Using high-powered lasers, this new method could help biologists study the body’s immune responses and develop new medicines.

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

Chronic diseases like diabetes are prevalent, costly, and challenging to treat. A common denominator driving them may be a promising new therapeutic target.

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

A detailed new look at dopamine signaling suggests neuroscientists’ model of reinforcement learning may need to be revised.

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

The MIT Health and Life Sciences Collaborative will bring together researchers from across the Institute to deliver health care solutions at scale.

The team’s detection method, which identified 138 space rocks ranging from bus- to stadium-sized, could aid in tracking potential asteroid impactors.

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

MIT engineers show how detailed mapping of weather conditions and energy demand can guide optimization for siting renewable energy installations.