Study shows the way forward in recovering precious metals from E-waste, a growing issue in our technology-rich society.

Top scientists and officials from government, academia, Alaskan Native communities, and industry are heading to Alaska to focus on driving energy technologies for a more sustainable Arctic region.

Machine learning happens a lot like erosion. Data is hurled at a mathematical model like grains of sand skittering across a rocky landscape. Some of those grains simply sail along with little or no impact. But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. Effective? Yes. Efficient? Not so much. Rick Blum, the Robert W. Wieseman Professor of Electrical and Computer Engineering at Lehigh University, seeks to bring efficiency to distributed learning techniques emerging as crucial to modern artificial intelligence (AI) and machine learning (ML). In essence, his goal is to hurl far fewer grains of data without degrading the overall impact.