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Recorded on Jan 17, 2024 in London, UK.

Video version available here

What makes so many predictions about the future of AI wrong?

And what's possible with the current paradigm?

From medical imaging to song recommendations, the association-based paradigm of learning can be helpful, but is not sufficient to answer our most interesting questions.

Meet Athanasios (Thanos) Vlontzos who looks for inspirations everywhere around him to build causal machine learning and causal inference systems at Spotify's Advanced Causal Inference Lab.

In the episode we discuss:
- Why is causal discovery a better riddle than causal inference?
- Will radiologists be replaced by AI in 2024 or 2025?
- What are causal AI skeptics missing?
- Can causality emerge in Euclidean latent space?

Ready to dive in?

About The Guest
Athanasios (Thanos) Vlontzos, PhD is a Research Scientist at Advanced Causal Inference Lab at Spotify. Previousl;y, he worked at Apple, at SETI Institute with NASA stakeholders and published in some of the best scientific journals, including Nature Machine Learning. He's specialized in causal modeling, causal inferernce, causal discovery and medical imaging.

Connect with Athanasios:
- Athanasios on Twitter/X
- Athanasios on LinkedIn
- Athanasios's web page

About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality.

Connect with Alex:
- Alex on the Internet

Links
The full list of links can be found here.

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Causal Bandits Podcast
Causal AI || Causal Machine Learning || Causal Inference & Discovery
Web: https://causalbanditspodcast.com

Connect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/
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The Causal Book: https://amzn.to/3QhsRz4