AI Magazine Volume 10 Number 4 (1989) ( AAAI) generated some controversy. Relative to the discussion of the role of strong syllables in lexical segmentation, Gerry Altmann of CSTR reviewed some of the evidence based on computational studies of large The 1988 Workshop on Cognitive bone. Evidence from human studies computerized lexicons (20,000 Models of Speech Processing was suggested that the spurious word is words). This evidence suggested that held at Park Hotel Fiorelle, Sperlonga, activated, even though in principle it a stressed syllable conveys more Italy, on 16-20 May 1988. Twentyfive would be possible to prevent this activation information about the identity of the participants gathered in this by only accessing the lexicon at word in which it occurs than an small coastal village, where the the offset of some previously found unstressed syllable.

The AI lab at the Courant Institute at New York University (NYU) is pursuing many different areas of artificial intelligence (AI), including natural language processing, vision, common sense reasoning, information structuring, learning, and expert systems. Other groups in the Computer Science Department are studying such AI-related areas as text analysis, parallel Lisp and Prolog, robotics, low-level vision, and evidence theory.

The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.

AI Magazine, 6(4):65-85

Why People Think Computers Can't MOST PEOPLE ARE CONVINCED computers cannot think. I think those specialists are too used t,o That is, really think. This leads them to believe that there can't "thinking." This essay explains why they are wrong . Can Computers Do Only What They're Told? concerned with huge numerical computations: that's why the things were called computers. Most people think that "creativity" Yet even then a fringe of people envisioned what's now If so, then no computer can create-since, clearly, they realized that computers could manipulate not only numbers anything machines can do can be explained. To see what's wrong with that, we'd better turn aside able to go beyond arithmetic, perhaps to imitate the informa-from those outstanding works our cuhure views as very best Con processes that happen inside minds.

Rep. No. TM-86, Laboratory for Computer Science, Massachusetts Institute of Technology.