The brain power behind sustainable AI
PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.
PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.
Incorporating machine learning, MIT engineers developed a way to 3D print alloys that are much stronger than conventionally manufactured versions.
Read MoreThe new “CRESt” platform could help find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.
Read MoreThe research center, sponsored by the DOE’s National Nuclear Security Administration, will advance the simulation of extreme environments, such as those in hypersonic flight and atmospheric reentry.
Read MoreThe MIT Energy Initiative’s annual research symposium explores artificial intelligence as both a problem and a solution for the clean energy transition.
Read MoreWith demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
Read MoreRapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.
Read MoreWith their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.
Read MoreAn electronic stacking technique could exponentially increase the number of transistors on chips, enabling more efficient AI hardware.
Read MoreProgress on the energy transition depends on collective action benefiting all stakeholders, agreed participants in MITEI’s annual research conference.
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