This article examines the near-term impact of expert system technology on work and the organization. First, an approach is taken for forecasting the likely extent of the diffusion, or success, of the technology. Next, the case of advanced manufacturing technologies and their effects is considered. From this analysis, a framework is constructed for viewing the impact of these technologies -- and technologies in general -- as a function of the technology itself; market realities; and personal, organizational, and societal values and policy choices. Two scenarios are proposed with respect to the application of this framework to expert systems. The first concludes that expert systems will have little impact on the nature of work and the organization. The second scenario posits that expert system diffusion will be pulled by, and will be a contributing factor toward, the evolution of the lean, flexible, knowledge-intensive, postindustrial organization.

In his new book "Alternate Realities: Mathematical Models of Nature and Man (New York: John Wiley and Sons, 1989, 493 pages, $34.95), John L. Casti gives us an impressive, up-to-date look at several areas of mathematics that are being applied to the study of biological and sociological systems.

Pattern Recognition (New York: John Wiley and Sons, 1987, 144 pages, ISBN 0-471-61120-4) by Mike James is a concise survey of the practice of image recognition.

Many barriers exist today that prevent effective industrial exploitation of current and future AI research. These barriers can only be removed by people who are working at the scientific forefront in AI and know potential industrial needs. The Knowledge Processing Laboratory's research and development concentrates in the following areas: (1) natural language interfaces to knowledge-based systems and databases; (2) theoretical and experimental work on qualitative modeling and nonmonotonic reasoning for future knowledge-based systems; (3) application-specific language design, in particular, Prolog extensions; and (4) desi gn and analysis of neural networks. This article gives the reader an overview of the main topics currently being pursued in each of these areas.

Part of the Media Laboratory's Steve Benton on an advanced beammixing information. Like Richard Feynman's heritage (its origins are in the television display), (4) movies two books of memoirs and School of Architecture) is a startling of the future (putting feature-length Gleick's Chaos, this book will be receptivity to the arts, especially movies on laser disks, thereby ushering passed among workers in computer music and the visual arts, and Brand in paperback movies), (5) the visible and engineering departments as a repeatedly returns to this subject.

Stewart Brand, of Whole Earth Catalog fame, is a technology enthusiast. In 1986, he spent three months in the fantasyland of his choice, MIT's Media Laboratory (formerly the Architecture Machine Group). In his latest book, The Media Lab: Inventing the Future at MIT (Viking/ Penguin, New York, 1988, 285 pp., $10, ISBN 0-14-009701-5), he tells the world what he found.

To be considered exceptional, a textbook must satisfy three basic requirements. First, it must be authoritative, written by one with a broad range of experience in, and knowledge of, a subject. Second, it must effectively communicate to the reader, in the same manner in which a course instructor must be capable of imparting knowledge to students in a classroom. Third, it must stimulate the reader into thinking more deeply about the subject and into viewing it from fresh perspectives. In Artificial Intelligence: A Knowledge-Based Approach (Boyd and Fraser, Boston, 740 pp., $48.95), author Morris W. Firebaugh has succeeded in meeting each of these requirements.

In a letter to the editor (AI Magazine, Winter 1989), Benjamin Kuipers criticizes various points made in an earlier paper of ours (Akman and ten Hagen 1989). First, a side (nonetheless important) remark: Although Kuipers asserts that he distributes QSIM to interested researchers, our experience has been otherwise. Akman has tried twice to obtain QSIM, without success. Although Kuipers promised to deliver a copy -- QSIM was under revision at the time of Akman's request (this being as early as winter 1988) -- the program was never sent. So much for the availability of QSIM. . . . Kuipers' letter is full of sweeping generalizations that are so much against the nature of scientific enterprise. We should also add that we are disappointed to see Kuipers employing universal truths and unarguable facts such as ". . . if you build the wrong model, the predictions derived from that model are likely to be wrong" or ". . . guarantees of mathematical validity [are] necessary for any science" as his main cheval de bataille. In the following we'll point out, one by one, the weaknesses of QSIM. Our task will be easy since we shall merely reproduce, almost verbatim, Kuipers' own sentences (Kuipers 1986) and, additionally, Janowski's (1987) views. (The latter reference gives an excellent review of QSIM's disadvantages.) Then, we'll let the reader judge.

In ICML-90, pp. 216–224.

A full book, available for free in PDF form.From the preface:A major problem in artificial intelligence is to endow computers with commonsense knowledge of the world and with the ability to use that knowledge sensibly. A large body of research has studied this problem through careful analysis of typical examples of reasoning in a variety of commonsense domains. The immediate aim of this research is to develop a rich language for expressing commonsense knowledge, and inference techniques for carrying out commonsense reasoning. This book provides an introduction and a survey of this body of research. It is, to the best of my knowledge, the first book to attempt this.The book is designed to be used as a textbook for a one-semester graduate course on knowledge representation.Morgan Kaufmann