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A Century Long Commitment to Assessing Artificial Intelligence and Its Impact on Society1 Barbara J
A Century Long Commitment to Assessing Artificial Intelligence and Its Impact on Society1 Barbara J. Grosz, Harvard University, Inaugural Chair of the AI100 Standing Committee Peter Stone, University of Texas at Austin, Chair of the Inaugural AI100 Study Panel The Stanford One Hundred Year Study on Artificial Intelligence, a project that launched in December 2014, is designed to be a century-long periodic assessment of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society. Colloquially referred to as "AI100", the project issued its first report in September 2016. A Standing Committee works with the Stanford Faculty Director of AI100 in overseeing the project and designing its activities. A little more than two years after the first report appeared, we reflect on the decisions made in shaping it, the process that produced it, its major conclusions, and reactions subsequent to its release. The inaugural AI100 report [4], which is titled “Artificial Intelligence and Life in 2030,” examines eight domains of human activity in which AI technologies are already starting to affect urban life. In scope, it encompasses domains with emerging products enabled by AI methods and ones raising concerns about technological impact generated by potential AI-enabled systems. The Study Panel members who authored the report and the AI100 Standing Committee, which is the body that directs the AI100 project, intend for it to act as a catalyst, spurring conversations on how we as a society might shape and share the potentially powerful technologies that AI could enable. In addition to influencing researchers and guiding decisions in industry and governments, the report aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential. -
“Reflections on the Status and Future of Artificial Intelligence”
STATEMENT OF: ERIC HORVITZ TECHNICAL FELLOW AND DIRECTOR MICROSOFT RESEARCH—REDMOND LAB MICROSOFT CORPORATION BEFORE THE COMMITTEE ON COMMERCE SUBCOMMITTEE ON SPACE, SCIENCE, AND COMPETITIVENESS UNITED STATES SENATE HEARING ON THE DAWN OF ARTIFICIAL INTELLIGENCE NOVEMBER 30, 2016 “Reflections on the Status and Future of Artificial Intelligence” NOVEMBER 30, 2016 1 Chairman Cruz, Ranking Member Peters, and Members of the Subcommittee, my name is Eric Horvitz, and I am a Technical Fellow and Director of Microsoft’s Research Lab in Redmond, Washington. While I am also serving as Co-Chair of a new organization, the Partnership on Artificial Intelligence, I am speaking today in my role at Microsoft. We appreciate being asked to testify about AI and are committed to working collaboratively with you and other policymakers so that the potential of AI to benefit our country, and to people and society more broadly can be fully realized. With my testimony, I will first offer a historical perspective of AI, a definition of AI and discuss the inflection point the discipline is currently facing. Second, I will highlight key opportunities using examples in the healthcare and transportation industries. Third, I will identify the important research direction many are taking with AI. Next, I will attempt to identify some of the challenges related to AI and offer my thoughts on how best to address them. Finally, I will offer several recommendations. What is Artificial Intelligence? Artificial intelligence (AI) refers to a set of computer science disciplines aimed at the scientific understanding of the mechanisms underlying thought and intelligent behavior and the embodiment of these principles in machines that can deliver value to people and society. -
Curriculum Vitae
6/14/21 CURRICULUM VITAE Edward Hance Shortliffe, MD, PhD, MACP, FACMI, FIAHSI [work] Chair Emeritus and Adjunct Professor, Department of Biomedical Informatics Vagelos College of Physicians and Surgeons, Columbia University in the City of New York [email protected] – https://www.dbmi.columbia.edu/people/edward-shortliffe/ Adjunct Professor of Biomedical Informatics College of Health Solutions Arizona State University, Phoenix, AZ [email protected] – https://isearch.asu.edu/profile/1098580 Adjunct Professor, Department of Healthcare Policy and Research (Health Informatics) Weill Cornell Medical College, New York, NY http://hpr.weill.cornell.edu/divisions/health_informatics/ [home] 272 W 107th St #5B, New York, NY 10025-7833 Phone: 212-666-8440 — Mobile: 917-640-0933 [email protected] – http://www.shortliffe.net Born: Edmonton, Alberta, Canada Date of birth: 28 August 1947 Citizenship: U.S.A. (naturalized - 1962) Spouse: Vimla L. Patel, PhD Education From To School/Institution Major Subject, Degree, and Date 9/62 6/65 The Loomis School, Windsor, CT. High School 9/65 7/66 Gresham's School, Holt, Norfolk, U.K. Foreign Exchange Student 9/66 6/70 Harvard College, Cambridge, MA. Applied Math and Computer Science, A.B., June 1970 9/70 1/75 Stanford University, Stanford, CA PhD, Medical Information Sciences, January 1975 9/70 6/76 Stanford University School of Medicine MD, June 1976. 7/76 6/77 Massachusetts General Hospital, Boston, MA Internship in Internal Medicine 7/77 6/79 Stanford University Hospital, Stanford, CA Residency in Internal Medicine Honors Graduation Magna Cum Laude, Harvard College, June 1970 Medical Scientist Training Program (MSTP), NIH-funded Stanford Traineeship, September 1971 - June 1976 Grace Murray Hopper Award (Distinguished computer scientist under age 30), Association for Computing Machinery, October 1976 Research Career Development Award, National Library of Medicine, July 1979—June 1984 Henry J. -
Machine Learning, Reasoning, and Intelligence in Daily Life: Directions and Challenges
Machine Learning, Reasoning, and Intelligence in Daily Life: Directions and Challenges Eric Horvitz Microsoft Research Redmond, Washington USA 98052 [email protected] Abstract An example of an implicit integration of ambient learning Technical developments and trends are providing a and reasoning is the effort by our team to create a probabil- fertile substrate for creating and integrating ma- istic action prediction and prefetching subsystem that is em- chine learning and reasoning into multiple applica- bedded deeply in the kernel of Microsoft’s Windows Vista tions and services. I will review several illustrative operating system. The predictive component, operating research efforts on our team, and focus on chal- within a component in the Vista operating system called lenges, opportunities, and directions with the Superfetch, learns by watching sequences of application streaming of machine intelligence into daily life. launches over time to predict a computer user’s application launches. These predictions, coupled with a utility model 1 Reflections on Trends and Directions that captures preferences about the cost of waiting, are used Over the last decade, technical and infrastructural develop- in an ongoing optimization to prefetch unlaunched applica- ments have come together to create a nurturing environment tions into memory ahead of their manual launching. The for developing and fielding applications of machine learning implicit service seeks to minimize the average wait for ap- and reasoning—and for harnessing automated intelligence -
The Dawn of Artificial Intelligence Hearing
S. HRG. 114–562 THE DAWN OF ARTIFICIAL INTELLIGENCE HEARING BEFORE THE SUBCOMMITTEE ON SPACE, SCIENCE, AND COMPETITIVENESS OF THE COMMITTEE ON COMMERCE, SCIENCE, AND TRANSPORTATION UNITED STATES SENATE ONE HUNDRED FOURTEENTH CONGRESS SECOND SESSION NOVEMBER 30, 2016 Printed for the use of the Committee on Commerce, Science, and Transportation ( U.S. GOVERNMENT PUBLISHING OFFICE 24–175 PDF WASHINGTON : 2017 For sale by the Superintendent of Documents, U.S. Government Publishing Office Internet: bookstore.gpo.gov Phone: toll free (866) 512–1800; DC area (202) 512–1800 Fax: (202) 512–2104 Mail: Stop IDCC, Washington, DC 20402–0001 VerDate Nov 24 2008 13:07 Feb 15, 2017 Jkt 075679 PO 00000 Frm 00001 Fmt 5011 Sfmt 5011 S:\GPO\DOCS\24175.TXT JACKIE SENATE COMMITTEE ON COMMERCE, SCIENCE, AND TRANSPORTATION ONE HUNDRED FOURTEENTH CONGRESS SECOND SESSION JOHN THUNE, South Dakota, Chairman ROGER F. WICKER, Mississippi BILL NELSON, Florida, Ranking ROY BLUNT, Missouri MARIA CANTWELL, Washington MARCO RUBIO, Florida CLAIRE MCCASKILL, Missouri KELLY AYOTTE, New Hampshire AMY KLOBUCHAR, Minnesota TED CRUZ, Texas RICHARD BLUMENTHAL, Connecticut DEB FISCHER, Nebraska BRIAN SCHATZ, Hawaii JERRY MORAN, Kansas EDWARD MARKEY, Massachusetts DAN SULLIVAN, Alaska CORY BOOKER, New Jersey RON JOHNSON, Wisconsin TOM UDALL, New Mexico DEAN HELLER, Nevada JOE MANCHIN III, West Virginia CORY GARDNER, Colorado GARY PETERS, Michigan STEVE DAINES, Montana NICK ROSSI, Staff Director ADRIAN ARNAKIS, Deputy Staff Director JASON VAN BEEK, General Counsel KIM LIPSKY, Democratic -
Report on the Future of AI Workshop
Report on The Future of AI Workshop Date: December 13 - 15, 2002 Venue: Amagi Homestead, IBM Japan Edited by the Future of AI Workshop Steering Committee Edward A. Feigenbaum Setsuo Ohsuga Hiroshi Motoda Koji Sasaki Compiled by AdIn Research, Inc. August 31, 2003 Please direct general inquiries to AdIn Research, Inc. 3-6 Kioicho, Chiyoda-ku, Tokyo 102-0094, Japan phone: +81-3-3288-7311 fax: +81-3-3288-7568 url: http://www.adin.co.jp/ Table of Contents Prospectus ………………………………………………………………………………………………… 4 Outline …………………………………………………………………………………………………… 5 Co-sponsors and Supporting Organizations ………………………………………………………………… 6 Schedule ……………………………………………………………………………………………………. 9 List of Panelists …………………………………………………………………………………………….. 10 Contact Information …………………………………….…………………………………………………... 12 Keynote Speech Edward A. Feigenbaum ………………………………………………………………... 17 Sessions 1. FOUNDATIONS OF AI 1. Hiroki Arimura ………………………………………………………………………… 27 2. Stuart Russell …………………………………………………………………………... 30 3. Naonori Ueda …………………………………………………………………………... 34 4. Akito Sakurai …………………………………………………………………………... 38 2. DISCOVERY 1. Einoshin Suzuki ……………………………………………………………………...… 45 2. Satoru Miyano …………………………………………………………………...…….. 49 3. Thomas Dietterich …………………………………………………………...………… 55 4. Hiroshi Motoda ………………………………………………………...……………… 62 3. HCL 1. Yasuyuki Sumi …………………………………………………...……………………. 68 2. Kumiyo Nakakoji ………………………………………………...……………………. 72 3. Toru Ishida ………………………………………………………..…………………… 77 4. Eric Horvitz ………………………………………………………..…………………... 83 4. AI SYSTEMS 1. Ron -
Public Response to RFI on AI
White House Office of Science and Technology Policy Request for Information on the Future of Artificial Intelligence Public Responses September 1, 2016 Respondent 1 Chris Nicholson, Skymind Inc. This submission will address topics 1, 2, 4 and 10 in the OSTP’s RFI: • the legal and governance implications of AI • the use of AI for public good • the social and economic implications of AI • the role of “market-shaping” approaches Governance, anomaly detection and urban systems The fundamental task in the governance of urban systems is to keep them running; that is, to maintain the fluid movement of people, goods, vehicles and information throughout the system, without which it ceases to function. Breakdowns in the functioning of these systems and their constituent parts are therefore of great interest, whether it be their energy, transport, security or information infrastructures. Those breakdowns may result from deteriorations in the physical plant, sudden and unanticipated overloads, natural disasters or adversarial behavior. In many cases, municipal governments possess historical data about those breakdowns and the events that precede them, in the form of activity and sensor logs, video, and internal or public communications. Where they don’t possess such data already, it can be gathered. Such datasets are a tremendous help when applying learning algorithms to predict breakdowns and system failures. With enough lead time, those predictions make pre- emptive action possible, action that would cost cities much less than recovery efforts in the wake of a disaster. Our choice is between an ounce of prevention or a pound of cure. Even in cases where we don’t have data covering past breakdowns, algorithms exist to identify anomalies in the data we begin gathering now. -
ACM-BCB 2016 the 7Th ACM Conference on Bioinformatics
ACM-BCB 2016 The 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics October 2-5, 2016 Organizing Committee General Chairs: Steering Committee: Ümit V. Çatalyürek, Georgia Institute of Technology Aidong Zhang, State University of NeW York at Buffalo, Genevieve Melton-Meaux, University of Minnesota Co-Chair May D. Wang, Georgia Institute of Technology and Program Chairs: Emory University, Co-Chair John Kececioglu, University of Arizona Srinivas Aluru, Georgia Institute of Technology Adam Wilcox, University of Washington Tamer Kahveci, University of Florida Christopher C. Yang, Drexel University Workshop Chair: Ananth Kalyanaraman, Washington State University Tutorial Chair: Mehmet Koyuturk, Case Western Reserve University Demo and Exhibit Chair: Robert (Bob) Cottingham, Oak Ridge National Laboratory Poster Chairs: Lin Yang, University of Florida Dongxiao Zhu, Wayne State University Registration Chair: Preetam Ghosh, Virginia CommonWealth University Publicity Chairs Daniel Capurro, Pontificia Univ. Católica de Chile A. Ercument Cicek, Bilkent University Pierangelo Veltri, U. Magna Graecia of Catanzaro Student Travel Award Chairs May D. Wang, Georgia Institute of Technology and Emory University JaroslaW Zola, University at Buffalo, The State University of NeW York Student Activity Chair Marzieh Ayati, Case Western Reserve University Dan DeBlasio, Carnegie Mellon University Proceedings Chairs: Xinghua Mindy Shi, U of North Carolina at Charlotte Yang Shen, Texas A&M University Web Admins: Anas Abu-Doleh, The -
David C. Parkes John A
David C. Parkes John A. Paulson School of Engineering and Applied Sciences, Harvard University, 33 Oxford Street, Cambridge, MA 02138, USA www.eecs.harvard.edu/~parkes December 2020 Citizenship: USA and UK Date of Birth: July 20, 1973 Education University of Oxford Oxford, U.K. Engineering and Computing Science, M.Eng (first class), 1995 University of Pennsylvania Philadelphia, PA Computer and Information Science, Ph.D., 2001 Advisor: Professor Lyle H. Ungar. Thesis: Iterative Combinatorial Auctions: Achieving Economic and Computational Efficiency Appointments George F. Colony Professor of Computer Science, 7/12-present Cambridge, MA Harvard University Co-Director, Data Science Initiative, 3/17-present Cambridge, MA Harvard University Area Dean for Computer Science, 7/13-6/17 Cambridge, MA Harvard University Harvard College Professor, 7/12-6/17 Cambridge, MA Harvard University Gordon McKay Professor of Computer Science, 7/08-6/12 Cambridge, MA Harvard University John L. Loeb Associate Professor of the Natural Sciences, 7/05-6/08 Cambridge, MA and Associate Professor of Computer Science Harvard University Assistant Professor of Computer Science, 7/01-6/05 Cambridge, MA Harvard University Lecturer of Operations and Information Management, Spring 2001 Philadelphia, PA The Wharton School, University of Pennsylvania Research Intern, Summer 2000 Hawthorne, NY IBM T.J.Watson Research Center Research Intern, Summer 1997 Palo Alto, CA Xerox Palo Alto Research Center 1 Other Appointments Member, 2019- Amsterdam, Netherlands Scientific Advisory Committee, CWI Member, 2019- Cambridge, MA Senior Common Room (SCR) of Lowell House Member, 2019- Berlin, Germany Scientific Advisory Board, Max Planck Inst. Human Dev. Co-chair, 9/17- Cambridge, MA FAS Data Science Masters Co-chair, 9/17- Cambridge, MA Harvard Business Analytics Certificate Program Co-director, 9/17- Cambridge, MA Laboratory for Innovation Science, Harvard University Affiliated Faculty, 4/14- Cambridge, MA Institute for Quantitative Social Science International Fellow, 4/14-12/18 Zurich, Switzerland Center Eng. -
Crowdsourcing General Computation
Crowdsourcing General Computation The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Zhang, Haoqi, Eric Horvitz, Rob C. Miller, and David C. Parkes. 2011. Crowdsourcing general computation. In Proceedings of the 2011 ACM Conference on Human Factors in Computing Systems: May 7-12, 2011, Vancouver, British Columbia. New York, NY: Association for Computing Machinery. Published Version http://www.chi2011.org/ Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:9961302 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP Crowdsourcing General Computation Haoqi Zhang∗, Eric Horvitzy, Rob C. Millerz, and David C. Parkes∗ ∗Harvard SEAS yMicrosoft Research zMIT CSAIL Cambridge, MA 02138, USA Redmond, WA 98052, USA Cambridge, MA 02139, USA fhq, [email protected] [email protected] [email protected] ABSTRACT With the growth of online task markets, such parallel solving We present a direction of research on principles and methods is becoming increasingly available to anyone with a task in that can enable general problem solving via human compu- mind, and the willingness to pay for its completion. tation systems. A key challenge in human computation is the effective and efficient coordination of problem solving. Although such systems allow a variety of tasks to be com- While simple tasks may be easy to partition across individ- pleted by humans, the tasks posed for solution are typically uals, more complex tasks highlight challenges and oppor- simple, and thus easy to divide and distribute across indi- tunities for more sophisticated coordination and optimiza- viduals. -
The Effect of AI Explanations on Complementary Team Performance
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance Gagan Bansal∗ Joyce Zhou† Besmira Nushi Tongshuang Wu∗ Raymond Fok† [email protected] [email protected] [email protected] Microsoft Research [email protected] [email protected] University of Washington University of Washington Ece Kamar Marco Tulio Ribeiro Daniel S. Weld [email protected] [email protected] [email protected] Microsoft Research Microsoft Research University of Washington & Allen Institute for Artificial Intelligence ABSTRACT ACM Reference Format: Many researchers motivate explainable AI with studies showing Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, that human-AI team performance on decision-making tasks im- Ece Kamar, Marco Tulio Ribeiro, and Daniel S. Weld. 2021. Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team proves when the AI explains its recommendations. However, prior Performance. In CHI Conference on Human Factors in Computing Systems studies observed improvements from explanations only when the (CHI ’21), May 8–13, 2021, Yokohama, Japan. ACM, New York, NY, USA, AI, alone, outperformed both the human and the best team. Can 16 pages. https://doi.org/10.1145/3411764.3445717 explanations help lead to complementary performance, where team accuracy is higher than either the human or the AI working solo? 1 INTRODUCTION We conduct mixed-method user studies on three datasets, where an Although the accuracy of Artificial Intelligence (AI) systems is AI with accuracy comparable to humans helps participants solve a rapidly improving, in many cases, it remains risky for an AI to op- task (explaining itself in some conditions). -
Douglas C Downey
Douglas C Downey Electrical Engineering and Computer Science Dept. [email protected] Northwestern University, Ford 3-345 (847)491-3710 2133 Sheridan Road http://www.cs.northwestern.edu/~ddowney Evanston, IL 60208 Research Natural Language Processing, Machine Learning, Artificial Intelligence Interests Education University of Washington September 2002 – Ph.D. Computer Science and Engineering, 2008 December 2008 Dissertation Title: Redundancy in Web-scale Information Extraction: Probabilistic Model and Experimental Results M.S. Computer Science and Engineering, 2004 Case Western Reserve University August 1996 – B.S./M.S. in Computer Science May 2000 Minors in Mathematics and Economics Professional Northwestern University, Associate Professor September 2014 – Experience Department of Electrical Engineering and Computer Science present Northwestern University, Assistant Professor September 2008 – Department of Electrical Engineering and Computer Science 2014 University of Washington, Research Assistant June 2003 – Advisor: Oren Etzioni September 2008 March 2006 – Microsoft Corporation, Research Intern July 2006 Mentor: Susan Dumais Manager: Eric Horvitz October 2000 – Intel Corporation, Internet Software Engineer September 2002 October 1998 – Case Western Reserve University, Research Assistant September 2000 Advisor: Randall D. Beer Honors and NSF CAREER Award, 2014 Awards Northwestern EECS “Teacher of the Year,” 2012 US SECRET Security Clearance, 2012-2016 DARPA Computer Science Study Panel, 2011 Microsoft New Faculty Fellowship,