A better method for identifying overconfident large language models
This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.
Download RSS feed: News Articles / In the Media / Audio
This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.
Academia-industry relationship is an early-stage accelerator, supporting professional progress and research.
Researchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance.
One year in, MIT’s hands-on 6-5 (Electrical Engineering With Computing) degree program is already one of the most popular majors among first-year students.
MIT professors Amos Winter and Nikolai Zeldovich are honored for exceptional undergraduate teaching.
MIT computer science students design AI chatbots to help young users become more social, and socially confident.
Light-emitting structures that curl off the chip surface could enable advanced displays, high-speed optical communications, and larger-scale quantum computers.
Faculty members and researchers were honored in recognition of their scholarship, service, and overall excellence.
A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
Through an interdisciplinary collaboration between MIT and the Museum of Fine Arts, Boston, researchers are creating playable physical and synthesized replicas.
The engineered tissue grafts could take on the liver’s function and help thousands of people with liver failure.
Offering substantial prize funding alongside workshops, classes, and mentorship, the initiative helps translate early-stage biotech research into venture-ready innovation.
Foray Bioscience, founded by Ashley Beckwith SM ’18, PhD ’22, is engineering single plant cells to create new materials and meet growing demand.
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
To help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.