As a physicist, I was originally trained to describe the world in terms of exact equations. Later, as an experimental high-energy particle physicist, I learned to deal with vast amounts of dataMercedes Ups Driverless Ambitions To Challenge Tesla Motors Inc and GM. Read more ... » to describe the data. Business data, taken in bulk, is often messier and harder to model than the physics data on which I cut my teeth. Simply put, human behaviorCombining virtual technologies to conquer the physical access world.
The Eighteenth National Conference on Artificial Intelligence (AAAI-2002) Robot Challenge is part of an annual series of robot challenges and competitions. It is intended to promote the development of robot systems that interact intelligently with humans in natural environments. The Challenge task calls for a robot to attend the AAAI conference, which includes registering for the conference and giving a talk about itself. In this article, we review the task requirements, introduce the robots that participated at AAAI-2002 and describe the strengths and weaknesses of their performance.
Bruce G. Buchanan, Professor of Computer Science, University of Pittsburgh Discovery in science is often described in such terms as serendipity, insight, creative impulse, and other attributes thought to be unmechanizable. The process of science, however, is much like other problem-solving processes in which large problems can be broken into subproblems and experienced scientists with more knowledge can be seen to be better than novices. In this talk I revisit several of the critical steps in mechanizing scientific discovery and describe prototype AI programs from our laboratory that address those steps. Paul Cohen, Professor of Computer Science, University of Massachusetts, Amherst When you program a computer, you make it process symbols in such a way that the results mean something to you. Whether the symbols and results mean anything to the computer is not a consideration to programmers, nor is it an easy philosophical question.
In this issue of AI Magazine, we continue our presentation of extended versions of papers presented at IAAI-12 (held in Toronto, Ontario, Canada) that were selected for their description of AI technologies that are in practical use. Our selections for this issue describe deployed applications. They explain the context, requirements, and constraints of the application, how the technology was adapted to satisfy those factors, and the impact that this innovation brought to the operation in terms of cost and performance. The articles also supply useful insights into use cases that we hope can also be translated to other work that the AI community is engaged in. In the first of these deployed application articles, eBird: A Human/Computer Learning Network to Improve Biodiversity Conservation and Research by Steve Kelling, Carl Lagoze, Weng-Keen Wong, Jun Yu, Theodoros Damoulas, Jeff Gerbracht, Daniel Fink, and Carla Gomes, the authors describe an intriguing application that successfully combines the best in human and artificial computing capabilities with an active feedback loop between people and machines.