Artificial Intelligence and Machine Learning

    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

    Jack Cook, Matthew Kearney, and Jupneet Singh will begin postgraduate studies at Oxford University next fall.

    Models trained on synthetic data can be more accurate than other models in some cases, which could eliminate some privacy, copyright, and ethical concerns from using real data.

    Yilun Du, a PhD student and MIT CSAIL affiliate, discusses the potential applications of generative art beyond the explosion of images that put the web into creative hysterics.

    On October 6, nearly 50 undergraduate and graduate students and postdocs, primarily from MIT, attended the MIT-IBM Watson AI Lab’s networking event. The goal was to connect young

    The Department of Electrical Engineering and Computer Science (EECS) recently announced the following crop of chair appointments, all effective July 1, 2022. Karl Berggren has been named the

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