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
A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.
Assistant Professor Priya Donti has been named an AI2050 Early Career Fellow by Schmidt Sciences, a philanthropic initiative from Eric and Wendy Schmidt aimed at helping to solve hard problems in AI.
MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.
The Department of Electrical Engineering and Computer Science (EECS) is proud to announce multiple promotions.
The MIT senior will pursue graduate studies in technology policy at Cambridge University.