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)
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This is the counter-Enlightenment theory of Language, Charles Taylor calls it “Hamann Herder Heidegger” and has lots of good, accessible essays on it Good to understand for interpretation, meaning in general
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2. DH notes google can’t understand meaning yet: true. He thinks the problem is they’re not even looking at meaning yet, just brute-forcing statistical connections: probably false.
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I had the exact same intuitions about Monte Carlo methods for Go. You can find lots of statisical correlations by brute force but plateau bc you can’t find obviously correct moves that are unusual but forced by context I was wrong
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If neural nets allow the AI to apply enough processing power, it can brute force its way into the context, too
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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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