The Workshop on Future Directions in NLP was held at Bolt Beranek and Newman, Inc. (BBN), in Cambridge, Massachusetts, from 29 November to 1 December 1989. The workshop was organized and hosted by Madeleine Bates and Ralph Weischedel of the BBN Speech and Natural Language Department and sponsored by BBN's Science Development Program.
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 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.
If classical planners are ever to automatically plan the actions of the smart machines, particularly robots for the automatic assembly of industrial objects, then they will have to know much more about geometry and topology as well as sensing. Consider that the simple act of changing an object's grasp -- the change might be necessitated by the nature of some assembly goal -- involves the interaction of the geometries of the grasping device and the object if the change is to occur without a collision between the device and the object. Of course, one could ask, Could geometric considerations be divorced from the highly developed symbolic-level planning? That is, could we first synthesize a symbolic plan and then plug in the geometry for the execution of the actions? Experience has shown the answer to, unfortunately, be a big no.