News

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IC@L Distinguished Speaker

Tom Mitchell

Tom Mitchell, E. Fredkin University Professor at Carnegie Mellon University, in a talk entitled Conversational Machine Learning, will discuss how if we wish to predict the future of machine learning, all we need to do is identify ways in which people learn but computers don’t, yet. Humans often learn from natural language instruction. Now that computers are finally able to have conversations (i.e., we routinely have simple conversations with our phones), it is time for research on how computers might also learn from conversations with human instructors. This talk will describe recent research on conversational machine learning, including development of a prototype personal agent that users can teach to perform new action sequences to achieve new commands, using solely natural language interaction.

Date: October 15, 2018
Time: 11am
Place: Senate Chambers, Ross N940

Bio: 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 (http://rtw.ml.cmu.edu), and uses brain imaging to study how the human brain understands what it reads.  He believes conversational machine learning will be the greatest new thrust in AI over the coming decade. 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).

The Centre for Innovation in Computing at Lassonde (IC@L) is a research unit focusing on the science of computing and its realization to enable novel solutions and technologies. The future of many disciplines depends on advances in computational science via theoretical and empirical research, and hardware and software development. IC@L joins computational scientists with hospitals, industry and government to address the next generation of computational problems.