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Like generated faces have… messed up ears Text generators do small arithmetic ok but screw up larger arithmetic problems Where does a coherent sounding academic-paper summarizer tend to go wrong? Dangling prepositions ok. Medical paper getting doses wrong, 😬😬 etc etc
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With humans we very quickly form trust models. Humans tend to be untrustworthy in predictable ways. With AIs we a) don’t b) might never be able to if the models are sensitive and fragile in the untrustworthiness distribution. This is like a meta-trust problem.
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Or like pharma ads. “May cause dizziness, nausea, weird ears, and impossible geometry. Ask your prompt engineer if dalletruda is right for you!” <generated image of oddly unsatisfying happy retirees hiking under non-Euclidean skies>
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Mostly this has been happening either via a 2nd order plumbing or prompt stylings, like the “step by step” prompt. But I don’t yet see a theory of error mapping and correction. It’s actually dual of explainability. If you can explain your reasoning you can map your wrongness.
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Replying to @vgr
They should. Tho it’s the same problem as detecting AI images. if you could make an AI that it knows it’s wrong, you also make an AI that isn’t wrong
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also this
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Good tools admit virtuosity — they have low floors and high ceilings, and are open to beginners but support mastery, so that experts can deftly close the gap between their taste and their craft. Prompt engineering does not admit virtuosity. We need something better.
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