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.

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.

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.

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.

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...

What Do We Know about Knowledge? In this article, I will examine the first of these questions. AI has been slow to embrace this principle. Programs demonstrating research ideas in AI are often too large and not well enough documented to allow replication or sharing. What I would like to in diverse conditions. I wish to clarify the knowledge example, it was pretty clearly articulated in Biblical principle and try to increase our understanding times: "A man of knowledge increaseth of what programmers and program strength" (Proverbs 24: 5). Greek philosophers based their lives on acquiring The "knowledge is power" principle is most and transferring knowledge. In the course closely associated with Francis Bacon, from his of teaching, they sought to understand the 1597 tract on heresies: "Nam et ipsa scientia nature of knowledge and how we can establish potestas est." ("In and of itself, knowledge is knowledge of the natural world. B," along with quantification, "All A's are B's," Euclid's geometry firmly established the concept In the intervening several centuries before Plato, Socrates's pupil and Aristotle's mentor, was the first to pose the question in writing of the Middle Ages and the rise of modern science what we mean when we say that a person in the West, He was distinguishing empirical knowledge, church to make new knowledge fit with established lacking complete certainty, from the certain dogma.

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.

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

Just as email traffic has increased over the years since its in ception, so has the proportion that is unsolicited; some estimations have plac ed the proportion as high as 60%, and the average cost of this to business at arou nd $2000 per year, per employee (see [29] for a range of numbers and statis tics on spam). Unsolicited emails - commonly know as spam - have thereby become a daily feature of every email user's inbox; and regardless of advan ces in email filtering, spam continues to be a problem in a similar way to comp uter viruses which constantly reemerge in new guises. This leaves the res earch community with the task of continually investigating new approac hes to sorting the welcome emails (known as ham) from the unwelcome spam. W e present just such an approach to email classification and fi ltering based on a well studied data structure, the suffix tree (see [1 6] for a brief introduction). The approach is similar to many existing one s, in that it uses training examples to construct a model or profile of the class and its features, then uses this to make decisions as to the class of new example s; but it differs in the depth and extent of the anaysis. For a good overview of a number of text classification methods, see [26, 1, 31]. Using a suffix tree, we are able to compare not only single word s, as in most current approaches, but substrings of an arbitrary len gth.