DeepSeek-R1, every mention

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Verbatim, from the transcripts: the passages where DeepSeek-R1 comes up

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▶ 13:15 unnamed speaker Between Gini one, two, like, you know, like in, for example, NLMs, like you have, like Dipsy card one, like they saw in this paper, like the longer they keep it running, they suddenly will see like these interesting behaviors.

▶ 9:48 Anjney Midha They're not that, they're not years behind, and so when R-I, DeepSeek R 2 times in the scene

▶ 0:05 Guido Appenzeller So, DeepSeek R-One last month really captured the zeitgeist. 2 times in the scene
▶ 0:51 Guido Appenzeller Some of the models they've been building, like Deep Seek Math, B-III and B-II, and now R-I. 2 times in the scene
▶ 4:51 Guido Appenzeller And we look specifically here at DeepSeq R-one. 6 times in the scene
▶ 7:05 Marco Mascorro And, and the idea, so, so DeepSafe came with B three in December, and then they, they released, uh, just a few weeks later, uh, R one. 3 times in the scene
▶ 8:33 Guido Appenzeller It's a classic LLM, although I think they took a little bit of reasoning from a precursor of DeepSeq R-One and applied it back. 3 times in the scene
▶ 9:49 Guido Appenzeller So here's a rough flowchart to see what the different steps are for, for training R one. 5 times in the scene
▶ 13:10 Marco Mascorro Some of these traces actually in R-one, when they decided to basically fix these R-one zero models, like, how do we improve now, or how do we make it usable for humans, right? 2 times in the scene
▶ 18:01 Guido Appenzeller So going from V-three to R-one is probably pretty cheap, is my guess, right?
▶ 25:06 Marco Mascorro Another, another interesting thing that, that came out of the paper from R-one was for these model models, the LAMAs, the seven Bs, the seven Bs, the 13 Bs. 4 times in the scene

▶ 2:43 Martin Casado You have, like, all this excitement about O-one and how that's gonna drive compute costs and NVIDIA, and then, you know, R-one comes out, and it looks pretty good, and then all of a sudden they're saying, well, you know, 2 times in the scene
▶ 3:52 unnamed speaker And so, we're, like, 10 days or so out from this release, which, by the way, as both of you said, it was, that was the R-one release.
▶ 9:47 Martin Casado Yeah, so there's, there's two things that are pretty remarkable about DeepSeq R-One that have implications on adoption. 2 times in the scene
▶ 18:59 Martin Casado And to bring it back to DeepSeq, I mean, that means like, this is very important to realize that, you know, the capabilities of China, it's a very credible player, but I don't think that R-One itself as a standalone is going to have that…

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