This post is a distillation of a recent work in AI-assisted human coordination from Google DeepMind.

The paper has received some press attention, and anecdotally, it has become the de-facto example that people bring up of AI used to improve group discussions.

Since this work represents a particular perspective/bet on how advanced AI could help improve human coordination, the following explainer is to bring anyone curious up to date. I’ll be referencing both the published paper as well as the supplementary materials.

Summary

The Habermas Machine[1] (HM) is a scaffolded pair of LLMs designed to find consensus among people who disagree, and help them converge to a common point of view. Human participants are asked to give their opinions in response to a binary question (E.g. “Should voting be compulsory?”). Participants give their level of agreement[2], as well as write a short 3-10 sentence opinion [...]

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Outline:

(00:35) Summary

(01:40) Full Process

(03:08) Automated Mediation

(04:33) Empirical Results

(05:50) Comparison to Gemini 1.5 Pro

(07:06) Embedding Space Analysis

(08:39) Training Details

(08:53) Generative Model

(09:47) Reward Model

(10:29) Example Session

(11:29) Question and Summary

(11:56) Initial Phase

(12:39) Critique Phase

(13:19) Final Survey

(13:43) Conclusion

The original text contained 5 footnotes which were omitted from this narration.

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First published:

Source:

Narrated by TYPE III AUDIO.

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