Stanford The Future of Artificial Intelligence Emerging Topics and Societal Benefit

A Partner Event of the 2016 Global Entrepreneurship Summit Hosted by The White House Office of Science and Technology Policy and Stanford University

June 23, 2016 5:30–9:30 p.m.

Paul Brest Hall 555 Salvatierra Walk Stanford University ABOUT THIS EVENT

Welcome to The Future of Artificial Intelligence Partner Event of the 2016 Global Entrepreneurship Summit at Stanford University. Today leading artificial intelligence (AI) researchers will discuss the most impactful research topics in AI and highlight the challenges and potentials of artificial intelligence.

There is a lot of excitement about AI and how to create computers capable of intelligent behavior. After years of steady but slow progress on making computers “smarter” at everyday tasks, a series of breakthroughs in the research community and industry have recently spurred momentum and investment in the development of this field.

There is a sense that AI has made sufficient inroads into everyday life that we should pause and take stock of the great opportunities and challenges before us. We are aware of self- driving cars, intelligent assistants on phones and mobile devices, and the use of data for many activities in academia, government and industry. Now is a good time to think about the anticipated evolution of these capabilities and how they might impact economic, social, political and cultural activities. We look forward to engaging with you about how best to harness the innovations and the array of considerations brought by artificial intelligence.

Best Regards,

Russ Altman, Co-Chair Fei-Fei Li, Co-Chair Professor, Bioengineering, Genetics, Medicine, and Associate Professor, Computer Science and Computer Science (by courtesy); Chair, Biomedical Psychology (by courtesy); Director, Stanford Artificial Informatics Training Program; Faculty Director, One Intelligence Lab; Director, Stanford-Toyota Center for Hundred Year Study on Artificial Intelligence (AI100) AI Research 2016 Conference Agenda

Thursday, June 23, 2016

5:30 p.m. Reception in the Rehnquist Courtyard outside of Paul Brest Hall

6:30 p.m. Opening Remarks and Keynote Presentations

Introduction: Russ Altman (Stanford) Government: Megan Smith (Office of Science and Technology Policy) Academic: Fei-Fei Li (Stanford)

7:05 p.m. Invited Talks

Daniela Rus (MIT) Anshul Kundaje (Stanford) Chris Manning (Stanford)

7:30 p.m. Panel: 100 Year Study of Artificial Intelligence

Moderator: Russ Altman Barbara Grosz (Harvard), panelist Yoav Shoham (Stanford), panelist Milind Tambe (USC), panelist

8:15 p.m. Invited Talks

Finale Doshi-Velz (Harvard) Stefano Ermon (Stanford) John Duchi (Stanford) Dieter Fox (University of Washington) Chris Ré (Stanford)

8:55 p.m. Keynote Presentation

Industry: Arvind Krishna (IBM)

9:10 p.m. Closing Remarks

9:15 p.m. Meeting ends Speakers

Russ Biagio Altman Kenneth Fong Professor of Bioengineering, Genetics, Medicine and, by courtesy, of Computer Science, Stanford University Russ Biagio Altman is a professor of bioengineering, genetics, & medicine (and of computer science, by courtesy) and past chairman of the Bioengineering Department at Stanford University. His primary research interests are in the application of computing and informatics technologies to problems relevant to medicine. Dr. Altman holds an A.B. from Harvard College, and M.D. from Stanford Medical School, and a Ph.D. in Medical Information Sciences from Stanford. He received the U.S. Presidential Early Career Award for Scientists and Engineers and a National Science Foundation CAREER Award. He has chaired the Science Board advising the FDA Commissioner, and currently serves on the NIH Director’s Advisory Committee. He is an organizer of the annual Pacific Symposium on Biocomputing (http://psb.stanford.edu/), and a founder of Personalis, Inc. Dr. Altman is board certified in Internal Medicine and in Clinical Informatics. Altman is the faculty director of the 100 year study of Artificial Intelligence (ai100.stanford.edu). https://profiles.stanford.edu/russ-altman

Finale Doshi-Velez Assistant Professor, Harvard University Finale Doshi-Velez leads the Data to Actionable Knowledge group at the Harvard Paulson School of Engineering and Applied Science. She completed her Ph.D. from MIT and her postdoc at Harvard Medical School. She was a Marshall Scholar at the University of Cambridge and was named one of IEEE’s “AI Top 10 to Watch” in 2013. Doshi-Velez is excited about methods to turn data into actionable knowledge. Her core research in machine learning, computational statistics, and data science is inspired by—and often applied to—the objective of accelerating scientific progress and practical impact in healthcare and other domains. https://www.seas.harvard.edu/directory/finale

John C. Duchi Assistant Professor of Statistics and Electrical Engineering, Stanford University John C. Duchi completed his Ph.D. in computer science at Berkeley in 2014. His research interests are a bit eclectic, and they span statistics, computation, optimization, and machine learning. At Berkeley, he worked in the Statistical Artificial Intelligence Lab (SAIL) under the joint supervision of Michael Jordan and Martin Wainwright. He obtained his master’s degree (MA) in statistics in Fall 2012. He was also an undergrad and a master’s student at Stanford University, where he worked with Daphne Koller in her research group, DAGS. He also spends some time at Google Research, where he had (and continue to have) the great fortune to work with Yoram Singer. https://profiles.stanford.edu/john-duchi

Stefano Ermon Assistant Professor of Computer Science, Stanford University Stefano Ermon is Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory and a fellow of the Woods Institute for the Environment. Ermon received his Bachelors and Master’s degrees in Electrical and Electronic Engineering from the Università degli Studi di Padova and he completed his Ph.D. in computer science at Cornell in 2014. His research is centered on techniques for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability. Stefano has won several awards, including two Best Student Paper Awards, one Runner-Up Prize, and a McMullen Fellowship. https://cs.stanford.edu/~ermon/

Dieter Fox Professor of Computer Science, University of Washington Dieter Fox is a Professor in the Department of Computer Science & Engineering at the University of Washington, Seattle, where he heads the UW Robotics and State Estimation Lab. From 2009 to 2011, he was also Director of the Intel Research Labs Seattle. Fox obtained his Ph.D. from the University of Bonn, Germany. Before joining the faculty of UW, he spent two years as a postdoctoral researcher at the CMU Robot Learning Lab. Fox’s research is in robotics and artificial intelligence, with a focus on state estimation and perception applied to various problems in robotics and activity recognition. Dieter is an IEEE Fellow, a Fellow of the AAAI, and he received several best paper awards at major robotics, AI, and computer vision conferences. He was an editor of the IEEE Transactions on Robotics, program co-chair of the 2008 AAAI Conference on Artificial Intelligence, and program chair of the 2013 Robotics: Science and Systems conference. https://homes.cs.washington.edu/~fox/ Speakers, continued

Barbara J. Grosz Higgins Professor of Natural Sciences, Harvard University Barbara J. Grosz’s contributions to AI include establishing the research field of computational modeling of discourse, developing some of the earliest computer dialogue systems, pioneering models of collaboration, and the development of collaborative multi-agent systems and collaborative systems for human-computer communication. Grosz is a member of the National Academy of Engineering, the American Philosophical Society, and the American Academy of Arts and Sciences and a corresponding fellow of the Royal Society of Edinburgh, and she is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the Association for Computing Machinery (ACM) and the American Association for the Advancement of Science. She is recipient of the University of California, Berkeley Distinguished Alumna Award in Computer Sciences and Engineering (1997), the ACM/AAAI Allen Newell Award (2009), and the 2015 IJCAI Research Excellence Award. http://grosz.seas.harvard.edu/

Arvind Krishna Senior Vice President and Director, IBM Research Arvind Krishna helps guide IBM’s overall technical strategy in core and emerging technologies, including cognitive computing, quantum computing, cloud platform services, data-driven solutions and blockchain. Krishna was general manager of IBM Systems and Technology Group’s development and manufacturing organization, responsible for the advanced engineering and development of a full technology portfolio, ranging from advanced semiconductor materials to leading-edge microprocessors, servers and storage systems. Krishna has an undergraduate degree from the Indian Institute of Technology, Kanpur, and a Ph.D. from the University of Illinois at Urbana-Champaign. He is the recipient of a distinguished alumni award from the University of Illinois, is the co-author of 15 patents, has been the editor of IEEE and ACM journals, and has published extensively in technical conferences and journals. https://www-03.ibm.com/press/us/en/biography/45780.wss

Anshul Kundaje Assistant Professor of Genetics and Computer Science, Stanford University Anshul Kundaje’s research focuses on deciphering the molecular and genetic basis of disease by integrative analysis of diverse types of large-scale genomic data. His lab develops statistical and machine learning methods to decipher functional elements in the human genome, understand their effects on cellular function across diverse cell types and interpret the molecular impact of natural and disease-associated genetic variation. Kundaje completed his Ph.D. in Computer Science in 2008 from Columbia University. As a postdoc at Stanford University from 2012-2014 and a research scientist at MIT and the Broad Institute from 2012-2014, he led the integrative analysis efforts for two of the largest functional genomics consortia - The Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Project. Dr. Kundaje is also a recipient of the 2014 Alfred Sloan Fellowship. https://profiles.stanford.edu/anshul-kundaje

Fei-Fei Li Associate Professor, Computer Science and Psychology (by courtesy); Director, Stanford Artificial Intelligence Lab, Director, Stanford-Toyota Center for AI Research Li’s main research areas are in machine learning, computer vision and cognitive and computational neuroscience. She has published more than 100 scientific articles, including Nature, PNAS, Journal of Neuroscience, CVPR, ICCV, NIPS, ECCV, IJCV, IEEE-PAMI. Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her Ph.D. degree in electrical engineering from California Institute of Technology (Caltech) in 2005. Li joined Stanford in 2009 and was promoted to associate professor with tenure in 2012. Prior to that, she was on faculty at Princeton University (2007-2009) and University of Illinois Urbana-Champaign (2005-2006). Li was a speaker at the TED2015 main conference, a recipient of the 2016 Nvidia Pioneer in AI Award, 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. https://profiles.stanford.edu/fei-fei-li

Christopher Manning Professor of Linguistics and Computer Science, Stanford University Christopher Manning’s Ph.D. is from Stanford and he held faculty positions at Carnegie Mellon University and the University of Sydney before returning to Stanford. His research goal is computers that can intelligently process, understand, and generate human language material. Manning concentrates on machine learning approaches to computational linguistic problems, including syntactic parsing, computational semantics and pragmatics, textual inference, machine translation, and deep learning for NLP. He is an ACM Fellow, a AAAI Fellow, and an ACL Fellow, and has coauthored leading textbooks on statistical natural language processing and information retrieval. He is a member of the Stanford NLP group (@stanfordnlp). https://profiles.stanford.edu/chris-manning Speakers, continued

Christopher Ré Assistant Professor of Computer Science, Stanford University Christopher (Chris) Ré’s work goal is to enable users and developers to build applications that more deeply understand and exploit data. Chris received his Ph.D. from the University of Washington in Seattle. He then spent four wonderful years on the faculty of the University of Wisconsin, Madison, before coming to Stanford in 2013. He helped discover the first join algorithm with worst-case optimal running time, which won the best paper at PODS 2012. He also helped develop a framework for feature engineering that won the best paper at SIGMOD 2014. Work from his group has been incorporated into scientific efforts including the IceCube neutrino detector and PaleoDeepDive, and into Cloudera’s Impala and products from Oracle, Pivotal, and Microsoft’s Adam. He received an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, a Moore Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, and the MacArthur Foundation Fellowship in 2015. https://profiles.stanford.edu/christopher-re

Daniela Rus Director, CSAIL, Andrew (1956) and Erna Viterbi Professor, Massachusetts Institute of Technology (MIT) Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus’s research interests are in robotics, mobile computing, and data science. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineering. She earned her Ph.D. in Computer Science from Cornell University. Prior to joining MIT, Rus was a professor in the Computer Science Department at Dartmouth College. http://danielarus.csail.mit.edu/

Yoav Shoham Professor Emeritus, Computer Science, Stanford University Yoav Shoham is Professor (emeritus) of Computer Science at Stanford University and Principal Scientist at Google. Previously a director of the Stanford AI Lab, Yoav’s research has focused on knowledge representation, game theory, and electronic commerce. He is Fellow of AAAI and of the ACM, and among his awards are AAAI/ACM Allen Newell award and the ACM/SIGART Autonomous Agents Research Award. He has published or edited five books and many articles, and the online Game Theory courses he co-teaches have had over 500,000 registrants. A serial entrepreneur, Yoav was co-founder and chairman of TradingDynamics (ARBA), Katango (GOOG), and Timeful (GOOG). Dr. Shoham is a member of the Standing Committee of the One Hundred Year Study of AI. http://robotics.stanford.edu/users/shoham/

Megan Smith United States Chief Technology Officer (CTO) in the Office of Science and Technology Policy Megan Smith focuses on how technology policy, data and innovation can advance the future of our nation. Smith is an award-winning entrepreneur, engineer, and tech evangelist. She most recently served as a Vice President at Google, first leading New Business Development—where she managed early-stage partnerships, pilot explorations, and technology licensing across Google’s global engineering and product teams. Smith previously served as CEO of PlanetOut, a leading LGBT online community. Smith was a member of the Massachusetts Institute of Technology (MIT) student team that designed, built, and raced a solar car 2000 miles across the Australian outback. She has served on the boards of MIT, MIT Media Lab, MIT Technology Review, and Vital Voices; as a member of the USAID Advisory Committee on Voluntary Foreign Aid; and as an advisor to the Joan Ganz Cooney Center and the Malala Fund. Smith holds a bachelor’s and master’s degrees in mechanical engineering from the Massachusetts Institute of Technology (MIT). https://www.whitehouse.gov/administration/eop/ostp/about/leadershipstaff/smith

Milind Tambe Helen N. and Emmett H. Jones Professor, University of Southern California (USC) Milind Tambe is Helen N. and Emmett H. Jones Professor in Engineering at the University of Southern California(USC), and Professor in the Computer Science and Industrial and Systems Engineering Departments. He is a fellow of AAAI (Association for Advancement of Artificial Intelligence) (2007), fellow of ACM (Association for Computing Machinery) (2013), recipient of the ACM Autonomous Agents Research Award (2005), Christopher Columbus Fellowship Foundation Homeland security award(2010), the INFORMS Wagner prize for excellence in Operations Research practice (2012), the Rist Prize of the Military Operations Research Society (2011), IBM Faculty Award(2012), Okawa foundation faculty research award (2004), the RoboCup scientific challenge award(1999), Orange County Engineering Council Outstanding Project Achievement Award (2015), USC Associates Award for Creativity in Research (2014) and USC Viterbi School of Engineering use-inspired research award (2009). Dr. Tambe is a member of the current Study Panel for the One Hundred Year Study of AI. http://teamcore.usc.edu/tambe/ About the Stanford Artificial Intelligence Lab

Artificial Intelligence comprises the complete loop from sensing to perception, learning, communications, and action. Stanford’s Artificial Intelligence Lab is devoted to the design of intelligent machines that serve, extend, expand, and improve human endeavor, making life more productive, safer, and healthier. These intelligent machines will learn everything about anything using multi-sensory information and the entire cyber world of information and knowledge.

The faculty members of the Stanford AI Lab are changing the world. Their research includes deep learning and machine learning; robotics; natural language processing; vision, haptics, and sensing; big data and knowledge base; and genomics, medicine, and healthcare. The approach is personalized, adaptive, anticipatory, communicative, and context aware.

Please contact Steve Eglash, Executive Director, Data Science Programs, Stanford University, [email protected], for further information.

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