Q of the underlying technology to go from a Roomba. We're pulling up the video here on the stream of, uh, what you've actually built. Uh, what were the foundational turning points in terms of the, like the different models and different breakthroughs? I imagine the transformer was really important, but there's probably a ton of other, uh, developments that excited you. Now is the time, like we're ready to go.
A Yeah, there's been a lot of things that we are, we are building on top of, um, things like transformers, things like vision language models, the concept of pre-training and post-training. A lot of those things transfer to the robotics world, but they're, they're not as well understood. We are still in the process of figuring out what that recipe should be like. We, we kind of have to rediscover this, some of the steps that, that language people had to do initially and see how we can map them onto the robotics world. We don't have the privilege of having an open internet full of data. We need to collect the data ourselves, which on one hand is a big challenge. Like the data isn't there. You can't e-trade nearly as fast. On the other hand, it also gives you more freedom in figuring out what kind of data is the most important and what data to collect. For, for this particular PI-O-V advancement, what we have to do is, one, collect very diverse data set, large diverse data set that involves not only model manipulators in homes, but also static robots in the office or data of the internet. And it turns out if you collect very diverse data across many different tasks from many different form factors, they all contribute to each other. And that they contribute to a better understanding for the model of what actually is happening and how to utilize all of the data to figure out what to…
AI assessment note: “things like transformers, things like vision language models, the concept of pre-training and post-training”