TechTakes

The answer was: the AI represents various features of the line breaking process as one-dimensional helical manifolds in a six-dimensional space, then rotates the manifolds in some way that corresponds to multiplying or comparing the numbers that they’re representing. You don’t need to understand what this means, so I’ve relegated my half-hearted attempt to explain it to a footnote1. From our point of view, what’s important is that this doesn’t look like “LOL, it just sees that the last token was ree and there’s a 12.27% of a line break token following ree.” Next-token prediction created this system, but the system itself can involve arbitrary choices about how to represent and manipulate data.

Human neuron interpretability is even harder than AI neuron interpretability, but probably your thoughts involve something at least as weird as helical manifolds in 6D spaces.I searched the literature for the closest human equivalent to Claude’s weird helical manifolds, and was able to find one team talking about how the entorhinal cells in the hippocampus, which help you track locations in 2D space, use “high-dimensional toroidal attractor manifolds”. You never think about these, and if Claude is conscious, it doesn’t think about its helices either2. These are just the sorts of strange hacks that next-token/next-sense-datum prediction algorithms discover to encode complicated concepts onto physical computational substrate.