Xref: utzoo comp.ai:4635 comp.ai.neural-nets:847 Path: utzoo!utgpu!watmath!att!tut.cis.ohio-state.edu!cica!ctrsol!IDA.ORG!potomac!jtn From: jtn@potomac.ads.com (John T. Nelson) Newsgroups: comp.ai,comp.ai.neural-nets Subject: Re: Connectionism, a paradigm shift? Summary: neural nets as a charade Message-ID: <7977@potomac.ads.com> Date: 15 Aug 89 14:33:05 GMT References: <24241@iuvax.cs.indiana.edu> <568@berlioz.nsc.com> <9143@thorin.cs.unc.edu> Organization: Advanced Decision Systems, Arlington VA Lines: 39 > 6. The "popularity" of neural net research is a consequence of the > miserable mathematical backgrounds of computer science students (and > some professors!). You don't need to know any math to be a hacker, but > you have to know math and statistics to work in statistical pattern > recognition. Thus, generations of computer science students are > susceptible to hoodwinking by neat demos based on simple mathematical > and statistical techniques that incorporate some engineering hacks > that can be tweaked forever. They'll think they are accomplishing > something by their endless tweaking because they don't know enough > math and statistics to tell what's really going on. A sweeping generalization. Computer scientists aren't the only ones working on neural networks and not all computer scientists are "student hackers." I wish people would stop confusing "programming" activities with thinking and research activities. They are distinctly different. One is engineering and the other is not. There are computer scientists who approach problems as theoreticians and there are computer scientists who approach problems with ad hoc solutions in mind. However...... (time to get up on my soapbox oh boy!).... In my opinion we don't have a deep macroscopic understanding of what neural nets are capable of doing or are doing even in the simplest networks. Researchers are spending a lot of time and effort focusing on the optimization of small techniques (e.g. backpropigation) and too little time on developing formalisms for describing and understanding NNs as a whole. A deep understanding of any complex paradigm will be reached only through the efforts of many researchers, tackling the problem from different viewpoints (like multiple sculptors chipping away at a block of marble to reveal the statue hidden inside). It's fairly useless for all of these metaphorical artists to chip away at a big toe all at once, yet they must also posses the same overall goal and understanding of the problem, otherwise the final piece will not be consistant and balanced. Well you get the idea.