• Workshop on Machine Learning and Mathematical Conjecture

    CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

    On April 15, 2022, the CMSA will hold a one-day workshop, Machine Learning and Mathematical Conjecture, related to the New Technologies in Mathematics Seminar Series. Location: Room G10, 20 Garden Street, Cambridge, MA 02138. Organizers: Michael R. Douglas (CMSA/Stony Brook/IAIFI) and Peter Chin (CMSA/BU). Machine learning has driven many exciting recent scientific advances. It has enabled […]

  • Breaking the one-mind-barrier in mathematics using formal verification

    CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

    https://youtu.be/D7dqadF5k9Q New Technologies in Mathematics Seminar Speaker: Johan Commelin, Mathematisches Institut, Albert-Ludwigs-Universität Freiburg Title: Breaking the one-mind-barrier in mathematics using formal verification Abstract: In this talk I will argue that formal verification helps break the one-mind-barrier in mathematics. Indeed, formal verification allows a team of mathematicians to collaborate on a project, without one person understanding all parts of […]

  • Minerva: Solving Quantitative Reasoning Problems with Language Models

    CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

    https://youtu.be/HUTWime3d6w New Technologies in Mathematics Seminar Speaker: Guy Gur-Ari, Google Research Title: Minerva: Solving Quantitative Reasoning Problems with Language Models Abstract: Quantitative reasoning tasks which can involve mathematics, science, and programming are often challenging for machine learning models in general and for language models in particular. We show that transformer-based language models obtain significantly better performance […]

  • Towards Faithful Reasoning Using Language Models

    CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

    New Technologies in Mathematics Seminar Speaker: Antonia Creswell, DeepMind Title: Towards Faithful Reasoning Using Language Models Abstract: Language models are showing impressive performance on many natural language tasks, including question-answering. However, language models – like most deep learning models – are black boxes. We cannot be sure how they obtain their answers. Do they reason […]