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 →

Matthew Zeiler 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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1exchanges match
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Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q themes that we've had in this event, uh, over the, over the years, I guess, um, is this question of horizontal technology versus vertical expertise. Um, and you showed a bunch of, of different, uh, verticals, but, for example, something like medical, Is the claim here that you could apply this fairly quickly, or does it need, do you need a bunch of specialists to come and train the system?

A Um, absolutely, we can apply it quickly to all these different verticals, because that's the beauty of neural networks. It's literally taking a pair of inputs and outputs, and they could be anything. Here we've been showing tags, but they could even be a quality prediction, what's a good quality image, a price prediction for A product on eBay or something like that where, ah, you don't know what to price it at to start. And it can learn these mappings in arbitrary ways. So, the medical, ah, domain might not have categories, the image pixels might be different, but it's just learning that mapping. So, it's just a matter of getting the data, ah, rather than understanding exactly the, the problems that are there.

AI assessment note: “absolutely, we can apply it quickly to all these different verticals”

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