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 →

David Luan 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 4 · P 4 · Cm 4 4.30

Q of horizontal AI layer? Um, I mean, you touch upon this a little bit, but, uh, what would it take to go, uh, you know, beyond sort of this vertical by vertical, uh, approach and have, you know, this one utility that, you know, sort of answers all questions? Or how far are we to, is that complete science fiction or is there a world where that, where that happens?

A I think there's definitely a world where that happens. The world's not quite 20 15 somewhere. I'm not quite sure exactly when that might be, but there's a lot of interesting work going on right now, um, with essentially combining multiple domains of solving a particular, uh, of solving a particular problem to get way more insight on, on, on the problem as a whole. And models like that that are continually, uh, just getting better over time could potentially be like that in the future. But At the same time, I think that it's important for us to draw a distinction between deep learning as a, as, as, as a, as a machine learning method versus the specific, um, in terms of a, in terms of a commercialization and, and, and helping partners standpoint. Drawing a difference between the machine learning method itself and the remainder of the last mile work in terms of both data collection and, and, and fine tuning that actually gets you something that solves customer problems really well. So I, I think that in terms of where we're going to see it In industry, it's likely to be, um, still in a kind of vertical by vertical, um, Um, system for a little while before we start seeing a little more convergence.

AI assessment note: “I think there's definitely a world where that happens.”

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