ACM has named Cornell University professor Jon Kleinberg the winner of the 2008 ACM-Infosys Foundation Award in the Computing Sciences. ACM and the Infosys Foundation created the award in 2007 to recognize personal contributions by young scientists and system developers to a contemporary innovation that exemplifies the greatest recent achievements in the computing field. Kleinberg’s models show how information is organized on the Web, how it spreads through large social networks, and how the structure of these networks leads to the six degrees of separation phenomenon. “With his innovative models and algorithms, he has broadened the scope of computer science to extend its influence to the burgeoning world of the Web and the social connections it enables,” says ACM President Dame Wendy Hall. “We are fortunate to have the benefit of his profound insights into the link between computer network structure and information that has transformed the way information is retrieved and shared online.” ACM will present Kleinberg with the ACM-Infosys Foundation Award at the annual ACM Awards Banquet on June 27 in San Diego, Calif. Financial support for the $150,000 award is provided by an endowment from the Infosys Foundation.
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Tom Mitchell, head of Carnegie Mellon University’s Machine Learning Department, says that advances in machine learning could bring about a transformation in psychology and neuroscience. Mitchell says that his group has trained an algorithm to study functional magnetic resonance imaging scans of a person’s brain activity and determine what object they are thinking about. “We can look inside your brain when you see the color red, and we can look inside my brain when I see the color red, and we can ask, ‘Is it or is it not the same pattern of neural activity?’ ” he notes. Mitchell speculates that people could conceivably be networked to exchange information so that one person can tell what the other is thinking. He observes that a number of researchers are developing brain-computer interfaces that can enable the decoding of a person’s thoughts. This could be particularly useful for “locked in” patients who are speech- and mobility-disabled.
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