The multi-layer learning algorithms known as "Deep learning" have catapulted to the front page of the New York times, formed the core
of the so-called "Google brain", and achieved impressive results on several classification tasks in vision, speech recognition, and more.
Yet others have offered conundrums like the following: The large ball crashed right through the table because it was made of styrofoam.
What was made of styrofoam?
_ the large ball
_ the table
The answer is obviously "the table", but if we change the word "Styrofoam" to "steel" the answers equally clearly is "the large ball". Being
able to answer this type of question relies intrinsically on an extensive body of common-sense knowledge, and on utilizing it to both understand the question
and derive the answer. Yet deep learning doesn't begin to address this challenge. Where will the field of AI go in the coming decade? Is a
grand synthesis possible here? My talk will lay explore these questions, and describe the approach we are taking in the new Allen Institute for AI towards the unprecedented acquisition of common-sense knowledge from text.
Dr. Oren Etzioni is the Executive Director of the Allen Institute for AI. He was a Professor at the University of Washington's Computer Science department starting in 1991, receiving several awards including Seattle's Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He was also the founder or co-founder of several companies including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013), and the author of over 100 technical papers that have garnered roughly 20,000 citations. The goal of Oren's research is to solve fundamental problems in AI, particularly the automatic learning of knowledge from text. Oren received his Ph.D. from Carnegie Mellon University in 1991 (under Tom Mitchell), and his B.A. from Harvard in 1986.
Faculty Host: Tom Mitchell
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