Introducing the MIT Generative AI Impact Consortium
The consortium will bring researchers and industry together to focus on impact.
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The consortium will bring researchers and industry together to focus on impact.
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.
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.
With their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
Inspired by the mechanics of the human vocal tract, a new AI model can produce and understand vocal imitations of everyday sounds. The method could help build new sonic interfaces…
Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.