I survey four viewpoints about what AI is. I describe a program exhibiting AI as one that can change as a result of interactions with the user. Such a program would have to process hundreds or thousands of examples as opposed to a handful. Because AI is a machine's attempt to explain the behavior of the (human) system it is trying to model, the ability of a program design to scale up is critical. Researchers need to face the complexities of scaling up to programs that actually serve a purpose. The move from toy domains into concrete ones has three big consequences for the development of AI. First, it will force software designers to face the idiosyncrasies of its users. Second, it will act as an important reality check between the language of the machine, the software, and the user. Third, the scaled-up programs will become templates for future work. For a variety of reasons, some of which I discuss one of the following four things: (1) AI means in this article, the newly formed Institute magic bullets, (2) AI means inference engines, for the Learning Sciences has been concentrating (3) AI means getting a machine to do something its efforts on building high-quality you didn't think a machine could do educational software for use in business and (the "gee whiz" view), and (4) AI means elementary and secondary schools. In the two having a machine learn.

AI research are discussed This article is individuals outside the field. Even Of course, linguists have never an introduction to Scientific DataLink's AI'S practitioners are somewhat confused thought of their field as having much microfiche publication of the Yale AI about what AI really is. to do with AI at all. However, as technical reports In this context, examples Is AI mathematics? A great many money for linguistics has begun to of research conducted at the Yale AI researchers believe strongly that disappear and money for AI has Artificial Intelligence Project relating to knowledge representations used in AI increased, it has become increasingly each of the research problems is presented. Suddenly, theories of know how the answer will turn out language that were never considered even before they have figured out by their creators to be process models what exactly the questions are. They at all are now proposed as AI models.

In this article I wish to address some of the problems that confront AI. I am giving, no doubt, what amounts to no more than one man's opinion. It is my hope, in expressing these opinions, that the issues begin to be discussed in some public forum. I will attempt to start this debate by answering some questions about the field that have been posed to me over time. In some cases, what follows are questions that I have simply posed to myself.

Book-length version of the central ideas presented in Roger C. Schank and Robert P. Abelson, "SCRIPTS, PLANS, AND KNOWLEDGE" http://oak.conncoll.edu/parker/com316/progassign/scripts.pdf Hillsdale, NJ: Lawrence Erlbaum Associates

Stanford AI Memo 83 or Computer Science Technical Note 130, Computer Science Department, Stanford University, Stanford, CA, 1969