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I also felt 's question strike at the heart! But I wonder if it is more about 'well-definedness of the problem' rather than cleanness of data that makes it unique. Finding a protein structure given an amino sequence seems almost as well defined as winning a chess game.
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I don't think so. Current LLMs are at least somewhat useful as substrates for exploring vague ideas and questions. I expect multi-model ones will get gradually better. Still, I agree with you that it's a lot easier to make progress when you have a well-defined problem
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The scale of modern GPU computation is so incomprehensible that I regularly find that even experts underestimate it. A 4090 can do ~150 THOUSAND fp32 ops per pixel per frame at 4k 60 Hz, and can load kilobytes for every single pixel from VRAM (and more from on-die SRAM).
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This is very tangential, but even among scientists, debunking doesn't work. Let's look at some of the empirical work on the topic of replication failures. First: papers that don't replicate receive more citations than ones that do.
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So uhm….A new meta-analysis of >200 effect-sizes (n > 60,000) in @NatureHumBehav found that on average debunking scientific misinformation had no effect 😳 Yikes! nature.com/articles/s4156
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