hakey the Robot, conceived fifty years ago, was a seminal contribution to AI. Shakey perceived its world, planned how to achieve a goal, and acted to carry out that plan. This was revolutionary. At the Twenty-Ninth AAAI Conference on Artificial Intelligence, attendees gathered to celebrate Shakey, and to gain insights into how the AI revolution moves ahead. The celebration included a panel, chaired by Benjamin Kuipers and featuring AI pioneers Ed Feigenbaum, Peter Hart, and Nils Nilsson. This article includes written versions of the contributions of those panelists.

In 1983, I gave the AAAI president's address titled "Artificial Intelligence Prepares for 2001." An article, based on that talk, was published soon after in "AI Magazine. In this article, I retract or modify some of the points made in that piece and reaffirm others. Specifically, I now acknowledge the many important facets of AI research beyond high-level reasoning but maintain my view about the importance of integrated AI systems, such as mobile robots.

I claim that achieving real human-level artificial intelligence would necessarily imply that most of the tasks that humans perform for pay could be automated. Rather than work toward this goal of automation by building special-purpose systems, I argue for the development of general-purpose, educable systems that can learn and be taught to perform any of the thousands of jobs that humans can perform. Joining others who have made similar proposals, I advocate beginning with a system that has minimal, although extensive, built-in capabilities. These would have to include the ability to improve through learning along with many other abilities.

Those of us engaged in artificial intelligence research have the historically unique privilege of asking and answering the most profound scientific and engineering questions that people have ever set for themselves--questions about the nature of those processes that separate us humans from the rest of the universe--namely intelligence, reason, perception, self-awareness, and language. It is clear--to most of us in AI, at least--that our field, perhaps together with molecular genetics, will be society's predominant scientific endeavor for the rest of this century and well into the next...

I claim that achieving real human-level artificial intelligence would necessarily imply that most of the tasks that humans perform for pay could be automated. Rather than work toward this goal of automation by building special-purpose systems, I argue for the development of general-purpose, educable systems that can learn and be taught to perform any of the thousands of jobs that humans can perform. Joining others who have made similar proposals, I advocate beginning with a system that has minimal, although extensive, built-in capabilities. These would have to include the ability to improve through learning along with many other abilities.

In the span of a few months, the AI community lost four important figures. The fall of 2002 marked the passing of Ray Reiter, for whom a memorial article by Jack Minker appears in this issue. As the issue was going to press, AI lost Saul Amarel, Norm Nielsen, and Charles Rosen. This section of AI Magazine commemorates these friends, leaders, and AI pioneers. We thank Tom Mitchell and Casimir Kulikowski for their memorial to Saul Amarel, Ray Perrault for his remembrance of Norm Nielsen, and Peter Hart and Nils Nilsson for their tribute to Charles Rosen. The AI community mourns our lost colleagues and gratefully remembers their contributions, which meant so much to so many and to the advancement of artificial intelligence as a whole.

In its early stages, the field of AI had as its main goal the invention of computer programs having the general problem-solving abilities of humans. Along the way, a major shift of emphasis developed from general-purpose programs toward performance programs, ones whose competence was highly specialized and limited to particular areas of expertise. In this article, I claim that AI is now at the beginning of another transition, one that will reinvigorate efforts to build programs of general, humanlike competence. These programs will use specialized performance programs as tools, much like humans do.

In its early stages, the field of AI had as its main goal the invention of computer programs having the general problem-solving abilities of humans. Along the way, a major shift of emphasis developed from general-purpose programs toward performance programs, ones whose competence was highly specialized and limited to particular areas of expertise. In this article, I claim that AI is now at the beginning of another transition, one that will reinvigorate efforts to build programs of general, humanlike competence. These programs will use specialized performance programs as tools, much like humans do.

Response to Drew McDermott's Review of Logical Foundations of Artificial Intelligence

McDermott makes some valid points in his review ... It's too bad that he chose to embed the helpful comments in the context of his by-now-tiresome doubts about the value of logic in AI.