The President's Distinguished Lecture Series brings our world’s most distinguished thought leaders in science and technology to create debate and spur discussion on the role of technology and its implications in 21st century society. This series was launched by President Nariman Farvardin in 2012.

Using Machine Learning to Study How Brains Represent Language Meaning

January 31, 2018

Lecture Summary: Machine Learning Scientist Tom Mitchell Delivers Talk on How the Human Brain Works→

Transcript: View PDF Transcript→

ABSTRACT: How does the human brain use neural activity to create and represent meanings of words, phrases, sentences and stories?  One way to study this question is to give people text to read while scanning their brain.  At Carnegie Mellon University, researchers in the machine learning department have been conducting such experiments with fMRI (1 mm spatial resolution) and MEG (1 msec time resolution) brain imaging, and developing novel machine learning approaches to analyzing this data.  These experiments are producing answers to questions such as:

  • "Are the neural encodings of word meaning the same in your brain and that of another?"
  • "Are neural encodings of word meaning built out of recognizable subcomponents, or are they randomly different for each word?,"  
  • "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 Carnegie Mellon’s machine learning approach, what has been learned, and newer questions that are currently being studied.

BIOGRAPHY: Dr. Tom M. Mitchell is the E. Fredkin University Professor at Carnegie Mellon University, where he founded the world's first machine learning department.  His research uses machine learning to develop computers that are learning to read the web and uses brain imaging to study how the human brain understands what it reads.  He co-chaired the 2017 U.S. National Academy study on “Information Technology, Automation, and the U.S. Workforce,” and has testified before the U.S. Congressional Research Service and the U.S. House Subcommittee on Veterans' Affairs on the potential uses and impacts of artificial intelligence. Dr. Mitchell is a member of the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, and a Fellow and past president of the Association for the Advancement of Artificial Intelligence (AAAI). For more information on Professor Mitchell’s research, please visit http://rtw.ml.cmu.edu.

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