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Friday, October 26, 2018 at 12:00pm to 1:30pm
Bloomberg Center, 061 2 West Loop Road, New York, NY 10044
Learning Machines Seminar Series
"Teaching Machines like we Teach People"
Today machine learning is largely about statistical pattern discovery and function approximation from large volumes of data. But as computing devices that interact with us in natural language become ubiquitous (e.g., Siri, Alexa, Google Now), and as computer perceptual abilities become more accurate, they open an exciting possibility of enabling end-users to teach machines similar to the way in which humans teach one another. Natural language conversations, gesturing, demonstrations, teleoperation and other modes of communication offer a new paradigm for machine learning through instruction from humans. In this talk I will discuss our effort and progress at CMU to build the next generation conversational agent that can learn from explicit verbal instruction and demonstration.
Igor Labutov’s interests are in building machine learning algorithms that can learn from natural human supervision, such as verbal or visual instructions. Most recently, he was a Postdoc at Carnegie Mellon Machine Learning department working with Tom Mitchell on problems of machine learning from flexible and natural forms of instruction. Prior to that, he obtained his PhD from Cornell University where he worked with Professors Hod Lipson and Christoph Studer, and where he was a recipient of the NSF Graduate Fellowship. In June 2018, he co-founded LAER AI., a startup focusing on developing next-general semantic search tools for enterprises.