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The Raymond and Beverly Sackler U.S.-U.K. Scientific Forum was established to help the scientific leadership of the United Kingdom and the United States forge an enduring and productive partnership on pressing topics of worldwide scientific concern with benefit to all people. These meetings are organized jointly by the National Academy of Sciences and the Royal Society, and alternate between locations in the U.S. and the U.K.
This program is made possible by a generous gift from Raymond and Beverly Sackler.
The Future of Machine Learning
January 31 - February 1, 2017
National Academy of Sciences Building
Webcast participants can submit questions for the speakers by e-mailing Michaelle Schwalbe at firstname.lastname@example.org.
Session outlines below:
The ubiquity of data, accessibility of computing power, and algorithmic advances have enabled revolutionary progress in machine learning over the past five years. As a result, technologies such as voice recognition or image perception, which a few years ago were performing at noticeably below-human levels, can now outperform people at some tasks. 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, considering both cutting-edge technology and near-term applications. The session will include a range of case studies, possibly including examples from transportation, medicine, public services, and finance. It will also consider what high profile advances in machine learning — such as the success of AlphaGo — indicate about the state of the science.
A key aim of this session will be to establish a common understanding amongst participants about applications of machine learning, both now and in the near-term, in order to frame subsequent discussions.
As machine learning is used in an increasing range of applications, it raises legal and ethical questions, re-frames discussions about uses of data, and poses new challenges for the governance of this technology.
This session will consider a range of issues relating to machine learning and society. It will be grounded in current approaches or best practice in addressing key governance issues, through talks by relevant experts and key players in the commercial world, which will demonstrate how these issues are handled in a practical way. It may also include issues such as liability, accountability, and public engagement.
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 to advancing this field. Establishing key research challenges and areas of commercial opportunity will therefore both be important in moving the frontiers of machine learning forward.
In light of discussions in sessions one and two, this session will explore areas of significant opportunities in both industry and research, highlight where machine learning could make a significant impact, and describe some of the skills required. It will consider what type of advances might be possible, and the roles of large corporations, research labs and start-ups, including university spin-outs, in driving these advances. The session may also enable discussions about the entrepreneurial environment, and incentives and funding for start-ups.