Watershed technologies like AlphaGo make it easy to forget that artificial intelligence (AI) isn't just a futuristic dream. It's already here -- and we interact with it every day. Sensing traffic lights, fraud detection, mobile bank deposits, and, of course, internet search -- each of these technologies involves AI of some kind. As we have grown used to AI in these instances, it has become part of the scenery -- we see it, but we no longer notice it. Expect that trend to continue: As AI grows increasingly ubiquitous, it'll become increasingly invisible.

Part of the Media Laboratory's heritage (its origins are in the School of Architecture) is a startling receptivity to the arts, especially music and the visual arts, and Brand repeatedly returns to this subject. Even here, intellectualism reigns: It is symptomatic that the lab members' interest in literature seems to be limited to science fiction. This lopsidedness echoes Turkle's complaint that hackers ignore the texture (emotion) of music in favor of its structure (intellect). Not an engineer himself, Brand is not always in a position to critically evaluate what he saw; I was reminded of persons who, on seeing ELIZA, concluded that computerized psychotherapy was just around the corner. As Brand points out, the Media Lab replaces the publish-orperish imperative with demo or die, and anyone who has produced a demo knows something about practical mendacity.

The growth in the amount of available databases far outstrips the growth of corresponding knowledge. This creates both a need and an opportunity for extracting knowledge from databases. Many recent results have been reported on extracting different kinds of knowledge from databases, including diagnostic rules, drug side effects, classes of stars, rules for expert systems, and rules for semantic query optimization. The importance of this topic is now recognized by leading researchers. Michie predicts that "The next area that is going to explode is the use of machine learning tools as a component of large scale data analysis'' (AI Week, March 15, 1990).

The Ipecac College Committee on Human Experimentation is mailing each faculty member the enclosed review of developments in the recent PortraitPrograms controversy. While the committee deplores the atmosphere of crisis, not to say hysteria, that now envelops the issue, the committee welcomes constructive comment: Damage control continues. Behavioral Taxidermy The PortraitPrograms Project grew out of hyperinterdisciplinarianism of the famed Gigabase Sculpture Group,l in turn stimulated by recent cutbacks in government support for the arts. The National Endowment for the Humanities and the National Science Foundation had jointly funded the Gigabase Sculpture Project to foster the literary/musical genre of composing genetic codes for novel organisms. Later, artists trained in recombinant DNA technology designed massive Brancusi-esque statues of living cytoplasmic jelly.

Sentences for the 50,000 offenders vary from community work release and probation to lifelong incarceration. Tennessee was one of 38 states required by court order to improve prison conditions and reduce overcrowding; it is the target of over 300 inmate lawsuits each year. The new $14 million system is the largest and most comprehensive computer system ever developed in the field of corrections. Sentences C and D are consecutive to sentence B, and sentence B is consecutive to sentence A. C, and D of an offender, as shown in figure 1, it must be determined which sentence is not consecutive to any others. In this case, A is the sentence that must first be calculated because its dates do not depend on a previous sentence.

AI Magazine Volume 9 Number 3 (1988) ( AAAI) The first part of the book is intended to be an introduction to computational jurisprudence for both groups. It identifies issues critical to the purpose, behavior, knowledge sources, knowledge structures, and reasoning processes of expert legal systems. The second part implements a simple prototype system for a well-defined area of contract law and is more appropriate for experienced developers of knowledge-based systems. Law is a domain in which the experts are supposed to disagree, and lawyers must be able to argue either side of a case. A judge or juror must decide which argument is "best."

The question comes from an old joke about a Boston politician talking to voters in his district. "Will you vote for me? I gave your father a job at city hall, I found jobs for your wife, your sons, and your daughter. Last year I directed a million dollars worth of business to your company. And I got the city to repair your street."

Editor: We are currently working on a project that attempts to integrate artificial intelligence and legal reasoning for the purpose of simulating judicial decision making. The project has defined legal reasoning and legal analysis-the former taking place before the latter begins. Using a historical approach with our legal system's basis founded in English common law, we attempted to examine the role of stare decisis in decision making. More extensively we examined the role of reasoning in legal analysis, relying on Wittgenstein and to some extend Hofstadter, for an explanation of the foundation of the thought behind man's reasoning process. Legal reasoning is a specialized thought process, but reasoning is generic to all processes that attempt to incorporate artificial intelligence.

Limited security resources prevent full security coverage at all times, which allows adversaries to observe and exploit patterns in selective patrolling or mon itoring; for example, they can plan an attack avoiding existing pa trols. Hence, randomized patrolling or monitoring is impor tant, but randomization must provide distinct weights to dif ferent actions based on their complex costs and benefits. To this end, this article describes a promising transition of the lat est in multiagent algorithms into a deployed application. In particular, it describes a software assistant agent called AR MOR (assistant for randomized monitoring over routes) that casts this patrolling and monitoring problem as a Bayesian Stackelberg game, allowing the agent to appropriately weigh the different actions in randomization, as well as uncertainty over adversary types. ARMOR combines two key features.

Toward that end, this article presents PROTECT, a game-theoretic system deployed by the United States Coast Guard (USCG) in the Port of Boston for scheduling its patrols. USCG has termed the deployment of PROTECT in Boston a success; PROTECT is currently being tested in the Port of New York, with the potential for nationwide deployment. PROTECT is premised on an attackerdefender Stackelberg game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary's behavior -- to the best of our knowledge, this is the first real-world deployment of the QR model. Second, to improve PROTECT's efficiency, we generate a compact representation of the defender's strategy space, exploiting equivalence and dominance.