Energy
Energy

New chip could help tiny robots traverse complex environments

Researchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power.

MIT in the media: For the future of tech, “Massachusetts can absolutely lead”

Leaders, faculty across MIT discuss fostering innovation and talent in Greater Boston in special series of articles published alongside the outlet’s annual list of ‘Tech Power Players’

Jinhua Zhao named head of the Department of Urban Studies and Planning

An expert in behavioral science and transportation, Zhao combines these studies with AI and public policy to address some of the most urgent challenges facing cities.

Startup’s nuclear-inspired cooling system could make data centers more sustainable

Founded by two researchers from MIT, Ferveret reduces the amount of energy and water required to cool the chips that power AI.

Q&A: Expanding MIT’s global reach through Universal Learning

Dimitris Bertsimas and Megan Mitchell discuss the motivation behind Universal Learning, and what sets the new MIT Open Learning educational initiative apart.

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.

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.

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.

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.

New method could increase LLM training efficiency

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