In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, that is, statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today's chaotic information universe, how one would understand which data sets can be meaningfully integrated, and how to communicate the results to humans and machines alike. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights work best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation. Equally important, however, are questions of how to publish data effectively and break up data silos, how to retrieve data, how to enable the exploration of unfamiliar data sets from different domains, how to access provenance information, how to determine whether data sets can be meaningfully reused and integrated, how to prevent data from being misunderstood, how to combine data with processing services and workflows on the fly, and finally how to make data readable and understandable by machines and humans.

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.

PROLOG can compute quantities as answers by extract,ing values from the variable bindings imroduced in the proof of p from S, and so serves as a general purpose programming language. Logical programming languages attract many people in artificial intelligence because of the relative ease of stating declarative information in them, as compared with traditional programming languages Since most knowledge-based, expert systems contain large numbers of essentially declarative statements, the designers of the FGC expect their choice of PROLOG to facilitate the construction and operation of knowledge-based systems Parallelism enters the picture because traditional PROLOG requires that all sentences be expressed in clausal form, searches for proofs of its goal by examining the input clauses in a fixed linear order, and within clauses, examining literals in left-to-right order Many of these imposed orderings have no purely logical basis, so that, as far as questions of deducibility are concerned, greater efficiency may be possible with separat,e deduction searches conducted concurrently. In such a reorganization of PROLOG, time of execution is ideally proportional to the depth of the proof found (the size of the answer), rather than proportional to the number of alternative proofs (the size of the search space). Does S t-p? may be needlessly interest to economists is not affected by such a change. In the second, ordinal utilities were abandoned in, favor of sets of binary preferences among alternatives.

Thcsc are the problems of investigating theories and techniques of natural and artificial psychologies by means of t,he most fit mathematical concepts. Rational psychology should not, be confused with logic-based presentations of artificial intelligence. While investigations based on mathematical logic are relatively familiar and certainly useful, using only that portion of mathematics to characterize psychologies presupposes that psychological questions are fundamentally logical. That presupposition is not, ncccssary for the development of an exact science of mind. Rational Psychology Hat,ional psychology is a part of mathematics, the conceptual investigation of psychology.

Within the time of a human generation, computer technology will be capable of producing computers with as many artificial neurons as there are neurons in the human brain. Within two human generations, intelligists (AI researchers) will have discovered how to use such massive computing capacity in brainlike ways. This situation raises the likelihood that twenty-first century global politics will be dominated by the question, Who or what is to be the dominant species on this planet? This article discusses rival political and technological scenarios about the rise of the artilect (artificial intellect, ultraintelligent machine) and launches a plea that a world conference be held on the socalled "artilect debate." Many years ago, while reading my first book on molecular biology, I realized not only that living creatures, including human beings, are biochemical machines, but also that one day, humanity would sufficiently understand the principles of life to be able to reproduce life artificially (Langton 1989) and even create a creature more intelligent than we are.

The following is a synopsis of the findings of the first AAAI Workshop on AI Applications to Battle Management held at the University of Washington, 16 July 1987. The workshop organizer, Pete Bonasso, sent a point paper to a number of invited presenters giving his opinion of what AI could and could not do for battle management . This paper served as a focus for the workshop presentations and discussions and was augmented by the workshop presentations; it can also serve as a roadmap of topics for future workshops. AI can provide battle management with such capabilities as sensor data fusion and adaptive simulations. Also, several key needs in battle management will be AI research topics for years to come, such as understanding free text and inferencing in real time.

This special issue of AI Magazine covers select applications from the IAAI conference held in 2008 in Chicago. The articles address a broad range of very challenging issues and contain great lessons for AI researchers and application developers. Your rice cooker, toaster, and washing machine have their own minds. Your car parks itself; its transmission adapts itself to your driving preferences, and it tells the dealership which parts it thinks it will need to have replaced three months from now. Your PDA knows your preferences and acts as your personal radio station, playing only music you like.

CHARLES A. ROSEN came to SRI in 1957 I arrived in 1961 Between these dates, Charlie organized an Applied Physics Laboratory and became interested in "learning machines" and "self-organizing systems." That interest launched a group that ultimately grew into a major world center of artificial intelligence research - a center that has endured twenty-five years of boom and bust in fashion, has "graduated" over a hundred AI research professionals, and has generated ideas and programs resulting in new products and companies as well as scientific articles, books, and this particular collection itself The SRI Artificial Intelligence Center has always been an extremely cohesive group, even though it is associated with many contrasting themes. Perhaps these very contrasts are responsible for its vitality. It is a group of professional researchers, but visiting doctoral candidates (mainly from Stanford University) have figured prominently in its intellectual achievements. It is not part of a university, yet its approach to AI has often been more academic and basic than those used in some of the prominent university laboratories.

Today, approximately 10 percent of the world's population is over the age of 60; by 2050 this proportion will have more than doubled. Moreover, the greatest rate of increase is amongst the "oldest old," people aged 85 and over. While many older adults remain healthy and productive, overall this segment of the population is subject to physical and cognitive impairment at higher rates than younger people. This article surveys new technologies that incorporate artificial intelligence techniques to support older adults and help them cope with the changes of aging, in particular with cognitive decline. This change poses both a challenge and an opportunity for the design of intelligent technology.

Many have used Kalman filter techniques based on the equations of vehicle motion; these techniques most accurately predict shortterm motion. With intelligent path prediction, the long-term mission objective of the vehicle is being predicted in addition to the short-term motion. Thus, when applied to predicting the motion of a car, an intelligent predictor will attempt to predict the final destination--say, for example, the vehicle appears to be going to the post office or the art museum--in addition to predicting which streets will be used. The theory is also applicable to predicting air vehicle travel, so that for a military application, the target (from a set of plausible targets) and the threat-avoidance policy (from a set of plausible policies), in addition to the route, can be predicted. The first investigation is to develop a method for identifying a decisionmaking strategy that seemingly explains the vehicle's motion.