Machine Learning, Advanced Algorithms Can Help Scientists Translate Large Quantities of Data into Solutions for Americans
WASHINGTON, D.C. — The U.S. Department of Energy (DOE) today announced $29 million to develop new tools to analyze massive amounts of scientific information, including artificial intelligence, machine learning, and advanced algorithms. All have the potential to reveal critical new insights—and even new discoveries—in research that can help tackle clean energy, climate, and national security challenges for the American people.
“As research tools like computers or microscopes have gotten more powerful, the amount of data they can gather has gotten overwhelming—and scientists need new capabilities to make sense of it all,” said Secretary of Energy Jennifer M. Granholm. “Advanced analysis methods will help them unlock the full potential behind all this data, so that we can solve even our most complex challenges.”
Modern scientific facilities, instruments, and high-performance computing tools are able to generate a volume of data that traditional analysis methods can struggle to interpret efficiently—for example, electron microscopes can generate a terabyte, or one million million bytes, of data in a single experiment. Advanced analysis methods can benefit multiple scientific fields and inform cutting-edge solutions to complex problems by:
- Identifying patterns that are impossible for humans to detect: Up to $21 million of today’s announcement will focus on machine learning, a process that allows researchers to identify patterns that are difficult or impossible for humans to detect, at speeds hundreds to thousands of times faster than traditional data analysis techniques. Up to $14 million of the $21 million in planned funding is contingent on congressional appropriations.
- Making large datasets simple to understand: Up to $8 million in funding will support the development of “randomized” algorithms, which use random sampling as a way of systematically simplifying extremely large datasets for practical analysis. These hold the potential to be significantly more accurate than current methods based on “best guess” or human intuition.
“I applaud this key investment to ensure 21st-century American competitiveness in artificial intelligence, machine learning and advanced algorithms. This will boost scientific breakthroughs, advance manufacturing and assist the United States with analyzing and solving some of the greatest challenges facing our nation, like climate change, new cures, quality healthcare and cybersecurity," said U.S. Representative Darren Soto.
DOE National Laboratories, universities, industry, and nonprofit research institutions may apply, with competitive funding awarded based on peer review.