Our subject has been compared to Mozart. His both in the popular media and its corrupting moral influence on society, proofs are ingenious, cleverly argued, among the cultured intelligentsia. Lanier's home page: While many of these attacks are cited "... machine decision making is widely, most of them are ridiculous running our household finances to anyone with an appropriate technical to a scary degree. Our demonstrated - Sir John Eccles for the "Mysterious willingness to accommodate machines Loom Theory." AI…lets strategists Thus, the Simon Newcomb award imagine less gruesome warfare this year goes to Jaron Lanier. He is an essayist whose views can be found in such magazines All of this ill-informed pseudo-political as Harpers and Wired; he is also, ranting is remarkably silly, but according to his web page, "available none of it can really be said to constitute for public speaking." He is widely regarded an argument. The first part of the argument is clearly inspired by Searle's magnificent reductio, though performed in a very different style. Recall that Searle's those numbers are a computer Obviously one is supposed to think argument amounts to the claim program. Lanier then makes the move that any sufficiently complex physical through all the possible computers which wins him this year's Award: system--the atoms in a wall, say that could exist … until you "You say the rainstorm is not --has within it a pattern which could find one that treats the raindrop really doing computation--it is be the encoding of a given piece of patterns as a program exactly just sitting there as a passive program--and software--a word-processor, for instance--and equivalent to your brain." Perhaps there's something "Yes, it can be done..." [Since the Now the raindrops are reduce to nonsense, that a program There's a technical slip here: the doing the computing."

Researchers and technology developers from the National Aeronautics and Space Administration (NASA), other government agencies, academia, and industry recently met in Pasadena, California, to take stock of past and current work and future challenges in the application of AI to highly autonomous systems. The meeting was catalyzed by new opportunities in developing autonomous spacecraft for NASA and was in part a celebration of the fictional birth year of the HAL-9000 computer.

CHEMREG is a large knowledge-based system used by Air Products and Chemicals, Inc., to support compliance with regulatory requirements for communicating health and safety information in the shipping and handling of chemical products. This article concentrates on one of the knowledge bases in this system: the case-based reasoner. The case-based reasoner addresses the issue of how proper communication of public health and safety information can be ensured while rapid and cost-effective product evaluation is allowed in the absence of actual hazard testing of the product. CHEMREG generates estimates of hazard data for new products from similar products using an existing relational database as a case library.

CHEMREG is a large knowledge-based system used by Air Products and Chemicals, Inc., to support compliance with regulatory requirements for communicating health and safety information in the shipping and handling of chemical products. This article concentrates on one of the knowledge bases in this system: the case-based reasoner. The case-based reasoner addresses the issue of how proper communication of public health and safety information can be ensured while rapid and cost-effective product evaluation is allowed in the absence of actual hazard testing of the product. CHEMREG generates estimates of hazard data for new products from similar products using an existing relational database as a case library. Implementation of the case-based reasoner in rules and objects using a commercial knowledge-based system shell is described. Although some refinements remain, the performance of the case-based reasoner has met its design goals.

To effectively generate a fast and consistent apartment construction project network, Hyundai Engineering and Construction and Korea Advanced Institute of Science and Technology developed a case- and constraint-based project-planning expert system for an apartment domain. The system, FAS-TRAK- APT, is inspired by the use of previous cases by a human expert project planner for planning a new project and the modification of these cases by the project planner using his/her knowledge of domain constraints. This large-scale, case-based, and mixed-initiative planning system, integrated with intensive constraint-based adaptation, utilizes semantic-level metaconstraints and human decisions for compensating incomplete cases imbedding specific planning knowledge. The case- and constraint-based architecture inherently supports cross-checking cases with constraints during system development and maintenance. This system has drastically reduced the time and effort required for initial project planning, improved the quality and completeness of the generated plans, and is expected to give the company the competitive advantage in contract bids for new contracts.

New areas such as knowledge management and multimedia also appeared in the emerging applications track.

Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner DERSNLP+EBL. DERSNLP+EBL extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure.

A number of approaches have been advanced for taking data about a user's likes and dislikes and generating a general profile of the user. These profiles can be used to retrieve documents matching user interests; recommend music, movies, or other similar products; or carry out other tasks in a specialized fashion. This article presents a fundamentally new method for generating user profiles that takes advantage of a large-scale database of demographic data. These data are used to generalize user-specified data along the patterns common across the population, including areas not represented in the user's original data. I describe the method in detail and present its implementation in the LIFESTYLE FINDER agent, an internet-based experiment testing our approach on more than 20,006 users worldwide.

Our goal is to predict the class membership of an observation with predictor vector Xo Nearest neighbor classification is a simple and appealing approach to this problem.

Our goal is to predict the class membership of an observation with predictor vector Xo Nearest neighbor classification is a simple and appealing approach to this problem.