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The Frontiers of Machine Learning 2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum (2018) / Chapter Skim
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Appendix
Pages 24-27

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From page 24...
... TUESDAY - JANUARY 31, 2017 9:00 AM Welcome from the National Academy of Sciences and Royal Society Diane Griffin, Vice President, National Academy of Sciences Richard Catlow, Foreign Secretary, Royal Society Welcome from the Co-Chairs Peter Donnelly, University of Oxford Michael Kearns, University of Pennsylvania Session 1: The Frontiers of Machine Learning The ubiquity of data, accessibility of computing power, and algorithmic advances have driven rapid progress in machine learning over the past five years. Not only does machine learning now underpin many applications that have become part of daily life, the field continues to evolve quickly and has the potential to play a trans formative role across a diverse range of sectors.
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From page 25...
... 2:25 PM Artificial Intelligence and Life in 2030 Peter Stone, University of Texas at Austin 3:00 PM Interpretable Machine Learning for Recidivism Prediction Cynthia Rudin, Duke University 3:35 PM Break 4:10 PM Protecting and Enhancing Our Humanity in an Age of Machine Learning Charis Thompson, University of California, Berkeley 25
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From page 26...
... 1:00 PM Building the Human Wiring Diagram from Linked Genomic and Healthcare Data Gil McVean, University of Oxford 1:35 PM Active Optimization and Self-Driving Cars Jeff Schneider, Carnegie Mellon University and Uber Advanced Technology Center 26
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From page 27...
... 2:10 PM Three Principles for Data Science: Predictability, Stability, and Computability Bin Yu, University of California, Berkeley 2:45 PM Experimental Design and Machine Learning Opportunities in Mobile Health Susan Murphy, University of Michigan 3:20 PM Break 3:40 PM A Deployable Decision Service John Langford, Microsoft Research 4:15 PM Panel Discussion Jeff Schneider, Carnegie Mellon University Bin Yu, University of California, Berkeley Susan Murphy, University of Michigan Gil McVean, University of Oxford John Langford, Microsoft Research 4:55 PM Adjourn Meeting 27
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Key Terms

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  • jeff schneider
  • commercial communities
  • daily life

  • probabilistic machine
  • interpretable machine
  • healthcare data
  • evolve quickly
  • key research

  • challenges associated
  • wiring diagram
  • research challenges
  • past five
  • recidivism prediction

  • social acceptability
  • linked genomic
  • technology center
  • learning algorithms
  • etal implications

  • statistical stereotyping
  • learning applications
  • algorithms interact
  • manage concerns
  • commercial opportunity

  • potential applications
  • learning forward
  • driven rapid
  • lishing key
  • human wiring


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