#3: Extropic - Why Thermodynamic Computing is the Future of AI (PUBLIC DEBUT)

Episode 3: Extropic is building a new kind of computer – not classical bits, nor quantum qubits, but a secret, more complex third thing. They call it a Thermodynamic Computer, and it might be many orders of magnitude more powerful than even the most powerful supercomputers today. 

Check out their “litepaper” to learn more: https://www.extropic.ai/future.

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(00:00) - Intro

(00:41) - Guillaume's Background

(02:40) - Trevor's Background

(04:02) - What is Extropic Building? High-Level Explanation

(07:07) - Frustrations with Quantum Computing and Noise

(10:08) - Scaling Digital Computers and Thermal Noise Challenges

(13:20) - How Digital Computers Run Sampling Algorithms Inefficiently

(17:27) - Limitations of Gaussian Distributions in ML

(20:12) - Why GPUs are Good at Deep Learning but Not Sampling

(23:05) - Extropic's Approach: Harnessing Noise with Thermodynamic Computers

(28:37) - Bounding the Noise: Not Too Noisy, Not Too Pristine

(31:10) - How Thermodynamic Computers Work: Inputs, Parameters, Outputs

(37:14) - No Quantum Coherence in Thermodynamic Computers

(41:37) - Gaining Confidence in the Idea Over Time

(44:49) - Using Superconductors and Scaling to Silicon

(47:53) - Thermodynamic Computing vs Neuromorphic Computing

(50:51) - Disrupting Computing and AI from First Principles

(52:52) - Early Applications in Low Data, Probabilistic Domains

(54:49) - Vast Potential for New Devices and Algorithms in AI's Early Days

(57:22) - Building the Next S-Curve to Extend Moore's Law for AI

(59:34) - The Meaning and Purpose Behind Extropic's Mission

(01:04:54) - Call for Talented Builders to Join Extropic

(01:09:34) - Putting Ideas Out There and Creating Value for the Universe

(01:11:35) - Conclusion and Wrap-Up

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Links:

• Christian Keil – https://twitter.com/pronounced_kyle
• Guillaume Verd - https://twitter.com/GillVerd
• Beff Jezos - https://twitter.com/BasedBeffJezos
• Trevor McCourt - https://twitter.com/trevormccrt1

First Principles:

• Gaussian Distribution: https://en.wikipedia.org/wiki/Normal_distribution
• Energy-Based Models: https://en.wikipedia.org/wiki/Energy-based_model
• Shannon’s Theorem: https://en.wikipedia.org/wiki/Noisy-channel_coding_theorem

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Production and marketing by The Deep View (https://thedeepview.co). For inquiries about sponsoring the podcast, email team@firstprinciples.fm

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Checkout the video version here → http://tinyurl.com/4fh497n9

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