To keep hardware safe, cut out the code’s clues
New “Oreo” method from MIT CSAIL researchers removes footprints that reveal where code is stored before a hacker can see them.
New “Oreo” method from MIT CSAIL researchers removes footprints that reveal where code is stored before a hacker can see them.
A deep neural network called CHAIS may soon replace invasive procedures like catheterization as the new gold standard for monitoring heart health.
New faculty member Kaiming He discusses AI’s role in lowering barriers between scientific fields and fostering collaboration across scientific disciplines.
MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.
“We need to both ensure humans reap AI’s benefits and that we don’t lose control of the technology,” says senior Audrey Lorvo.
The consortium will bring researchers and industry together to focus on impact.
Longtime AeroAstro professor brings deep experience with academic and student life.
By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.
New research could improve the safety of drone shows, warehouse robots, and self-driving cars.
MIT CSAIL Principal Research Scientist Una-May O’Reilly discusses how she develops agents that reveal AI models’ security weaknesses before hackers do.
The course challenges students to commercialize technologies and ideas in one whirlwind semester. Alumni of the class have founded more than 150 companies.
Associate Professor Luca Carlone is working to give robots a more human-like awareness of their environment.
Starting with a single frame in a simulation, a new system uses generative AI to emulate the dynamics of molecules, connecting static molecular structures and developing blurry pictures into videos.
Gifted Caribbean high schoolers become SPISE alumni at MIT, and many go on to advanced academic and professional careers.
Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.