Causal Bandits Podcast
Causal Models, Biology, Generative AI & RL || Robert Ness || Causal Bandits Ep. 011 (2024)
Mar 4, 2024
Robert Ness discusses the broad perspective on causal inference encompassing graphical models, Bayesian inference, reinforcement learning, generative AI, and cognitive science. The conversation explores the challenges and importance of causal inference in AI models for understanding complex scenarios and human decision-making processes, with a focus on bridging computational models with human reasoning for Artificial General Intelligence (AGI). Delve into the integration of causality in identifying latent representations in generative AI for image manipulation, emphasizing the importance of understanding causal relationships in creating realistic images.
01:13:32
Episode guests
AI Summary
AI Chapters
Episode notes
Podcast summary created with Snipd AI
Quick takeaways
- Dr. Robert Ness emphasizes the importance of creating practical causal solutions in biological research beyond learning structures.
- Designing workflows for experimental design and interpreting causal graphs are crucial for actionable insights in biology.
Deep dives
Transition from Economics to Causal Inference
Dr. Robert Ness discusses his journey from studying economics to focusing on statistics and causal inference, driven by a natural aptitude for statistics and an interest in modeling real-world problems. His transition to systems biology during his PhD was influenced by the desire to work on complex and dynamic systems with grounding in natural sciences.
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
