Audio note: this article contains 36 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. Imagine using an ML-like training process to design two simple electronic components, in series. The parameters <span>_theta^1_</span> control the function performed by the first component, and the parameters <span>_theta^2_</span> control the function performed by the second component. The whole thing is trained so that the end-to-end behavior is that of a digital identity function: voltages close to logical 1 are sent close to logical 1, voltages close to logical 0 are sent close to logical 0.  Background: Signal Buffering We’re imagining electronic components here because, for those with some electronics background, I want to summon to mind something like this:  This electronic component is called a signal buffer. Logically, it's an identity function: it maps 0 to 0 and 1 to 1. But crucially, it maps a wider range of logical-0 voltages to a narrower (and lower) range of logical-0 voltages, and correspondingly for logical-1. So if noise in the circuit upstream might make a logical-1 voltage a little too low or a logical-0 voltage a little too [...] ---Outline:(01:09) Background: Signal Buffering(02:26) Back To The Original Picture: Introducing Interfaces(05:58) The Stat Mech Part(07:50) Why Is This Interesting? ---
First published:
November 6th, 2025

Source:
https://www.lesswrong.com/posts/r3PMS8wXGviHEvz5Z/toward-statistical-mechanics-of-interfaces-under-selection
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Narrated by TYPE III AUDIO.
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