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

New method assesses and improves the reliability of radiologists’ diagnostic reports

The framework helps clinicians choose phrases that more accurately reflect the likelihood that certain conditions are present in X-rays.

For this computer scientist, MIT Open Learning was the start of a life-changing journey

Ana Trišović, who studies the democratization of AI, reflects on a career path that she began as a student downloading free MIT resources in Serbia.

At the core of problem-solving

Stuart Levine ’97, director of MIT’s BioMicro Center, keeps departmental researchers at the forefront of systems biology.

Robotic helper making mistakes? Just nudge it in the right direction

New research could allow a person to correct a robot’s actions in real-time, using the kind of feedback they’d give another human.

Like human brains, large language models reason about diverse data in a general way

A new study shows LLMs represent different data types based on their underlying meaning and reason about data in their dominant language.

AI model deciphers the code in proteins that tells them where to go

Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.

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.