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.
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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
