00:18:32 · Bernt Øivind Børnich
[00:18:32] S1: I think it just comes down to, like, have you understood the fundamental principles of how the machine operates and why? And if you really understand the fundamentals, then everything else is just logical, like, if you have a good design. So then the complexity kind of goes... Logic goes away. And then you look at it in the end, and you're like, actually, how do we spend this much time? Like, this isn't that complicated. And, like, that should really be the goal. Right? And maybe other people look at it and are like, oh my god. This is complex. But once you really know how it works, then the magic goes away. Right? And it's just like this pretty simple machine. I think that takes a lot of time. So you also need to create an environment where kind of people stick around and get that intuition and under... Deep understanding of the system. But you can also do a lot on the tooling side, which is quite important. Like, how do you build the design tools on the electromechanical side that allows you to iterate on the product in a in a way that's, like, verifiably correct? And this is increasingly becoming more and more productive because you have so good tools, especially on the coding side. But that's always been a challenge, like, finding people who are very deep on electromechanical but are still general enough that they can kinda develop their own tools to solve the problem. Because most people that are very deep on the electromechanical side, they're not necessarily software people. But by far, the most valuable people are the people who are very broad across all of these domains, preferably even down to, like, material science and everything else. Right? Like, how many domains have you actually mastered? And can you start to see kinda, like, the correlation between them? So kinda like the boundaries disappear. The best people generally end up building a lot of tools for how to do your job.
00:20:15 · Ty Morse, Bernt Øivind Børnich
[00:20:15] S0: If long term millions of people are going to have these things in their homes, ideally, is something that humans, like, form a connection with, and they start to love. One of the videos that I saw, you were, like, hugging NEO. Mhmm. [00:20:27] S1: And I [00:20:27] S0: was thinking to myself, how do you go through the process of trying to design something that is fundamentally something that humans can learn to love or decide to love?
00:20:38 · Bernt Øivind Børnich
[00:20:38] S1: Yeah. So there's... There there there there... There's the obvious answer, which is like, oh, you do the right things with respect to, like, warmth and materials. And it's easy to design things that are scary, and then they look kinda science fiction and modern because they're scary. Like, that's kinda like the simple cheat code. We don't do that. We try to design things with warmth, like technology that just blends into society, and you shouldn't even think about it as technology. Like, that will be the success. So that's kinda like on the hardware side, and that's hard to do, but I think it's pretty straightforward. I think on the intelligence side, it's incredibly interesting. And one of the things I'm so bullish on with the world models and what we're doing there is the robot actually learns social behaviors also through all this data. And a good example is just, like, if you get the robot to, like... I mean, there's some bags over there, and so we have the robot to go over and, like, give you a a goody bag of swag. It hands it to you, and it doesn't let go. Right? And then you grab the handle, and then the robot lets go. And there's there's no data on the training data. Right? The the the robot doesn't have data of the specific tasks. It's just these kinda, like, social behaviors and how to interact with people just falls out of the model. Body language falls out of the model. Like, everything kinda collapses into one big omni model that understands everything from how to do the task, but also how to, like, how to be social, how to simulate older agents like older people. Like, if you want to move around and do things in an efficient manner, then you need to understand and predict what will other people do. Right? And we do this all the time. And I think that part of the intelligence is very, very interesting, but also very, very hard to get right. I think, ultimately, that will matter a lot. Right? Because that's how you create the trust. That's how you kinda, like, create that warmth. And I think it's also gonna be quite individual. Like, people don't... Like, it... It's personalizable. Like, peep... People don't necessarily want the exact same thing. But I do think that these machines will they will learn and last for a long time, and they will become a part of the family and a part of society.
00:22:47 · Bernt Øivind Børnich, Ty Morse
And it's gonna be very interesting to see see how that evolves. I think it's gonna be quite beautiful to have, like, this companion throughout your life that kinda, like, is always on your side, remembers everything that's happened, and is always there for you. And I think I think we could all need that. [00:23:03] S0: I think there's very few products that most people would describe as delightful to have in their lives. And I I I think this is gonna be one of them where when people think of the experience of interacting with humanoid robots and NEOs, it's gonna feel like delight. Like, things just are happening. You didn't really tell it to do something. Just, like, magically did some task that you weren't expecting, and you have this little surprise and joy. How do you think about designing something that is just going to be, like, a delightful product?
00:23:32 · Bernt Øivind Børnich, Ty Morse
[00:23:32] S1: I think, ultimately, it's all in the data. Right? So it becomes a data problem of how do you kinda, like, create the desired behaviors through how you tune your data. I think there's still a lot of uns unsolved research there. Just like a lot of work to do. I don't have the solution. Like, everyone else, we're working on it. But I think it's something that will really, really, really greatly improve the enjoyability of using the models. And it... It's been a lot of discussion lately. Right? When you look at the best LLMs now, they're kinda becoming worse personality wise because you have to be so careful to make sure they don't do anything wrong. And we're clearly gonna have the same problem here. Like, we want to make sure these robots are caretakers of humanity and, like, they help us. And finding the balance there, I think, is gonna be be very hard. So, yeah, that's that's something we're gonna work a lot on. And I do hope that through kinda exposing the robot the right kinda, like, behaviors that we would like to see in other people. This is emergent behaviors. [00:24:46] S0: It actually just occurred to me. If you have a robot in a home environment, it doesn't... Not all homes are actually happy. Right. And there might be learned behaviors that are not good.
00:24:46 · Ty Morse, Bernt Øivind Børnich
[00:24:46] S0: It actually just occurred to me. If you have a robot in a home environment, it doesn't... Not all homes are actually happy. Right. And there might be learned behaviors that are not good. [00:24:56] S1: Well, you know, remember, like, most of, like, the the LLMs are mainly, like, trained on, I don't know, like, Discord forums, Reddit. Right. Like, worse forums than that. Like, it's, like, all over place. Humanity. All over the place. Right? So, like, it's like... It is like the dark side of humanity. And then you take that, and you know there's this, like, great meme, right, where you have, like, the big gatula monster, and then someone, like, slaps a smiley on it and says, like, now it's ready. And that's kinda like what a modern model is. Right? You have this, like, beast, and then you're just like, no. No. You're gonna behave well. And I think there's better ways to do that long term as you get more data. Right now, everyone is extremely data constrained. So are so are we. Right? But as you get this incredible tool to learn about how to operate in our world. Maybe you can be more selective on your data. I don't know. But maybe just encourage everyone to, like, be be kind to our robots, and your robots will be kind to you. But there is a balance here because the willingness to kinda, like, break rules and explore is a large part of how we learn. Right? It's how you get new ideas and creativity. And maybe that's one of the reasons you don't see that much creativity in models today. You don't really kinda encourage and expose that kind of behavior.