#110 Unpacking Bayesian Methods in AI with Sam Duffield

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Jul 10, 2024

Expert Sam Duffield discusses leveraging Bayesian methods in AI, focusing on mini-batch techniques, approximate inference, thermodynamic computing, and the Posteriors python package. He simplifies complex concepts for non-expert audiences and highlights the role of temperature in Bayesian models, stochastic gradient MCMC, and uncertainty quantification for improved predictions.

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ANECDOTE

Introduction to Bayes

Sam Duffield was initially drawn to Bayesian statistics due to its intuitive nature and perceived mathematical simplicity.
Bayes' theorem elegantly handles updates, requiring minimal effort: define likelihood and prior, and the theorem does the rest.

INSIGHT

Time Series Models

Gaussian Processes (GPs) extend static Bayesian models to continuous time, offering advantages for time series analysis.
State-space and hidden Markov models also handle time series data efficiently by incorporating uncertainty from past observations.

INSIGHT

Posteriors and Large-Scale AI

Posteriors is designed for large-scale AI models, focusing on mini-batch processing for scalability.
It prioritizes approximate inference techniques for practicality in enterprise settings.

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Takeaways:

Use mini-batch methods to efficiently process large datasets within Bayesian frameworks in enterprise AI applications.
Apply approximate inference techniques, like stochastic gradient MCMC and Laplace approximation, to optimize Bayesian analysis in practical settings.
Explore thermodynamic computing to significantly speed up Bayesian computations, enhancing model efficiency and scalability.
Leverage the Posteriors python package for flexible and integrated Bayesian analysis in modern machine learning workflows.
Overcome challenges in Bayesian inference by simplifying complex concepts for non-expert audiences, ensuring the practical application of statistical models.
Address the intricacies of model assumptions and communicate effectively to non-technical stakeholders to enhance decision-making processes.

Chapters:

00:00 Introduction to Large-Scale Machine Learning

11:26 Scalable and Flexible Bayesian Inference with Posteriors

25:56 The Role of Temperature in Bayesian Models

32:30 Stochastic Gradient MCMC for Large Datasets

36:12 Introducing Posteriors: Bayesian Inference in Machine Learning

41:22 Uncertainty Quantification and Improved Predictions

52:05 Supporting New Algorithms and Arbitrary Likelihoods

59:16 Thermodynamic Computing

01:06:22 Decoupling Model Specification, Data Generation, and Inference

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, 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, Steven Rowland, 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, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, 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 and Francesco Madrisotti.

Links from the show:

Sam on Twitter: https://x.com/Sam_Duffield
Sam on Scholar: https://scholar.google.com/citations?user=7wm_ka8AAAAJ&hl=en&oi=ao
Sam on Linkedin: https://www.linkedin.com/in/samduffield/
Sam on GitHub: https://github.com/SamDuffield
Posteriors paper (new!): https://arxiv.org/abs/2406.00104
Blog post introducing Posteriors: https://blog.normalcomputing.ai/posts/introducing-posteriors/posteriors.html
Posteriors docs: https://normal-computing.github.io/posteriors/
Paper introducing Posteriors – Scalable Bayesian Learning with posteriors: https://arxiv.org/abs/2406.00104v1
Normal Computing scholar: https://scholar.google.com/citations?hl=en&user=jGCLWRUAAAAJ&view_op=list_works
Thermo blogs: https://blog.normalcomputing.ai/posts/2023-11-09-thermodynamic-inversion/thermo-inversion.html
https://blog.normalcomputing.ai/posts/thermox/thermox.html
Great paper on SGMCMC: https://proceedings.neurips.cc/paper_files/paper/2015/file/9a4400501febb2a95e79248486a5f6d3-Paper.pdf
David MacKay textbook on Sustainable Energy: https://www.withouthotair.com/
LBS #107 - Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/
LBS #98 - Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié: https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/

Transcript

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