two thoughts on hofstadter’s take on AI translation in the Atlantic: 1. His examples are excellent and show ways in which languages is not just mastering syntax-lexicon-morphology code but requires real understanding of subject areas under discussion (and *not* under discussion)
DH points out intended audiences the AI fails to speak to, questions it fails to settle, emphatic distinctions it ignores Well with enough processing power you’ll be able to have a representational model of a text by audience, by implied questions, by semantic contrast
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If Google can’t pull it off it won’t be because they didn’t try to hard-code a knowledge base into the nets, but because they aren’t feeding it enough data, or data of the wrong type
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For example they will have very few translations of casual conversations and personal messages - especially not spoken convos Possible that modal written text has “haplax legomena” that are nonetheless frequent in verbal exchanges
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Could also be effects based on being unable to classify texts by generation - might be helpful to only use contemporary translations as targets, or at least distinguish between contemporary and modernized transl as two diff targets
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