Computer Science and Artificial Intelligence Laboratory (CSAIL)

clear all
November 29, 2023

New method uses crowdsourced feedback to help train robots

Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.

October 11, 2023

Twelve with MIT ties elected to the National Academy of Medicine for 2023

Five MIT faculty, along with seven additional affiliates, are honored for outstanding contributions to medical research.

Doctoral Thesis: Building Blocks for Human-AI Alignment: Specify, Inspect, Model, and Revise

Serena Booth Abstract: The learned behaviors of AI systems and robots should align with the intentions of their human designers. In service of this goal, people—especially experts—must be

Doctoral Thesis: A Language and Logic for Programming and Reasoning with Partial Observability

Eric H. Atkinson Abstract: Computer systems are increasingly deployed in partially-observable environments, in which the system cannot directly determine the environment’s state but receives partial information from observations.

August 31, 2023

AI helps robots manipulate objects with their whole bodies

With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.

August 31, 2023

SMART launches research group to advance AI, automation, and the future of work

Mens, Manus and Machina (M3S) will design technology, training programs, and institutions for successful human-machine collaboration.

August 31, 2023

How machine-learning models can amplify inequities in medical diagnosis and treatment

MIT researchers investigate the causes of health care disparities among underrepresented groups.

August 23, 2023

Artificial intelligence for augmentation and productivity

The MIT Schwarzman College of Computing awards seed grants to seven interdisciplinary projects exploring AI-augmented management.

August 23, 2023

M’Care and MIT students join forces to improve child health in Nigeria

The effort aims to transform micronutrient dosing to children by harnessing the power of data.

Doctoral Thesis: Approximate Bayesian Modeling with Embedded Gaussian Processes

Rujian Chen Thesis Supervisor: Dr. John W. Fisher III