Schocken, Shimon

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Association for Information SystemsAIS Electronic Library (AISeL)ICIS 1990 Proceedings International Conference on Information Systems(ICIS)1990PANEL 16 BUSINESS APPLICATIONS OFNEURAL NETWORKS: PROBLEMS ANDOPPORTUNITIESShimon SchockenNew York University, USAFollow this and additional works at: http://aisel.aisnet.org/icis1990This material is brought to you by the International Conference on Information Systems (ICIS) at AIS Electronic Library (AISeL). It has been acceptedfor inclusion in ICIS 1990 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contactelibrary@aisnet.org.Recommended CitationSchocken, Shimon, "PANEL 16 BUSINESS APPLICATIONS OF NEURAL NETWORKS: PROBLEMS AND OPPORTUNITIES"(1990). ICIS 1990 Proceedings. 10.http://aisel.aisnet.org/icis1990/10PANEL 16BUSINESS APPLICATIONS OF NEURAL NETWORKS:PROBLEMS AND OPPORTUNITIESPanel Chair: Shimon Schocken, New York University, USAPANELISTS: John Hertz, Nordita and Neurotech, DenmarkLudwik J. Liszka, Swedish Institute of Space Physics, SwedenDouglas L. Reilly, Nestor Inc., USALeo Vercauteren, State University of Ghent, BelgiumBACKGROUNDThe term "neural network" means different things to different people. Neural networks are popularly perceived asdescriptive models, designed to mimic the mechanism of the human brain. Most researchers, however, view thesimilarity of the neural network architecture to the brain circuitry as no more than a useful analogy. This is becauseneural networks are powerful computational models in their own right, regardless o[ their biological justification. Theyoffer novel solutions to many problems which defy standard algorithmic techniques and they lend themselves nicely tonew developments in parallel and optical hardware.During the past decade, neural networks were constructed to carry out a wide variety of computational tasks, primarilyin scientific and engineering applications, Most of this work, however, is experimental; it remains to bc seen whetheror not neural networks will become a widely-used computational paradigm. Notwithstanding its slow assimilation in thefield, the neural networks area is presently bursting with research activity, attracting scores of people from computerscience, neuroscience, physics, and psychology. Since the conception of the idea in the mid-1940s, many architecturesand training algorithms were developed. A partial list of several major paradigms and their respective developersindudesperceptioiu, feed-forward networks, recurrent networks, adaptive resonance theoiy, andfeature maps.Several hardware and software vendors presently offer off-the-shelf products which implement some of these models.The typical product is a software shell that enables end-users to develop and test neural networks in the context ofspecific problems. Is it possible that these programs will eventually become popular tools, like statistical packages andspreadsheet programs? Thc answer sccms to go far beyond user-interface and performance considerations; it is relatedto deeper issues regarding the appropriateness of the neural network approach to the special nature of businessapplications. Hence, we take the position that neural networks offer a great deal of promise, as well as a certain dcgrccof uncertainty. In this panel, we wish to explore the limitations and potential of this technology in the context ofbusiness applications.WHAT IS A NEURAL NETWORK?Broadly speaking, a neural network consists of three things: a task, an architecture, and a training algorithm. The taskspccifics what the network is supposed to do: convert hand-written text into an ASCII file, classify sonar images ontosubmarine profiles, or determine the credit-worthiness of personal loan applications. These problems seem to be verydifferent from cach other. Surprisingly, it has been shown that one can successfully model all of them using genericneural architectures and domain-frcc training algorithms.The neural architecture consists of a set of processors which are linked to each other, forming a network topology.Each processor is capable of computing a simple function and transmitting the result of its computation to many otherprocessors. The strength of these messages is modulated by a set of weights which parameterize the inter-processorconnections. The topology of the network, and the functions that the processor compute, are specified by the networkdesigner a priori; they become a fixed part of the network's architecture. In contrast, the weights of the inter-processorconnections are data-driven; they are continuously adjusted by a mathematical procedure which is typically referred toas a training a/gon'thm. Many traioing algorithms exist and all of them are related in one way or another to statisticalerror-minimizing procedures.342To illustrate, consider a neural network designed to fine-tune an expensive direct-mail campaign. Our objective is toclassify the company's customers into three classes: high, medium, and low priority targets. This classification can bemodeled through a neural network architecture consisting of three layers of processors. The input layer, consisting ofsome fifty processors, is used to represent demographic and fmancial data items, which are pulled out from thecompany's file. The output layer consists of three processors which are labeled "high," "medium," and "low." The inputand output layers are connected to each other through an intermediate set of processors, designed to capture complexand possibly non-linear interactions among the input and output variables. Each connection is parameterized by anumeric weight. At the beginning of the traimng session, all of the weights are initially set to small random values.The training session proceeds by showing the network a set of hundreds or thousands of customers whose true prioritiesare known ex-post from historical data. The customer's data are fed into the network incrementally, one data-set at atime. The network computes the customer's priority by turning on one of the output processors. Next, a feedbackmechanism tells the network whether or not this was a correct classification. In the case of a disagreement, a trainingalgorithm adjusts the weights in an attempt to correct the error. This is a delicate operation, because the new set ofweights must be chosen so that previously correct classifications will not be reversed. The training session continuesuntil the network reaches a satisfactory level of performance or until the designer runs out of examples. At that point,the network's performance is tested on the entire training data-set as well as on real data.PANEL OVERVIEWThe goal of the panel is to explore, demonstrate, and discuss the potential and limitations of neural networks in businessapplications. In spite of the complexity of the underlining subject, the panelists will attempt to minimize the use oftechnical clutter and stay away from esoteric terminology. This is based on the assumption that every equation ortechnical term is likely to decrease the audience's interest in the subject matter.The panel will commence with a mini-tutorial covering the history, technology, and state-of-the-art of the neuralnetworks field. This introduction will set the stage for the second part of the panel, in which we will present neuralnetwork solutions to problems in finance, marketing, and operations management. Specifically, we will survey the useof neural networks in such applications as credit scoring, market analysis, prediction, classification, queuing, andscheduling.In the final part of the panel, the panelists will draw on their collective work experiences to propose specificrecommendations to companies who are presently considering the use of neural networks technology. Each panelist willdiscuss such issues as face-validity, accountability, robustness, user interface, and management acceptance.The panel includes researchers, practitioners, and vendors of neural networks and represents five different countries.The panelists have extensive experience in developing and testing neural networks in realistic applications. Together,they offer a very broad perspective on the state of the art of the field in Western Europe and in the United States.343

PANEL 16 BUSINESS APPLICATIONS OF NEURAL NETWORKS: PROBLEMS AND OPPORTUNITIES

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PANEL 16 BUSINESS APPLICATIONS OF NEURAL NETWORKS: PROBLEMS AND OPPORTUNITIES

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