCP> Reply-To: kpfleger@phoenix.Princeton.EDU (Karl Robert Pfleger) Organization: Princeton University, NJ Lines: 21 In article <79@nrl-cmf.UUCP> tedwards@cmsun.UUCP (Thomas Edwards) writes: >In article ted@nmsu.edu (Ted Dunning) writes: >>unfortunately, in most of the networks exhibited so far, the scaling >>of the size of the neural net or the accuracy required is prohibitive. > >I'll be the first one to admit that backpropagation learning can be truly tedious, and >using it on anything but the most toy problems will definately leave one with a >bad taste in the mouth for neural networks. There is one giant problem with back-prop. In the (admittedly long term) goal of actual artificial intelligence, back-prop will have to be abandoned as the general method of altering the network. The reason is that back-prop requires at all time that the 'correct' output of the system be known, so that it can be compared with the network's own output. This is obviously not the way any natural intelligence learns (all of the time, anyway). We need a method of alterning the network without this problem. -Karl kpfleger@phoenix.princeton.edu kpfleger@pucc (bitnet)