Petar Veličković

Joining the discussion are Petar Veličković from DeepMind, renowned for his work on graph neural networks, Taco Cohen from Qualcomm AI Research, specializing in geometric deep learning, and Joan Bruna, an influential figure in data science at NYU. They delve into geometric deep learning, exploring its foundations in symmetry and invariance. The conversation highlights innovative mathematical frameworks, the unification of geometries, and their implications in AI. Insights on dimensionality, algorithmic reasoning, and historical perspectives on geometry further enrich this engaging dialogue.

Dr. Petar Veličković, a Staff Research Scientist at DeepMind known for his work on Graph Attention Networks, discusses fascinating advancements in deep learning. He explores how category theory enhances geometric deep learning and innovates graph neural networks. The conversation dives into algorithmic reasoning, exposing the shift from manual feature engineering to automated processes. Petar also addresses the challenges of neural networks with extrapolation versus interpolation and shares insights on expander graphs to overcome obstacles in information propagation.

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.