- Daniel Lowd
How does the human brain use neural activity to create and represent meanings of the words, phrases, sentences and stories it reads? One way to study this question is to give people text to read while observing their brain activity. We have been doing such experiments with fMRI (1 mm spatial resolution) and MEG (1 msec time resolution) brain imaging, and developing novel machine learning approaches to analyze this data. As a result, we have learned answers to questions such as "Are the neural encodings of word meaning the same in your brain and mine?", "Are neural encodings of word meaning built out of recognizable subcomponents?," "What sequence of neurally encoded information flows through the brain during the half-second in which the brain comprehends a word?," “How are meanings of multiple words combined when reading phrases, sentences, and stories?” This talk will summarize our machine learning approaches, some of what we have learned, and newer questions we are currently studying.
Tom M. Mitchell is the Founders University Professor in the School of Computer Science at Carnegie Mellon University, where he founded the world's first academic Machine Learning Department, and does research in AI, machine learning, and cognitive science. His research has been featured in the popular press from the New York Times, to CCTV (China's national television network), to CBS's 60 Minutes. Mitchell is a member of the U.S. National Academy of Engineering, a member of the American Academy of Arts and Sciences, and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI).