User-friendly system can help developers build more efficient simulations and AI models
By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.
By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.
A new approach, which takes minutes rather than days, predicts how a specific DNA sequence will arrange itself in the cell nucleus.
MIT CSAIL Principal Research Scientist Una-May O’Reilly discusses how she develops agents that reveal AI models’ security weaknesses before hackers do.
Sometimes, it might be better to train a robot in an environment that’s different from the one where it will be deployed.
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.
Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.
With their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.
As the use of generative AI continues to grow, Lincoln Laboratory’s Vijay Gadepally describes what researchers and consumers can do to help mitigate its environmental impact.