Episode description

In this podcast episode, I interview Petar Veličković, a research scientist at DeepMind. We discuss his academic background and his journey from competitive programming to machine learning. Petar shares insights on the value of a PhD, emphasizing its role as an entry ticket into research and the opportunity it provides to build connections and adaptability. He also highlights the evolving landscape of AI research, where diverse backgrounds and contributions are essential. Overall, the interview offers valuable perspectives on academia, industry, and the importance of curiosity in driving impactful research. ►Twitter: https://twitter.com/PetarV_93, https://twitter.com/Whats_AI ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/ ►Support me on Patreon: https://www.patreon.com/whatsai ►Join Our AI Discord: https://discord.gg/learnaitogether How to start in AI/ML - A Complete Guide: ►https://www.louisbouchard.ai/learnai/

Timestamps:

00:05 Intro

00:37 Academic Background of Petar

02:07 The switch into the Machine Learning

05:33 Was Ph.D. worth it?

14:26 Is a Ph.D. worth it if you want to become a MLOps or a developer?

19:15 What was more useful to you to find research work at DeepMind?

22:28 Skills and portfolio tips for non-Ph.D. person to get into Google DeepMind

30:08 Work of research scientist at DeepMind

33:52 Promising avenue for AGI

42:36 Travel time prediction algorithm that was implemented in Google Maps

48:26 Why Petar wants to do more applied research projects?

51:07 Biggest challenges in AI research vs AI in the industry

53:37 Do you believe Graph Neural Network is the future?

58:07 Upcoming Projects of Petar