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The Frontiers of Machine Learning

January 31 - February 1, 2017
Washington, DC

Meeting Summary»

Tuesday, January 31

Welcome and Introduction
Diane Griffin, National Academy of Sciences
Richard Catlow, The Royal Society
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 transformative role across a diverse range of sectors. This session will explore the frontiers of machine learning, in terms of both cutting-edge technology and near-term applications, and discuss the state of the art of machine learning.

I Know it's an Idiot but it's MY Artificial Idiot!
Vint Cerf, Google

Towards Affordable Self-Driving Cars
Raquel Urtasun, University of Toronto

Probabilistic Machine Learning: Foundations and Frontiers
Zoubin Ghahramani, University of Cambridge

Words, Pictures, and Common Sense
Devi Parikh, Georgia Institute of Technology

Applied Machine Learning at Google
Greg Corrado, Google

Session 2: Machine learning and society

People and machine learning systems are increasingly interacting through a range of applications or contexts. This expansion of machine learning raises legal and ethical questions, re-frames discussions about uses of data, and poses new challenges for the governance of this technology. The social acceptability of different machine learning applications, desirability of automated decision-making processes, adequacy of processes to manage concerns about statistical stereotyping or privacy, and more, will all influence how and where society has confidence in the deployment of machine learning systems. This session will explore the societal implications of increased use of machine learning, and the opportunities and challenges associated with advances in the field.

Artificial Intelligence and Life in 2030
Peter Stone, University of Texas at Austin

Interpretable Machine Learning for Recidivism Prediction
Cynthia Rudin, Duke University

Protecting and Enhancing Our Humanity in an Age of Machine Learning
Charis Thompson, University of California, Berkeley

Using Machine Learning in Criminal Justice Risk Assessments
Richard Berk, University of Pennsylvania

Wednesday, February 1

Privacy and Machine Learning: Promise, Peril, and the Path Forward
Pam Dixon, World Privacy Forum

Algorithmic Regulation: A Critical Interrogation
Karen Yeung, King's College London

Session 3: Machine learning in the research and commercial communities

There are enormous opportunities in machine learning in academia, research labs, and industry. While much of the research and development of machine learning to date has been done in the commercial world, each of these communities will continue advancing this field. Establishing key research challenges and areas of commercial opportunity will therefore be important in moving the frontiers of machine learning forward. This session will explore key areas of interest in machine learning in the research and commercial communities.

Building the Human Wiring Diagram from Linked Genomic and Healthcare Data
Gil McVean, University of Oxford

Three Principles for Data Science: Predictability, Stability, and Computability
Bin Yu, University of California, Berkeley

Experimental Design and Machine Learning Opportunities in Mobile Health
Susan Murphy, University of Michigan

Active Optimization and Self-Driving Cars
Jeff Schneider, Carnegie Mellon University and Uber Advanced Technology Center

A Deployable Decision Service
John Langford, Microsoft Research

This website contains both edited and unedited video of presentations made by forum participants.  It is not an official report of the National Academy of Sciences. Opinions and statements included in this material are solely those of the individual authors. They have not been verified as accurate, nor do they necessarily represent the views of other forum participants, the forum planning group, or the National Academy of Sciences.

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