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People on Twitter are speculating breathlessly about using this for social science. I don't immediately see uses for it outside of fiction, esp. video games.

It would be cool if some kind of law of large numbers (an LLN for LLMs) implied that the decisions made by a thing trained on the internet will be distributed like human decisions. But the internet seems a very biased sample. Reporters (rightly) mostly write about problems. People argue endlessly about dumb things. Fiction is driven by unreasonably evil characters and unusually intense problems. Few people elaborate the logic of ordinary common sense, because why would they? The edge cases are what deserve attention.

A close model of a society will need a close model of beliefs, preferences and material conditions. Closely modeling any one of those is far, far beyond us.



> But the internet seems a very biased sample.

It also seems to me (acknowledging my lack of expertise) that LLMs trained from online resources are likely to weight text that is frequent vs text that represents "truth". Or perhaps I should say repetition should not be considered evidence of truth. I have no idea how to drive LLM models or other ML models to incorporate truth -- humans have a hard time agreeing on this and ML researchers providing guided reinforcement learning don't have any special ability to discern truth.


I have long suspected that it will be necessary to deliberately create a new type of model that is aware of the trivium and then uses logic, grammar and rhetoric to begin to create a closer model of reality than a LLM can.


The way I see it, LLMs are similar to what the boundary between our unconscious and conscious processing is: that voice which snaps to suggest associations, whether they make sense or not, and can, with work, be coaxed into following a path involving some logic or algorithmic procedure.


Hey now, I turned out all right.




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