MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.

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

By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.

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 new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.

Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data.

Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.

Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.

MIT engineers developed the largest open-source dataset of car designs, including their aerodynamics, that could speed design of eco-friendly cars and electric vehicles.

This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.