Panel: Progress in AI: Myths, Realities, and Aspirations


July 10, 2015


Christopher Bishop, Eric Horvitz, Fei Fei Li, Josh Tenenbaum, Michael L. Littman, and Oren Etzioni


Microsoft Research, Allen Institute for Artificial Intelligence, Stanford University, Brown University, Massachusetts Institute of Technology


Moderated by Eric Horvitz, Managing Director, Microsoft Research

Fei-Fei Li, Associate Professor, Stanford University
Michael Littman, Professor of Computer Science, Brown University
Josh Tenenbaum, Professor, Massachusetts Institute of Technology
Oren Etzioni, Chief Executive Officer, Allen Institute for Artificial Intelligence
Christopher Bishop, Distinguished Scientist, Microsoft Research


Christopher Bishop, Eric Horvitz, Fei Fei Li, Josh Tenenbaum, Michael L. Littman, and Oren Etzioni

“Eric Horvitz is serving as the managing director of the Microsoft Research lab at Redmond, balancing lab-wide responsibilities with ongoing research on machine intelligence and on opportunities to leverage the complementarities of human and machine intelligence. Visit his home page.

His ongoing research builds on representations of probability and utility, and centers on identifying ideal actions under uncertainty and bounded informational, computational, and cognitive resources. Beyond curiosity-driven research on foundations of machine perception, learning, and reasoning, he has been excited about building real-world systems that provide value to people, organizations, and society, working in multiple areas, including human-computer interaction, information retrieval, healthcare, transportation, operating systems, and aerospace.

See the Microsoft Research home page as a starting point for browsing through projects, events, and people and contact information for the Microsoft Research lab at Redmond—and other Microsoft labs in the United States and throughout the world. “

Chris Bishop is a Distinguished Scientist and Deputy Managing Director at Microsoft Research Cambridge, where he is head of the Machine Learning and Perception group. His research interests include probabilistic approaches to machine learning, as well as their application to fields such as biomedical sciences and healthcare. He is also Professor of Computer Science at the University of Edinburgh where he is a member of the Institute for Adaptive and Neural Computation in the School of Informatics. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. Chris is the author of the influential textbooks Neural Networks for Pattern Recognition (Oxford University Press, 1995) which has over 23,000 citations, and Pattern Recognition and Machine Learning (Springer, 2006), which has over 16,000 citations. He has an MA in Physics from Oxford, and a PhD in quantum field theory from the University of Edinburgh.

Dr. Oren Etzioni is Chief Executive Officer of the Allen Institute for Artificial Intelligence. He was a Professor at the University of Washington’s Computer Science department starting in 1991, receiving several awards including GeekWire’s Hire of the Year (2014), 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, and his B.A. from Harvard in 1986.

Fei-Fei Li is an Associate Professor in the Computer Science Department at Stanford, and the Director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab. Her research areas are in machine learning, computer vision and cognitive and computational neuroscience, with an emphasis on Big Data analysis. Dr. Fei-Fei Li has published more than 100 scientific articles in top-tier journals and conferences, including Nature, PNAS, Journal of Neuroscience, CVPR, ICCV, NIPS, ECCV, IJCV, IEEE-PAMI, etc. Research from Fei-Fei’s lab have been featured in New York Times, New Scientists and a number of popular press magazines and newspapers. Dr. Fei-Fei Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. Dr. Fei-Fei Li is a recipient of the 2014 IBM Faculty Fellow Award, 2011 Alfred Sloan Faculty Award, 2012 Yahoo Labs FREP award, 2009 NSF CAREER award, the 2006 Microsoft Research New Faculty Fellowship, and a number of Google Research awards.

Michael Littman works mainly in reinforcement learning, but has done work in machine learning, game theory, computer networking, partially observable Markov decision process solving, computer solving of analogy problems and other areas. He is currently a professor of computer science at Brown University. Before graduate school, Littman worked with Thomas Landauer at Bellcore and was granted a patent for one of the earliest systems for cross-language information retrieval. Littman received his Ph.D. in computer science from Brown University in 1996. From 1996 to 1999, he was a professor at Duke University. During his time at Duke, he worked on an automated crossword solver PROVERB, which won an Outstanding Paper Award in 1999 from AAAI and competed in the American Crossword Puzzle Tournament. From 2000 to 2002, he worked at AT&T. From 2002 to 2012, he was a professor at Rutgers University; he chaired the department from 2009 to 2012. In summer 2012 he returned to Brown University as a full professor.

Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data – learning concepts and word meanings, inferring causal relations or goals – and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or ‘intuitive theories’. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).