The People's AI: The Decentralized AI Podcast
00:10:11 · Jeff Wilser, Bayley Wang
[00:10:11] S0: For those who are not familiar, what was the mini cheetah and talk about kinda what it did and and what it Yeah. Yes. Yeah. [00:10:16] S1: So you've probably seen these robot dogs that have been running around. The mini cheetah was the was the first, like, commercially manufacturable robot dog. It was built using advanced, what they call quasi direct drive actuators. So high force, but also very, easy to measure, like, what force they're they're exerting. And it it was built using mass production techniques using parts off of, like, drone drone, drone supply chains. So it was a little little dog about the size of, like, a... I don't know. About about two feet long. And it could do all kinds of things. It could run. It could jump. It could do a backflip. But as I was saying, when it came time to figure out what this machine could actually do in the real world, all we could think of was very expensive robotic pet for rich people. And we floated we floated the idea of, like, putting it on Kickstarter for, several thousand dollars, but decided it just wasn't a scalable market. And and in the end, we were right. There were... There are a lot of companies in this crowded space building robot dogs, but they're just being marketed as as toys right now because no one can figure out the the the software to make them do it. [00:11:23] S0: In in fairness, I guess some of these designer lap dogs, you know, if you if you if you're wanting to buy a particular breed of, like, hypoallergenic dogs, you do spend, $4,000. So I guess, like, hey. Is it cheaper than Yeah.
00:11:23 · Jeff Wilser, Bayley Wang
[00:11:23] S0: In in fairness, I guess some of these designer lap dogs, you know, if you if you if you're wanting to buy a particular breed of, like, hypoallergenic dogs, you do spend, $4,000. So I guess, like, hey. Is it cheaper than Yeah. [00:11:36] S1: Yeah. There there is there is a [00:11:38] S0: require dog food. [00:11:39] S1: There is a small market that I would say is about the same size as people buy $5,000 hypoallergenic dogs, but that's also not a like venture scale market. Yeah, is Venture fund your dog. And you'd be the [00:11:51] S0: one North Star for an addressable universe. Yeah. Yeah. Okay. So you're just like, alright. This is a fairly hyper niche thing. We can probably find a... Or hopefully find a a more scalable, better use case and and and better kind of, like, type of robot that does more than just, like, freaking backflips for the hyper wealthy.
00:11:51 · Jeff Wilser, Bayley Wang
[00:11:51] S0: one North Star for an addressable universe. Yeah. Yeah. Okay. So you're just like, alright. This is a fairly hyper niche thing. We can probably find a... Or hopefully find a a more scalable, better use case and and and better kind of, like, type of robot that does more than just, like, freaking backflips for the hyper wealthy. [00:12:16] S1: Yeah. Yeah. Yeah. So after... Right. After the after that, we, just sort of went off and did did our own things. I went and did a... Did, like, worked on a company doing displays and optics. But robotics has always been top of my mind and things that I'm interested in and I... Things that I think are really high impact. So, you know, a couple years ago, g... GPT three showed the world that large scale unsupervised training on huge datasets was incredibly powerful, and the resulting emergent behavior could do crazy things. So everyone everyone started thinking about robotics again and how the same types of software and same types of workflow could make robotics better. But when you come and do robotics, you immediately run into a data problem, like a a data problem, a scaling problem, where if you wanna train a model which handles text, you go download the whole Internet and you go do your training. When you wanna train a robotics model, step one is to collect data and then you like stop because it's really annoying to collect data. It requires hiring hundreds or thousands of people to do the data collection to really do anything meaningful.
00:13:21 · Jeff Wilser
[00:13:21] S0: Famously, Google had to... Like, when originally to try and get Google Maps to work. Right? It had to... And Google Street View, they had to get, like, cameras on cars and drive thousands or probably millions of miles around the world, in the physical world to get to see, okay, here is the real world. It's one it's one... Prior to that, Google was like, cool. We can just... On web searches, we will own this. But then you try and get the real world. It's a lot more physical infrastructure you have to to stand up to do that. And so that's relatively simple compared to the idea of streets and highways is a relatively simple thing, I'm guessing, compared to the messiness of your robot and kitchen example earlier. Imagining all of the granular... If if if a countertop is sloped a bit. Right? Or if there's winds, air friction, how fast the robot's arm moves. That sounds in a way much more complicated than certainly than, like, predicting the next word in a sentence and arguably more complicated than cool. I'm on Main Street, and next to me is 1st Street, and here's how the... Here's what the the the neighborhood looks like. I mean, I warm as far as my idea of how complicated this is in a physical sense and where I was to struggle to figure out how do you, hoover up enough data to be able to make meaningful predictions.
00:14:51 · Bayley Wang
[00:14:51] S1: Yeah. So robotics is a little more complicated in in terms of the sensor modalities that are really needed relative to some of the other tasks. But the way you think about it is this. Right? Like, say you wanted to learn how to make a make a French omelet. Omelet. You would probably go watch a video of how to make a French omelet. And because you have additional knowledge about how your fingers interact with the world and how to pick things up by just watching that video, you can learn how to complete the task. And I think with robotics, it's going to be a similar situation. We're going to have low level models that are trained for that particular hardware probably on data from simulations in order to learn how to, like, hold objects without crushing them and understand things like friction. But the higher level models, the ones that require a huge amount of data are the ones that then teach the robot how to complete tasks. Mhmm. It's it's... It it also circles back to this question of like, hey. Do you need force data to be able to train a robot? And my answer to this is probably not because when humans want to learn how to complete new tasks, you... I don't have to, like, pick up your arms and, carry you through the motion of making that omelet in order to teach you how to make an omelet. Right? Like, you're smart enough about the world from your interaction, like, basic interactions with it to learn how to do this task. And sure, for very complex tasks, like like like if I ask you to, like, complete a advanced manufacturing task, I would probably have to also, like, give you a few trials. But I think that parallel also draws the robotics where maybe you do large scale training on, like, visual data. Then for the final run, you go operate the robot to go interact with the environment a bit in order to learn a little more more about the the forces and the friction in the materials.
00:16:40 · Jeff Wilser
[00:16:40] S0: So tell us about what brought you to and what kinda led to PrismaX your and light bulb of, okay, given the challenges of gathering data in the real world, we need some other solution.