Melding data, systems, and society
A new book from Professor Munther Dahleh details the creation of a unique kind of transdisciplinary center, uniting many specialties through a common need for data science.
A new book from Professor Munther Dahleh details the creation of a unique kind of transdisciplinary center, uniting many specialties through a common need for data science.
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.
The winning essay of the Envisioning the Future of Computing Prize puts health care disparities at the forefront.
Coactive, founded by two MIT alumni, has built an AI-powered platform to unlock new insights from content of all types.
A team of MIT researchers founded Themis AI to quantify AI model uncertainty and address knowledge gaps.
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.
PhD student Sarah Alnegheimish wants to make machine learning systems accessible.
Through collaborations with organizations like BREIT in Peru, the MIT Institute for Data, Systems, and Society is upskilling hundreds of learners around the world in data science and machine learning.
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.