In the past fifteen years artificial intelligence has changed from being the preoccupation of a handful of scientists to a thriving enterprise that has captured the imagination of world leaders and ordinary citizens alike. While corporate and government officials organize new projects whose potential impact is widespread, to date few people have been more affected by the transition than those already in the field. I review here some aspects of this transition, and pose some issues that it raises for AI researchers, developers, and leaders.

The state of the art in any science includes the criteria for evaluating research. Like every other aspect of the science, it An example is the alpha-beta heuristic for game playing. The criteria for evaluating AI research Humans use it, but it wasn't identified by the writers of the are not in very good shape. I had intended to produce four first chess programs. It doesn't constitute a game playing presidential messages during my term but have managed only program, but it seems clearly necessary, because without two, because this one has proved so difficult to write.

At Vanderbilt University we are exploring the use of expert systems in a broad range of application areas. Programming is in Franz Lisp on a VAX 11/790, UCI LISP on a DEC-10, and IQ LISP on an IBM XT. Currently, personnel from four schools in the University are participating. Listed are brief descriptions of current projects.

Knowledge engineers qualified to build expert systems are currently in short supply. The production of useful and trustworthy expert systems can be significantly increased by pursing the idea of articulate apprenticeship independent of computer implementations. Making theoretical progress in artificial intelligence should also help.

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.

The prototype system is written in Prolog, a language that has proved to be very powerful and easy to use for problem /rule development. The resulting prototype system (called EXPRS-Expert Prolog System) uses English-like rule constructs of Prolog code. This approach enables the system to generate answers automatically to "why" a ruled fired, and "how" that rule fired. In addition, a rule clause construct is provided which allows direct access to Prolog code routines.

During the past year, a prototype expert system for tactical data fusion has been under development,. This computer program combines various messages concerning electronic intelligence (ELINT) to aid in decision making concerning enemy actions and intentions. The prototype system is written in Prolog, a language that has proved to be very powerful and easy to use for problem /rule development. The resulting prototype system (called EXPRS-Expert Prolog System) uses English-like rule constructs of Prolog code. This approach enables the system to generate answers automatically to "why" a ruled fired, and "how" that rule fired. In addition, a rule clause construct is provided which allows direct access to Prolog code routines. This paper describes the structure of the rules used and provides typical user interactions.

At Vanderbilt University we are exploring the use of expert systems in a broad range of application areas. Programming is in Franz Lisp on a VAX 11/790, UCI LISP on a DEC-10, and IQ LISP on an IBM XT. Currently, personnel from four schools in the University are participating. Listed are brief descriptions of current projects.

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.