Artificial intelligence (AI) will have profound societal effects. It promises potential benefits (and may also pose risks) in education, defense, business, law and science. In this article we explore how AI is likely to affect employment and the distribution of income. We argue that AI will indeed reduce drastically the need of human toil. We also note that some people fear the automation of work by machines and the resulting of unemployment. Yet, since the majority of us probably would rather use our time for activities other than our present jobs, we ought thus to greet the work-eliminating consequences of AI enthusiastically. The paper discusses two reasons, one economic and one psychological, for this paradoxical apprehension. We conclude with discussion of problems of moving toward the kind of economy that will be enabled by developments in AI.

The Steamer project is a research effort concerned with exploring the use of AI software and hardware technologies in the implementation of intelligent computer-based training systems. While the project addressed a host of research issues ranging from how people understand complex dynamic systems to the use of intelligent graphical interfaces, it is focused around the construction of a system to assist in propulsion engineering instruction. The purpose of this article is to discuss the underlying ideas which motivated us to initiate the Steamer effort, describe the current status of the project, provide a glimpse of our planned directions for the future, and discuss the implications of Steamer for AI applications in other instructional domains.

Abstract, Editors' Note: In this provocative article Doyle suggests that many of the benefits of current expert systems technology Knowledge engineers qualified to build expert systems are currently in could be achieved without computer-based implementations. Is there not an intermediary position? This revolution is very Namely, that the problems encountered by today's expert important. The views and conclusions contained manpower. The novice still botches the task, but explains in detail of knowledge engineers in the current fashion.

Over the past few years, the character of the AI community has changed. AI researchers used to be able to go about their work in peace, while the rest of the world ignored them. As the promise of partical applications of AI has slowly become reality, new players have entered the field, changing its nature forever. The quiet, intellectual community of AI researchers has been augmented by a hoard of other interested parties, including the press, the financial community, and the technology entrepreneurs. Since we cannot go back and hide in the ivory tower, we may as well take the time to explore our new environment. I invite you to join me in a guided tour of the new AI community. Let's begin by analyzing the basic motivation for commercial interest in AI.

Ellen L. Walker Linda T. Powell counter.

AI technology transfer Is the diffusion of AI research techniques into commercial products. I have been involved in this process since 1975, when the Artificial Intelligence Corporation began to develop ROBOT, the prototype of INTELLECT, a commercially viable natural language interface to data base systems which has been on the market since 1981. In this article, I will discuss AI technology transfer with particular reference to my experiences with the commercialization of INTELLECT. I will begin with the historical perspective of where the field of AI came from, where it is now, and where it is going. Next, I will describe my interpretation of the present market structure for AI products and some specific marketing perspectives. I will then briefly describe the product INTELLECT and its capabilities as an example of a state-of-the-art commercial system. Next, I will describe some of the experiences, which I think are typical, that my company has encountered in commercialize their systems.

Most work in automatic programming has focused primarily on the roles of deduction and programming knowledge. However, the role played by knowledge of the task domain seems to be at least as important, both for the usability of an automatic programming system and for the feasibility of building one which works on non-trivial problems. This perspective has evolved during the course of a variety of studies over the last several years, including detailed examination of existing software for a particular domain (quantitative interpretation of oil well logs) and the implementation of an experimental automatic programming system for that domain. The importance of domain knowledge has two important implications: a primary goal of automatic programming research should be to characterize the programming process for specific domains; and a crucial issue to be addressed in these characterizations is the interaction of domain and programming knowledge during program synthesis.

Founded in 1972 to develop and disseminate new ideas in computer science, the Information Sciences Institute (ISI) is an off-campus research center of the University of Southern California, with a combined research and support staff of over one hundred. The Institute engages in a broad set of research and application-oriented projects in the computer sciences. The Institute AI research focuses on program synthesis user interfaces, programming environments, natural language, and expert systems. AI researchers are supported by ten personal Lisp workstations, several VAXs, two TOPS-20 systems, and a magnificent view of Marina del Rey.

Artificial Intelligence Research at the University of Maryland

Most work in automatic programming has focused primarily on the roles of deduction and programming knowledge. However, the role played by knowledge of the task domain seems to be at least as important, both for the usability of an automatic programming system and for the feasibility of building one which works on non-trivial problems. This perspective has evolved during the course of a variety of studies over the last several years, including detailed examination of existing software for a particular domain (quantitative interpretation of oil well logs) and the implementation of an experimental automatic programming system for that domain. The importance of domain knowledge has two important implications: a primary goal of automatic programming research should be to characterize the programming process for specific domains; and a crucial issue to be addressed in these characterizations is the interaction of domain and programming knowledge during program synthesis.