Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!swrinde!zaphod.mps.ohio-state.edu!usc!elroy.jpl.nasa.gov!aero!robert From: robert@aerospace.aero.org (Bob Statsinger) Newsgroups: comp.ai.neural-nets Subject: Re: What good are neural nets? Message-ID: <69379@aerospace.AERO.ORG> Date: 23 Mar 90 01:09:46 GMT References: Reply-To: robert@aero.UUCP (Bob Statsinger) Organization: Ahh...wouldntya like to know! Lines: 30 In article rr2p+@andrew.cmu.edu (Richard Dale Romero) writes: >I think Ted is ignoring some very important aspects of the neural network. >It seems that we will be looking more and more towards parallel processing >in order to increase our computing power. But, solving problems on a parallel >machine leads to really *big* complications in how to structure the program. >Simulating a neural network on a parallel machine is something that it is >beautifully suited for, though. With more computing power, we can begin >to solve more types of problems that would have previously taken much to >long to do on today's von-Neumann (sp?) computers. > Parallel processors, in and of themselves, will not let us tackle new kinds of problems; they will only run the "old" problems faster. I think the point of the above posting is that, as we simulate NN's on faster and faster processors, their results may become more and more satisfactory. At the very least the same unsatisfactory results will arrive faster :-) So very soon the questions we ask will be things like: does our implementation of (backprop, or nettalk, etc) on our Massively Parallel Processor (MPP) do anything for us that our implementation of (genetic algorithmsm, dectalk, linear regression, etc) on the MPP does not? -- Bob Statsinger Robert@aerospace.aero.org The employers expressed herein are strictly mine and are not necessarily those of my opinion's....uh..er...whatever...