Explained: Generative AI’s environmental impact
Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.
Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.
As the use of generative AI continues to grow, Lincoln Laboratory’s Vijay Gadepally describes what researchers and consumers can do to help mitigate its environmental impact.
Associate Professor Matteo Bucci’s research sheds new light on an ancient process, to improve the efficiency of heat transfer in many industrial systems.
Progress on the energy transition depends on collective action benefiting all stakeholders, agreed participants in MITEI’s annual research conference.
Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.
Analysis and materials identified by MIT engineers could lead to more energy-efficient fuel cells, electrolyzers, batteries, or computing devices.
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
Iwnetim Abate aims to stimulate natural hydrogen production underground, potentially unearthing a new path to a cheap, carbon-free energy source.
MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.
The Energy and Climate Hack presented opportunities for students and companies to collaborate and develop innovative solutions.