Introducing the MIT Generative AI Impact Consortium
The consortium will bring researchers and industry together to focus on impact.
Research
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.
With generative AI, MIT chemists quickly calculate 3D genomic structures
A new approach, which takes minutes rather than days, predicts how a specific DNA sequence will arrange itself in the cell nucleus.
New training approach could help AI agents perform better in uncertain conditions
Sometimes, it might be better to train a robot in an environment that’s different from the one where it will be deployed.
Toward video generative models of the molecular world
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.
New computational chemistry techniques accelerate the prediction of molecules and materials
With their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.
For healthy hearing, timing matters
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
Teaching AI to communicate sounds like humans do
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 for entertainment and education.
A new computational model can predict antibody structures more accurately
Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
Ecologists find computer vision models’ blind spots in retrieving wildlife images
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.