Cosmos

“That number that was 50% that came from cosmos's interpretation of some of the analysis. So like it might go in literature and find this result. And then it would say, wow, this is super exciting. This is amazing. Or it might do data analysis, but this is a no…”

“In cosmos, we basically, we had all the pieces sitting around. We working on world models, we working on data analysis agent, working on literature agent. And then we're working on, you know, we built a platform for scientific agents. So we had things that can…”

“I think in in Cosmos, we use Lama or we use mix, mix through. And the Cosmos video model itself is only seven B, and the model, the language model is a prompt rewriter. It's bigger than that.”

“And if you look at a number of tokens we disclose that in cosmos, it's also like tens of trillions of tokens. On the visual tokens.”

“So that's in the protocol of Cosmos labeling. We required the objective we gave to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text, can reconstruct what the video is like from their he…”

“There, once I built the Cosmos one, I realized as this thing also has a scaling law similar to language model.”

“One thing I say, like, thanks to my experience at NVIDIA, because first time when we were building Cosmos together, we built it for about a year.”

“Yeah, for example, like in Cosmos, I think just five seconds of video is like a 50, 50 K or a 60 K number of tokens. So like, if you do 50 seconds as a 500 K tokens, if you do longer than that, easily explode.”

“In Cosmos, I believe we have like four steps and eight steps. If you do some simpler task, like image to image translation, it can even run in first step, that one step in, in Cosmos transfer.”