Summary
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. A planning committee of distinguished scholars from the United States and the United Kingdom organized the forum.
The forum drew over 60 in-person attendees and over 500 webcast participants from academia, government, industry, and philanthropy. Participants included industry leaders, machine learning researchers, and experts in privacy and the law, and this report summarizes their high-level interdisciplinary discussions.
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.
Novel technical and societal challenges are arising as machine learning advances. These relate to the ethics of using certain types of data (because of privacy or biased data collection), of managing data in different ways, and of automating certain processes (e.g., life and death decision making). They are also rooted in questions about how humans and machine learning systems interact as well as the societal challenges of adapting to a world in which these systems are increasingly ubiquitous.
Alongside these challenges come exciting opportunities across a range of industries and areas of research, with stimulating cross-disciplinary research taking place in academia and business. A supportive environment for this research—based on open standards and tools, effective education, public confidence through effective engagement, and an understanding of varied users—can help secure the social and economic benefits of machine learning.
This report is a summary of contributions to the forum. It does not reflect the views of the National Academy of Sciences or the Royal Society.
