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🚀 @SBERLOGASCI webinar on mathematics and data science:
👨‍🔬 Sergei Gukov "What makes math problems hard for reinforcement learning: a case study"
⌚️ 19 September, Thursday 19.00 Moscow time

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Can AI solve hard and interesting research-level math problems? While there is no mathematical definition of what makes a mathematical problem hard or interesting, we can provisionally define such problems as those that are well known to an average professional mathematician and have remained open for N years. The larger the value of N, the harder the problem. Using examples from combinatorial group theory and low-dimensional topology, in this talk I will explain that solving such hard long-standing math problems holds enormous potential for AI algorithm development, providing a natural path toward Artificial General Intelligence (AGI).

The talk is based on a recent paper: https://arxiv.org/abs/2408.15332

О докладчике: Сергей Гуков - профессор КалТех, выпускник МФТИ и Принстона, один из наиболее известных специалистов по теории струн и математической физике, в последние годы занимающийся применением методов Reinforcement Leaning к задачам математики и физики.

Zoom link will be in @sberlogabig just before start. Video records: https://www.youtube.com/c/SciBerloga and in telegram: https://www.group-telegram.com/sberlogasci/19688 - subscribe !

Анонс на твиттер:
https://x.com/sberloga/status/1835702457260765359
Ваши лайки и репосты - очень welcome !

www.group-telegram.com/us/datastorieslanguages.com/291

899 viewsSep 17, 2024 at 06:32

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Create: 2024-09-17
Last Update: 2025-06-28 16:33:21

🚀 @SBERLOGASCI webinar on mathematics and data science:
👨‍🔬 Sergei Gukov "What makes math problems hard for reinforcement learning: a case study"
⌚️ 19 September, Thursday 19.00 Moscow time

Add to Google Calendar

Can AI solve hard and interesting research-level math problems? While there is no mathematical definition of what makes a mathematical problem hard or interesting, we can provisionally define such problems as those that are well known to an average professional mathematician and have remained open for N years. The larger the value of N, the harder the problem. Using examples from combinatorial group theory and low-dimensional topology, in this talk I will explain that solving such hard long-standing math problems holds enormous potential for AI algorithm development, providing a natural path toward Artificial General Intelligence (AGI).

The talk is based on a recent paper: https://arxiv.org/abs/2408.15332

О докладчике: Сергей Гуков - профессор КалТех, выпускник МФТИ и Принстона, один из наиболее известных специалистов по теории струн и математической физике, в последние годы занимающийся применением методов Reinforcement Leaning к задачам математики и физики.

Zoom link will be in @sberlogabig just before start. Video records: https://www.youtube.com/c/SciBerloga and in telegram: https://www.group-telegram.com/sberlogasci/19688 - subscribe !

Анонс на твиттер:
https://x.com/sberloga/status/1835702457260765359
Ваши лайки и репосты - очень welcome !

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