Eric Horvitz is a technical fellow and director at Microsoft Research. He has made contributions in areas of machine learning, perception, natural language understanding, decision making, and human-AI collaboration. His efforts and collaborations have led to fielded systems in healthcare, transportation, ecommerce, operating systems, and aerospace. He received the Feigenbaum Prize and the Allen Newell Prize for contributions to AI. He has been elected fellow of the National Academy of Engineering (NAE), the Association of Computing Machinery (ACM) , Association for the Advancement of AI (AAAI), and the American Academy of Arts and Sciences. He has served as president of the AAAI, and on advisory committees for the National Science Foundation, National Institutes of Health, President’s Council of Advisors on Science and Technology, DARPA, and the Allen Institute for AI. Beyond technical work, he has pursued efforts and studies on the influences of AI on people and society, including issues around ethics, law, and safety. He established the One Hundred Year Study on AI and served as a founder and co-chair of the Partnership on AI to Support People and Society. Eric received PhD and MD degrees at Stanford University. More information can be found on his home page. A list of publications can be found here.
The Microsoft Research home page is a starting point for browsing through projects, events, and people at the lab at Redmond, and for our sister labs in the U.S. and throughout the world.
Students may find these career reflections by computer scientists interesting and inspirational–stemming from an event that we organized to mark the 20th anniversary of Microsoft Research.
I’ve long been curious about the computational foundations of intelligence: How do our minds work? What computational principles and architectures underly thinking and intelligent behavior? I’ve pursued answers via studies of machine perception, learning, reasoning, and decision making. Many questions remain unanswered and much research is to be done. On the way to a deeper understanding, I work to field working systems that can immediately deliver value in the world. Projects include efforts in time-critical decisions, information retrieval, healthcare, urban infrastructure, sustainability, and development–with goals of understanding how computational models perform amidst real-world complexities, and of deploying systems that deliver value to people and society. A key focus of my work has been on opportunities to leverage the complementarities of human and machine intelligence. Related interests include machine learning and decision making for crowdsourcing and human computation, information triage and alerting that takes human attention into consideration, spanning work on notification systems, surprise modeling, multitasking, and psychological studies of interruption and recovery.
On the more theoretical front, I’ve been long interested in offline and real-time optimization of the expected value of computational systems under limited and varying resources. Areas of concentration in this realm include flexible or anytime computation, ideal metareasoning for guiding computation, compilation for reducing real-time deliberation, ongoing, continual computation, and the construction of bounded-optimal reasoning systems–systems that maximize the expected utility of the people they serve, given the expected costs of reasoning, the problems encountered over time, and assertions about a system’s constitution. Research in this arena includes tackling hard reasoning problems with learning and decision making methods.
Completed cycle of service as president and then past-president of the Association for the Advancement of Artificial Intelligence (AAAI), and remain active with AAAI strategic planning. Recently completed service as chair of the Section on Information, Computing, and Communication of the American Association for the Advancement of Science (AAAS), representing AAAS members with interests in data and computation. Serving this year on the PCAST NITR&D Working Group, NIH Advisory Committee to the Director (ACD) NLM Working Group, and National Academies Committee on Information Technology, Automation, and the Workforce. Served on the Computing Community Consortium, an organization that works to envision computing futures–and to stoke the fires of creativity within our community. As part of that effort, assisted with creating a whitepaper series for communicating the value of research on machine learning and decision support to government leaders and the public.
I’ve worked with colleagues to kick off a new AAAI conference in 2013 human computation and crowdsourcing, to serve an exciting and evolving community. Fellow of the AAAI, ACM, AAAS, the American Academy of Arts and Sciences, and National Academy of Engineering (NAE), and elected to the CHI Academy.
Recent news and events
- Panel discussion: Progress in AI: Myths, Realities, and Aspirations (Faculty Summit video)
- 100 Year Study of AI (Science, NYTimes, press release, framing memo)
- TEDx talk on People, Computing, and Intelligence
- Situated Interaction project, recent story
- BBC Report on Artificial intelligence: How to turn Siri into Samantha
Blast to the relevant past
Featuring this month:
- Challenge Problems for AI. AAAI (1996).
- Artificial Intelligence in the Open World, AAAI Presidential Address.
- Six degrees of separation (2008).
- Notification Platform demo at CHI 2001 keynote with Bill Gates
- Wearable computing for decision support (1995).
- Melding Logic and Probability for Software Debugging. CACM (1995).
- Summer Institute on Crowdsourcing Personalized Online Education (2012).
- Patterns of Search, User Modeling (1999).
- Privacy in Online Services, JAIR (2010).
- Continual Computation, AI Journal (2001).
- Models of Attention in Computing, CACM (2003).