#137 Causal AI & Generative Models, with Robert Ness
Learning Bayesian Statistics
Intro
This chapter delves into the crucial interplay between causal inference and artificial intelligence, highlighting key insights from a recent book. It examines the role of causal assumptions, probabilistic programming, and the implications for evaluating large language models within the context of Bayesian statistics and deep learning.
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Takeaways:
- Causal assumptions are crucial for statistical modeling.
- Deep learning can be integrated with causal models.
- Statistical rigor is essential in evaluating LLMs.
- Causal representation learning is a growing field.
- Inductive biases in AI should match key mechanisms.
- Causal AI can improve decision-making processes.
- The future of AI lies in understanding causal relationships.
Chapters:
00:00 Introduction to Causal AI and Its Importance
16:34 The Journey to Writing Causal AI
28:05 Integrating Graphical Causality with Deep Learning
40:10 The Evolution of Probabilistic Machine Learning
44:34 Practical Applications of Causal AI with LLMs
49:48 Exploring Multimodal Models and Causality
56:15 Tools and Frameworks for Causal AI
01:03:19 Statistical Rigor in Evaluating LLMs
01:12:22 Causal Thinking in Real-World Deployments
01:19:52 Trade-offs in Generative Causal Models
01:25:14 Future of Causal Generative Modeling
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Joshua Meehl, Javier Sabio, Kristian Higgins, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli and Adam Tilmar Jakobsen.
Links from the show:
- Robert’s profile on Microsoft Research: https://www.microsoft.com/en-us/research/people/robertness/
- Robert’s Causal AI book: https://www.manning.com/books/causal-ai
- Associated GitHub repo: https://github.com/altdeep/causalML
- Robert on Google Scholar: https://scholar.google.com/citations?hl=en&user=8gWTOBAAAAAJ
- Robert on LinkedIn: https://www.linkedin.com/in/osazuwa/
- Robert on GitHub: https://github.com/robertness
- LBS #56 Causal & Probabilistic Machine Learning, with Robert Osazuwa Ness: https://learnbayesstats.com/episode/56-causal-probabilistic-machine-learning-robert-ness
- A Causal AI Suite for Decision-Making, NeurIPS 2022: https://openreview.net/forum?id=-gVJ1_lD1RH
- Causal Reasoning with ChiRho: https://basisresearch.github.io/chirho/getting_started.html
- PyWhy, An Open Source Ecosystem for Causal Machine Learning: https://www.pywhy.org/
Transcript
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