Causal Bandits Podcast Why Hinton Was Wrong, Causal AI & Science | Thanos Vlontzos Ep 15 | CausalBanditsPodcast.com
May 6, 2024
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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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.
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.
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.
