General Electric is engaged in a broad range of research and development activities in artificial intelligence, with the dual objectives of improving the productivity of its internal operations and of enhancing future products and services in its aerospace, industrial, aircraft engine, commercial, and service sectors. Many of the applications projected for AI within GE will require significant advances in the state of the art in advanced inference, formal logic, and architectures for real-time systems. New software tools for creating expert systems are needed to expedite the construction of knowledge bases. Further, new application domains such as computer -aided design (CAD), computer- aided manufacturing (CAM), and image understanding based on formal logic require novel concepts in knowledge representation and inference beyond the capabilities of current production rule systems. Fundamental research in artificial intelligence is concentrated at Corporate Research and Development (CR&D), with advanced development and applications pursued in parallel efforts by operating departments. The fundamental research and advanced applications activities are strongly coupled, providing research teams with opportunities for field evaluations of new concepts and systems. This article summarizes current research projects at CR&D and gives an overview of applications within the company.

At the start of a project looking into the development of an expert system, the knowledge engineering project team must investigate one or several possible expert system domains. To evaluate the potential of possible application domains, it has proved very useful to have a set of desired attributes for good expert domain. The attribute set was developed as part of a major expert system development project at GTE Laboratories. It was used recurrently (and modified and expanded continually) throughout an extensive application domain evaluation and selection process.

It is interesting to note that there is no agreed upon definition of artificial intelligence. Why is this interesting? Because government agencies ask for it, software shops claim to provide it, popular magazines and newspapers publish articles about it, dreamers base their fantasies on it, and pragmatists criticize and denounce it. Such a state of affairs has persisted since Newell, Simon and Shaw wrote their first chess program and proclaimed that in a few years, a computer would be the world champion. Not knowing exactly what we are talking about or expecting is typical of a new field; for example, witness the chaos that centered around program verification of security related aspects of systems a few years ago. The details are too grim to recount in mixed company. However, artificial intelligence has been around for 30 years, so one might wonder why our wheels are still spinning. Below, an attempt is made to answer this question and show why, in a serious sense, artificial intelligence can never demonstrate an outright success within its own discipline. In addition, we will see why the old bromide that "as soon as we understand how to solve a problem, it's no longer artificial intelligence" is necessarily true.

By-Laws of Association for the Advancement of Artificial Intelligence

A pattern recognition algorithm is described that learns a transition net grammar from positive examples. Two sets of examples -- one in English and one in Chinese -- are presented. It is hoped that language learning will reduce the knowledge acquisition effort for expert systems and make the natural language interface to database systems more transportable. The algorithm presented makes a step in that direction by providing a robust parser and reducing special interaction for introduction of new words and terms.

Review of A Practical Guide to Designing Expert Systems

Letters to the Editor

This article describes one person's experience in coming from an academic environment to work at Digital Equipment Corporation. The author feels his own experience has paralleled the transfer of AI technology from academia to industry, where AI researchers must live up to very different expectations, but also enjoy very different rewards. This article covers the historical background of DEC's involvement with AI, the development of R1- known internally and henceforth in this article as XCON-and DEC's experiences with it and its consequences. Finally, the article offers advice for other corporations planning to develop their own capabilities in AI.

Jim Kornell, Robert Park, Christopher Dungan, Joop Schopman, David Drager, Nils J. Nilsson, Marty Kalin, John Gavin, Bernard Meltzer, Robert Salmansohn, Keith McCammon, Loren Martindale Abstract Subjects include AI's impact on employment, the AAAI conference, a response to McCarthy's Presidential Message, AI going public, and computerless expert systems. Subjects include AI's impact on employment, the AAAI conference, a response to McCarthy's Presidential Message, AI going public, and computerless expert systems.

This article describes one person's experience in coming from an academic environment to work at Digital Equipment Corporation. The author feels his own experience has paralleled the transfer of AI technology from academia to industry, where AI researchers must live up to very different expectations, but also enjoy very different rewards. This article covers the historical background of DEC's involvement with AI, the development of R1- known internally and henceforth in this article as XCON-and DEC's experiences with it and its consequences. Finally, the article offers advice for other corporations planning to develop their own capabilities in AI.