Computer Science and Artificial Intelligence Laboratory (CSAIL)
Computer Science and Artificial Intelligence Laboratory (CSAIL)

Creating a common language

New faculty member Kaiming He discusses AI’s role in lowering barriers between scientific fields and fostering collaboration across scientific disciplines.

Validation technique could help scientists make more accurate forecasts

MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.

Introducing the MIT Generative AI Impact Consortium

The consortium will bring researchers and industry together to focus on impact.

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.

3 Questions: Modeling adversarial intelligence to exploit AI’s security vulnerabilities

MIT CSAIL Principal Research Scientist Una-May O’Reilly discusses how she develops agents that reveal AI models’ security weaknesses before hackers do.

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.

Explained: Generative AI’s environmental impact

Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.

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.