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Suggested Citation:"Front Matter." 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.
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The Frontiers of

MACHINE LEARNING

2017 Raymond and Beverly Sackler U.S.-U.K. Scientific Forum

imagesNATIONAL ACADEMY OF SCIENCE images
Suggested Citation:"Front Matter." 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.
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Foreword

Rapid advances in machine learning—the form of artificial intelligence that allows computer systems to learn from data—are capturing scientific, economic, and public interest. Recent years have seen machine learning systems enter everyday usage, while further applications across healthcare, transportation, finance, and more appear set to shape the development of these fields over the coming decades. The societal and economic opportunities that follow these advances are significant, and nations are grappling with how artificial intelligence might affect society.

There are emerging policy debates in the United States and the United Kingdom about how and where society can make best use of machine learning. As the capabilities of machine learning systems and the range of their applications continue to grow, it is therefore particularly timely for the National Academy of Sciences and the Royal Society to bring together leading figures in these fields.

Since 2008, the Raymond and Beverly Sackler U.S.-U.K. Scientific Forum has brought together thought leaders from a variety of scientific fields to exchange ideas on topics of international scientific concern and to help forge an enduring partnership between scientists in the United States and the United Kingdom.

The forum on “The Frontiers of Machine Learning” took place in the United States on January 31 and February 1, 2017, at the National Academy of Sciences in Washington, D.C. This event brought together leading researchers and policy experts to explore the cutting edges of machine learning research and the implications of technological advances in this field. This report summarizes the high-level discussions at the event focusing on some of the exciting areas of progress in machine learning and the societal debates that follow.

The National Academy of Sciences and the Royal Society share a mission to promote the use of science to benefit society and to inform important policy debates. As Presidents of the National Academy of Sciences and the Royal Society, we are pleased to introduce the latest piece of work supported through the inspired generosity of the Raymond and Beverly Sackler Foundation.

Marcia McNutt Venki Ramakrishnan
President, National Academy of Sciences President, Royal Society
Suggested Citation:"Front Matter." 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.
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Suggested Citation:"Front Matter." 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.
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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.

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