Algorithmic Reasoning, Graph Neural Nets, AGI and Tips to researchers | Petar Veličković

Dr. Petar Veličković is a Staff Research Scientist at Googe DeepMind and an Affiliated lecturer at 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 featured in numerous media articles and been impactful in many ways including Google Map’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 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/S26...
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:   / shahjay22  
Twitter:   / jaygshah22  
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.

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