Technical report TR-47, Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology.

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.

Subjects include AI's impact on employment, the AAAI conference, a response to McCarthy's Presidential Message, AI going public, and computerless expert systems.

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.

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. Since log interpretation is expert skill, the emergence of expert systems technology prompted Schlumberger's initial interest in Artificial Intelligence. Our first full- scale attempt at a commercial-quality expert system was the Dipmeter Advisor. Following these initial efforts, Schlumberger has expanded its Artificial Intelligence activities, and is now engaged in both basic and applied research in a wide variety of areas.

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).

I rise now to speak with the assumption that all of you know very well what I am going to say. I am the humanist here, the professor of English. We humanists, when asked to speak on questions of science and technology, are notorious for offering an embarrassed and ignorant respect toward those matters, a respect, however, which can all too quickly degenerate into insolent condescension. Face to face with the reality of computer technology, say, or with "artificial intelligence," we humanists are the kind of soreheads who compulsively point out that human beings aren't machines, that computers will never possess the uniquely human powers of intuition and common sense, and that we smelly, hairy and other wise organic people are simply more interesting, more clever and more mysterious than electricpowered, binary-formatted, digital computers with their tidy little green phosphorescent screens. So now you know what I'm going to say, or at least part of it. For, to be even more candid with you this morning, I want to go on to say that while I do believe many of the things that we humanists are reputed to believe, I also happen to believe that humanists are wrong, and very foolish, to fear computers. But they are also wrong, to underestimate the power of those computers and their important utility in the world that is now ours and the word that we will soon be turning over to our descendents.

This article surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding. The emphasis of this article is on conceptual representation of objects based on the semantic interpretation of natural language input. Six programs serve as case studies for guiding the course of the article. Within the framework of describing each of these programs, several other programs, ideas, and theories that are relevant to the program in focus are presented.

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. This article examines the structural similarities and dissimilarities in the two subjects and briefly summarizes the history of the commercialization of genetic engineering. It then proposes some lessons that would benefit the artificial intelligence industry.

Probability concepts for ruled-based expert systems are developed that are compatible with probability used in data fusion of imprecise information. Procedures for treating probabilistic evidence are presented, which include the effects of statistical dependence. Confidence limits are defined as being proportional to root-mean-square errors in estimates, and a method is outlined that allows the confidence limits in the probability estimate of the hypothesis to be expressed in terms of the confidence limits in the estimate of the evidence. Procedures are outlined for weighting and combining multiple reports that pertain to the same item of evidence. The illustrative examples apply to tactical data fusion, but the same probability procedures can be applied to other expert systems.