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Because unless you have a way to quantify the manifold ways in which specific human beings "understand things", there's no way to express that to chatGPT in a language symbol form that it can use to help best reexpress a larger piece of information and tailor it specific to you.

E.g. some people learn best through narrative story form, other people through visual imagery, etc. at a minimum yo'd need some kind of a neural biofeedback device to gauge and "AB test" different strategies of expressing information that would optimize encoding from short-term to long-term memory for each individual person.

Basically what you're suggesting is entirely to hand wavy and not nearly concrete enough to be applicable.



OK I'll try again:

Forward: fine tune responses base on new prompts with more details (output from human) - learn from human to create context

human -> machine

Reverse: condense large inputs with details to best fit understanding base on feedback (input into human) - teach human from context

machine -> human




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