Pitman.

Review of A Practical Guide to Designing Expert Systems

Letters to the Editor

The AI field needs major breakthroughs in its thinking to achieve continuous, sensory-gathered, machine learning from the environment on unlimited subjects. The way motivate such dramatic progress is to articulate and endorse research goals for machine behavior so ambitious that limited-domain, problemsolving knowledge representation methods are disqualified at the outset, thus forcing ourselves to produce valuable new "thoughtware." After exploring why the tendency to associate intelligence with problem-solving may be a mental roadblock to further progress in AI science, some preliminary thinking tools are introduced more suitable for sensory learning machine research. These include lifelong sensorimotor data streams, representation as a symbolic recording process, knowledge transmission, and the totality of knowledge.

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.

Schlumberger is a large, multinational corporation concerned primarily with the measurement, collection, and interpretation of data. For the past fifty years, most of the activities have been related to hydrocarbon exploration. The efficient location and production of hydrocarbons from an underground formation requires a great deal of knowledge about the formation, ranging in scale from the size and shape of the rock's pore spaces to the size and shape of the entire reservoir. Schlumberger provides its clients with two types of information: measurements, called logs, of the petrophysical properties of the rock around the borehole, such as its electrical, acoustical, and radioactive characteristics; and in terpretations of these logs in terms of geophysical properties such as porosity and mineral composition.

Artificial Intelligence is rapidly leaving its academic home and moving into the marketplace. There are few precedents for an arcane academic subject becoming commercialized so rapidly. But, genetic engineering, which recently burst forth from academia to become the foundation for the hot new biotechnology industry, provides useful insights into the rites of passage awaiting the commercialization of artificial intelligence. It then proposes some lessons that would benefit the artificial intelligence industry.

Canadians have made many contributions to artificial intelligence over the years. This article presents a summary of current research in artificial intelligence in Canada and acquaints readers with the Canadian organization for artificial intelligence -- the Canadian Society for the Computational Studies of Intelligence / Societe Canadienne pour l' Etude de l'Intelligence par Ordinateur (CSCSI/ SCEIO).

The initial results from a few AI research projects in statistics have been quite interesting to statisticians: Feasibility demonstration systems have been built at Stanford University, AT-T bell Laboratories, and the University of Edinburgh. Several more design studies have been completed. A conference devoted to expert systems in statistics was sponsored by the Royal Statistical Society. On the other hand, statistic as a domain may be of particular interest to AI researchers, for it offers both tasks well suited to current AI capabilities and tasks requiring development of new AI techniques.