Path: utzoo!attcan!uunet!snorkelwacker!bloom-beacon!bu.edu!nntp-xfer.bu.edu!slehar From: slehar@bucasd.bu.edu (Lehar) Newsgroups: comp.ai.neural-nets Subject: Re: What good are neural nets? Message-ID: Date: 22 Mar 90 17:24:04 GMT References: <68764@aerospace.AERO.ORG> Sender: news@bu.edu.bu.edu Organization: Boston University Center for Adaptive Systems Lines: 106 In-reply-to: ted@nmsu.edu's message of 21 Mar 90 18:33:53 GMT **** WHAT GOOD ARE NEURAL NETS? **** There are generally two reasons for studying neural nets, 1) to better understand how natural systems compute, and 2) to perhaps gain from this knowledge to possibly enhance our own computational techniques. At the present time, our knowledge of natural systems is so rudimentary that our progress in emulating them is somewhat limited. We have however discovered several fundamental principles of natural computation which are either being currently used to advantage, or show promise of future utility. Some of these principles are summarized below. 1 That computation can be performed by numerous simple asynchronous analog processors which are richly interconnected, as an alternative to the more traditional approach of fewer, more complex time-locked digital processors which communicate using highly encoded protocols. 2 That an advantage of this approach is greater fault tolerance to the loss of individual components or data corruption. 3 That such systems can be designed to program themselves, thus eliminating the need for a highly skilled and time consuming task. I.e. the problem domain does not have to be explicitly understood and modelled in order to build a system to work with it. 4 That such systems are potentially much easier to connect together because of the simple nature of the signals between units. (You can insert an electrode into many points in the brain and elicit simple responses- hunger, fear, motor or sensory response, etc. A similar intrusion into the complex components of a computer is likely to elicit nothing more than a crash) 5 That certain problem domains are best solved by a parallel distributed approach instead of a sequential analytical approach. Generally, if there is uncertainty in the data, then all interim decisions are suspect, and alternative choices should continue to be considered until the final decision is made. When the data is more precise and reliable, interim results can be trusted, and alternatives can be safely discarded as in digital computation. Given the above observations, I cannot imagine a good reason for NOT studying neural nets. Ted Dunning (ted@nmsu.edu) says: ------------------------------------------------------------------------ | the biological plausibility of artificial neural nets is essentially | nil. a linear sum followed by a soft limiter is nothing like what a | neuron does. it may be that the collective behavior of all sorts of | different neurons will converge, but there is no indication yet that | neural nets in the popular style will do this. ------------------------------------------------------------------------ Current models may not model biological neurons in all their complexity, but would you not agree that they are much more like neural computation than say, a logic chip or an expert system? Are we not moving in the right direction? If current models are a simplification, do they not at least capture the essentials of some of the fundamental aspects of neural processing? (like the above mentioned 5) And are they not therefore worth investigating? Perhaps your problem is that you think current models are the final product, the last word on brain modeling. Dunning continues... ------------------------------------------------------------------------ | ...but let us not over-hype the idea to the point that | people will be so incredibly disillusioned that they won't work with | it for decades. remember the history of neural nets (aka | perceptrons). ------------------------------------------------------------------------ Nobody disagrees with this point. Will you point out for us what is being said that is over-hyped? Give us some specifics. Is there anything in my contentions that is over-hyped for instance? If we look back at the source of all this discussion, could it not be said perhaps that some people are UNDER-hyping neural nets... Dunning... ------------------------------------------------------------------------ | In article <68764@aerospace.AERO.ORG> abbott@aerospace.aero.org | (Russell J. Abbott) writes: | | | Is there a good characterization of the kinds of problems for which | neural nets are better than more traditional computational systems? | | yes. | | | none. <========|| -------------------||--------------------------------------------------- || ====== Surely you jest! -- (O)((O))(((O)))((((O))))(((((O)))))(((((O)))))((((O))))(((O)))((O))(O) (O)((O))((( slehar@bucasb.bu.edu )))((O))(O) (O)((O))((( Steve Lehar Boston University Boston MA )))((O))(O) (O)((O))((( (617) 424-7035 (H) (617) 353-6425 (W) )))((O))(O) (O)((O))(((O)))((((O))))(((((O)))))(((((O)))))((((O))))(((O)))((O))(O)