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

    Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds.

    A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.

    The MIT Stephen A. Schwarzman College of Computing has named Costis Daskalakis as the inaugural holder of the Avanessians Professorship. His chair began on July 1. Daskalakis is

    Four early-career researchers shared their work on improving the social outcomes of artificial intelligence and machine learning at a research summit hosted by EECS Thriving Stars.

    MIT alumni-founded Overjet analyzes and annotates dental X-rays to help dentists offer more comprehensive care.

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