Eric Horvitz

Biography

Horvitz received his PhD in 1990 and his MD degree at Stanford University. His doctoral dissertation, Computation and action under bounded resources, and follow-on research introduced models of bounded rationality founded in probability and decision theory. He did his doctoral work under advisors Ronald A. Howard, George B. Dantzig, Edward H. Shortliffe, and Patrick Suppes.

He is currently Technical Fellow at Microsoft, where he serves as director of Microsoft Research's main Redmond lab. He has been elected Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the National Academy of Engineering (NAE), the American Academy of Arts and Sciences, and of the American Association for the Advancement of Science (AAAS). He was elected to the ACM CHI Academy in 2013 and ACM Fellow 2014 For contributions to artificial intelligence, and human-computer interaction.

In 2015, he was awarded the AAAI Feigenbaum Prize, a biennial award for sustained and high-impact contributions to the field of artificial intelligence through the development of computational models of perception, reflection and action, and their application in time-critical decision making, and intelligent information, traffic, and healthcare systems.

In 2015, he was also awarded the ACM - AAAI Allen Newell Award.

He serves on the Scientific Advisory Committee of the Allen Institute for Artificial Intelligence (AI2) and is also chair of the Section on Information, Computing, and Communications of the American Association for the Advancement of Science (AAAS).

He has served as president of the Association for the Advancement of AI (AAAI), on the NSF Computer & Information Science & Engineering (CISE) Advisory Board, and on the council of the Computing Community Consortium (CCC).

Work

Horvitz's research interests span theoretical and practical challenges with developing systems that perceive, learn, and reason. His contributions include advances in principles and applications of machine learning and inference, information retrieval, human-computer interaction, bioinformatics, and e-commerce.

Horvitz played a significant role in the use of probability and decision theory in artificial intelligence. His work raised the credibility of artificial intelligence in other areas of computer science and computer engineering, influencing fields ranging from human-computer interaction to operating systems. His research helped establish the link between artificial intelligence and decision science. As an example, he coined the concept of bounded optimality, a decision-theoretic approach to bounded rationality.

He studied the use of probability and utility to guide automated reasoning for decision making. The methods include consideration of the solving of streams of problems in environments over time. In related work, he applied probability and machine learning to identify hard problems and to guide theorem proving.

He has issued long-term challenge problems for AI—and has espoused a vision of open-world AI, where machine intelligences have the ability to understand and perform well in the larger world where they encounter situations they have not seen before.

He has explored synergies between human and machine intelligence, with methods that learn about the complementarities between people and AI. He is a founder of the AAAI conference on Human Computation and Crowdsourcing.

He co-authored probability-based methods to enhance privacy, including a model of altruistic sharing of data called community sensing and stochastic privacy.

Horvitz speaks on the topic of artificial intelligence, including on NPR and the Charlie Rose show. Online talks include both technical lectures and presentations for general audiences (TEDx Austin: Making Friends with Artificial Intelligence). His research has been featured in the New York Times and the Technology Review.

Asilomar AI Study

He served as President of the AAAI from 2007-2009. As AAAI President, he called together and co-chaired the Asilomar AI study which culminated in a meeting of AI scientists at Asilomar in February 2009. The study considered the nature and timing of AI successes and reviewed concerns about directions with AI developments, including the potential loss of control over computer-based intelligences, and also efforts that could reduce concerns and enhance long-term societal outcomes. The study was the first meeting of AI scientists to address concerns about superintelligence and loss of control of AI and attracted interest by the public.

In coverage of the Asilomar study, he said that scientists must study and respond to notions of superintelligent machines and concerns about artificial intelligence systems escaping from human control. In a later NPR interview, he said that investments in scientific studies of superintelligences would be valuable to guide proactive efforts even if people believed that the probability of losing of control of AI was low because of the cost of such outcomes.

One Hundred Year Study on Artificial Intelligence

In 2014, he defined and funded with his wife the One Hundred Year Study of Artificial Intelligence at Stanford University. A Stanford press release stated that sets of committees over a century will "study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play." A framing memo for the study calls out 18 topics, including monitoring and addressing possibilities of superintelligences and loss of control of AI.

Founding of Partnership on AI

He served as founding co-chair of an effort to establish the Partnership on AI, an organization that brought Amazon, Facebook, DeepMind, Google, IBM, and Microsoft together to found and fund a non-profit, multi-stakeholder organization. A press release in September 2016 states that the non-profit organization will be guided by balanced leadership that includes “academics, non-profits, and specialists in policy and ethics.” The goals are stated as including research activities, recommending best practices, and publishing in “areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness” of AI technologies.

Publications

Books:

Articles, a selection: