Patreon: https://www.patreon.com/mlst The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact tractable given enough computational horsepower. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning and second, learning by local gradient-descent type methods, typically implemented as backpropagation. While learning generic functions in high dimensions is a cursed estimation problem, most tasks of interest are not uniform and have strong repeating patterns as a result of the low-dimensionality and structure of the physical world. Geometric Deep Learning unifies a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases. This week we spoke with Professor Michael Bronstein (head of graph ML at Twitter) and Dr. Petar Veličković (Senior Research Scientist at DeepMind), and Dr. Taco Cohen and Prof. Joan Bruna about their new proto-book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. See the table of contents for this (long) show at https://youtu.be/bIZB1hIJ4u8 

Dr. Petar Veličković  is a Staff Research Scientist at DeepMind, he has firmly established himself as one of the most significant up and coming researchers in the deep learning space. He invented Graph Attention Networks in 2017 and has been a leading light in the field ever since pioneering research in Graph Neural Networks, Geometric Deep Learning and also Neural Algorithmic reasoning. If you haven’t already, you should check out our video on the Geometric Deep learning blueprint, featuring Petar. I caught up with him last week at NeurIPS. In this show, from NeurIPS 2022 we discussed his recent work on category theory and graph neural networks. https://petar-v.com/ https://twitter.com/PetarV_93/ TOC: Categories  (Cats for AI) [00:00:00] Reasoning [00:14:44] Extrapolation [00:19:09] Ishan Misra Skit [00:27:50] Graphs (Expander Graph Propagation) [00:29:18] YT: https://youtu.be/1lkdWduuN14 MLST Discord: https://discord.gg/V25vQeFwhS Support us! https://www.patreon.com/mlst References on YT description, lots of them!  Host: Dr. Tim Scarfe

In this engaging discussion, former professional volleyball player Abby Bertics, sports data analyst James Tozer, and Google DeepMind's Petar Veličković delve into the transformative impact of AI on sports. They explore how AI is revolutionizing strategies in basketball and soccer, enhancing player recruitment and game tactics. The guests share insights on predicting injuries, analyzing player interactions using graph neural networks, and the challenges AI faces in capturing the complexities of human performance on the field.