The emerging Semantic Web focuses on bringing knowledge representationlike capabilities to Web applications in a Web-friendly way. The ability to put knowledge on the Web, share it, and reuse it through standard Web mechanisms provides new and interesting challenges to artificial intelligence. In this paper, I explore the similarities and differences between the Semantic Web and traditional AI knowledge representation systems, and see if I can validate the analogy "The Semantic Web is to KR as the Web is to hypertext."

L. Frank Baum, who gave us the Wizard he history of AI is a history of fantasies, promise. Ever since Homer wrote of mechanical of Oz. Baum wrote of several robots and described "tripods" waiting on the gods at dinner, the mechanical man Tiktok in 1907, for imagined mechanical assistants have been example, as an "Extra-Responsive, Thought-a part of our culture. However, only in the last Creating, Perfect-Talking Mechanical Man … half century have we, the AI community, been Thinks, Speaks, Acts, and Does Everything but able to build experimental machines that test Live." These writers have inspired many AI researchers.

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.

AI and AAAI are coevolving. As AI matures, its focus is shifting from inward-looking to outwardlooking. Some of the new concerns of the field are social awareness, networking, cross-disciplinarity, globalization, and open access. AAAI must reflect and support those concerns. AI is now a mature discipline.

For many years, the very idea of artificial intelligence has been provocative and exciting. However, with a continually increasing focus on specialized subareas and somewhat narrow technical problems (both of which are inevitable and in many ways healthy), we may be torpedoing our core research agenda: the creation of a true synthetic intelligence. I reflect briefly on the essential interdependencies of the components of intelligence, the important roles of architecture and integration, and the need to get back to thinking about the very idea of AI. AAAI's role in the field has evolved over the years, but after a quarter-century as an organization, and a half-century as a field, it seems like AAAI is in an ideal situation to bring AI as a whole back to its roots. In 1985, the philosopher John Haugeland wrote a thoughtprovoking treatise on AI that he titled Artificial Intelligence: The Very Idea.

The most important challenge facing AI today is enabling components to interact in larger scale systems, where modules built with multiple alternative methodologies can be incorporated into robust applications.

If it is true that good problems produce good science, then it will be worthwhile to identify good problems, and even more worthwhile to discover the attributes that make them good problems. This discovery process is necessarily empirical, so we examine several challenge problems, beginning with Turing's famous test, and more than a dozen attributes that challenge problems might have. We are led to a contrast between research strategies -- the successful "divide and conquer" strategy and the promising but largely untested "developmental" strategy -- and we conclude that good challenge problems encourage the latter strategy.

AAAI has seen great ups and downs, based largely on the perceived success of AI in business applications. Great early success allowed AAAI to weather the "AI winter" to enjoy the current "thaw." Other challenges to AAAI have resulted from its success in spinning out international conferences, thereby effectively removing several key AI areas from the AAAI National Conference. AAAI leadership continues to look for ways to deal with these challenges. AAI began life intending to be completely societies (such as ACM).

The 1993-95 period presented various "identity challenges" to the field of AI and to AAAI as a leading scientific society for the field. The euphoric days of the mid-1980s AI boom were over, various expectations of those times had not been met, and there was continuing concern about an AI "winter." The major challenge of these years was to chart a path for AI, designed and endorsed by the broadest spectrum of AI researchers, that built on past progress, explained AI's capacity for addressing fundamentally important intellectual problems and realistically predicted its potential to contribute to technological challenges of the coming decade. This reflection piece considers these challenges and the ways in which AAAI helped the field to move forward. Adolescence, the twenties, and the forties each bring particular "developmental" challenges to people, and, though surely coincidentally, elements of those life stages seem also to characterize the period of my presidency.

The long-term goal of AI is human-level AI. This is still not directly definable, although we still know of human abilities that even the the best present programs on the fastest computers have not been able to emulate, such as playing master-level go and learning science from the Internet. Basic researchers in AI should measure their work as to the extent to which it advances this goal.