This article contains an overview of recent and ongoing projects at Boeing's Huntsville Advanced Computing Group (ACG). In addition, it contains an overview of some of the work being conducted by Boeing's Advanced Civil Space Systems Group. One aspect of ACG's charter is to support the efforts of other groups at Boeing. Thus, AI is not considered a stand-alone field but, instead, is considered an area that can be used to find both long- and short-term solutions for Boeing and its customers. All the projects listed here represent a team effort on the part of both ACG researchers and members of other Boeing organizations.

The first Robotics Exhibition and Competition sponsored by the Association for the Advancement of Artificial Intelligence was held in San Jose, California, on 14-16 July 1992 in conjunction with the Tenth National Conference on AI. This article describes the history behind the competition, the preparations leading to the competition, the threedays during which 12 teams competed in the three events making up the competition, and the prospects for other such competitions in the future.

The system has reduced time The system, developed by Stereo-spent by store personnel on the telephone seeking answers to point-of-sale Graphics (San Rafael, Calif.), uses a technical problems, and it allows the help desk analysts to handle a wider range video projector equipped with a of responsibilities for the company. Viewers wear passive eye Swedish stock exchange, has developed an intelligent system to advise on how wear which allows each eye to view to deal in stocks and shares. The company's "hit rate" of dealing correctly with the appropriate image, thereby providing stocks has reportedly increased from 60% to 90%. IntelliCorp Inc. (Mountain View, Calif.), an expert system vendor, and James Inference Corp. (El Segundo, Calif.), Martin & Co. (Reston, Va.), a computer-aided software engineering (CASE) consulting a supplier of expert system development group, have launched a field test program for an object-oriented information tools, has teamed up with IDS engineering environment. The new product, called Object Management Financial Services (Minneapolis, Workbench, directly supports rapid application development, and will either Minn.) to jointly develop Macintosh generate a system immediately or draw information from currently available versions of Inference products.

In VISION-21 Symposium. NASA Lewis Research Center and the Ohio Aerospace Institute.

Computational Linguistics, 19 (2), 313-30.

The field of AI is directed at the fundamental problem of how the mind works; its approach, among other things, is to try to simulate its working -- in bits and pieces. History shows us that mankind has been trying to do this for certainly hundreds of years, but the blooming of current computer technology has sparked an explosion in the research we can now do. The center of AI is the wonderful capacity we call learning, which the field is paying increasing attention to. Learning is difficult and easy, complicated and simple, and most research doesn't look at many aspects of its complexity. However, we in the AI field are starting. Let us now celebrate the efforts of our forebears and rejoice in our own efforts, so that our successors can thrive in their research. This article is the substance, edited and adapted, of the keynote address given at the 1992 annual meeting of the Association for the Advancement of Artificial Intelligence on 14 July in San Jose, California. AI Magazine 14(2): 36-48.

In AAAI-93, pp. 811–816.

Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI93), American Association for Artificial Intelligence, Washington, DC, July 1993, pp. 392-397.

Kai-Yeung Siu Dept. of Electrical & Computer Engineering University of California, Irvine Irvine, CA 92717 Jehoshua Bruck IBM Research Division Almaden Research Center San Jose, CA 95120-6099 Abstract An important issue in neural computation is the dynamic range of weights in the neural networks. Many experimental results on learning indicate that the weights in the networks can grow prohibitively large with the size of the inputs. Here we address this issue by studying the tradeoffs between the depth and the size of weights in polynomial-size networks of linear threshold elements (LTEs). We show that there is an efficient way of simulating a network of LTEs with large weights by a network of LTEs with small weights. To prove these results, we use tools from harmonic analysis of Boolean functions.

We describe in this paper a novel application of neural networks to system health monitoring of a large antenna for deep space communications. The paper outlines our approach to building a monitoring system using hybrid signal processing and neural network techniques, including autoregressive modelling, pattern recognition, and Hidden Markov models. We discuss several problems which are somewhat generic in applications of this kind - in particular we address the problem of detecting classes which were not present in the training data. Experimental results indicate that the proposed system is sufficiently reliable for practical implementation. 1 Background: The Deep Space Network The Deep Space Network (DSN) (designed and operated by the Jet Propulsion Laboratory (JPL) for the National Aeronautics and Space Administration (NASA)) is unique in terms of providing end-to-end telecommunication capabilities between earth and various interplanetary spacecraft throughout the solar system. The ground component of the DSN consists of three ground station complexes located in California, Spain and Australia, giving full 24-hour coverage for deep space communications.