By the early 1960s there were several active research groups in AI, including those at Carnegie Mellon University (CMU), the Massachusetts Institute of Technology (MIT), Stanford University, Stanford Research Institute (later SRI International), and a little later the University of Southern California Information Sciences Institute (USC-ISI). My own involvement in AI began in 1963, when I joined Stanford as a graduate student working with John McCarthy. After completing my Ph.D. in 1966, I joined the faculty at Stanford as an assistant professor and stayed there until 1969 when I left to join Allen Newell and Herb Simon at Carnegie Mellon University

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...

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.

This article reprints a section of the January 1980 "Chairman's Message" of the SIGART Newsletter (No. 69). SIGART is the Special Interest Group on Artificial Intelligence, of the Association for Computing Machinery. At the time of AAAI's formation, SIGART, with its 3,800 members, was the principal AI organization in the United States, and its primary activity was publishing the "Newsletter.

The AAAI-06 workshops will be held rather than a regular AAAI paper.

This article provides a personal perspective, in three stories, on the origins of AAAI. In the first story, I explain the reasons justifying AAAI's existence. In the second story. In the second story, I recount some of the controvery over the name artificial intelligence, and explain why it was chosen as the new society's moniker. In the third story, I note that AI has not suffered from the applied versus research scism that has affected other societies. Finally, in the fourth story, I mention some of the early issues of finance.

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.

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.

I systems, the importance of controlling the had previously worked in cybernetics, data acquisition, and introduced an new control theory, and pattern recognition, paradigm: active perception. We stated that where we modeled intelligence, perception, we not just see but we also look, and we and action as signal processing.

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.