Athanasios (Thanos) Vlontzos, a Research Scientist at Spotify's Advanced Causal Inference Lab, tackles intriguing questions about AI's future and causal modeling. He discusses why many AI predictions miss the mark and explores the evolving role of radiologists amid AI advancements. Thanos dives into challenges in medical AI, the humor of causal model pitfalls, and the essence of interdisciplinary collaboration. The conversation also highlights the connection between music and ideas, emphasizing the drive for exploration in science.
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question_answer ANECDOTE
Hinton's Inaccurate Prediction
In 2016, Geoffrey Hinton predicted radiologists' obsolescence within five years due to deep learning.
Eight years later, radiologists remain essential, highlighting the inaccuracy of some AI predictions.
insights INSIGHT
Irreplaceable Expertise
Radiologists' expertise extends beyond identifying lesions; their decision-making and evaluation are crucial.
These skills, honed through years of training, are difficult for current AI systems to replicate.
insights INSIGHT
Challenges in Medical AI
Building robust AI systems requires good, representative data, careful modeling, and thoughtful implementation.
Athanasios Vlontzos emphasizes the importance of a holistic approach, not just focusing on modeling.
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This book by Douglas Hofstadter is a comprehensive and interdisciplinary work that explores the interrelated ideas of Kurt Gödel, M.C. Escher, and Johann Sebastian Bach. It delves into concepts such as self-reference, recursion, and the limits of formal systems, particularly through Gödel's Incompleteness Theorem. The book uses dialogues between fictional characters, including Achilles and the Tortoise, to intuitively present complex ideas before they are formally explained. It covers a wide range of topics including cognitive science, artificial intelligence, number theory, and the philosophy of mind, aiming to understand how consciousness and intelligence emerge from formal systems[2][4][5].
The Name of the Rose
Umberto Eco
The novel, set in 1327, follows Adso of Melk, a young Benedictine novice, and William of Baskerville, a Franciscan friar, as they investigate a series of murders at an abbey in northern Italy. The story is framed by a complex narrative structure and involves debates on theology, science, and politics. The abbey's labyrinthine library, which houses a forbidden book, is central to the mystery. As the investigation unfolds, William and Adso uncover a conspiracy related to the library and a hidden manuscript that could undermine religious and societal order[2][3][5].
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.
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