00:04:08 · Priya, Anika
[00:04:08] S1: The entire Internet. Literally, the entire Internet. Yeah. And so the different types of data that are needed for foundational models, world models, humanoids, all of those different things fall into, like, best to worst quality of data, I would say. So, like, at the top, you have actual real robotics data. So that's like from the sensors and from like an actual robot taking action on something. Because then you can see, did it fail? Did it succeed? Like, what do I need to change to make sure that it does succeed next time? That's why that's so helpful. Then after that, you have robotics human operated data. So you you might have, like, seen those videos of people in the different suits, like, moving around. And so that's, like, simulating what a robot would do. And because you actually have, like, the depth perception and you have, like, the three d world actually around you, that's why that's valuable. [00:04:51] S2: And that was a whole controversy when one x launched and said they're gonna be the first consumer humanoid robot in people's houses where people were like, oh, it's all teleoperated. Do you want someone in India, like, peering into your house? The reason it's teleoperated, it's because anything the robot can't do, and right now the robot can't do most things on its own, ends up being teleoperated. But then that data that gets collected is, like, the second highest quality data. But it's not like immediate feedback loop. It's like you collect all of it and then you feed it back to the model and then Yeah.
00:04:51 · Anika, Priya
[00:04:51] S2: And that was a whole controversy when one x launched and said they're gonna be the first consumer humanoid robot in people's houses where people were like, oh, it's all teleoperated. Do you want someone in India, like, peering into your house? The reason it's teleoperated, it's because anything the robot can't do, and right now the robot can't do most things on its own, ends up being teleoperated. But then that data that gets collected is, like, the second highest quality data. But it's not like immediate feedback loop. It's like you collect all of it and then you feed it back to the model and then Yeah. [00:05:20] S1: Yeah. Which is like the same thing that happens with Waymo. It's like Waymo's get stuck all the time, and I call support all the time. There's this one specific turn right next to my office that I get stuck on every single time. Oh, really? It's happened three times now, but every time I call them, they count my ride and give me $10. So I'm like, five minutes of Yeah. Additional time, but I get a free ride. Yeah. But like that, someone comes in and actually tell her operates it. So it's like the same approach that's taken there.
00:05:20 · Priya, Maya, Anika
[00:05:20] S1: Yeah. Which is like the same thing that happens with Waymo. It's like Waymo's get stuck all the time, and I call support all the time. There's this one specific turn right next to my office that I get stuck on every single time. Oh, really? It's happened three times now, but every time I call them, they count my ride and give me $10. So I'm like, five minutes of Yeah. Additional time, but I get a free ride. Yeah. But like that, someone comes in and actually tell her operates it. So it's like the same approach that's taken there. [00:05:45] S0: Yeah. And then the the thing I was gonna say actually is in that robotic data, you get force torque, which is like how hard you're gripping the things or how how hard you're like pushing things. And you also get like tactile, like different tactile data, like pressure distribution across your different fingers and stuff. These are just things that you cannot get from a human. So there are, there is so much need for a human operated robot specific data. [00:06:08] S2: That makes sense. [00:06:10] S0: But but... Keep going.
00:06:10 · Maya, Priya
[00:06:10] S0: But but... Keep going. [00:06:11] S1: Yeah. And then after that, it's simulated data. So there's a lot... That's a whole category of its own startups that are working on being able to simulate data. NVIDIA Cosmos is like one of the big platforms that does that. And it's basically just synthetic data. The reason that people use it is because you can actually get it at scale. It's super cheap. Like the first two that we discussed are super expensive and difficult to get. And so at scale, it's just like the best thing that you can get. And then the last one is egocentric data. So that's what, like, all of these startups that have been in the news recently are focused on, and that's when you have a camera either strapped on your head or like metaclasses or something like that to record what's going on. But then like Maya was saying, you don't get the force... Was it forced work? Yeah. You don't get forced work. You don't have depth perception. You don't have all access to all of that. And so it's not ideal data. It's like the worst quality. But when you have millions of people in India with cameras on them, it's scalable.
00:07:02 · Maya, Anika, Priya
[00:07:02] S0: Yeah. And then there's the final thing I would actually say is third person view where it's just like a video of like people moving through a room. And so this is just... People will like like people... Data labelers will label like what the light is, what a rug is, what a cup is, and then someone moving a cup from left to right. And this is mainly for like planning or understanding how like things move in an environment. And that's like, what creates like a world model. And you need like a world model in order to like have a humanoid work because you need to understand like how things move in a three d space rather than just like texts. But yeah. Yeah. [00:07:34] S2: Yeah. It's funny. One of my friends has a robotics data labeling company. And some of his workers in India were whattapping him, they were like, oh, like, we've realized it's, like, more sturdy and a better angle if we just set it up with a tripod. [00:07:47] S1: And he was like, no. No. No. [00:07:50] S0: The phone of it has to be egocentric. [00:07:52] S1: Yeah. Yeah. That's so funny. And I wanted to talk a [00:07:56] S2: little And then it turned out it was just because they were getting headaches from the camera. I know. That sucks. It's really sad. [00:08:02] S1: Wait. How many hours do they have it on a day? [00:08:05] S2: The whole time. He said, like, he can see them going to the bathroom going to the bathroom doing drugs, like watching, like That's crazy. Inappropriate things on their phone. [00:08:14] S1: And do they clean it at all or they send it all to the labs?
00:08:14 · Priya, Anika, Maya
[00:08:14] S1: And do they clean it at all or they send it all to the labs? [00:08:17] S2: They don't send it to the labs. Like Okay. Like, he does a really low level stuff where, like, he has another job and he's just, like, doing this for cash on the side. So, like, he doesn't do anything, but he sends it to, like, other companies. Like, what... For example, like, y c companies that have... Like, basically, there's, like, a whole chain, right, with the labs at the top. Yeah. He's at the bottom, and then there's, like, companies in the middle there's companies in the middle that are, like, they do some of the labeling, the cleaning [00:08:48] S1: Yeah. [00:08:48] S2: The... And then they're kinda like going up in the chain. Right? Like, they're more like a Mercor. Like, I don't know if Mercor wholesale buys video footage, but I'm sure they do. Because it's like, the people that have contracts with, like, labs, physical intelligence, like, all these big companies, they actually usually will have like really high demand, more high demand than what they can produce with their own operations. [00:09:09] S0: Yeah. Yeah. Like, the... It it makes sense to split out the actual collection to the labeling, to the cleaning, to the processing. [00:09:16] S2: Right. Like everyone wants to do as much of it as they can. But realistically, not every company can do all of Yeah.