Helping machines understand visual content with AI
Coactive, founded by two MIT alumni, has built an AI-powered platform to unlock new insights from content of all types.
Teaching AI models what they don’t know
A team of MIT researchers founded Themis AI to quantify AI model uncertainty and address knowledge gaps.
AI stirs up the recipe for concrete in MIT study
With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
Teaching AI models the broad strokes to sketch more like humans do
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.
3 Questions: How to help students recognize potential bias in their AI datasets
Courses on developing AI models for health care need to focus more on identifying and addressing bias, says Leo Anthony Celi.
Rationale engineering generates a compact new tool for gene therapy
Researchers redesign a compact RNA-guided enzyme from bacteria, making it an efficient editor of human DNA.
An anomaly detection framework anyone can use
PhD student Sarah Alnegheimish wants to make machine learning systems accessible.
Building networks of data science talent
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.
MIT announces the Initiative for New Manufacturing
The Institute-wide effort aims to bolster industry and create jobs by driving innovation across vital manufacturing sectors.
AI learns how vision and sound are connected, without human intervention
This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.
Learning how to predict rare kinds of failures
Researchers are developing algorithms to predict failures when automation meets the real world in areas like air traffic scheduling or autonomous vehicles.
The sweet taste of a new idea
Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.
With AI, researchers predict the location of virtually any protein within a human cell
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
Study shows vision-language models can’t handle queries with negation words
Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.
MIT Department of Economics to launch James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work
With support from the Stone Foundation, the center will advance cutting-edge research and inform policy.