The inaugural SERC Symposium convened experts from multiple disciplines to explore the challenges and opportunities that arise with the broad applicability of computing in many aspects of society.

The scientists used a natural language-based logical inference dataset to create smaller language models that outperformed much larger counterparts.

A new multimodal technique blends major self-supervised learning methods to learn more similarly to humans.

Statistics tools support the idea that all radio bursts may repeat if observed long enough.

Fifteen principal investigators from across MIT will conduct early work to solve issues ranging from water contamination to aquaculture monitoring and management.

A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue.

Citadel founder and CEO Ken Griffin visits MIT, discusses how technology will continue to transform trading and investing.

Models trained using common data-collection techniques judge rule violations more harshly than humans would, researchers report.

The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.

A collaborative research team from the MIT-Takeda Program combined physics and machine learning to characterize rough particle surfaces in pharmaceutical pills and powders.