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Notational Intelligence (2022)


spent the last month wondering and investigating how we might design better workflows for creative work that meld the best of human intuition and machine intelligence. I think a promising path is in the design of notation.

If we can endow the whole universe of human notation with the strengths of programming languages like verifiability or interactivity, I think it would represent a huge jump in the way we work with knowledge as a society. To effectively compress English text, the model might be advantaged to “learn” abstractions like common words and frequent grammatical constructs, so it can avoid inefficient, rote memorization of letters as much as possible. When I’m studying the mathematics of ray tracing or fluid dynamics, I want to be able to place my favorite 3D model into an equation in front of me and see normal vectors and force fields laid out before my eyes.

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