Computer Scaence Department Yale University THE COGNITION AND PROGRAMMING PROJECT (CAPP) in the Computer Science Department at Yale University is an interdisciplinary group exploring a wide range of issues in programming. 'This project is currently being funded by NSF RISE, under grant number SED-81-12403 'This project is currently being funded by NSF IST, under grant number IST-81-14840 We have also shown that when the language construct, agrees with people's natural problem solving strategies they can learn to use such constructs effectively. The implication is that language dcsigners should be more sensitive to cognitive capabilities which people bring to programming and that computing educators should be aware of the systematic misconceptions which arise due to cognztively poor programming language constructs. Using our theory of programming plans, we are developing measures of program complexity that are based on the underlying mental effort needed to understand programs. This approach is in contrast to typical measures of program complexity which are sensitive to only surface features of programs.

Automated Problem Solving Group Jet Propulsion Laboratory 4800 Oak Grove Dr. Pasadena, California 91109 AI research at JPL started in 1972 when design and construction of an experimental "Mars Rover" began. Early in that effort, it was recognized that rover planning capabilities were inadequate. Research in planning was begun in 1975, and work on a succession of AI expert systems of steadily increasing power has continued to the present. Within the group, we have concentrated our efforts on expert systems, although work on vision and robotics has continued in a separate organization, with which we have maintained informal contacts. The thrust of our work has been to build expert systems that can be applied in a real-world environment, and to actually put our systems into such environments, taking a consultative responsibility for meeting user requirements.

In terms of basic research, our current focus is the development, of broadly applicable techniques for description and matching of structure in sensory data. Such techniques appear to lmderlie virtually every aspect of early and intermediate vision, such as edge and region finding, perceptual organization and grouping, and the recovery of 3-D shape from contour, texture, stereo and motion They appear to be equally important in other sensory domains, such as audition (e g, for describing the structure in spectrograms.) In particular, we are dealing with the problem of grey-level inspection, and are constructing a vision workbench to allow rapid experimentation with alternative techniques Finally, WC are examining a variety of special-purpose architectures for image processing. These range from a SUN (MC68000-based) workstation, augment,cd with high-speed pipelined VLSI components, to a massively parallel architerture involving a thousand processors and a novel interconnection network. Knowledge Representation Contact: Ronald J. Brachman Having had experience with knowledge representation syst,ems designed to support "common sense" reasoning, we are developing and implementing a new framework for representation and reasoning in arcas requiring "expertise."

Conspicuously absent from the 5th Generation Computer Project's proclaimed goals is one vitally important in a 1990's knowledge-intensive society.....the ability to help people tame mountains of video-based information. A decade from now, the nation will be crisscrossed with fiberoptic bundles capable of simultaneously carrying thousands of hiresolution video conversations, and solid-state video cameras will be as abundant as microphone pickup devices are today. In short, the voice-telephone and printed-page information networks over which we communicate will be joined by 2-way, super-narrowcast video, where each knowledge worker both receives product from myriad sources ad reshapes and originates his own unique product. The main activities interactive video will support are the same ones that will occupy people a decade from nowlearning and teaching. Already, one can "walk through" homes for sale thousands of miles away, learn how to assemble, operate and fix complex machinery, drive around the streets of Aspen, Colorado, and learn facial communication skills using this powerful medium.

Artificial Intelligence Department, Computer Resenrch Laboratory, Tektronix, 1, Post Office Box 500, Beaverton, Oregon 97077 Getting started on a new knowledge engineering project is a difficult and challenging task, even for those who have done it before. For those who haven't, the task can often prove impossible. One reason is that the requirementsoriented methods and intuitions learned in the development of other types of software do not carry over well to the knowledge engineering task. Another reason is that methodologies for developing expert systems by extracting, representing, and manipulating an expert's knowledge have been slow in coming. At Tektronix, we have been using a step-by-step approach to prototyping expert systems for over two years now.

As evidenced by the articles in this special issue, transfer learning has come a long way in the past five or so years, partially because of DARPA's Transfer Learning program, which sponsored much of the work reported in this issue. There is a Transfer Learning Toolkit for Matlab available on the web. Transfer learning has developed techniques for classification, regression, and clustering (as summarized in Pan and Yang's 2009 survey) and for complex interactive tasks that are often best addressed by reinforcement learning techniques. However, there is a more practical and more feasible goal for transfer learning against which progress is being made. An engineering-oriented goal of artificial intelligence that could be enabled by transfer learning is the ability to construct a large number of diverse applications not from scratch, but by taking advantage of knowledge already acquired and formally represented for other purposes.

In this article I argue that achieving human-level AI is equivalent to learning how to create sufficiently smart software social organisms. This implies that no single test will be sufficient to measure progress. Instead, evaluations should be organized around showing increasing abilities to participate in our culture, as apprentices. This provides multiple dimensions within which progress can be measured, including how well different interaction modalities can be used, what range of domains can be tackled, what human-normed levels of knowledge they are able to acquire, as well as others. I begin by motivating the idea of software social organisms, drawing on ideas from other areas of cognitive science, and provide an analysis of the substrate capabilities that are needed in social organisms in terms closer to what is needed for computational modeling.

The following article briefly describes the developments of tools and knowledge in human history and states that these two phenomena coexist only in intelligent tools. Moreover, since human beings were unable to produce an intelligent tool capable of outperforming man' as a tool, the technological basis of slavery continued to persist throughout history. The article then examines the current achievements of computer technology in producing intelligent tools It argues that the production of intelligent tools makes it possible to bypass the social and natural limztatzons of all past intelligent tools. Once these tools outperform humans as intelligent tools, man will no longer be indispensable as a production tool. This eradication may start a new civilization by effecting higher human intelligence, more economic wealth and greater socio-political freedom in man's future society.

Through this collection of programmatic statements from key figures in the field, we chart the progress of AI and survey current and future directions for AI research and the AI community. You're on the lookout for fanciful prognostications about technology: Someday computers will fit in a suitcase and have a whole megabyte of memory. And you're wary of lurid Hollywood visions of "the day the robots come": A spiderlike machine pins you to the wall and targets a point four inches behind your eyes with long and disturbing spikes. Your last memory before it uploads you is of it asking, with some alien but unmistakable existential agony, what does this all mean? We are not here to offer a collection of fiction.

The goal of AI as a whole is to produce machines that. Hy "act intelligently," we mean to cover a broad range of activities, only some of which are directly humanlike; our machines may eventually he far better at certain intelligence tasks than people are (much as a calculator does ' Authors of t,hc rrpol t. arc as follows: David Waltz, Chairman, IJniversity of Illinois, LJrbana; Michael Genesereth, Stanford IJniversit.y; ' Recausc we have rosearchers primarily from thcsr two areas, in turn because these two areas wci-c specifically select,rd for conrent. Al research are i clutlcd t.o some AI would never produce useful results, and (2) t,he applications themselves have high int,rinsic value. There are in addition a number of companies t.hat, plan t,o introduce or use internally AI products in the near future, including However, the significant, progress cxperiencccl in the last decade demonstrates the feasibilit,y of dealing with natural language in restricted contexts, employing today's coniput,ers. Each of these goals has import arrcc in isolation; pursuing them simultaneously enables progress on each t,o support, progress towards the other.