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Since the start of the year, a team of researchers at Carnegie Mellon Universitysupported by grants from the Defense Advanced Research Projects Agency and Google, and tapping into a research supercomputing cluster provided by Yahoohas been fine-tuning a computer system that is trying to master semantics by learning more like a human. Its beating hardware heart is a sleek, silver-gray computercalculating 24 hours a day, seven days a weekthat resides in a basement computer center at the university, in Pittsburgh. The computer was primed by the researchers with some basic knowledge in various categories and set loose on the Web with a mission to teach itself. For all the advances in computer science, we still dont have a computer that can learn as humans do, cumulatively, over the long term, said the teams leader, Tom M. Mitchell, a computer scientist and chairman of the machine learning department. The Never-Ending Language Learning system, or NELL, has made an impressive showing so far. NELL scans hundreds of millions of Web pages for text patterns that it uses to learn facts, 390,000 to date, with an estimated accuracy of 87 percent. These facts are grouped into semantic categoriescities, companies, sports teams, actors, universities, plants and 274 others. The category facts are things like San Francisco is a city and sunflower is a plant. NELL also learns facts that are relations between members of two categories. For example, Peyton Manning is a football player . The Indianapolis Colts is a football team . By scanning text patterns, NELL can infer with a high probability that Peyton Manning plays for the Indianapolis Coltseven if it has never read that Mr. Manning plays for the Colts. Plays for is a relation, and there are 280 kinds of relations. The number of categories and relations has more than doubled since earlier this year, and will steadily expand. | 
