Friday, November 4, 2016 - 2:30pm
Location:100 Baker-Porter Hall
Speaker:Jordan Boyd-Graber University of Colorado
ABSTRACT Machine learning is ubiquitous, but most users treat it as a black box: a handy tool that suggests purchases, flags spam, or autocompletes text. I present qualities that ubiquitous machine learning should have to allow for a future filled with fruitful, natural interactions with humans: interpretability, interactivity, and an understanding of human qualities. After introducing these properties, I present machine learning applications that begin to fulfill these desirable properties. I begin with a traditional information processing task -- making sense and categorizing large document collections -- and show that machine learning methods can provide interpretable, efficient techniques to categorize large document collections with a human in the loop. From there, I turn to techniques to help computers understand and detect when texts reveal their writer's ideology or duplicity. Finally, I end with a setting combining all of these properties: language-based games and simultaneous machine translation.
BIO Jordan Boyd-Graber is an assistant professor in the University of Colorado Boulder's Computer Science Department, formerly serving as an assistant professor at the University of Maryland. Before joining Maryland in 2010, he did his Ph.D. thesis on "Linguistic Extensions of Topic Models" with David Blei at Princeton. Jordan's research focus is in applying machine learning and Bayesian probabilistic models to problems that help us better understand social interaction or the human cognitive process. He and his students have won "best of" awards at NIPS (2009, 2015), NAACL (2016), and CoNLL (2015), and Jordan won the British Computing Society's 2015 Karen Spärk Jones Award. His research has been funded by DARPA, IARPA, NSF, NCSES, ARL, OMO, NIH, and Lockheed Martin and has been featured by CNN, Huffington Post, New York Magazine, Talking Machines, and the Wall Street Journal.