Source: AIP | Nathan Foster | August 3, 2018
This summer, the House Science Committee has turned its attention to the implications of advanced computing, holding two hearings on the subject. The firstfocused on artificial intelligence, while the second dealt with the opportunities and challenges posed by big data and machine learning techniques.
At the second hearing, committee members explored the role the Department of Energy and its national laboratories play in developing cutting-edge computing methods and infrastructure to help advance science. Expressing optimism in future applications of machine learning to the analysis of scientific data, Energy Subcommittee Chair Randy Weber (R-TX) remarked that DOE is “uniquely equipped to fund robust fundamental research in machine learning.”
Witnesses highlighted applications of machine learning in a number of disciplines, including computer science, neuroscience, materials science, and astronomy. They also emphasized the competition in advanced computing posed by other countries.
Witnesses point to variety of applications, DOE’s role
Katherine Yelick, associate laboratory director for computing sciences as Lawrence Berkeley National Laboratory, said that machine learning “requires three things: large amounts of data, fast computers, and good algorithms,” adding “DOE has all of these.”
Bobby Kasthuri, a neuroscience professor at the University of Chicago and researcher at Argonne National Laboratory, pointed to his field as one that could benefit from DOE support, saying it suffers from a lack of tools and computing infrastructure needed to map the human brain.
“Although many fields of science have learned how to leverage the expertise and the resources available in the national labs system, neuroscientists have not,”Kasthuri remarked. “A national center for brain mapping situated within the DOE labs system could actually be a sophisticated clearinghouse to ensure that the correct physics and engineering and computer science tools are vetted and accessible for measuring brain structure and brain function.”