Computer Science and Artificial Intelligence Laboratory (CSAIL)


Three from MIT elected to the National Academy of Sciences for 2022

May 12, 2022

Faculty members Angela Belcher, Pablo Jarillo-Herrero, and Ronitt Rubinfeld elected by peers for outstanding contributions to research.

3 Questions: How the MIT mini cheetah learns to run

March 21, 2022

CSAIL scientists came up with a learning pipeline for the four-legged robot that learns to run entirely by trial and error in simulation.

Q&A: Climate Grand Challenges finalists on building equity and fairness into climate solutions

March 4, 2022

Faculty leaders discuss the opportunities and obstacles in developing, scaling, and implementing their work rapidly.

Injecting fairness into machine-learning models

March 3, 2022

A new technique boosts models’ ability to reduce bias, even if the dataset used to train the model is unbalanced.

A security technique to fool would-be cyber attackers

February 25, 2022

Researchers demonstrate a method that safeguards a computer program’s secret information while enabling faster computation.

Team creates 3D objects that change their appearance from different viewpoints

February 9, 2022

Editing tool makes fabrication process available to all.

Seeing into the future: Personalized cancer screening with artificial intelligence

February 4, 2022

Scientists demonstrate that AI-risk models, paired with AI-designed screening policies, can offer significant and equitable improvements to cancer screening.

This image contrasts the appearance of a coffee cup to the naked human eye (a black, nondescript cup) to the eye of a IR camera (where the cup appears white with a distinctive black mark).

Invisible machine-readable labels that identify and track objects

January 31, 2022

An MIT team develops 3D-printed tags to classify and store data on physical objects.

When should someone trust an AI assistant’s predictions?

January 19, 2022

Researchers have created a method to help workers collaborate with artificial intelligence systems.

Nonsense can make sense to machine-learning models

January 5, 2022

Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.