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Abstract
The sciences that study the brain are experiencing a significant revolution, caused mainly by the invention of new instruments for observing and manipulating brain function. For example, function Magnetic Resonance Imaging (fMRI) now provides a safe, non-invasive tool to observe human brain activity, allowing scientists to capture a 3D image of activity across the entire human brain at a spatial resolution of 1mm, once per second. Brain probes now allow direct recording simultaneously from hundreds of individual neurons in laboratory animals as they move about their environment, genetic knock-out experiments allow studying lab mice missing specific neuro-transmitters, and new dyes provide new ways to study neural pathways and neural metabolism. Brain implants now allow tens of thousands of humans to hear for the first time, and the FDA recently approved the first human retinal implants intended to help blind people.
The thesis of my talk is that research over the coming decade in the brain sciences will have a significant impact on Artificial Intelligence research, and that AI will have an even more significant impact on studies of the brain. Well examine two distinct ways in which this synergy between AI and brain sciences is already beginning to take shape. First, AI architectures and algorithms for specific tasks are providing a basis for interpreting new data on brain activity in animals in several cases leading to the conclusion that animals may use approaches surprisingly similar to these engineered AI solutions. Second, machine learning methods are providing new ways to discover regularities in the huge volume of new data for example, automatically discovering the spatial-temporal patterns of brain activity associated with reading a confusing sentence, or determining the semantic category of a word.

Biography
Tom M. Mitchell is the Fredkin Professor of Computer Science at Carnegie Mellon University, and Founding Director of CMU’s Center for Automated Learning and Discovery, an interdisciplinary research center specializing in statistical machine learning and data mining. He is President of the American Association of Artificial Intelligence (AAAI), author of the textbook “Machine Learning,” and a member of the National Research Council, Computer Science and Telecommunications Board. During 1999-2000 he served as Vice President and Chief Scientist at WhizBang! Labs, a company that employs machine learning to extract information from the web.
Mitchell’s research interest lies in the area of machine learning and data mining. He has developed specific learning algorithms such as inductive inference methods, learning methods that combine data with background knowledge, methods that learn from combinations of labeled and unlabeled training data, and methods for learning probabilistic first-order logic rules from relational data. He has also explored the application of these methods to complex time series data, including studies of pneumonia mortality and C-section risk from time series data in medical records, to studies of brain function from complex functional MRI time series data, to robot learning.