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.

Founded early in 1983, the center for the Study of Language and Information (CSLI) at Stanford University grew out of a long-standing collaboration between scientists at research laboratories in the Palo Alto area and the faculty and students of several Stanford University departments and out of a need for an institutional focus for this work on natural and computer languages. At present, CSLI has 17 senior members and about as many associate members, from SRI International, Xerox PARC, Fairchild, and the Department of Computer Science, Linguistics, and Philosophy at Stanford. Since the Center's research will overlap with the work of other researchers around the world, an important goal of CSLI is to initiate a major outreach, whereby members of CSLI both inform themselves of work done elsewhere and share their own results with others.

Letters to the Editor

Minutes of the Fourth Annual Meeting of the American Association for Artificial Intelligence

Between these dates, Charlie organized an Applied Physics Laboratory and became interested in "learning machines" and "self-organizing systems." That interest launched a group that ultimately grew into a major world center of artificial intelligence research - a center that has endured twenty-five years of boom and bust in fashion, has "graduated" over a hundred AI research professionals, and has generated ideas and programs resulting in new products and companies as well as scientific articles, books, and this particular collection itself.

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.

This article presents a dual dependency between AI and programming methodologies. AI is an important source of ideas and tools for building sophisticated support facilities which make possible certain programming methodologies. These advanced programming methodologies in turn can have profound effects upon the methodology of AI research. Finally speculations about a more direct connection between AI and partial evaluation are presented.

Artificial Intelligence Research at the University of Maryland

How to Get the Most Out of IJCAI-83 Vol IV, No 1, p 35, Winter-Spring, 1983.