Tim Scarfe: preference

Transcript context

…I think that is kind of the vision that we have. So I think in robotics in particular, there's a lot of focus on deploying robots in somewhat constrained settings. So it might be, for example, in someone's apartment that's very staged. Right? Almost as staged as a podcast recording. You know, got all these support staff watching around this robot achieve 1 goal. Right? And from a control perspective, it might be very impressive. But in terms of the stochasticity of the world that it's in, it's very limited. Right? And if we look at simulated simulation environments, they might accurately model physics, but they definitely don't model things like weather or other agents or animals or these kinds of things. Right? Whereas a model like Genie 3, because it's it has world knowledge, that world knowledge extends beyond physics actually to also the behavior of other agents. And as we showed you in that example at the beginning, with the the world events that we can also inject, right, you can actually prompt to have, you know, another agent crosses in front of you or like, you know, we had a herd of deer run down the ski slope or something like that. And I think these are the kind of things that for robots to be deployed at large scale in the real world. The real world is fundamentally populated by people and other agents. And this is something that we can gain from training on this general purpose wormhole that we just have no other approach, I think, to scalably get this data in a safe way as well. Right? Because the safety is a critical element of this that we can simulate things in a realistic way without having to actually deploy agents in the real world. Yes. And that was a very important detail. So you you can put a prompt event in. And you gave me an example of that. There's a skier going down a slope, and then here here's a guy with a Gemini t shirt. And I I guess what I'm thinking about here is if we did train these robot policies, we would need to do probably some kind of curriculum learning and some kind of diversity. So, you know, we would start off with a simple environment and then we'd add the guy with the Gemini t shirt and then there'd be a car coming along. And maybe in reality, there would be some kind of meta process, you know, creating some gradient of complexity complexity and, and, you you know, know, diversifying environments. I love that Ken Stanley paper, you know, the poet paper doing something like that. But is is is that like a fairly reasonable intuition? So I think it's still early to say exactly how word models like GenieFree will be actually used for AI research. I think we can only kind of directionally say. We I I in general, I think we we still we see it in also in other generative models that there are some capabilities that we actually discover. Right? And we don't necessarily know that they're there. And then through the interaction development, we're actually seeing them emerge. For example, you know, with with Vell, we just recently, like, few days ago, we them we we kinda, like, shared that you can write, like, some text on a on a photo and provide it to Vowel, and it just, like, it's it's it reads the the the text, and it follows also the spatial instructions. Right? And I think that's that's, for example, something that we didn't necessarily explicitly train them all to do, but it it's capable of doing. And I think here as well, the capabilities of Gini Free that we're exploring are still we still discover new things. And I think that's something that we hope that by first, by having more, like, you know, testers and external testers that we already shared some some like, we basically previewed the model to and give us feedback. So we hope that through this kind of engagement with the community, and we can better see how those models will be useful. And that's something that I expect to take some time as we we basically try and understand the best application.…

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