US Department of Energy to invest $40m in AI and machine learning research

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Categories: Energy,
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The US Department of Energy (DOE) has decided to provide funds up to $40 million over a three-year period for new research in data, artificial intelligence (AI), and machine learning to address the challenges associated with issues related to data production and management at DOE scientific user facilities.

Dr. Chris Fall, Director of DOE’s Office of Science, said: “Major scientific facilities at our DOE national laboratories are generating vast and growing amounts of data for researchers every day. Artificial intelligence and machine learning hold out new promise for managing this wealth of data as well as improving facility operations and aiding in experimental design.”

Proposals are likely to cover a wide variety of different challenges, including extracting information from complex data sets, managing facility operations in real-time, and optimising experiments through the creation of virtual laboratory environments, among other topics. The funding opportunity focuses on 18 DOE Office of Science user facilities, comprising of particle accelerators, accelerator test facilities, x-ray light sources, neutron scattering sources, and nanoscale science research centres, overseen by three major programme offices: basic energy sciences, high energy physics, and nuclear physics.

According to the latest Ericsson Mobility Report, data volumes in mobile networks are increasing at an exceptional rate and mobile data traffic is expected to grow fourfold by 2025, reaching up to 160 exabytes per month. This seems interesting and in fact offers all sorts of opportunities for communications service providers; however, there is a potential disadvantage of this rapidly increasing data traffic due to the impact on energy consumption and carbon footprint of mobile networks. But AI has the ability to solve this problem, Ericsson notes, as when deployed, communications service providers will be able to realise energy efficiencies on the radio network proactively. The technology does not just address site-related energy savings, but also operational efficiencies.

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