MIT CSAIL researchers discuss frontiers of generative AI
Experts convene to peek under the hood of AI-generated code, language, and images as well as its capabilities, limitations, and future impact.
Experts convene to peek under the hood of AI-generated code, language, and images as well as its capabilities, limitations, and future impact.
Martin Luther King Jr. Scholar Brian Nord trains machines to explore the cosmos and fights for equity in research.
Principal Research Scientist Audun Botterud tackles a range of cross-cutting problems — from energy market interactions to designing batteries — to get closer to a decarbonized power grid.
The three-fingered robotic gripper can “feel” with great sensitivity along the full length of each finger – not just at the tips.
“DribbleBot” can maneuver a soccer ball on landscapes such as sand, gravel, mud, and snow, using reinforcement learning to adapt to varying ball dynamics.
MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.
Researchers create a trajectory-planning system that enables drones working together in the same airspace to always choose a safe path forward.
Associate Professor Tamara Broderick and colleagues build a “taxonomy of trust” to identify where confidence in the results of a data analysis might break down.
New LiGO technique accelerates training of large machine-learning models, reducing the monetary and environmental cost of developing AI applications.
By keeping data fresh, the system could help robots inspect buildings or search disaster zones.
Researchers used machine learning to build faster and more efficient hash functions, which are a key component of databases.
In MIT’s 2023 Killian Lecture, Peter Shor shares a brief history of quantum computing from a personal viewpoint.
MIT researchers trained logic-aware language models to reduce harmful stereotypes like gender and racial biases.
A process that seeks feedback from human specialists proves more effective at optimization than automated systems working alone.
The Advanced Computing Users Survey, sampling sentiments from 120 top-tier universities, national labs, federal agencies, and private firms, finds the decline in America’s advanced computing lead spans many areas.