Mechanism for Discussing with a Large Number of People

• Things to consider:

  • Ideas pondered in selfsynth and Lab Use 20240801.

• Considerations:

  • The data structure of a shared database in [medium-chat] is crucial.
    • Personal information is not that relevant.
      • It is sufficient to record the premises associated with a person’s assertions.
  • Accumulating “assertions” that arise during discussions as a graph.
    • Using LLM and sentence embedding to aggregate similar assertions.
    • Imagining a chain of graphs following a syllogistic structure: “major premise,” “minor premise” → “conclusion.”
    • It is entirely possible to arrive at the same conclusion from different premises.
  • Each node is also an embedded vector, so the graph exists on a vector space.
    • If A->B and C->D, and A is very similar to C and B is very similar to D, they should merge.
      • Adjusting weights, for example.
    • Expressing “discussions” as a “weighted graph on a vector space.”
  • If someone agrees with a response, they should express it.
    • Continuously doing this should lead to gradually finding “convincing arguments” that persuade many people.
      • Individuals can then follow paths through the graph to reach those arguments.
  • Mechanism of conversation:
    • The concept of a “consistent personality of assertions” seems useful for humans to organize the current situation.

• Implementation proposal:

  • Extract many pairs of “assertion (AI)” → “response (human)” from logs of discussions between humans and AI.