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Published online by Cambridge University Press: 05 August 2013
Ai technology has matured markedly in the past couple of decades and now includes an impressive array of powerful computational tools. These can be deployed with great effectiveness because of the increasing power of relatively inexpensive computers, the availability of large databases, and the growth of the World Wide Web. Today's AI programs are capable of approximating many human cognitive abilities, automating some of them completely, and even bettering what humans can do in others. Because AI is now capable of contributing to the solution of many real-world problems, many graduates who have specialized in AI studies go to work for companies and start-ups instead of pursuing academic AI research. Google and Microsoft, just to name two examples, have hired many of these graduates.
Just as other branches of engineering gradually develop a number of subspecialties, so has AI. For example, the July 2009 International Joint Conference on Artificial Intelligence (IJCAI) had papers in the following “theme” areas: Agent-based and Multi-agent Systems; Constraint, Satisfiability, and Search; Knowledge Representation, Reasoning, and Logic; Machine Learning; Multidisciplinary Topics and Applications; Natural-Language Processing; Planning and Scheduling; Robotics and Vision; Uncertainty in AI; and Web and Knowledge-based Information Systems. (Note that many of the theme areas combine two or more broad topics, and the topics themselves are further articulated in the call for papers.) Of course, many of these subspecialties draw on each other so the field as a whole stays connected.
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