Common sense has always been of interest in AI, but has rarely taken center stage. Despite its mention in one of John McCarthy's earliest papers and years of work by dedicated researchers, arguably no AI system with a serious amount of general common sense has ever emerged. Why is that? What's missing? Examples of AI systems' failures of common sense abound, and they point to AI's frequent focus on expertise as the cause. Those attempting to break the brittleness barrier, even in the context of modern deep learning, have tended to invest their energy in large numbers of small bits of commonsense knowledge. But all the commonsense knowledge fragments in the world don't add up to a system that actually demonstrates common sense in a human-like way. We advocate examining common sense from a broader perspective than in the past. Common sense is more complex than it has been taken to be and is worthy of its own scientific exploration.

But, I argue, the consequences are actually far worse: because of the very nature of intelligence, the centrifugal force on the field could thwart the very mission that drives it by leaving no place for the study of the interaction and synergy of the many coupled components that individually in isolation are not intelligent but, when working together, yield intelligent behavior. To raise awareness of the need to reintegrate AI, I contemplate the role of systems integration and the value and challenge of architecture. Illustrating some reason for optimism, I briefly outline some promising developments in large projects that are helping to increase the centripetal force on AI. I conclude by discussing how it is critical that the field focus its attention back on its original mission, led by a heavy dose of integrated systems thinking and grand challenges, and why after its first quarter century, AAAI is more essential than ever.

This is a wonderful opportunity, yet a position is very hard to match in any other. The first AAAI conference was held at Stanford University; it was very much a research conference, a scientific event that generated a lot of excitement. The conference was small and intimate, with few parallel sessions. There were excellent opportunities for us to talk to one another. AAAI-80 gave real substance to the organization, clearly getting AAAI off on the right foot, and it gave new identity and cohesiveness to the field. This year--2006--has also been a big year, celebrating the 50th anniversary of the original meeting at Dartmouth College, where the name "artificial intelligence" first came into common use. Numerous events around the world, including a celebratory symposium at Dartmouth and an AAAI Fellows Symposium associated with AAAI-05, have marked this important milestone in the history of the field. Progress since our first AAAI conference has The First AAAI Conference was Held at Stanford University. While each year's results may have seemed incremental, when we look back over the entire period we see some truly amazing plate the big picture and, perhaps more importantly things. In job at DARPA), to identify gaps in our national hindsight this may no longer look so exciting computing research agenda. It also occurred to (purists will say that it was not an "AI" system me that that perspective was a very special that beat Garry Kasparov but rather a highly asset to use in drafting this presidential engineered special-purpose machine largely address. Looking forward from back then, no want to raise a broad issue and consider matter how Deep Blue actually worked, playing some larger questions regarding the nature of chess well was clearly an AI problem--in fact, a the field itself and the role that AAAI as an classical one--and our success was historic.

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.

During the balmy summer of 1980, about 800 AI researchers pose of the new format, the Committee's recommendation, met on the Stanford campus to hold the first and some expanded ways for members to participate in the AAAI conference. The conference program had no more conference this year. For many of Conference Goals those attendees, it was a special, unique opportunity to have deep colleagial interactions in a very comfortable setting. The most radical change that was considered, but not adopted, was the division of the science and engineering interests into two separate conferences at different times of Even the first national conference, however, was more the year. Many Council members expressed concern that than a gathering of researchers.

Over the past few years, the notion of a "prototype" (e.g., TYPICAL-ELEPHANT) seems to have caught on securely in knowledge representation research. Along with a way to specify default properties for instances of a description, proto-representations allow overriding, or "canceling" of properties that don't apply in particular cases. This supposedly makes representing exceptions ( three-legged elephants and the like) easy; but, alas, it makes one crucial type of representation impossible-that of composite descriptions whose meanings are functions of the structure and interrelation of their parts. This article explores this and other ramifications of the emphasis on default properties and "typical" objects.

Over the past few years, the notion of a "prototype" (e.g., TYPICAL-ELEPHANT) seems to have caught on securely in knowledge representation research. Along with a way to specify default properties for instances of a description, proto-representations allow overriding, or "canceling" of properties that don't apply in particular cases. This supposedly makes representing exceptions ( three-legged elephants and the like ) easy; but, alas, it makes one crucial type of representation impossible-that of composite descriptions whose meanings are functions of the structure and interrelation of their parts. This article explores this and other ramifications of the emphasis on default properties and "typical" objects.

The third national conference promotes research in the field of AI by bringing together individuals from government, industry, and academia and by providing a published record of the conference as proceedings.

The third national conference promotes research in the field of AI by bringing together individuals from government, industry, and academia and by providing a published record of the conference as proceedings.

The Fairchild Laboratory for Artificial Intelligence Research (FLAIR) was inaugurated in October, 1980, with the purposes of introduction AI Technology into Fairchild Camera and Instrument Corporation, and of broadening the AI base of its parent company, Schlumberger Ltd. The charter of the laboratory includes basic and applied research in all AI disciplines. Currently, we have significant efforts underway in several areas of computational perception, knowledge representation and reasoning, and AI-related architectures. The current computational environment includes several large mainframes dedicated to AI research, a number of high-performance personal scientific machines, and extensive graphics capabilities.