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 […]
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 […]
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 […]
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 […]
https://youtu.be/h3jcSg359E8 New Technologies in Mathematics Seminar Speakers: João Araújo, Mathematics Department, Universidade Nova de Lisboa and Michael Kinyon, Department of Mathematics, University of Denver Title: From Engine to Auto Abstract: Bill McCune produced the program EQP that deals with first order logic formulas and in 1996 managed to solve Robbins' Conjecture. This very powerful tool reduces […]
https://youtu.be/g8zdumOAWzw New Technologies in Mathematics Seminar Speaker: Cyril Zhang, Microsoft Research Title: How do Transformers reason? First principles via automata, semigroups, and circuits Abstract: The current "Transformer era" of deep learning is marked by the emergence of combinatorial and algorithmic reasoning capabilities in large sequence models, leading to dramatic advances in natural language understanding, program synthesis, […]
The CMSA hosted a series of three 90-minute lectures on the subject of machine learning for protein folding. Thursday Feb. 9, Thursday Feb. 16, & Thursday March 9, 2023, 3:30-5:00 pm ET Location: G10, CMSA, 20 Garden Street, Cambridge MA 02138 & via Zoom Speaker: Nazim Bouatta, Harvard Medical School Abstract: AlphaFold2, a […]
https://youtu.be/ztk5TPYTKZA New Technologies in Mathematics Seminar Speaker: Jimmy Ba, University of Toronto Title: How to steer foundation models? Abstract: By conditioning on natural language instructions, foundation models and large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depends significantly on the quality of the prompt used to steer the model. […]
https://youtu.be/UID_oXuN-0Y New Technologies in Mathematics Seminar Speaker: Timo Schick, Meta AI Title: Toolformer: Language Models Can Teach Themselves to Use Tools Abstract: Language models exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where […]
https://youtu.be/5LXiQUsnHrI New Technologies in Mathematics Seminar Speaker: Dmitry Krotov, IBM Research - Cambridge Title: Modern Hopfield Networks for Novel Transformer Architectures Abstract: Modern Hopfield Networks or Dense Associative Memories are recurrent neural networks with fixed point attractor states that are described by an energy function. In contrast to conventional Hopfield Networks, which were popular in […]