A platform to expedite clean energy projects
Station A, founded by MIT alumni, makes the process of buying clean energy simple for property owners.
Station A, founded by MIT alumni, makes the process of buying clean energy simple for property owners.
Starting with a single frame in a simulation, a new system uses generative AI to emulate the dynamics of molecules, connecting static molecular structures and developing blurry pictures into videos.
Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.
The startup NALA, which began as an MIT class project, directly matches art buyers with artists.
With their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
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.
Inspired by 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 for entertainment and education.
AeroAstro PhD student Sydney Dolan uses an interdisciplinary approach to develop collision-avoidance algorithms for satellites.
Associate Professor Matteo Bucci’s research sheds new light on an ancient process, to improve the efficiency of heat transfer in many industrial systems.
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.
MIT engineers developed AI frameworks to identify evidence-driven hypotheses that could advance biologically inspired materials.
With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.