National Academies Press: OpenBook
« Previous: Appendix
Suggested Citation:"Participants." National Academy of Sciences. 2018. The Frontiers of Machine Learning: 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum. Washington, DC: The National Academies Press. doi: 10.17226/25021.
×

Participants

Richard Berk, University of Pennsylvania

Joanna Bryson, University of Bath

Joaquin Candela, Facebook

Linda Casola, National Academies

Richard Catlow, Royal Society

Vint Cerf, Google

Greg Corrado, Google

Claire Craig, Royal Society

Nello Cristianini, University of Bristol

Kim Dai, U.S. Department of Defense

Thomas Dietterich, Oregon State University

Pam Dixon, World Privacy Forum

Peter Donnelly, University of Oxford

Jon Eisenberg, National Academies

Avi Feller, University of California, Berkeley

Edward Felten, Princeton University

Kay Firth-Butterfield, AI-Austin

Zoubin Ghahramani, University of Cambridge

Fernand Gouveia, British Embassy, Washington, D.C.

Arthur Gretton, University College London

Diane Griffin, Johns Hopkins University

Brian Hall, U.S. Department of Defense

Sabine Hauert, University of Bristol

Kenneth Heafield, University of Edinburgh

Rodney Howard, National Academies

Luke Huan, National Science Foundation

Nick Jennings, Imperial College London

Subbarao Kambhampati, Arizona State University

Behzad Kamgar-Parsi, Office of Naval Research

Michael Kearns, University of Pennsylvania

John Langford, Microsoft Research

Po-Ling Loh, University of Wisconsin, Madison

Gil McVean, University of Oxford

Mitch Mellen, Office of the Director of National Intelligence

Lynette Millett, National Academies

Parsa Mirhaji, Yeshiva University

Tom Mitchell, Carnegie Mellon University

Jessica Montgomery, Royal Society

Miranda Mowbray, Mowbray Ventures

Susan Murphy, University of Michigan

Predrag Neskovic, Office of Naval Research

Regina Nuzzo, Gallaudet University

Susannah Odell, Royal Society

Sofia Olhede, University College London

Devi Parikh, Georgia Institute of Technology

Jerome Pesenti, BenevolentAI

Kate Piblett, British Defence Service

Cynthia Rudin, Duke University

Jeff Schneider, Carnegie Mellon University

Michelle Schwalbe, National Academies

Peter Stone, University of Texas, Austin

Charis Thompson, University of California, Berkeley

Raquel Urtasun, University of Toronto

Andrew H. Van Scyoc, Department of the Navy

Suresh Venkatasubramanian, University of Utah

David Vladek, Georgetown University

Scott Weidman, National Academies

Adrian Weller, University of Cambridge

Patrick Wolfe, University College London

Karen Yeung, King’s College London

Catharine Young, British Embassy, Washington, D.C.

Bin Yu, University of California, Berkeley

Rapela Zaman, Royal Society

Jerry Zhu, University of Wisconsin, Madison

Suggested Citation:"Participants." National Academy of Sciences. 2018. The Frontiers of Machine Learning: 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum. Washington, DC: The National Academies Press. doi: 10.17226/25021.
×

FOR FURTHER READING

For more detailed discussion of many of the topics addressed in this document, see the following publications:

The Fourth Industrial Revolution: Proceedings of a Workshop–in Brief, National Academies of Sciences, Engineering, and Medicine, 2017

Information Technology and the U.S. Workforce: Where Are We and Where Do We Go from Here? National Academies of Sciences, Engineering, and Medicine, 2017

Machine Learning: The Power and Promise of Computers That Learn by Example, Royal Society, 2017

Public Views of Machine Learning, Royal Society and Ipsos MORI, 2017

Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop, National Academies of Sciences, Engineering, and Medicine, 2017

Continuing Innovation in Information Technology: Workshop Report, National Academies of Sciences, Engineering, and Medicine, 2016

How Modeling Can Inform Strategies to Improve Population Health: Workshop Summary, National Academies of Sciences, Engineering, and Medicine, 2016

Privacy Research and Best Practices: Summary of a Workshop for the Intelligence Community, National Academies of Sciences, Engineering, and Medicine, 2016

Progress and Research in Cybersecurity, Royal Society, 2016

A 21st Century Cyber-Physical Systems Education, National Academies of Sciences, Engineering, and Medicine, 2016

A Look at the Legal Environment for Driverless Vehicles, National Academies of Sciences, Engineering, and Medicine, 2015

Preparing the Workforce for Digital Curation, National Research Council, 2015

Complex Operational Decision Making in Networked Systems of Humans and Machines: A Multidisciplinary Approach, National Research Council, 2014

Emerging and Readily Available Technologies and National Security: A Framework for Addressing Ethical, Legal, and Societal Issues, National Research Council and National Academy of Engineering, 2014

For further information, please contact:

Michelle Schwalbe, National Academies, mschwalbe@nas.edu

Jessica Montgomery, Royal Society, jessica.montgomery@royalsociety.org

Suggested Citation:"Participants." National Academy of Sciences. 2018. The Frontiers of Machine Learning: 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum. Washington, DC: The National Academies Press. doi: 10.17226/25021.
×

This page intentionally left blank.

Suggested Citation:"Participants." National Academy of Sciences. 2018. The Frontiers of Machine Learning: 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum. Washington, DC: The National Academies Press. doi: 10.17226/25021.
×
Page 28
Suggested Citation:"Participants." National Academy of Sciences. 2018. The Frontiers of Machine Learning: 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum. Washington, DC: The National Academies Press. doi: 10.17226/25021.
×
Page 29
Suggested Citation:"Participants." National Academy of Sciences. 2018. The Frontiers of Machine Learning: 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum. Washington, DC: The National Academies Press. doi: 10.17226/25021.
×
Page 30
The Frontiers of Machine Learning: 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum Get This Book
×
 The Frontiers of Machine Learning: 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum
Buy Ebook | $9.99
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

The field of machine learning continues to advance at a rapid pace owing to increased computing power, better algorithms and tools, and greater availability of data. Machine learning is now being used in a range of applications, including transportation and developing automated vehicles, healthcare and understanding the genetic basis of disease, and criminal justice and predicting recidivism. As the technology advances, it promises additional applications that can contribute to individual and societal well-being.

The Raymond and Beverly Sackler U.S.-U.K. Scientific Forum “The Frontiers
 of Machine Learning” took place on January 31 and February 1, 2017, at the Washington, D.C., headquarters of the National Academies of Sciences, Engineering, and Medicine. Participants included industry leaders, machine learning researchers, and experts in privacy and the law, and this report summarizes their high-level interdisciplinary discussions.

READ FREE ONLINE

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    Switch between the Original Pages, where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

    « Back Next »
  6. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  7. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  8. ×

    View our suggested citation for this chapter.

    « Back Next »
  9. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!