MIT engineers propose a new “local electricity market” to tap into the power potential of homeowners’ grid-edge devices.

A new study shows LLMs represent different data types based on their underlying meaning and reason about data in their dominant language.

Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.

Assistant Professor Sara Beery is using automation to improve monitoring of migrating salmon in the Pacific Northwest.

Sometimes, it might be better to train a robot in an environment that’s different from the one where it will be deployed.

MAD Design Fellow Zane Schemmer writes algorithms that optimize overall function, minimize carbon footprint, and produce a manufacturable design.

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 for entertainment and education.

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.

A small fleet of autonomous surface vessels forms a large sonar array for finding submerged objects.

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.