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.
Mens, Manus and Machina (M3S) will design technology, training programs, and institutions for successful human-machine collaboration.
How machine-learning models can amplify inequities in medical diagnosis and treatment
MIT researchers investigate the causes of health care disparities among underrepresented groups.
The MIT Schwarzman College of Computing awards seed grants to seven interdisciplinary projects exploring AI-augmented management.
The effort aims to transform micronutrient dosing to children by harnessing the power of data.
Machine-learning system based on light could yield more powerful, efficient large language models
MIT system demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems.