This paper reviews the current state of the art in natural language access to databases. This has been a long-standing area of work in natural language processing. But though some commercial systems are now available, providing front ends has proved much harder than was expected, and the necessary limitations on front ends have to be recognized. The paper discusses the issues, both general to language and task-specific, involved in front end design, and the way these have been addressed, concentrating on the work of the last decade. The focus is on the central process of translating a natural language question into a database query, but other supporting functions are also covered. The points are illustrated by the use of a single example application. The paper concludes with an evaluation of the current state, indicating that future progress will depend on the one hand on general advances in natural language processing, and on the other on expanding the capabilities of traditional databases.

Expert systems technology has been around for a long time, becoming increasingly easy to use, inexpensive and reliable in recent years. It would seem to provide an ideal vehicle for the dissemination of expertise in developing countries, particularly in the field of medicine, which was the focus of much early work in diagnostic systems. Despite the apparent match of a real problem and a credible solution, however, remarkably few AI systems for medicine in developing countries have been researched, designed and implemented. This paper addresses why this might be the case, reviews some of the extant systems and explores some of the design issues. Particular emphasis is placed on the question of “Appropriate Technology”. Various criteria for Appropriate Technology are explored, and an optimal set used to guide principles of design. It is argued that medical AI systems can satisfy these criteria, provided that sufficient care is taken in their design for the country of application.

It is a common Establishment practice in the UK to ask an expert, recognized for his/her sound views, to give a view on controversial matters of public interest. There are recent examples in such varied fields as accounting standards, export credits, and microgravity. Administrations—for all the current fashion for “built-in evaluation”—have no time or inclination to take a look backwards at these autographs. In this paper the author, who was a civil servant, and has worked for ten years as a consultant, including two years in the European Commission, considers, with the help of hindsight, the Lighthill Report of 1972 on AI.

This review implies making judgements about the progress of AI which cannot help being contentious. First, there will be disagreements about judgements, or even assertions—the author cannot claim authority over the whole field, unless as a voyeur; there may be important developments he has failed to take account of. Second, there will be arguments that Lighthill was to an extent self-fulfilling—that is, that there would have been more advance if some aspects of AI had not been checked at his advice. Third, it will be said that it takes time to work up a new computational concept—or any other new thing.

However, whatever the reservations, the main conclusion put forward is that there was an element of over-optimism about AI in general which Lighthill had reason to castigate. In directing his comments to the utility of AI in specific fields Lighthill got some things wrong, but, on balance—which is not easy to strike—probably more things right than wrong.

Extrapolating to the present day the writer questions whether, in the UK after Alvey, and not least in Japan, resources are not being expended on AI objectives which are intellectually stimulating but remain too far from the application's sharp end; and therefore whether there is still not too much “jam tomorrow” about AI. Whether or not this questioning is judged to be wrongheaded, perhaps it will prove useful to have stirred discussion on the subject.