Problem solving as a translation task

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Where: 503/1-001 at CERN

Neural architectures designed for machine translation can be used to solve problems of mathematics, by considering that solving amounts to translating the problem, a sentence in some mathematical language, into its solution, another sentence in mathematical language. Presenting examples from symbolic and numerical mathematics, and theoretical physics, I show how such techniques can be applied to develop AI for Science, and help understand the inner workings of language models.

Francois Charton is a research engineer in Meta AI, working on applications of language models to mathematics and science. He is a former student of Ecole Polytechnique, and ENSAE (Ecole Nationale de la Statistique et de l'Administration Economique). He has been in Meta since 2019, and had a career in industry prior to that.

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