Machine Learning and Theorem Proving in Formal Languages

Watch a thought-provoking panel discussion from the CMSA Mathematics and Machine Learning Closing Workshop featuring experts Amaury Hayat (Ecole des Ponts Paristech), Melanie Matchett Wood (Harvard University), Alex Davies (DeepMind), and Jeremy Avigad (Carnegie Mellon University) exploring the intersection of machine learning and mathematical theorem proving. Delve into the potential of neural networks to assist mathematicians in proof development, with particular focus on formal languages like Lean that provide automatic verification. Examine the current challenges in applying machine learning to theorem proving, including the disconnect between verification-focused formal languages and the simultaneous reasoning and solution-finding approach used by mathematicians. Gain insights into recent applications of machine learning in the Lean theorem prover and contemplate how these technological developments might shape the future of mathematical practice.