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DTSTART;TZID=America/New_York:20210909T140000
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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/
LOCATION:MA
CATEGORIES:New Technologies in Mathematics Seminar
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DTSTART;TZID=America/New_York:20210915T150000
DTEND;TZID=America/New_York:20210915T160000
DTSTAMP:20240517T200145Z
CREATED:20240214T091846Z
LAST-MODIFIED:20240517T200145Z
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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/
LOCATION:MA
CATEGORIES:New Technologies in Mathematics Seminar
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DTSTART;TZID=America/New_York:20210929T150000
DTEND;TZID=America/New_York:20210929T160000
DTSTAMP:20240517T200354Z
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/
LOCATION:MA
CATEGORIES:New Technologies in Mathematics Seminar
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