Dr. Petar Veličković is a Staff Research Scientist at Googe DeepMind and an Affiliated lecturer at the University of Cambridge. He is known for his research contributions in graph representation learning; particularly graph neural networks and graph attention networks. At DeepMind, he has been working on Neural Algorithmic Reasoning which we talk about more in this podcast. Petar’s research has been featured in numerous media articles and has been impactful in many ways including Google Maps’s improved predictions. Time stamps 00:00:00 Highlights 00:01:00 Introduction 00:01:50 Entry point in AI 00:03:44 Idea of Graph Attention Networks 00:06:50 Towards AGI 00:09:58 Attention in Deep learning 00:13:15 Attention vs Convolutions 00:20:20 Neural Algorithmic Reasoning (NAR) 00:25:40 End-to-end learning vs NAR 00:30:40 Improving Google Map predictions 00:34:08 Interpretability 00:41:28 Working at Google DeepMind 00:47:25 Fundamental vs Applied side of research 00:50:58 Industry vs Academia in AI Research 00:54:25 Tips to young researchers 01:05:55 Is a PhD required for AI research? More about Petar: https://petar-v.com/ Graph Attention Networks: https://arxiv.org/abs/1710.10903 Neural Algorithmic Reasoning: https://www.cell.com/patterns/pdf/S2666-3899(21)00099-4.pdf TacticAI paper: https://arxiv.org/abs/2310.10553 And his collection of invited talks:  @petarvelickovic6033  About the Host: Jay is a PhD student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***