“Federated Learning at Scale” Prof. Mike Rabbat, Meta AI
Day 1 - June 13, 2022 “Federated Learning at Scale” Prof. Mike Rabbat, Research Scientist & Manager, Meta AI - FAIR | 1h30min CIS Edge AI Summer School June 13-15 @EPFL The summer school covers broad topics related to Edge AI. You will learn about designing Edge AI algorithms, systems, and applications. We cover efficiency and performance aspects, co-design of algorithms and hardware, as well as robustness and privacy aspects, federated learning, decentralization, different client topologies and custom neural network architectures, as well as sustainability and ethics aspects of Edge AI. cis.epfl.ch

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“Model Pruning and the Hunt for Lottery Tickets” Prof. Dimitris Papailiopoulos

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Training Sand to Think: Artificial General Intelligence & Future of Physics

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Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker

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Yann LeCun: World Models: Enabling the next AI revolution

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Something is jamming GPS over Europe. Here's what we found

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AlphaFold - The Most Useful Thing AI Has Ever Done

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Visualizing transformers and attention | Talk for TNG Big Tech Day '24

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Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026
![Yann LeCun's $1B Bet Against LLMs [Part 1]](https://i.ytimg.com/vi/kYkIdXwW2AE/hqdefault.jpg?sqp=-oaymwEjCNACELwBSFryq4qpAxUIARUAAAAAGAElAADIQj0AgKJDeAE=&rs=AOn4CLDbV4izF3i-wxevCVIn7FJjoy1vlA)
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Yann LeCun's $1B Bet Against LLMs [Part 1]

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This is not the AI we were promised | The Royal Society

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The Uncomfortable Truth About AI “Reasoning” | World Science Festival

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If You Have A Bad Memory, I’ll Help You Fix It In 28 Minutes

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Creator of C++: Bell Labs, Negative Overhead Abstraction, Mistakes | Bjarne Stroustrup

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All 7 Dimensions Explained in Detail (From 0D to Infinity)

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Building an AI Dark Factory: A Codebase That Writes Its Own Code, Live

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Don't learn AI Agents without Learning these Fundamentals

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A visual Introduction to Federated or Collaborative Learning

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Richard Sutton - The future of AI - IPAM at UCLA

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EPFL AI Center - A Physical perspective on Graph Neural Networks - Prof Michael Bronstein

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