Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!bu.edu!slehar From: slehar@park.bu.edu (Steve Lehar) Newsgroups: comp.ai.neural-nets Subject: Re: application feasibility question Message-ID: Date: 24 Apr 91 17:21:09 GMT References: <9659@castle.ed.ac.uk> Sender: news@bu.edu.bu.edu Organization: Boston University Center for Adaptive Systems Lines: 76 In-reply-to: own@castle.ed.ac.uk's message of 18 Apr 91 12:01:30 GMT The prime consideration in whether or not to use a neural paradigm for a database implementation is the nature of the information to be stored. If the information is essentially deterministic and reliable, and the operations performed on such data explicit and well defined, then you are better off using a conventional database program. The neural approach holds promise only for those kinds of applications where a conventional database is no good, i.e. where the information stored or the retrieval operations are in some sense fuzzy or imprecise. For instance, if your data represents the color of a farmer's fields as detected by satellite sensors at various bands, and the information you would like to retrieve is the viability of his crops, this information is full of ambiguity. First of all, the color of the fields does not fall into discrete categories, but represents a continuum. Secondly, the mapping from color to viability is by no means explicit and well defined, and depends on a host of other considerations, such as geographic location, current season, recent weather, etc. No conventional database package could retrieve this kind of information without an explicit algorithm for how to compute viability from color. A neural algorithm however can be trained to correlate viability and color from known data, and can sometimes generalize this to new data (*note). On the other hand, if your data represents names, addresses, incomes, crops, etc. and you would like to retrieve the names of all corn farmers in Idaho with income exceeding 50,000 and with names between A and K, then a conventional database is the best way to go. The translation from fuzzy to hard has already been made implicitly in the gathering of the data - (Did the income figure include that swap of the old pick-up with his brother-in-law's tractor, and how much was that old tractor REALLY worth, and so forth...) so that now you would like to retrieve only those farmers whose REPORTED income exceeds 50,000, and you would consider it an error if the program infered from other sources that this figure was wrong and therefore included other names in the list. In other words, you would like the program to be the perfect bureaucrat, "Hey, I'm just doin' my job- those are the rules, I'm not paid to think...". If you do choose to go the neural route, you should be aware that many of the paradigms in the literature are famous for their historical importance or the interesting issues they raise, but are not ripe for practical application. For example the so-called "Hopfield net" (Actually the Grossberg Additive model [1], see [2] for explaination) introduces the interesting notion of auto-associative feedback networks. In their raw form however these networks have been shown to have a very limited storage capacity, and an "unfriendly" behavior when over-loaded, i.e. they don't complain, they just start giving wrong answers. I am less familiar with the Interactive Competition model (IAC) of Rummelhart and McClelland, except that I know that it was very influential in introducing connectionist concepts to linguists and psychologists, and is much referenced in those fields. I am not familiar with the practical implementations of the model, but I would suspect that these have been of a prototype nature. Such multi-level feedback models are fraught with dynamic subtleties that are often difficult to balance, and I would be interested to hear if someone had made practical use of this paradigm. [1] Grossberg, Math. Biosci. 4, 201 (1969); Grossberg and Pepe, J. Stat. Phys. 3, 95 (1971) [2] Carpenter, Cohen, & Grossberg "COMPUTING WITH NEURAL NETWORKS" (1987) Science, 235, 1226-1227. *note to "AI-vs-NN flamesters: this sentence is not to be construed as a claim that the neural model cannot be implemented with conventional algorithms, nor indeed be expressed in more conventional mathematical terms. Of course it can. Sometimes however it is more convenient to express these operations as a "neural" paradigm. -- (O)((O))(((O)))((((O))))(((((O)))))(((((O)))))((((O))))(((O)))((O))(O) (O)((O))((( slehar@park.bu.edu )))((O))(O) (O)((O))((( Steve Lehar Boston University Boston MA )))((O))(O) (O)((O))((( (617) 424-7035 (H) (617) 353-6741 (W) )))((O))(O) (O)((O))(((O)))((((O))))(((((O)))))(((((O)))))((((O))))(((O)))((O))(O)