The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Gideon Mann no published score: only 1 usable exchange on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q think this, um, acceleration we've experienced is that in some ways just the beginning or, uh, you know, did everything come together, uh, in the last few months that had been building up for the last 10 years and then this is gonna plateau? In other words, are we at the beginning of the exponential in terms of AI progress or was that just like a moment we just had?

A I, I think, I think we're still early. Um, I think, um, On, on, on, for two reasons. I think one is, ah, the, you know, to, to Melanie's point, the, the amount of tuning and optimization across the entire pipeline is, is really premature. There really haven't been, there hasn't been a lot of work. Um, even the, you know, things like, um, I don't know, maybe, maybe you guys know better than us, but like, you know, how to set your hyper, the learning rate hyper parameters, And how to tune your network, you know, efficiently for these kinds of models, uh, and how to parallelize even, there, there's so many very fundamental questions, technical questions, that, that really no one, no one has seriously investigated. Um, so on a technical level, I think it's very early. Um, and then the, the other part is on a deployment level, um, if you look at, The, the number of startups, the startups that are formed every day and announced every day, um, it, it, there's kind of like a startup formed for every different piece of software interface. Um, so, you know, um, uh, Jasper I think is the first, one of the first to, to kind of make it big, but it, it was like a startup in the, the, in marketing. Um, there's a really nice one spell book in legal. Um, there's another, um, uh, you know, there's another one. I, I don't know how adept, uh, you know, contextualizes itself, but it's, it's either …

AI assessment note: “I think we're still early.”

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