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
Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.
MIT Sea Grant works with the Woodwell Climate Research Center and other collaborators to demonstrate a deep learning-based system for fish monitoring.
By quickly generating aesthetically accurate previews of fabricated objects, the VisiPrint system could make prototyping faster and less wasteful.
Jointly led by the MIT Morningside Academy for Design, MIT Schwarzman College of Computing, and the Hasso Plattner Institute in Potsdam, the hub will foster a dynamic community where computing, creativity, and human-centered innovation meet.
Researchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance.