Prof. BERT DE VRIES - ON ACTIVE INFERENCE

episode

Machine Learning Street Talk (MLST) not yet transcribed

▲ 0

Transcript

This episode hasn't been transcribed yet Upvotes decide what gets transcribed next — 0 so far. Sign in to upvote

More from Machine Learning Street Talk (MLST)

VAEs Are Energy-Based Models? [Dr. Jeff Beck] 46 min · 25 Jan 2026

Abstraction & Idealization: AI's Plato Problem [Mazviita Chirimuuta] 53 min · 23 Jan 2026

Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION] 42 min · 23 Jan 2026

Adversarial Examples and Data Modelling - Andrew Ilyas (MIT) 1h 28m · 22 Aug 2024

All episodes from Machine Learning Street Talk (MLST) →

Report any issue

Source

Recorded20 Nov 2023

Publisheranchor.fm ↗

Handlerec:2107267

Show notes

Watch behind the scenes with Bert on Patreon: https://www.patreon.com/posts/bert-de-vries-93230722 https://discord.gg/aNPkGUQtc5 https://twitter.com/MLStreetTalk Note, there is some mild background music on chapter 1 (Least Action), 3 (Friston) and 5 (Variational Methods) - please skip ahead if annoying. It's a tiny fraction of the overall podcast. YT version: https://youtu.be/2wnJ6E6rQsU Bert de Vries is Professor in the Signal Processing Systems group at Eindhoven University. His research focuses on the development of intelligent autonomous agents that learn from in-situ interactions with their environment. His research draws inspiration from diverse fields including computational neuroscience, Bayesian machine learning, Active Inference and signal processing. Bert believes that development of signal processing systems will in the future be largely automated by autonomously operating agents that learn purposeful from situated environmental interactions. Bert received nis M.Sc. (1986) and Ph.D. (1991) degrees in Electrical Engineering from Eindhoven University of Technology (TU/e) and the University of Florida, respectively. From 1992 to 1999, he worked as a research scientist at Sarnoff Research Center in Princeton (NJ, USA). Since 1999, he has been employed in the hearing aids industry, both in engineering and managerial positions. De Vries was appointed part-time professor in the Signal Processing Systems Group at TU/e in 2012. Contact: https://twitter.com/bertdv0 https://www.tue.nl/en/research/researchers/bert-de-vries https://www.verses.ai/about-us Panel: Dr. Tim Scarfe / Dr. Keith Duggar TOC: [00:00:00] Principle of Least Action [00:05:10] Patreon Teaser [00:05:46] On Friston [00:07:34] Capm Peterson (VERSES) [00:08:20] Variational Methods [00:16:13] Dan Mapes (VERSES) [00:17:12] Engineering with Active Inference [00:20:23] Jason Fox (VERSES) [00:20:51] Riddhi Jain Pitliya [00:21:49] Hearing Aids as Adaptive Agents [00:33:38] Steven Swanson (VERSES) [00:35:46] Main Interview Kick Off, Engineering and Active Inference [00:43:35] Actor / Streaming / Message Passing [00:56:21] Do Agents Lose Flexibility with Maturity? [01:00:50] Language Compression [01:04:37] Marginalisation to Abstraction [01:12:45] Online Structural Learning [01:18:40] Efficiency in Active Inference [01:26:25] SEs become Neuroscientists [01:35:11] Building an Automated Engineer [01:38:58] Robustness and Design vs Grow [01:42:38] RXInfer [01:51:12] Resistance to Active Inference? [01:57:39] Diffusion of Responsibility in a System [02:10:33] Chauvinism in "Understanding" [02:20:08] On Becoming a Bayesian Refs: RXInfer https://biaslab.github.io/rxinfer-website/ Prof. Ariel Caticha https://www.albany.edu/physics/faculty/ariel-caticha Pattern recognition and machine learning (Bishop) https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf Data Analysis: A Bayesian Tutorial (Sivia) https://www.amazon.co.uk/Data-Analysis-Bayesian-Devinderjit-Sivia/dp/0198568320 Probability Theory: The Logic of Science (E. T. Jaynes) https://www.amazon.co.uk/Probability-Theory-Principles-Elementary-Applications/dp/0521592712/ #activeinference #artificialintelligence

Ask this episode

“What did the council decide on item 42?” — answered with the speaker and the moment.