A new framework from the MIT-IBM Watson AI Lab supercharges language models, so they can reason over, interactively develop, and verify valid, complex travel agendas.

Forget optimists vs. Luddites. Most people evaluate AI based on its perceived capability and their need for personalization.

The system automatically learns to adapt to unknown disturbances such as gusting winds.

With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.

SketchAgent, a drawing system developed by MIT CSAIL researchers, sketches up concepts stroke-by-stroke, teaching language models to visually express concepts on their own and collaborate with humans.

Courses on developing AI models for health care need to focus more on identifying and addressing bias, says Leo Anthony Celi.

Researchers redesign a compact RNA-guided enzyme from bacteria, making it an efficient editor of human DNA.

The Institute-wide effort aims to bolster industry and create jobs by driving innovation across vital manufacturing sectors.

This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.

Researchers are developing algorithms to predict failures when automation meets the real world in areas like air traffic scheduling or autonomous vehicles.