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 method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
Before joining MIT in 2025, Assistant Professor Omar Khattab had already created several transformative tools that made their mark on industry.
Awarded jointly by the Bavarian State Government and the Bavarian Academy of Sciences and Humanities, it is the most highly endowed award for technology and engineering in Germany.
“SceneSmith” system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores.
A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.