Logan Kilpatrick: belief

Transcript context

…Yeah, there's almost been a conversation every time we scale up of like, should we call it ultra? And what does that brand mean? Because we could. But there's sort of also a question of how do we keep consistency for users also kind of series to series? Yeah, and I think actually also to re-articulate a point that Tulsi made, like Google and specifically Google DeepMind's mission is to like build AI responsibly and make sure it benefits like all of humanity. And I think like that is like so deeply tied to the like Google product surfaces in which like we're serving, what is it like 8, 2 plus billion user products or whatever it is. And so at the same time, obviously the frontier matters, obviously having great models that are really expensive and really, really intelligent matter, and there's tons of use cases for that internally and for our customers. You also need to do the scaling up to billions of users for us to actually do the thing that Google needs to do to achieve the mission. And I feel like we've done a good job hopefully of trying to walk the fine line of actually continuing to push the frontier and build great flash models. And I actually think those two things are like more tied together. You know this better, more than I do, but like more tied together technically. Like it's, you know, it's hard to make great flash models if you actually don't have a great pro model and vice versa. So we'll definitely keep pushing the frontier on both of those things. I think the perception from outside is by analogy too, it's hard to make a good flash model if you don't have a good pro model. People sort of think that there's like an ultra model internally that's the mega training run that's then being used to like help train pro, which maybe in turn is being used to help train flash. Is that true? Is there like a bigger thing inside that is only for these sort of distillation to the mid-size?…

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