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
AI agents could soon become indistinguishable from humans online. Could “personhood credentials” protect people against digital imposters?
The department is pleased to announce the four inaugural recipients of the Transformative Research Fund, an exciting new funding opportunity designed to facilitate bold and pivotal research, especially that which applies recent breakthrough technologies (such as generative AI) to important problems with broad societal impact.
A new algorithm helps robots practice skills like sweeping and placing objects, potentially helping them improve at important tasks in houses, hospitals, and factories.
In controlled experiments, MIT CSAIL researchers discover simulations of reality developing deep within LLMs, indicating an understanding of language beyond simple mimicry.
The approach can detect anomalies in data recorded over time, without the need for any training.