Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, etc.); statistical learning (inference, graphical models, causal analysis, etc.); deep learning; reinforcement learning; symbolic reasoning ML systems; as well as diverse hardware implementations of ML.
Latest news in artificial intelligence and machine learning
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.
As the Director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Rus leads over 1,700 researchers in pioneering innovations to advance computing and improve global well-being.
Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.