MIT-Takeda Program heads into fourth year with crop of 10 new projects
The program leverages MIT’s research expertise and Takeda’s industrial know-how for research in artificial intelligence and medicine.
The program leverages MIT’s research expertise and Takeda’s industrial know-how for research in artificial intelligence and medicine.
Lincoln Laboratory seeks ways to build non-contact screening methods that can detect concealed explosives at airports.
The method enables a model to determine its confidence in a prediction, while using no additional data and far fewer computing resources than other methods.
MIT spinout Verta offers tools to help companies introduce, monitor, and manage machine-learning models safely and at scale.
A new study shows how large language models like GPT-3 can learn a new task from just a few examples, without the need for any new training data.
A new tool brings the benefits of AI programming to a much broader class of problems.
A new computational framework could help researchers design granular hydrogels to repair or replace diseased tissues.
Computer scientists want to know the exact limits in our ability to clean up, and reconstruct, partly blurred images.
“I wouldn’t let the aggressor in the war squash my dreams,” says Ukrainian mathematician and MITx MicroMasters learner Tetiana Herasymova.
Deep-learning model takes a personalized approach to assessing each patient’s risk of lung cancer based on CT scans.
A new experiential learning opportunity challenges undergraduates across the Greater Boston area to apply their AI skills to a range of industry projects.
Study shows that if autonomous vehicles are widely adopted, hardware efficiency will need to advance rapidly to keep computing-related emissions in check.
New fellows are working on health records, robot control, pandemic preparedness, brain injuries, and more.
AeroAstro major and accomplished tuba player Frederick Ajisafe relishes the community he has found in the MIT Wind Ensemble.
Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.