Energy
Energy

Energy

Algorithms Artificial Intelligence Computer science and technology Data Electrical engineering and computer science (EECS) Energy Environment machine learning MIT Schwarzman College of Computing MIT-IBM Watson AI Lab Research School of Engineering Sustainability Sustainable computing Technology and society

A faster way to estimate AI power consumption

The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.

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Sixteen new START.nano companies are developing hard-tech solutions with the support of MIT.nano

Startup accelerator program grows to over 30 companies, almost half of them with MIT pedigrees.

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Working to advance the nuclear renaissance

Dean Price, assistant professor in the Department of Nuclear Science and Engineering, sees a bright future for nuclear power, and believes AI can help us realize that vision.

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Sustaining diplomacy amid competition in US-China relations

At MIT, former U.S. ambassador to China Nicholas Burns highlights climate change as an area for diplomatic engagement, while exploring areas including China’s emphasis on STEM education.

Algorithms Artificial Intelligence Computer science and technology Data Electrical engineering and computer science (EECS) Energy machine learning MIT Schwarzman College of Computing MIT-IBM Watson AI Lab National Science Foundation (NSF) Research Research Laboratory of Electronics School of Engineering

New method could increase LLM training efficiency

By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.

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3 Questions: How AI could optimize the power grid

While the growing energy demands of AI are worrying, some techniques can also help make power grids cleaner and more efficient.

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Working to eliminate barriers to adopting nuclear energy

Nuclear waste continues to be a bottleneck in the widespread use of nuclear energy, so doctoral student Dauren Sarsenbayev is developing models to address the problem.

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New materials could boost the energy efficiency of microelectronics

By stacking multiple active components based on new materials on the back end of a computer chip, this new approach reduces the amount of energy wasted during computation.

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Helping power-system planners prepare for an unknown future

Macro, a modeling tool developed by the MIT Energy Initiative, enables energy-system planners to explore options for developing infrastructure to support decarbonized, reliable, and low-cost power grids.

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Driving American battery innovation forward

At MITEI’s Fall Colloquium, General Motors’ battery development expert emphasized how affordability, accessibility, and commercialization can position the US as a leader in battery tech.