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.
Machine learning
Algorithms and AI for a better world
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.
Making the art world more accessible
The startup NALA, which began as an MIT class project, directly matches art buyers with artists.
New computational chemistry techniques accelerate the prediction of molecules and materials
With their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.
For healthy hearing, timing matters
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
Q&A: The climate impact of generative AI
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.
Teaching AI to communicate sounds like humans do
Inspired by the mechanics of the human vocal tract, a new AI model can produce and understand vocal imitations of everyday sounds. The method could help build new sonic interfaces…
Unlocking the hidden power of boiling — for energy, space, and beyond
Associate Professor Matteo Bucci’s research sheds new light on an ancient process, to improve the efficiency of heat transfer in many industrial systems.
Ecologists find computer vision models’ blind spots in retrieving wildlife images
Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.
Need a research hypothesis? Ask AI.
MIT engineers developed AI frameworks to identify evidence-driven hypotheses that could advance biologically inspired materials.