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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