Digitized | Spotlight
00:07:54 · Michael Cho, Winnie
So it makes a big difference if the AI models are trained using data that's collected from various environments. And so in our case, the SAWO robot, we now have robots in more than 50 cities because people are starting to buy them. These are cheap, like like toys. And it turns out that diversity in environments, it's... It it makes a huge difference on building a model that it's already quite generalizable even though you don't have that huge amount of quantitative data, but they are already quite generalizable because of where the data is collected from. And I think crypto is the best way to diversify and and get people around the world to contribute. Yeah. [00:08:36] S1: Yeah. No. I think crypto and kind of like a lot of deepened products have proved one of the early use cases around dramatically reducing upfront capital costs, incentivizing the right players. I'm curious to kinda understand how you guys think about kind of that incentive design. Like, how do people earn? What are the data that you guys are collecting? Walk me through what uniquely suits kind of robotics r and d and how... What the data looks like in comparison to some other projects that maybe listeners are more familiar with, like Helium that you mentioned?
00:09:08 · Amanda
[00:09:08] S0: With BitRobot, we're building a platform for distributed open robotics research and development. We're doing this through what we call a network of subnets. You can think of a subnet as a market that's focused on a particular type of output that's pushing embodied AI forward. So creating a specific type of dataset, facilitating, evaluation of models in the real world. And then the types of data that we're collecting, and across that include teleoperation, video, and simulation. So these are the three types of data that are needed for robotics. And then the benefit of having a network of subnets is there's many different approaches right now to how to move robotics forward. And so we can basically pursue all of them in parallel. So like pursuing teleoperation, but also egocentric video. And then another trend is cross embodiment or co training. And so this is like, okay, let's make sure that the model works on different types of embodiments, different types of tasks. And so we're able to kind of, through our different subnets, showcase, different types of data and then pull that together in something that can be a more general purpose AI model. So yeah, would say those are some of the strengths that we have. I don't know if, Michael, you want to add?
00:10:26 · Michael Cho, Winnie
[00:10:26] S2: Yeah, I mean, to double tap, but it's basically still back to this point I was trying to make earlier, which is diversity. Diversity is not just in the environments where you collect this data, should also be diversity in terms of the robotic types right, humanoids or saurobots or drones. It turns out even if you are gathering data from these very different robotic types, actually you can do a more generalized model that can potentially work across all hybrid robots. [00:10:54] S1: Yeah. [00:10:57] S2: Yeah. And BitRobot is set up in a way that allows that flexibility and and, know [00:11:02] S1: Very interesting. Today, I think I read that anyone can kinda contribute to subnet one, which is, like, basically a Pokemon style go sidewalk where we gather real world human... Real world urban data and then subnet five. What has been the response so far? And maybe you can tell folks a little bit more about the two of those.
00:11:02 · Winnie, Michael Cho
[00:11:02] S1: Very interesting. Today, I think I read that anyone can kinda contribute to subnet one, which is, like, basically a Pokemon style go sidewalk where we gather real world human... Real world urban data and then subnet five. What has been the response so far? And maybe you can tell folks a little bit more about the two of those. [00:11:22] S2: Yeah. I can talk about the first one. So because I've been doing the longest and cyber robot. So it is actually quite a fun game. It just... Yeah. Like you say, it's it's very much like Pokemon GO in the real world. So the idea is, like, the cyber robot is literally like an RC car with four gs basically. So what that means is that you can be, let's say, sitting in your home while controlling a robot either around your neighborhood or even halfway across the world because these things are just running, you know, through the Internet. And that makes it super fun because, you know, it it happens in the real world. You're collecting some virtual items, you know, on the back. But, yeah, this happens in the real world. So I think it makes... Yeah. I I find it, like, I still find it really fun to to drive. You know, I don't drive it as often as I when I first started out. I've been to many cities that I would probably would never get a chance to go to. I remember the most memorable one I tried was, it was super hot one day in Singapore and then I was driving at this robot that we have in Sweden. Was snow everywhere, magical, it was so magical, but it's probably a very ordinary place like [00:12:33] S1: you
00:12:33 · Winnie, Michael Cho
[00:12:33] S1: you [00:12:33] S2: know the suites are probably just used to snow everywhere. I was like sweating in Singapore but it was. So that... Yeah. I think that aspect is And [00:12:44] S1: And how do people access it? Is it just kind of a platform, a portal, app, website? Walk us through what that looks like. [00:12:53] S2: Yeah, from the gamer perspective is right, just think of it like a cloud based game. Like you assess it on the browser, you don't need to download any app, just log in. Yep. And then just drive. So what you see is the front camera, the back camera. [00:13:09] S1: Yeah. That's [00:13:09] S2: awesome. Link it up with your game controller, you start driving. Yeah. [00:13:12] S1: And, like, how has the response been so far from, you know, the community and folks who have been kind of playing around with it and kind of helping you guys contribute and gather data?
00:13:24 · Michael Cho
[00:13:24] S2: Yeah. I would say there are few distinct... Very different different distinct groups there who are very interested in what we're building. So you have one group who are just gamers who who who just treat it like a pure game. I kind of think of it myself mostly falling into that group. You know? I... I'm wrestling with the... Because I really love the game. And then you have folks who are interested in the hardware. So these are the hardware makers who look at it as a as a as a hackable thing, you know, that that you can do. I can also say we're open sourcing the hardware very very very efficient. So so you have that group as well. And then you have the researchers. So researchers look at it as, again, like a very cheap, affordable data collection device that can be used to other data that they will then use to train the model. And so we have a lot of, I'll say, world class researchers doing urban application, like, you know, model building, that kind of thing, who are very involved on the platform. So you have these kind of different distinct groups. And, of course, you have the the top people who look at it as a potential deep in thing. Yeah. I mean, right now, we don't have the token as a lot live yet. So I I think it's it's quite interesting to to get all these different people getting involved for these different reasons and... But I I think it's it's... Yeah. It's it's quite cool. Maybe maybe, Amanda, you guys talk about about the Seesaw app as well. Think that one is quite cool as well. Very different from the Sour Robot.