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DTSTART;TZID=America/New_York:20231011T140000
DTEND;TZID=America/New_York:20231011T150000
DTSTAMP:20260510T214628
CREATED:20240223T114336Z
LAST-MODIFIED:20240223T114336Z
UID:10002868-1697032800-1697036400@cmsa.fas.harvard.edu
SUMMARY:LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Alex Gu\, MIT Dept. of EE&CS \nTitle: LeanDojo: Theorem Proving with Retrieval-Augmented Language Models \nAbstract: Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean. However\, existing methods are difficult to reproduce or build on\, due to private code\, data\, and large compute requirements. This has created substantial barriers to research on machine learning methods for theorem proving. We introduce LeanDojo: an open-source Lean playground consisting of toolkits\, data\, models\, and benchmarks. LeanDojo extracts data from Lean and enables interaction with the proof environment programmatically. It contains fine-grained annotations of premises in proofs\, providing valuable data for premise selection: a key bottleneck in theorem proving. Using this data\, we develop ReProver (Retrieval-Augmented Prover): the first LLM-based prover that is augmented with retrieval for selecting premises from a vast math library. It is inexpensive and needs only one GPU week of training. Our retriever leverages LeanDojo’s program analysis capability to identify accessible premises and hard negative examples\, which makes retrieval much more effective. Furthermore\, we construct a new benchmark consisting of 96\,962 theorems and proofs extracted from Lean’s math library. It features a challenging data split requiring the prover to generalize to theorems relying on novel premises that are never used in training. We use this benchmark for training and evaluation\, and experimental results demonstrate the effectiveness of ReProver over non-retrieval baselines and GPT-4. We thus provide the first set of open-source LLM-based theorem provers without any proprietary datasets and release it under a permissive MIT license to facilitate further research. \n 
URL:https://cmsa.fas.harvard.edu/event/nt-101123-2/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-10.11.2023.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230927T140000
DTEND;TZID=America/New_York:20230927T150000
DTSTAMP:20260510T214628
CREATED:20240227T082824Z
LAST-MODIFIED:20240227T082824Z
UID:10002872-1695823200-1695826800@cmsa.fas.harvard.edu
SUMMARY:Transformers for maths\, and maths for transformers
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: François Charton\, Meta AI \nTitle:  Transformers for maths\, and maths for transformers \nAbstract: Transformers can be trained to solve problems of mathematics. I present two recent applications\, in mathematics and physics: predicting integer sequences\, and discovering the properties of scattering amplitudes in a close relative of Quantum ChromoDynamics. \nProblems of mathematics can also help understand transformers. Using two examples from linear algebra and integer arithmetic\, I show that model predictions can be explained\, that trained models do not confabulate\, and that carefully choosing the training distributions can help achieve better\, and more robust\, performance. \n  \n  \n 
URL:https://cmsa.fas.harvard.edu/event/nt-92723/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-09.27.2023.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230920T140000
DTEND;TZID=America/New_York:20230920T150000
DTSTAMP:20260510T214628
CREATED:20240227T083355Z
LAST-MODIFIED:20240227T083355Z
UID:10002873-1695218400-1695222000@cmsa.fas.harvard.edu
SUMMARY:The TinyStories Dataset: How Small Can Language Models Be And Still Speak Coherent
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Ronen Eldan\, Microsoft Research \nTitle: The TinyStories Dataset: How Small Can Language Models Be And Still Speak Coherent \nAbstract: While generative language models exhibit powerful capabilities at large scale\, when either the model or the number of training steps is too small\, they struggle to produce coherent and fluent text: Existing models whose size is below a few billion parameters often do not generate coherent text beyond a few sentences. Hypothesizing that one of the main reasons for the strong reliance on size is the vast breadth and abundance of patterns in the datasets used to train those models\, this motivates the following question: Can we design a dataset that preserves the essential elements of natural language\, such as grammar\, vocabulary\, facts\, and reasoning\, but that is much smaller and more refined in terms of its breadth and diversity? \nIn this talk\, we introduce TinyStories\, a synthetic dataset of short stories that only contain words that 3 to 4-year-olds typically understand\, generated by GPT-3.5/4. We show that TinyStories can be used to train and analyze language models that are much smaller than the state-of-the-art models (below 10 million parameters)\, or have much simpler architectures (with only one transformer block)\, yet still produce fluent and consistent stories with several paragraphs that are diverse and have almost perfect grammar\, and demonstrate certain reasoning capabilities. We also show that the trained models are substantially more interpretable than larger ones\, as we can visualize and analyze the attention and activation patterns of the models\, and show how they relate to the generation process and the story content. We hope that TinyStories can facilitate the development\, analysis and research of language models\, especially for low-resource or specialized domains\, and shed light on the emergence of language capabilities in LMs. \n 
URL:https://cmsa.fas.harvard.edu/event/nt-92023/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-09.20.2023.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230510T140000
DTEND;TZID=America/New_York:20230510T150000
DTSTAMP:20260510T214628
CREATED:20230809T105349Z
LAST-MODIFIED:20240228T104953Z
UID:10001225-1683727200-1683730800@cmsa.fas.harvard.edu
SUMMARY:Modern Hopfield Networks for Novel Transformer Architectures
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Dmitry Krotov\, IBM Research – Cambridge \nTitle: Modern Hopfield Networks for Novel Transformer Architectures \nAbstract: 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 the 1980s\, their modern versions have a very large memory storage capacity\, which makes them appealing tools for many problems in machine learning and cognitive and neurosciences. In this talk\, I will introduce an intuition and a mathematical formulation of this class of models and will give examples of problems in AI that can be tackled using these new ideas. Particularly\, I will introduce an architecture called Energy Transformer\, which replaces the conventional attention mechanism with a recurrent Dense Associative Memory model. I will explain the theoretical principles behind this architectural choice and show promising empirical results on challenging computer vision and graph network tasks.
URL:https://cmsa.fas.harvard.edu/event/nt-51023/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-05.10.23.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230426T140000
DTEND;TZID=America/New_York:20230426T150000
DTSTAMP:20260510T214628
CREATED:20230809T103350Z
LAST-MODIFIED:20240209T151145Z
UID:10001224-1682517600-1682521200@cmsa.fas.harvard.edu
SUMMARY:Toolformer: Language Models Can Teach Themselves to Use Tools
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Timo Schick\, Meta AI \nTitle: Toolformer: Language Models Can Teach Themselves to Use Tools \nAbstract: 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 much simpler and smaller models excel. In this talk\, we show how these limitations can be overcome by letting language models teach themselves to use external tools via simple APIs. We discuss Toolformer\, a model trained to independently decide which APIs to call\, when to call them\, what arguments to pass\, and how to best incorporate the results into future token prediction. Through this\, it achieves substantially improved zero-shot performance across a variety of downstream tasks without sacrificing its core language modeling abilities. \n 
URL:https://cmsa.fas.harvard.edu/event/nt-42623/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-04.26.23.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230308T140000
DTEND;TZID=America/New_York:20230308T150000
DTSTAMP:20260510T214628
CREATED:20230808T190051Z
LAST-MODIFIED:20240223T154858Z
UID:10001812-1678284000-1678287600@cmsa.fas.harvard.edu
SUMMARY:How to steer foundation models?
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Jimmy Ba\, University of Toronto \nTitle: How to steer foundation models? \nAbstract: 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. Due to the lack of knowledge of how foundation models work\, most effective prompts have been handcrafted by humans through a demanding trial-and-error process. To reduce the human effort in this alignment process\, I will discuss a few approaches to steer these powerful models to excel in various downstream language and image tasks. \n 
URL:https://cmsa.fas.harvard.edu/event/nt-3823/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/03.08.2023.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221207T140000
DTEND;TZID=America/New_York:20221207T150000
DTSTAMP:20260510T214628
CREATED:20230808T185642Z
LAST-MODIFIED:20240116T060930Z
UID:10001215-1670421600-1670425200@cmsa.fas.harvard.edu
SUMMARY:How do Transformers reason? First principles via automata\, semigroups\, and circuits
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Cyril Zhang\, Microsoft Research \nTitle: How do Transformers reason? First principles via automata\, semigroups\, and circuits \nAbstract: 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\, and theorem proving. What is the nature of these models’ internal representations (i.e. how do they represent the states and computational steps of the algorithms they execute)? How can we understand and mitigate their weaknesses\, given that they resist interpretation? In this work\, we present some insights (and many further mysteries) through the lens of automata and their algebraic structure. \nSpecifically\, we investigate the apparent mismatch between recurrent models of computation (automata & Turing machines) and Transformers (which are typically shallow and non-recurrent). Using tools from circuit complexity and semigroup theory\, we characterize shortcut solutions\, whereby a shallow Transformer with only o(T) layers can exactly replicate T computational steps of an automaton. We show that Transformers can efficiently represent these shortcuts in theory; furthermore\, in synthetic experiments\, standard training successfully finds these shortcuts. We demonstrate that shortcuts can lead to statistical brittleness\, and discuss mitigations. \nJoint work with Bingbin Liu\, Jordan Ash\, Surbhi Goel\, and Akshay Krishnamurthy.
URL:https://cmsa.fas.harvard.edu/event/nt-12722/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/12.07.2022.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221026T140000
DTEND;TZID=America/New_York:20221026T150000
DTSTAMP:20260510T214628
CREATED:20230808T185319Z
LAST-MODIFIED:20240115T103149Z
UID:10001214-1666792800-1666796400@cmsa.fas.harvard.edu
SUMMARY:From Engine to Auto
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeakers: João Araújo\, Mathematics Department\, Universidade Nova de Lisboa and Michael Kinyon\, Department of Mathematics\, University of Denver \n\nTitle: From Engine to Auto \n\n\nAbstract: 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 to triviality any result that can be obtained by encoding the assumptions and the goals. The next step was to turn the program into a genuine assistant for the working mathematician: find ways to help the prover with proofs; reduce the lengths of the automatic proofs to better crack them;  solve problems in higher order logic; devise tools that autonomously prove results of a given type\, etc.\n\nIn this talk we are going to show some of the tools and strategies we have been producing. There will be real illustrations of theorems obtained for groups\, loops\, semigroups\, logic algebras\, lattices and generalizations\, quandles\, and many more.
URL:https://cmsa.fas.harvard.edu/event/nt-102622/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-10.26.2022.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221019T140000
DTEND;TZID=America/New_York:20221019T150000
DTSTAMP:20260510T214628
CREATED:20230808T184955Z
LAST-MODIFIED:20240215T095357Z
UID:10001213-1666188000-1666191600@cmsa.fas.harvard.edu
SUMMARY:Towards Faithful Reasoning Using Language Models
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Antonia Creswell\, DeepMind \nTitle: Towards Faithful Reasoning Using Language Models \nAbstract: 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 over relevant knowledge to construct an answer or do they rely on prior knowledge – baked into their weights – which may be biased? An alternative approach is to develop models whose output is a human interpretable\, faithful reasoning trace leading to an answer. In this talk we will characterise faithful reasoning in terms of logically valid reasoning and demonstrate where current reasoning models fall short. Following this\, we will introduce Selection-Inference\, a faithful reasoning model\, whose causal structure mirrors the requirements for valid reasoning. We will show that our model not only produces more accurate reasoning traces but also improves final answer accuracy. \n  \n 
URL:https://cmsa.fas.harvard.edu/event/nt-101922/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/10.19.2022.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221005T140000
DTEND;TZID=America/New_York:20221005T160000
DTSTAMP:20260510T214628
CREATED:20230808T184616Z
LAST-MODIFIED:20240214T110102Z
UID:10001212-1664978400-1664985600@cmsa.fas.harvard.edu
SUMMARY:Minerva: Solving Quantitative Reasoning Problems with Language Models
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Guy Gur-Ari\, Google Research \nTitle: Minerva: Solving Quantitative Reasoning Problems with Language Models \nAbstract: 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 on math and science questions when trained in an unsupervised way on a large\, math-focused dataset. Performance can be further improved using prompting and sampling techniques including chain-of-thought and majority voting. Minerva\, a model that combines these techniques\, achieves SOTA on several math and science benchmarks. I will describe the model\, its capabilities and limitations.
URL:https://cmsa.fas.harvard.edu/event/minerva-solving-quantitative-reasoning-problems-with-language-models/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/10.05.2022.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220928T140000
DTEND;TZID=America/New_York:20220928T150000
DTSTAMP:20260510T214628
CREATED:20230808T184138Z
LAST-MODIFIED:20240214T110335Z
UID:10001211-1664373600-1664377200@cmsa.fas.harvard.edu
SUMMARY:Statistical mechanics of neural networks: From the geometry of high dimensional error landscapes to beating power law neural scaling
DESCRIPTION:New Technologies in Mathematics \nSpeaker: Surya Ganguli\, Stanford University \n\nTitle: Statistical mechanics of neural networks: From the geometry of high dimensional error landscapes to beating power law neural scaling\n\n\n\n\nAbstract: Statistical mechanics and neural network theory have long enjoyed fruitful interactions.  We will review some of our recent work in this area and then focus on two vignettes. First we will analyze the high dimensional geometry of neural network error landscapes that happen to arise as the classical limit of a dissipative many-body quantum optimizer.  In particular\, we will be able to use the Kac-Rice formula and the replica method to calculate the number\, location\, energy levels\, and Hessian eigenspectra of all critical points of any index.  Second we will review recent work on neural power laws\, which reveal that the error of many neural networks falls off as a power law with network size or dataset size.  Such power laws have motivated significant societal investments in large scale model training and data collection efforts.  Inspired by statistical mechanics calculations\, we show both in theory and in practice how we can beat neural power law scaling with respect to dataset size\, sometimes achieving exponential scaling\, by collecting small carefully curated datasets rather than large random ones.\n\n\n\nReferences: Y. Bahri\, J. Kadmon\, J. Pennington\, S. Schoenholz\, J. Sohl-Dickstein\, and S. Ganguli\, Statistical mechanics of deep learning\, Annual Reviews of Condensed Matter Physics\, 2020.\n\nSorscher\, Ben\, Robert Geirhos\, Shashank Shekhar\, Surya Ganguli\, and Ari S. Morcos. 2022. Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning https://arxiv.org/abs/2206.14486 (NeurIPS 2022).
URL:https://cmsa.fas.harvard.edu/event/8303/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-09.28.2022.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220914T140000
DTEND;TZID=America/New_York:20220914T150000
DTSTAMP:20260510T214628
CREATED:20230808T183823Z
LAST-MODIFIED:20240301T091205Z
UID:10001210-1663164000-1663167600@cmsa.fas.harvard.edu
SUMMARY:Breaking the one-mind-barrier in mathematics using formal verification
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Johan Commelin\, Mathematisches Institut\, Albert-Ludwigs-Universität Freiburg \nTitle: Breaking the one-mind-barrier in mathematics using formal verification \nAbstract: 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 the project. At the same time\, it also allows a mathematician to rapidly free mental RAM in order to work on a different component of a project. It thus also expands the one-mind-barrier. \nI will use the Liquid Tensor Experiment as an example\, to illustrate the above two points. This project recently finished the formalization of the main theorem of liquid vector spaces\, following up on a challenge by Peter Scholze. \nVideo
URL:https://cmsa.fas.harvard.edu/event/breaking-the-one-mind-barrier-in-mathematics-using-formal-verification/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:New Technologies in Mathematics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220330T140000
DTEND;TZID=America/New_York:20220330T150000
DTSTAMP:20260510T214628
CREATED:20230808T183529Z
LAST-MODIFIED:20240515T202223Z
UID:10001209-1648648800-1648652400@cmsa.fas.harvard.edu
SUMMARY:Memorizing Transformers
DESCRIPTION:Speaker: Yuhuai Wu\, Stanford and Google \nTitle: Memorizing Transformers \nAbstract: Language models typically need to be trained or fine-tuned in order to acquire new knowledge\, which involves updating their weights. We instead envision language models that can simply read and memorize new data at inference time\, thus acquiring new knowledge immediately. In this talk\, I will discuss how we extend language models with the ability to memorize the internal representations of past inputs. We demonstrate that an approximate NN lookup into a non-differentiable memory of recent (key\, value) pairs improves language modeling across various benchmarks and tasks\, including generic webtext (C4)\, math papers (arXiv)\, books (PG-19)\, code (Github)\, as well as formal theorems (Isabelle). We show that the performance steadily improves when we increase the size of memory up to 262K tokens. We also find that the model is capable of making use of newly defined functions and theorems during test time.
URL:https://cmsa.fas.harvard.edu/event/3-30-2022-new-technologies-in-mathematics-seminar/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-03.30.2022-1583x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220323T140000
DTEND;TZID=America/New_York:20220323T150000
DTSTAMP:20260510T214628
CREATED:20230808T183247Z
LAST-MODIFIED:20240515T202339Z
UID:10001208-1648044000-1648047600@cmsa.fas.harvard.edu
SUMMARY:Formal Mathematics Statement Curriculum Learning
DESCRIPTION:Speaker: Stanislas Polu\, OpenAI \nTitle: Formal Mathematics Statement Curriculum Learning \nAbstract: We explore the use of expert iteration in the context of language modeling applied to formal mathematics. We show that at same compute budget\, expert iteration\, by which we mean proof search interleaved with learning\, dramatically outperforms proof search only.  We also observe that when applied to a collection of formal statements of sufficiently varied difficulty\, expert iteration is capable of finding and solving a curriculum of increasingly difficult problems\,  without the need for associated ground-truth proofs. Finally\, by applying this expert iteration to a manually curated set of problem statements\, we achieve state-of-the-art on the miniF2F benchmark\,  automatically solving multiple challenging problems drawn from high school olympiads.
URL:https://cmsa.fas.harvard.edu/event/3-23-2022-new-technologies-in-mathematics-seminar/
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-03.23.2022-1553x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220309T140000
DTEND;TZID=America/New_York:20220309T140000
DTSTAMP:20260510T214628
CREATED:20230808T182829Z
LAST-MODIFIED:20240813T160025Z
UID:10001207-1646834400-1646834400@cmsa.fas.harvard.edu
SUMMARY:Machine Learning 30 STEM Courses in 12 Departments
DESCRIPTION:Speaker: Iddo Drori\, MIT EE&CS and Columbia School of Engineering \nTitle: Machine Learning 30 STEM Courses in 12 Departments \nAbstract: We automatically solve\, explain\, and generate university-level course problems from thirty STEM courses (at MIT\, Harvard\, and Columbia) for the first time.\nWe curate a new dataset of course questions and answers across a dozen departments: Aeronautics and Astronautics\, Chemical Engineering\, Chemistry\, Computer Science\, Economics\, Electrical Engineering\, Materials Science\, Mathematics\, Mechanical Engineering\, Nuclear Science\, Physics\, and Statistics.\nWe generate new questions and use them in a Columbia University course\, and perform A/B tests demonstrating that these machine generated questions are indistinguishable from human-written questions and that machine generated explanations are as useful as human-written explanations\, again for the first time.\nOur approach consists of five steps:\n(i) Given course questions\, turn them into programming tasks;\n(ii) Automatically generate programs from the programming tasks using a Transformer model\, OpenAI Codex\, pre-trained on text and fine-tuned on code;\n(iii) Execute the programs to obtain and evaluate the answers;\n(iv) Automatically explain the correct solutions using Codex;\n(v) Automatically generate new questions that are qualitatively indistinguishable from human-written questions.\nThis work is a significant step forward in applying machine learning for education\, automating a considerable part of the work involved in teaching.\nOur approach allows personalization of questions based on difficulty level and student backgrounds\, and scales up to a broad range of courses across the schools of engineering and science. \nThis is joint work with students and colleagues at MIT\, Harvard University\, Columbia University\, Worcester Polytechnic Institute\, and the University of Waterloo.
URL:https://cmsa.fas.harvard.edu/event/3-9-2022-new-technologies-in-mathematics-seminar/
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-03.09.2022.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220302T140000
DTEND;TZID=America/New_York:20220302T150000
DTSTAMP:20260510T214628
CREATED:20230808T182233Z
LAST-MODIFIED:20240517T193649Z
UID:10001206-1646229600-1646233200@cmsa.fas.harvard.edu
SUMMARY:Scaling Laws and Their Implications for Coding AI
DESCRIPTION:Speaker: Jared Kaplan\, Johns Hopkins Dept. of Physics & Astronomy \nTitle: Scaling Laws and Their Implications for Coding AI \nAbstract:  Scaling laws and associated downstream trends can be used as an organizing principle when thinking about current and future ML progress.  I will briefly review scaling laws for generative models in a number of domains\, emphasizing language modeling.  Then I will discuss scaling results for transfer from natural language to code\, and results on python programming performance from “codex” and other models.  If there’s time I’ll discuss prospects for the future — limitations from dataset sizes\, and prospects for RL and other techniques.
URL:https://cmsa.fas.harvard.edu/event/3-2-2022-new-technologies-in-mathematics-seminar/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/03.2.2022-1553x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220216T140000
DTEND;TZID=America/New_York:20220216T150000
DTSTAMP:20260510T214628
CREATED:20230808T181915Z
LAST-MODIFIED:20240515T205523Z
UID:10001205-1645020000-1645023600@cmsa.fas.harvard.edu
SUMMARY:Bootstrapping hyperbolic manifolds
DESCRIPTION:Speaker: James Bonifacio\, Cambridge DAMTP \nTitle: Bootstrapping hyperbolic manifolds \nAbstract: Hyperbolic manifolds are a class of Riemannian manifolds that are important in mathematics and physics\, playing a prominent role in topology\, number theory\, and string theory. Associated with a given hyperbolic metric is a sequence of numbers corresponding to the discrete eigenvalues of the Laplace-Beltrami operator. While these eigenvalues usually cannot be calculated exactly\, they can be found numerically and must also satisfy various bounds. In this talk\, I will discuss a new approach for finding numerical bounds on the eigenvalues of closed hyperbolic manifolds using general consistency conditions and semidefinite programming\, inspired by the approach of the conformal bootstrap from physics. Although these bootstrap bounds follow from seemingly trivial consistency conditions\, they are surprisingly strong and are sometimes almost saturated by actual manifolds; for example\, one such bound implies that the first nonzero eigenvalue of a closed hyperbolic surface must be less than 3.83890\, and this is very close to being saturated by a particular genus-2 surface called the Bolza surface. I will show how to derive this and other bounds and will discuss some possible future directions for this approach.
URL:https://cmsa.fas.harvard.edu/event/2-16-2022-new-technologies-in-mathematics-seminar/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-02.16.2022-1553x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220209T140000
DTEND;TZID=America/New_York:20220209T150000
DTSTAMP:20260510T214628
CREATED:20230808T181534Z
LAST-MODIFIED:20240517T193404Z
UID:10001204-1644415200-1644418800@cmsa.fas.harvard.edu
SUMMARY:Toward Demystifying Transformers and Attention
DESCRIPTION:Speaker: Ben Edelman\, Harvard Computer Science \nTitle: Toward Demystifying Transformers and Attention \nAbstract: Over the past several years\, attention mechanisms (primarily in the form of the Transformer architecture) have revolutionized deep learning\, leading to advances in natural language processing\, computer vision\, code synthesis\, protein structure prediction\, and beyond. Attention has a remarkable ability to enable the learning of long-range dependencies in diverse modalities of data. And yet\, there is at present limited principled understanding of the reasons for its success. In this talk\, I’ll explain how attention mechanisms and Transformers work\, and then I’ll share the results of a preliminary investigation into why they work so well. In particular\, I’ll discuss an inductive bias of attention that we call sparse variable creation: bounded-norm Transformer layers are capable of representing sparse Boolean functions\, with statistical generalization guarantees akin to sparse regression.
URL:https://cmsa.fas.harvard.edu/event/2-9-2022-new-technologies-in-mathematics-seminar/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-02.09.2022-1553x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220202T140000
DTEND;TZID=America/New_York:20220202T150000
DTSTAMP:20260510T214628
CREATED:20230808T181044Z
LAST-MODIFIED:20240515T204958Z
UID:10000006-1643810400-1643814000@cmsa.fas.harvard.edu
SUMMARY:Neural diffusion PDEs\, differential geometry\, and graph neural networks
DESCRIPTION:Speaker: Michael Bronstein\, University of Oxford and Twitter \nTitle: Neural diffusion PDEs\, differential geometry\, and graph neural networks \nAbstract: In this talk\, I will make connections between Graph Neural Networks (GNNs) and non-Euclidean diffusion equations. I will show that drawing on methods from the domain of differential geometry\, it is possible to provide a principled view on such GNN architectural choices as positional encoding and graph rewiring as well as explain and remedy the phenomena of oversquashing and bottlenecks.
URL:https://cmsa.fas.harvard.edu/event/2-2-2022-new-technologies-in-mathematics/
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-02.02.2022-2-1583x2048-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220126T140000
DTEND;TZID=America/New_York:20220126T150000
DTSTAMP:20260510T214628
CREATED:20230808T180637Z
LAST-MODIFIED:20240517T193321Z
UID:10001203-1643205600-1643209200@cmsa.fas.harvard.edu
SUMMARY:Machine learning with mathematicians
DESCRIPTION:Speaker: Alex Davies\, DeepMind \nTitle: Machine learning with mathematicians \nAbstract: Can machine learning be a useful tool for research mathematicians? There are many examples of mathematicians pioneering new technologies to aid our understanding of the mathematical world: using very early computers to help formulate the Birch and Swinnerton-Dyer conjecture and using computer aid to prove the four colour theorem are among the most notable. Up until now there hasn’t been significant use of machine learning in the field and it hasn’t been clear where it might be useful for the questions that mathematicians care about. In this talk we will discuss the results of our recent Nature paper\, where we worked together with top mathematicians to use machine learning to achieve two new results – proving a new connection between the hyperbolic and geometric structure of knots\, and conjecturing a resolution to a 50-year problem in representation theory\, the combinatorial invariance conjecture. Through these examples we demonstrate a way that machine learning can be used by mathematicians to help guide the development of surprising and beautiful new conjectures.
URL:https://cmsa.fas.harvard.edu/event/1-26-2022-new-technologies-in-mathematics/
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-New-Technologies-Seminar-01.26.2022-1553x2048-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211215T140000
DTEND;TZID=America/New_York:20211215T150000
DTSTAMP:20260510T214629
CREATED:20230808T180208Z
LAST-MODIFIED:20240515T204057Z
UID:10001202-1639576800-1639580400@cmsa.fas.harvard.edu
SUMMARY:Unreasonable effectiveness of the quantum complexity view on quantum many-body physics
DESCRIPTION:Speaker: Anurag Anshu\, Department of EECS & Challenge Institute for Quantum Computation\, UC Berkeley \nTitle: Unreasonable effectiveness of the quantum complexity view on quantum many-body physics \nAbstract: A central challenge in quantum many-body physics is to estimate the properties of natural quantum states\, such as the quantum ground states and Gibbs states. Quantum Hamiltonian complexity offers a computational perspective on this challenge and classifies these natural quantum states using the language of quantum complexity classes. This talk will provide a gentle introduction to the field and highlight its success in pinning down the hardness of a wide variety of quantum states. In particular\, we will consider the gapped ground states and Gibbs states on low dimensional lattices\, which are believed to exhibit ‘low complexity’ due to the widely studied area law behaviour. Here\, we will see the crucial role of complexity-theoretic methods in progress on the ‘area law conjecture’ and in the development of efficient algorithms to classically simulate quantum many-body systems.
URL:https://cmsa.fas.harvard.edu/event/12-15-21-new-technologies-in-mathematics/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-12.15.21-2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211208T140000
DTEND;TZID=America/New_York:20211208T150000
DTSTAMP:20260510T214629
CREATED:20230808T175752Z
LAST-MODIFIED:20240515T203917Z
UID:10001201-1638972000-1638975600@cmsa.fas.harvard.edu
SUMMARY:Hierarchical Transformers are More Efficient Language Models
DESCRIPTION:Speaker: Piotr Nawrot\, University of Warsaw \nTitle: Hierarchical Transformers are More Efficient Language Models \nAbstract: Transformer models yield impressive results on many NLP and sequence modeling tasks. Remarkably\, Transformers can handle long sequences which allows them to produce long coherent outputs: full paragraphs produced by GPT-3 or well-structured images produced by DALL-E. These large language models are impressive but also very inefficient and costly\, which limits their applications and accessibility. We postulate that having an explicit hierarchical architecture is the key to Transformers that efficiently handle long sequences. To verify this claim\, we first study different ways to upsample and downsample activations in Transformers so as to make them hierarchical. We use the best performing upsampling and downsampling layers to create Hourglass – a hierarchical Transformer language model. Hourglass improves upon the Transformer baseline given the same amount of computation and can yield the same results as Transformers more efficiently. In particular\, Hourglass sets new state-of-the-art for Transformer models on the ImageNet32 generation task and improves language modeling efficiency on the widely studied enwik8 benchmark.
URL:https://cmsa.fas.harvard.edu/event/12-8-21-new-technologies-in-mathematics/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-12.08.21.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211201T140000
DTEND;TZID=America/New_York:20211201T143000
DTSTAMP:20260510T214629
CREATED:20230808T175251Z
LAST-MODIFIED:20240515T203641Z
UID:10001200-1638367200-1638369000@cmsa.fas.harvard.edu
SUMMARY:The Principles of Deep Learning Theory
DESCRIPTION:Speaker: Dan Roberts\, MIT & Salesforce \nTitle: The Principles of Deep Learning Theory \nAbstract: Deep learning is an exciting approach to modern artificial intelligence based on artificial neural networks. The goal of this talk is to provide a blueprint — using tools from physics — for theoretically analyzing deep neural networks of practical relevance. This task will encompass both understanding the statistics of initialized deep networks and determining the training dynamics of such an ensemble when learning from data. \nIn terms of their “microscopic” definition\, deep neural networks are a flexible set of functions built out of many basic computational blocks called neurons\, with many neurons in parallel organized into sequential layers. Borrowing from the effective theory framework\, we will develop a perturbative 1/n expansion around the limit of an infinite number of neurons per layer and systematically integrate out the parameters of the network. We will explain how the network simplifies at large width and how the propagation of signals from layer to layer can be understood in terms of a Wilsonian renormalization group flow. This will make manifest that deep networks have a tuning problem\, analogous to criticality\, that needs to be solved in order to make them useful. Ultimately we will find a “macroscopic” description for wide and deep networks in terms of weakly-interacting statistical models\, with the strength of the interactions between the neurons growing with depth-to-width aspect ratio of the network. Time permitting\, we will explain how the interactions induce representation learning. \nThis talk is based on a book\, The Principles of Deep Learning Theory\, co-authored with Sho Yaida and based on research also in collaboration with Boris Hanin. It will be published next year by Cambridge University Press.
URL:https://cmsa.fas.harvard.edu/event/12-1-21-new-technologies-in-mathematics-seminar-series/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-12.01.21.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211103T171200
DTEND;TZID=America/New_York:20211103T181200
DTSTAMP:20260510T214629
CREATED:20240214T094241Z
LAST-MODIFIED:20240813T155909Z
UID:10002643-1635959520-1635963120@cmsa.fas.harvard.edu
SUMMARY:When Computer Algebra Meets Satisfiability: A New Approach to Combinatorial Mathematics
DESCRIPTION:Speakers: Curtis Bright\, School of Computer Science\, University of Windsor and Vijay Ganesh\, Dept. of Electrical and Computer Engineering\, University of Waterloo \nTitle: When Computer Algebra Meets Satisfiability: A New Approach to Combinatorial Mathematics \nAbstract: Solvers for the Boolean satisfiability (SAT) problem have been increasingly used to resolve problems in mathematics due to their excellent search algorithms.  This talk will describe a new method for mathematical search that couples SAT solvers with computer algebra systems (CAS)\, thereby combining the expressiveness of CASs with the search power of SAT solvers.  This paradigm has led to a number of results on long-standing mathematical questions such as the first computer-verifiable resolution of Lam’s problem and the discovery of a new infinite class of Williamson matrices.
URL:https://cmsa.fas.harvard.edu/event/11-3-21-cmsa-new-technologies-in-mathematics/
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-11.03.21.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211027T140000
DTEND;TZID=America/New_York:20211027T150000
DTSTAMP:20260510T214629
CREATED:20240214T093929Z
LAST-MODIFIED:20240517T193105Z
UID:10002641-1635343200-1635346800@cmsa.fas.harvard.edu
SUMMARY:Why explain mathematics to computers?
DESCRIPTION:Speaker: Patrick Massot\, Laboratoire de Mathématiques d’Orsay and CNRS \nTitle: Why explain mathematics to computers? \nAbstract: A growing number of mathematicians are having fun explaining mathematics to computers using proof assistant softwares. This process is called formalization. In this talk I’ll describe what formalization looks like\, what kind of things it teaches us\, and how it could even turn out to be useful (in our usual sense of “useful”). This will not be a talk about foundations of mathematics\, and I won’t assume any prior knowledge about formalization.
URL:https://cmsa.fas.harvard.edu/event/10-27-2021-new-technologies-in-mathematics-seminar/
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-10.27.21.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211013T140000
DTEND;TZID=America/New_York:20211013T150000
DTSTAMP:20260510T214629
CREATED:20240214T093531Z
LAST-MODIFIED:20240515T204354Z
UID:10002637-1634133600-1634137200@cmsa.fas.harvard.edu
SUMMARY:Computer-Aided Mathematics and Satisfiability
DESCRIPTION:Speaker: Marijn Heule\, Carnegie Mellon University \nTitle: Computer-Aided Mathematics and Satisfiability \nAbstract: Progress in satisfiability (SAT) solving has made it possible to determine the correctness of complex systems and answer long-standing open questions in mathematics. The SAT solving approach is completely automatic and can produce clever though potentially gigantic proofs. We can have confidence in the correctness of the answers because highly trustworthy systems can validate the underlying proofs regardless of their size. We demonstrate the effectiveness of the SAT approach by presenting some recent successes\, including the solution of the Boolean Pythagorean Triples problem\, computing the fifth Schur number\, and resolving the remaining case of Keller’s conjecture. Moreover\, we constructed and validated a proof for each of these results. The second part of the talk focuses on notorious math challenges for which automated reasoning may well be suitable. In particular\, we discuss our progress on applying SAT solving techniques to the chromatic number of the plane (Hadwiger-Nelson problem)\, optimal schemes for matrix multiplication\, and the Collatz conjecture.
URL:https://cmsa.fas.harvard.edu/event/10-13-2021-new-technologies-in-mathematics-seminar/
CATEGORIES:New Technologies in Mathematics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211006T150000
DTEND;TZID=America/New_York:20211006T160000
DTSTAMP:20260510T214629
CREATED:20240214T092921Z
LAST-MODIFIED:20240517T200621Z
UID:10002630-1633532400-1633536000@cmsa.fas.harvard.edu
SUMMARY:New results in Supergravity via ML Technology
DESCRIPTION:Speaker: Thomas Fischbacher\, Google \nTitle: New results in Supergravity via ML Technology \nAbstract: The infrastructure built to power the Machine Learning revolution has many other uses beyond Deep Learning. Starting from a general architecture-level overview over the lower levels of Google’s TensorFlow machine learning library\, we review how this has recently helped us to find all the stable vacua of SO(8) Supergravity in 3+1 dimensions\, has allowed major progress on other related questions about M theory\, and briefly discuss other applications in field theory and beyond.
URL:https://cmsa.fas.harvard.edu/event/10-6-2021-new-technologies-in-mathematics-seminar/
CATEGORIES:New Technologies in Mathematics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210929T150000
DTEND;TZID=America/New_York:20210929T160000
DTSTAMP:20260510T214629
CREATED:20240214T092650Z
LAST-MODIFIED:20240517T200354Z
UID:10002626-1632927600-1632931200@cmsa.fas.harvard.edu
SUMMARY:Constructions in combinatorics via neural networks
DESCRIPTION:Speaker: Adam Wagner\, Tel Aviv University \nTitle: Constructions in combinatorics via neural networks \nAbstract: Recently\, significant progress has been made in the area of machine learning algorithms\, and they have quickly become some of the most exciting tools in a scientist’s toolbox. In particular\, recent advances in the field of reinforcement learning have led computers to reach superhuman level play in Atari games and Go\, purely through self-play. In this talk I will give a very basic introduction to neural networks and reinforcement learning algorithms. I will also indicate how these methods can be adapted to the ““game” of trying to find a counterexample to a mathematical conjecture\, and show some examples where this approach was successful.
URL:https://cmsa.fas.harvard.edu/event/9-29-2021-new-technologies-in-mathematics-seminar/
CATEGORIES:New Technologies in Mathematics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210915T150000
DTEND;TZID=America/New_York:20210915T160000
DTSTAMP:20260510T214629
CREATED:20240214T091846Z
LAST-MODIFIED:20240517T200145Z
UID:10002618-1631718000-1631721600@cmsa.fas.harvard.edu
SUMMARY:Why abstraction is the key to intelligence\, and what we’re still missing
DESCRIPTION:Speaker: Francois Chollet\, Google \nTitle: Why abstraction is the key to intelligence\, and what we’re still missing \nAbstract: This talk provides a personal perspective on the way forward towards more human-like and more intelligent artificial systems. Traditionally\, symbolic and probabilistic methods have dominated the domains of concept formation\, abstraction\, and automated reasoning. More recently\, deep learning-based approaches have led to significant breakthroughs\, including successes in games and combinatorial search tasks. However\, the resulting systems are still limited in scope and capabilities — they remain brittle\, data-hungry\, and their generalization capabilities are limited. We will address a set of questions: why is conceptual abstraction essential for intelligence? What is the nature of abstraction\, and its relationship to generalization? What kind of abstraction can deep learning models generate\, and where do they fail? What are the methods that are currently successful in generating strong conceptual abstraction? Finally\, we will consider how to leverage a hybrid approach to reinforce the strength of different approaches while compensating for their respective weaknesses.
URL:https://cmsa.fas.harvard.edu/event/9-15-2021-new-technologies-in-mathematics-seminar/
CATEGORIES:New Technologies in Mathematics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210909T140000
DTEND;TZID=America/New_York:20210909T150000
DTSTAMP:20260510T214629
CREATED:20240214T092250Z
LAST-MODIFIED:20240517T200424Z
UID:10002621-1631196000-1631199600@cmsa.fas.harvard.edu
SUMMARY:The complexity of matrix multiplication approached via algebraic geometry and representation theory
DESCRIPTION:Speaker: JM Landsberg\, Texas A&M \nTitle: The complexity of matrix multiplication approached via algebraic geometry and representation theory \nAbstract: In 1968 V. Strassen discovered the way we usually multiply matrices is not the most efficient possible\, and after considerable work by many authors\, it is generally conjectured by computer scientists that as the size of matrices becomes large\, it becomes almost as easy to multiply them as it is to add them. I will give a brief history of the problem\, explain how this conjecture is naturally understood in the framework of classical algebraic geometry and representation theory\, and conclude by describing recent advances using more sophisticated tools from algebraic geometry. For most of the talk\, no knowledge of algebraic geometry or representation theory will be needed.
URL:https://cmsa.fas.harvard.edu/event/9-22-2021-new-technologies-in-mathematics-seminar/
CATEGORIES:New Technologies in Mathematics Seminar
END:VEVENT
END:VCALENDAR