Kathleen Mckeown
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Bit.Ly/Pm19hms Can AI Accelerate Precision Medicine?
Department of Biomedical Informatics 10 Shattuck Street, Suite 514 Boston, MA 02115 dbmi.hms.harvard.edu @HarvardDBMI TWITTER #pm19hms LINK bit.ly/pm19hms Can AI Accelerate Precision Medicine? Harvard Medical School June 18, 2019 Welcome Recent progress in artificial intelligence has been touted as the solution for many of the challenges facing clinicians and patients in diagnosing and treating disease. Concurrently, there is a growing concern about the biases and lapses caused by the use of algorithms that are incompletely understood. Yet precision medicine* requires sifting through millions of individuals measured with thousands of variables: an analysis that defeats human cognitive capabilities. In this conference, we ask the question whether AI can be used effectively to accelerate precision medicine in ways that are safe, non-discriminatory, affordable and in the patient’s best interest. To answer the question, we have three panels and three keynote speakers. The first panel addresses the question ofPolicy and the Patient: Who’s in Control? We will review the intersection of consent, transparency and regulatory oversight in this very dynamic ethical landscape. The second panel, Is There Value in Prediction?, addresses a widely-shared challenge in medicine, which is to predict the patient’s future, and most importantly, their response to therapy. Can AI actually make an important contribution here? The third panel on Hyperindividualized Treatments asks the question of how to think about a future that is fast becoming the present, where therapy is “hyper-individualized,” so it is the very uniqueness of your genome that determines your therapy. As always, we have a patient representative for our opening keynote, Matt Might, who returns to this annual conference to discuss the development of an algorithm for conducting precision medicine, through the lens of a personal story: discovering that his child was the first case of a new, ultra-rare genetic disorder. -
Sentence Fusion for Multidocument News Summarization
Sentence Fusion for Multidocument News Summarization ∗ † Regina Barzilay Kathleen R. McKeown Massachusetts Institute of Technology Columbia University A system that can produce informative summaries, highlighting common information found in many online documents, will help Web users to pinpoint information that they need without extensive reading. In this article, we introduce sentence fusion, a novel text-to-text generation technique for synthesizing common information across documents. Sentence fusion involves bottom-up local multisequence alignment to identify phrases conveying similar information and statistical generation to combine common phrases into a sentence. Sentence fusion moves the summarization field from the use of purely extractive methods to the generation of abstracts that contain sentences not found in any of the input documents and can synthesize information across sources. 1. Introduction Redundancy in large text collections, such as the Web, creates both problems and opportunities for natural language systems. On the one hand, the presence of numer- ous sources conveying the same information causes difficulties for end users of search engines and news providers; they must read the same information over and over again. On the other hand, redundancy can be exploited to identify important and accurate information for applications such as summarization and question answering (Mani and Bloedorn 1997; Radev and McKeown 1998; Radev, Prager, and Samn 2000; Clarke, Cormack, and Lynam 2001; Dumais et al. 2002; Chu-Carroll et al. 2003). Clearly, it would be highly desirable to have a mechanism that could identify common information among multiple related documents and fuse it into a coherent text. In this article, we present a method for sentence fusion that exploits redundancy to achieve this task in the context of multidocument summarization. -
Artificial Intelligence: Implications for Business Strategy Online Short Course
MIT SLOAN SCHOOL OF MANAGEMENT MIT COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LABORATORY (CSAIL) ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR BUSINESS STRATEGY ONLINE SHORT COURSE Gain the knowledge and confdence to support the integration of AI into your organization. Certifcate Track: Management and Leadership ABOUT THIS COURSE What is artifcial intelligence (AI)? What does it mean for business? And how can your company take advantage of it? This online program, designed by the MIT Sloan School of Management and the MIT Computer Science and Artifcial Intelligence Laboratory (CSAIL), will help you answer these questions. Through an engaging mix of introductions to key technologies, business insights, case examples, and your own business-focused project, your learning journey will bring into sharp focus the reality of central AI technologies today and how they can be harnessed to support your business needs. Focusing on key AI technologies, such as machine learning, natural language processing, and robotics, the course will help you understand the implications of these new technologies for business strategy, as well as the economic and societal issues they raise. MIT expert instructors examine how artifcial intelligence will complement and strengthen our workforce rather than just eliminate jobs. Additionally, the program will emphasize how the collective intelligence of people and computers together can solve business problems that not long ago were considered impossible. WHAT THE PROGRAM COVERS Upon completion of the program, you’ll be ready to apply your knowledge to support informed This 6-week online program presents you with a foundational understanding of where we are today strategic decision making around the use of key AI with AI and how we got here. -
Kathleen Mckeown
Kathleen McKeown Department of Computer Science Columbia University New York, NY. 10027 U.S.A. Phone: 212-939-7118 Fax: 212-666-0140 email: [email protected] website: http://www.cs.columbia.edu/ kathy Current position Henry and Gertrude Rothschild Professor of Computer Science, Columbia University, New York Research Interests Computational Linguistics/Natural-Language Processing: Text Summarization; Language Genera- tion; Social Media Analysis; Open-ended question answering; Sentiment Analysis Education 1982 Ph.D., Computer and Information Science, University of Pennsylvania 1979 M.S., Computer and Information Science, University of Pennsylvania 1976 A.B., Comparative Literature, Brown University Appointments held 2012-2018 Founding Director, Columbia Data Science Institute 2011-2012 Vice Dean for Research, School of Engineering and Applied Science 2005-present Henry and Gertrude Rothschild Professor of Computer Science, Columbia University 1997-present Professor, Columbia University 7/2003-12/2003 Acting Chair, Department of Computer Science, Columbia University 1997-2002 Chair, Department of Computer Science, Columbia University 1987-1997 Associate Professor, Columbia University 1982-1987 Assistant Professor, Columbia University Honors & awards 2018 Keynote Speaker, ROCLING-18 speaker, Hsinchu, Taiwan. 2016 Keynote Speaker, International Semantic Web Conference, Kobe, Japan 20162014 Grace Hopper Distinguished Lecture, University of Pennsylvania Philadelphia, Pa., Nov. 2016. Distin- guished Lecturer, University of Edinburgh, Launch of the Centre for Doctoral Training in Data Science, Edinburgh, Scotland 2014 Keynote Speaker, 10th IEEE International Conference on eScience, Guaruja, Sao Paolo, Brazil, October 2013 Keynote Speaker, The 2013 Conference of the North American Association for Computational Linguistics K. McKeown 2 2012 Best paper award, Kristen Parton, Nizar Habash, Kathleen McKeown, Gonzalo Iglesias, and Adria de Gis- pert. -
Gregory Ward
Gregory Ward Department of Linguistics Tel.: (847) 491-8055 Northwestern University Fax: (847) 491-3770 2016 Sheridan Road, Suite 21 Email: [email protected] Evanston, IL 60208-4090 Internet: http://www.gregoryward.org Education 1985 Ph.D. in Linguistics, University of Pennsylvania. Thesis: The Semantics and Pragmatics of Preposing. Advisor: Ellen F. Prince. 1978 B.A. in Linguistics (with Honors) and Comparative Literature, University of California, Berkeley. Graduated with Great Distinction in General Scholarship from the College of Letters & Sciences. Phi Beta Kappa. Appointments Current: 1997 — Professor, Department of Linguistics, Northwestern University. 2014 — Professor, by courtesy, Department of Philosophy, Northwestern University (Affiliated Faculty since 2007). 2014 — Member of the Advisory Board, Program in Gender & Sexuality Studies, Northwestern University. Other: 2015 Faculty, LSA Linguistic Institute, University of Chicago, Chicago. 2013 Faculty, LSA Linguistic Institute, University of Michigan, Ann Arbor. 2011 Faculty, LSA Linguistic Institute, University of Colorado, Boulder. 2011 Distinguished Visiting Professor, University of Vigo, Spain. June. 2010 Distinguished Visiting Professor, University of Osnabrück, Germany. July. 2010 Visiting Professor, Department of Cognitive and Linguistic Sciences, Brown University. May. 2009 Faculty, LSA Linguistic Institute, University of California, Berkeley. 2008 Distinguished Visiting Professor, University of Santiago de Compostela, Spain. June. 2007 Faculty, LSA Linguistic Institute, Stanford University. 2003 Faculty, LSA Linguistic Institute, Michigan State University. 1999 – 04 Chair, Department of Linguistics, Northwestern University. 1997 Faculty, LSA Linguistic Institute, Cornell University. Gregory Ward, CV 2 1996 Visiting Professor, UFR Angellier (Department of English), Université Charles de Gaulle – Lille 3. 1993 Faculty, LSA Linguistic Institute, The Ohio State University. 1991 – 97 Associate Professor, Department of Linguistics, Northwestern University. -
Proceedings of the Human Language Technology Conference of The
HLT-NAACL 2006 Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics Proceedings of the Main Conference Robert C. Moore, General Chair Jeff Bilmes, Jennifer Chu-Carroll and Mark Sanderson Program Committee Chairs June 4-9, 2006 New York, New York, USA Published by the Association for Computational Linguistics http://www.aclweb.org Production and Manufacturing by Omnipress Inc. 2600 Anderson Street Madison, WI 53704 c 2006 The Association for Computational Linguistics Order copies of this and other ACL proceedings from: Association for Computational Linguistics (ACL) 209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 [email protected] ii Preface from the General Chair This year marks the third time that the conference on Human Language Technology has combined with the North American chapter meeting of the Association for Computational Linguistics. The roster of accepted papers reveals an eclectic mix of topics in natural-language processing, speech processing, and information retrieval. A gratifying number of the papers are difficult to classify because they span more than one of these three major areas of human language technology. For example, the boundary between natural-language processing and information retrieval is hard to draw in the papers that focus on the World Wide Web as a corpus; moreover, several of these include speech-related aspects as well. The crazy thing about putting on a conference like this is that you start out with a group of people who have never done it before, and by the time they really figure out what they are doing, the conference is over and you replace them with another group of people who have never done it before! To do a good job as general chair, however, there is only one really important thing to learn: pick really good people to do all the other jobs, sit back, and let them do all the work. -
Download a PDF of This Issue
Massachusetts Institute of Technology Spectrum Spring 2021 PLUS EMPLOYING AI AND SYNTHETIC BIO TO FIGHT THE PANDEMIC P. 2 2 EARTH From left: Kripa Varanasi, MIT professor of mechanical engineering; Karim Khalil PhD ’18; and Maher Damak PhD ’18 cofounded Infinite Cooling to capture and recycle vaporized water from thermoelectric power plants. PHOTO: COURTESY OF INFINITE COOLING LOOKING FOR MORE? Infinite Cooling is just one of many sustainability-focused companies with MIT roots. Read about them and find additional stories of MIT’s extraordinary faculty, researchers, and students at work exclusively at spectrum.mit.edu SPECIAL SECTION Breakthroughs Wide Angle Earth and Insights 2 Community Building 8 Faculty experts paint big picture 22 Synthetic biologist Jim Collins engineers disease fighters 10 MIT accelerates climate action 11 Campus becomes test bed Subjects for flood-risk assessment 12 Engineering projects Inside the MIT span climate landscape Campaign for 4 14.13 Psychology and 14 Improving Earth system modeling Economics a Better World 15 Doctoral student helps architects design cooler buildings 24 David ’69 and Jeanne-Marie 16 Why care about climate change? Brookfield: MIT family gives back John Sterman explains 25 Thuan ’90, SM ’91 and Nicole Pham: 18 Desirée Plata devises new Financial aid success story methods for decontaminating air, water 19 New J-PAL initiative zeros in on climate impacts in Spectrum is printed on 100% recycled vulnerable regions paper by DS Graphics | Universal Wilde 20 PhD candidate uses optical in Lowell, MA. DSG | UW is certified by the sensor to assess oceans’ Forest Stewardship Council®, the chemical changes Sustainable Forestry Initiative®, and the Program for the Endorsement of Forest FRONT COVER Blooms of cynobacteria, a type Certification standards. -
Decoding Algorithms for Complex Natural Language Tasks Pawan
Decoding Algorithms for Complex Natural Language Tasks by Pawan Deshpande Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Masters of Engineering in Computer Science and Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY January 2007 @ Massachusetts Institute of Technology 2007. A]l1rights reserved. Author .................................... Department of Electrical Engineering and Computer Science January 30, 2007 Certified by... ............. .... Assetgina Barzilay Assistant Professor Thesis Supervisor *y;o77 / Accepted by .......... .(. ......... ........ .. - Arthur C. Smith MASSACHUSETTS INSTITUTE Chairman, Department Committee on Graduate Students OF TECHNOLOGY OCT 0 3 2007 ARCHIVES LIBRARIES Decoding Algorithms for Complex Natural Language Tasks by Pawan Deshpande Submitted to the Department of Electrical Engineering and Computer Science on January 30, 2007, in partial fulfillment of the requirements for the degree of Masters of Engineering in Computer Science and Engineering Abstract This thesis focuses on developing decoding techniques for complex Natural Language Processing (NLP) tasks. The goal of decoding is to find an optimal or near optimal solution given a model that defines the goodness of a candidate. The task is chal- lenging because in a typical problem the search space is large, and the dependencies between elements of the solution are complex. The goal of this work is two-fold. First, we are interested in developing decoding techniques with strong theoretical guarantees. We develop a decoding model based on the Integer Linear Programming paradigm which is guaranteed to compute the opti- mal solution and is capable of accounting for a wide range of global constraints. As an alternative, we also present a novel randomized algorithm which can guarantee an ar- bitrarily high probability of finding the optimal solution. -
31St Annual Meeting of the Association for Computational Linguistics
31st Annual Meeting of the Association for Computational Linguistics Proceedings of the Conference 22-26 June 1993 Ohio State University Columbus, Ohio, USA Published by the Association for Computational Linguistics © 1993, Association for Computational Linguistics Order copies of this and other ACL proceedings from: Donald E. Walker (ACL) Bellcore 445 South Street, MRE 2A379 Morristown, NJ 07960, USA PREFACE This volume contains the papers prepared for the 31 st Annual Meeting of the Association for Compu- tational Linguistics, held 22-26 June 1993 at The Ohio State University in Columbus, Ohio. The cluster of papers in the final section stems from the student session, featured at the meeting for the 3rd successive year and testifying to the vigor of this emerging tradition. The number and quality of submitted papers was again gratifying, and all authors deserve our collec- tive plaudits for the efforts they invested despite the well-known risks of submitting to a highly selective conference. It was their efforts that once again ensured a Meeting (and Proceedings) reflecting the highest standards in computational linguistics, offering a tour of some of the most significant recent ad- vances and most lively research frontiers. Special thanks go to our invited speakers, Wolfgang Wahlster, Geoff Nunberg and Barbara Partee, for contributing their insights and panache to the conference; to Philip Cohen for concocting and coordinat- ing a varied and relevant tutorial program, and to Helen Gigley, Steve Small, Alexis Manaster Ramer, Wlodek Zadrozny, Kent Wittenburg, David D. Lewis, and Elizabeth D. Liddy for offering the tutorials; to Linda Suri and Sandra Carberry for very effectively co-organizing the program for the student sessions (see the separate preface for the student session papers); to Terry Patten for directing local arrangements, and to Robert Kasper for helping with local arrangements and coordinating exhibits and demonstrations. -
Julia Hirschberg
JULIA HIRSCHBERG September 12, 2021 Columbia University Department of Computer Science 1214 Amsterdam Avenue, M/C 0401 450 CS Building New York, NY 10027 email: [email protected] phone: (212) 853-8464 FAX: (212) 853-8440 http://www.cs.columbia.edu/ julia Biographical Sketch Julia Hirschberg has been a professor in the Department of Computer Science at Columbia University since 2002, serving as Chair from July 2012 through June 2018. She was appointed Percy K. and Vida L. W. Hudson Professor of Computer Science in 2013. She received her PhD in Computer Science from the University of Pennsylvania. She worked at Bell Laboratories and AT&T Laboratories { Research from 1985-2003 as a Member of Technical Staff, Department Head, and Technology Leader, creating the Human-Computer Interface Research Department at Bell Labs. She has done research on prosody, dis- course structure, conversational implicature, text-to-speech synthesis, speech search, spoken dialogue systems, emotional speech, deceptive speech, charismatic speech and speech summarization. She served as editor-in-chief of Computational Linguistics from 1993-2003 and was an editor-in-chief of Speech Communication from 2003-2006 and is now on the Editorial Board. She served on the Executive Board of the Association for Computational Linguistics (ACL) from 1993-2003, has served on the Permanent Council of International Conference on Spoken Language Processing (ICSLP) since 1996, the IEEE Speech and Language Processing Technical Committee (SLTC) from 2011{, the executive board of the -
Ai Governance in 2019 a Year in Review
The report editor can be reached at [email protected] We welcome any comments on this report and any communication related to AI AI GOVERNANCE IN 2019 governance. A YEAR IN REVIEW OBSERVATIONS OF 50 GLOBAL EXPERTS SHANGHAI INSTITUTE FOR SCIENCE OF SCIENCE April, 2020 Shanghai Institute for Science of Science ALL LIVING THINGS ARE NOURISHED WITHOUT INJURING ONE ANOTHER, AND ALL ROADS RUN PARALLEL WITHOUT INTERFERING WITH ONE ANOTHER. CHUNG YUNG, SECTION OF THE LI CHI TABLE OF CONTENTS FOREWORD China Initiative: Applying Long-Cycle, Multi-Disciplinary Social Experimental on Exploring 21 the Social Impact of Artificial Intelligence By SHI Qian By SU Jun Going Beyond AI Ethics Guidelines 23 INTRODUCTION 01 By Thilo Hagendorff By LI Hui and Brian Tse 25 PART 1 TECHNICAL PERSPECTIVES FROM WORLD-CLASS SCIENTISTS 07 By Petra Ahrweiler 27 The Importance of Talent in the Information Age 07 By John Hopcroft The Impact of Journalism 29 From the Standard Model of AI to Provably Beneficial Systems 09 By Colin Allen By Stuart Russell and Caroline Jeanmaire Future of Work in Singapore: Staying on Task 31 The Importance of Federated Learning 11 By Poon King Wang By YANG Qiang Developing AI at the Service of Humanity 33 Towards A Formal Process of Ethical AI 13 By Ferran Jarabo Carbonell By Pascale Fung Enhance Global Cooperation in AI Governance on the Basis of Further Cultural Consensus 35 From AI Governance to AI Safety 15 By WANG Xiaohong By Roman Yampolskiy Three Modes of AI Governance 37 By YANG Qingfeng PART 2 INTERDISCIPLINARY ANALYSES FROM PROFESSIONAL RESEARCHERS 17 PART 3 RESPONSIBLE LEADERSHIP FROM THE INDUSTRY 39 The Rapid Growth in the Field of AI Governance 17 By Allan Dafoe & Markus Anderljung Companies Need to Take More Responsibilities in Advancing AI Governance 39 Towards Effective Value Alignment in AI: From "Should" to "How" 19 By YIN Qi By Gillian K. -
Our Digital Future in a Divided World 22Nd APRU ANNUAL JUNE 24-26, 2018 PRESIDENTS’ MEETING National TAIWAN UNIVERSIT Y
Our Digital Future in a Divided World 22nd APRU ANNUAL JUNE 24-26, 2018 PRESIDENTS’ MEETING NATIONAL TAIWAN UNIVERSIT Y MEETING REPORT APRU Members Australia Korea Australian National University KAIST The University of Melbourne Korea University The University of Sydney POSTECH UNSW Sydney Seoul National University Yonsei University Canada The University of British Columbia Malaysia University of Malaya Chile University of Chile Mexico Tecnológico de Monterrey China and Hong Kong SAR Fudan University New Zealand Nanjing University The University of Auckland Peking University The Chinese University of Hong Kong Philippines The Hong Kong University of Science and Technology University of the Philippines The University of Hong Kong Tsinghua University Russia University of Chinese Academy of Sciences Far Eastern Federal University University of Science and Technology of China Zhejiang University Singapore Nanyang Technological University Taiwan National University of Singapore National Taiwan University National Tsing Hua University Thailand Chulalongkorn University Indonesia University of Indonesia United States of America California Institute of Technology Japan Stanford University Keio University University of California, Berkeley Nagoya University University of California, Davis Osaka University University of California, Irvine Tohoku University University of California, Los Angeles The University of Tokyo University of California, San Diego Waseda University University of California, Santa Barbara University of Hawaiʻi at Mãnoa University