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 →

Alex Hanna 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 4 · C 5 · P 4 · Cm 4 4.30

Q size of the data centers, size of the models. And of course there is an associated Uh, energy cost that must be paid to use these things. And so I'm curious if you Emily or you Anna, Alex, Alex, you worked at Google, right? So, uh, you probably have a good sense of this. Can you both share like quantify how much energy is being used to run these models?

A So part of the problem is that even, you know, even if you're working at Google, you've, you are directly working on this. They're not very public estimates of how much cost there is. I mean, the costs vary quite widely, and the only cost I think that we know was an estimate being made by folks at Hugging Face, um, that worked on the Bloom's model because they were able to actually have some kind of insight into the energy consumption Of these models. So part of the problem is the transparency of companies on this. You know, as a response at Google after, after the Stochastic Parents paper was published, one of the complaints from people like Jeff Dean, the SVP of research at Google, and David Patterson, who's the lead author of Google's kind of rebuttal to that, was that, you know, well, you didn't factor in XYZ, you didn't factor in renewables, That only we talk about at this one data center in Iowa. We didn't, you didn't factor into off-peak training. And so it's part of the problem. I mean, we could try to put numbers on it, but there's so much guardedness about what's actually happening here. We can't quantify it. We don't know when it comes to model training. I mean, we might have something like we know the number of parameters that are in a new model. Are in an open weights model like LAMA, but, um, we don't know how many kind of fits and starts there were with stopping …

AI assessment note: “We can't quantify it. We don't know when it comes to model training.”

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