Path: utzoo!utgpu!news-server.csri.toronto.edu!mailrus!ames!ucsd!cogsci!cutrell From: cutrell@cogsci.ucsd.EDU (Doug Cutrell) Newsgroups: comp.ai.neural-nets Subject: Re: What good are neural nets? Message-ID: <103@cogsci.ucsd.EDU> Date: 22 Mar 90 20:29:58 GMT References: <68764@aerospace.AERO.ORG> Reply-To: cutrell@cogsci (Doug Cutrell) Organization: University of California, San Diego Lines: 33 Ted Dunning has repeatedly asked for specific examples of where neural network approaches perform better than traditional approaches. The following spring to mind as immediate examples: Le Cun, Boser, Denker, Henderson, Howard, Hubbard, and Jackel of AT&T Bell Labs recently report achieving 9% rejection rate for a 1% error criterion on a U.S. Postal Service hand-written zip-code data base, with a throughput of a dozen digits per second on a 25 MFLOP DSP, including image aquistion and normalization. This data set is *EXTREMELY* noisy and consists of undoctored digitized images of zipcodes exactly as they are scribbled on real envelopes. (See Nerual Compuataion 1:4, pp. 541-551 ) Gerald Tesauro's backgamman playing network recently defeated all other computer implementations at the recently held First Computer Olympiad in London. (See Neural Computation 1:3 pp. 321-323). And finally, Sejnowski's original NetTalk, while admittedly inferior to DECTalk, did not require teams of professionals in excess of one decade in order to achieve its performance! This list is not meant to be complete. I do not contend that the neural network framework is essential to the above successes. Many approaches may be applied with similar results -- neural nets are capable of implementing general Turing computation. Their value comes from the type of approaches that the neural network paradigm inspires. Doug Cutrell Dept. of Cog.Sci., D-015 UCSD La Jolla, CA 92093 cutrell@cogsci.ucsd.edu (internet)