Dream Machines: Physical AI, Robotics, Being Human
01:03:20 · Charles Wong, Evan Helda
[01:03:20] S2: And then you feed that directly into your model, to your your data generator, you go from there. Right? That's Got you know, hinting back to earlier about that systematic loop. How do we get people motivated to have this success? [01:03:30] S1: That's that's also helpful. And I think, at least for me, so many people... It's like simulation is is a sexy topic, but so few people see into the black box of exactly how to set this up for success. So I appreciate that breakdown. Let's let's look into the future as we as we wind things down here. I use this term, data diet between synthetic versus real and, like, what that what that right balance is today versus tomorrow. What do you think... If we look... Let's say, I know it's so hard to predict the future because the future is here and it's evolving so fast. But if you had to if you had to guess five years in the future, what is that data diet? And do you think that we're gonna get to a point where it's all simulation, it's all synthetic, and you don't even need to touch real world data? Or do think real world data is gonna always have a place even in even in five years when it's incredibly easy to create everything and edge cases.
01:03:30 · Evan Helda, Charles Wong
[01:03:30] S1: That's that's also helpful. And I think, at least for me, so many people... It's like simulation is is a sexy topic, but so few people see into the black box of exactly how to set this up for success. So I appreciate that breakdown. Let's let's look into the future as we as we wind things down here. I use this term, data diet between synthetic versus real and, like, what that what that right balance is today versus tomorrow. What do you think... If we look... Let's say, I know it's so hard to predict the future because the future is here and it's evolving so fast. But if you had to if you had to guess five years in the future, what is that data diet? And do you think that we're gonna get to a point where it's all simulation, it's all synthetic, and you don't even need to touch real world data? Or do think real world data is gonna always have a place even in even in five years when it's incredibly easy to create everything and edge cases. [01:04:29] S2: I have a I have a bit of a left field answer here. Maybe this is less technical, but this is more like... It's just we're talking five years out. Let's talk vision a little bit. Right? Mhmm. When we think about real versus synthetic data, we're trying to think about what do we need to, like, dream up or hallucinate for robots to learn from rather than just experiencing the real world for themselves. [01:04:48] S1: Mhmm.
01:04:48 · Evan Helda, Charles Wong
[01:04:48] S1: Mhmm. [01:04:48] S2: Right? And for humans, when we humans learn, we just call this, like, either... You know, it's called either doing the thing or imagining it. Right? Like, the difference between the way robotics and human... The robot and humans learn. Right? Like, if you're a human... If you're a robot... Like, let's let's start with robots. Right? Let's say you're a robot. You make a mistake in in production, and you have to go back, and then your, you know, your your engineer has to figure out where did it fail, how do we improve this, how do we collect more real data, you know, improve the dataset, retrain, redeploy, yada yada. Right? For humans, let's say we go to work, we make a mistake, what happens? We go home, we reflect, we ruminate, we play out several scenarios in our mind of how this could have been better, and then tomorrow we come back to work, we do a better job. There was no retraining, right? There was no, you know, figuring out, you know, collecting more real data and practicing until we get it right. You could just... For many permitting tasks, right, you can just sit and reflect and imagine. Right? So I like to think of imagination as a proxy for this, where, like, eventually robots will get to such a point where they are able to autonomously imagine and self learn and self correct on the job, where this paradigm of, like, taking it back and retraining whether with real or synthetic data, that will subside or, like, reduce significantly. Yeah. Yeah.
01:06:08 · Evan Helda, Aravind Kandiah, Charles Wong
[01:06:08] S1: What's the role of your of your business in that future? I mean, that could come pretty quickly. Like, I just had a meeting with a customer that's building a, not a VLA, but a video action model where this is what it does. It it it imagines the split second future. It's about a good experience, and then it knows how to go do the thing. And then you have, like, world models that are starting to accelerate, in quite a way. And and I don't mean the world labs world models. I mean, like, the world models understand physics and cause and effect in lots of settings. To me, this sounds like this is a huge risk to Bifrost, and Bifrost doesn't really need to exist in that world. Tell me where I'm right or wrong. [01:06:54] S0: Yeah. So there's there's two parts of that. Right? There's two futures. One future is imagine intelligence is solved. Right? You you have a brain that can work anywhere. Same in physical intelligence and skill, they release the the final API. Right? Yeah. The final API. They can they can do anything and everything. That's a [01:07:15] S1: cool idea. I'm writing that I'm writing that down. Final API. [01:07:17] S0: That's a that's a [01:07:18] S1: catchy title for some kind of essay. [01:07:20] S0: All these credits, man. I gotta I gotta look [01:07:22] S1: at these credits. Is what I'm not getting credit for. I might just steal it, but that's okay. [01:07:28] S2: I'm I'm a professional world, everyone. Groupies, so, you know, do it that way you will.
01:07:28 · Charles Wong, Aravind Kandiah, Evan Helda
[01:07:28] S2: I'm I'm a professional world, everyone. Groupies, so, you know, do it that way you will. [01:07:31] S0: Indeed. Indeed. So the... So you mentioned you have the final API. Right? And you are working in a in a science lab. Right? And you're like, okay. You know, I I can't hire any lab assistants. Let's go buy Unitree G1, and then we're gonna load up the physical AI API, and I want it to do all these very domain specific tasks for me. Are you gonna, a, just load it up and hope it works in the lab? Or are you going to load it up and run it through a whole bunch of specific scenario tests and make sure you can do the task first before loading it into the real robot? So our thesis is actually that you won't need training at all in the future, but you will need very thorough rigorous evaluations for domain specific things. [01:08:19] S1: Mhmm. [01:08:19] S2: Right? So it's
01:08:19 · Charles Wong, Aravind Kandiah, Evan Helda
[01:08:19] S2: Right? So it's [01:08:19] S0: like, you wanna do it for domain specific testing, will need to evaluate it for your domain specific things, and you can't just evaluate it in real life across the thousand scenarios that you wanted to do. And every time a new update comes or a new, like, know, you just fine tune on the edge and you update the model, you still want to CICD your model. Right? So there will always be a need for testing simply because we're just deploying the systems into, like, the real world where real infrastructure, real lives are all at risk. Right? So for us... And we see that happening today as well. Right? Like, most of our customers actually use us for eval more than they use us for training. Right? So we're seeing that shift slowly [01:09:00] S1: That's fine.