Unlocking the Potential of Energy-Based Models in Modern Computing

Dr. Maxwell Ramstead grills Guillaume Verdon (AKA “Beff Jezos”) who's the founder of Thermodynamic computing startup Extropic.

Guillaume shares his unique path – from dreaming about space travel as a kid to becoming a physicist, then working on quantum computing at Google, to developing a radically new form of computing hardware for machine learning. He explains how he hit roadblocks with traditional physics and computing, leading him to start his company – building "thermodynamic computers." These are based on a new design for super-efficient chips that use the natural chaos of electrons (think noise and heat) to power AI tasks, which promises to speed up AND lower the costs of modern probabilistic techniques like sampling. He is driven by the pursuit of building computers that work more like your brain, which (by the way) runs on a banana and a glass of water! 

Guillaume talks about his alter ego, Beff Jezos, and the "Effective Accelerationism" (e/acc) movement that he initiated. Its objective is to speed up tech progress in order to “grow civilization” (as measured by energy use and innovation), rather than “slowing down out of fear”. Guillaume argues we need to embrace variance, exploration, and optimism to avoid getting stuck or outpaced by competitors like China. He and Maxwell discuss big ideas like merging humans with AI, decentralizing intelligence, and why boundless growth (with smart constraints) is “key to humanity's future”.

REFS:

1. John Archibald Wheeler - "It From Bit" Concept

00:04:45 - Foundational work proposing that physical reality emerges from information at the quantum level

Learn more: https://cqi.inf.usi.ch/qic/wheeler.pdf 

2. AdS/CFT Correspondence (Holographic Principle)

00:05:15 - Theoretical physics duality connecting quantum gravity in Anti-de Sitter space with conformal field theory

https://en.wikipedia.org/wiki/Holographic_principle 

3. Renormalization Group Theory

00:06:15 - Mathematical framework for analyzing physical systems across different length scales

https://www.damtp.cam.ac.uk/user/dbs26/AQFT/Wilsonchap.pdf 

4. Maxwell's Demon and Information Theory

00:21:15 - Thought experiment linking information processing to thermodynamics and entropy

https://plato.stanford.edu/entries/information-entropy/ 

5. Landauer's Principle

00:29:45 - Fundamental limit establishing minimum energy required for information erasure

https://en.wikipedia.org/wiki/Landauer%27s_principle 

6. Free Energy Principle and Active Inference

01:03:00 - Mathematical framework for understanding self-organizing systems and perception-action loops

https://www.nature.com/articles/nrn2787 

7. Max Tegmark - Information Bottleneck Principle

01:07:00 - Connections between information theory and renormalization in machine learning

https://arxiv.org/abs/1907.07331 

8. Fisher's Fundamental Theorem of Natural Selection

01:11:45 - Mathematical relationship between genetic variance and evolutionary fitness

https://en.wikipedia.org/wiki/Fisher%27s_fundamental_theorem_of_natural_selection 

9. Tensor Networks in Quantum Systems

00:06:45 - Computational framework for simulating many-body quantum systems

https://arxiv.org/abs/1912.10049 

10. Quantum Neural Networks

00:09:30 - Hybrid quantum-classical models for machine learning applications

https://en.wikipedia.org/wiki/Quantum_neural_network 

11. Energy-Based Models (EBMs)

00:40:00 - Probabilistic framework for unsupervised learning based on energy functions

https://www.researchgate.net/publication/200744586_A_tutorial_on_energy-based_learning 

12. Markov Chain Monte Carlo (MCMC)

00:20:00 - Sampling algorithm fundamental to modern AI and statistical physics

https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo 

13. Metropolis-Hastings Algorithm

00:23:00 - Core sampling method for probability distributions

https://arxiv.org/abs/1504.01896

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