Sources with Alex Heath
00:25:11 · Ellis Hamburger, Tony Zhao
[00:25:11] S1: you haven't worn it Urban Outfitters, but go ahead. But [00:25:14] S0: I I think the the place you have the most amount of attention is how, like, dishwasher should be loaded, Essentially, like, especially when there are, like, a a ton of dishes. Right? But I think when we we actually need to design these tasks when we teach the robot, it's not just like, hey. We got a lot of people, collect data, whatever you want it to be, and then we just put the... Whatever the end result is and... Into the robot. That's not what we do. And it's actually a a big trade off between whether the specific way we fold is easy to learn for the robot and whether on the product side is actually something people want. And at the same time, it shouldn't be, like like, ridiculously difficult that it might take, like, infinite data to to learn. And I think we see that commonly that when we make all these decisions about a robot, it always tends to be, like, three or four categories of things that we need to jointly consider. And, like, even how we fold the t shirt was, like, a big part of it. [00:26:17] S1: I know there's drama around the dishwasher methodology as you cited. I actually, I found a meme online that I see go around every so often. It was from Twitter. This person said, in every partnership, there is a person who stacks the dishwasher like a Scandinavian architect, that's me. And the other person who stacks the dishwasher like a raccoon on meth. I would apologize to my wife, but she's not listening.
00:26:17 · Ellis Hamburger, Alex Heath
[00:26:17] S1: I know there's drama around the dishwasher methodology as you cited. I actually, I found a meme online that I see go around every so often. It was from Twitter. This person said, in every partnership, there is a person who stacks the dishwasher like a Scandinavian architect, that's me. And the other person who stacks the dishwasher like a raccoon on meth. I would apologize to my wife, but she's not listening. [00:26:45] S2: Oh, my... Yeah. Chloe's the same way. So you and I are both, like, dishwasher orderly people. That's interesting to know about us because we're different in a lot of ways, but we we do that the exact same way. [00:26:56] S1: The right way, you mean? [00:26:57] S2: The right way. Glasses in the back. You gotta you have to put the stuff that's dirty first in the back. You don't wanna put it in the front. You can tell someone's personality by how they stack dishes, whether it's in the front or the back, in my opinion. [00:27:10] S1: Well, does Memo have a point of view on whether you should rinse before loading or load with the food on? Because I know there's controversy around that as well with the Cascade Platinum Reddit crew.
00:27:10 · Ellis Hamburger, Tony Zhao, Alex Heath
[00:27:10] S1: Well, does Memo have a point of view on whether you should rinse before loading or load with the food on? Because I know there's controversy around that as well with the Cascade Platinum Reddit crew. [00:27:21] S0: To to add context here, there's this whole, I think, whole debate, right, of, like, the dishwashers will be able to clean your dishes better if you don't rinse as much. There's a whole, like, theory around it. But I think for us, like, when we teach a memo, I think it will rinse. We we do not have, like, big chunk of food in the... Inside the dishwasher, which I think is is more more... There's more downside than the upside it can give. [00:27:49] S2: Yeah. Well, you know that's big dishwasher that tells you that you need to rinse. That's that's the lobbying operation. [00:27:57] S1: That's Bosch and co. For those who missed the last episode, Bosch's tagline from CES. The more you Bosch, the more you feel like a Bosch. That's not a joke. That was their actual tagline from the last time around. Well, I know like a lot of the models for robots and otherwise, we also talked to the CEO of Rivian recently. They talk about how they don't actually program rules of any kind. They just gather more data. Is that similar with you guys as well with the Glove? Absolutely.
00:27:57 · Ellis Hamburger, Tony Zhao, Alex Heath
[00:27:57] S1: That's Bosch and co. For those who missed the last episode, Bosch's tagline from CES. The more you Bosch, the more you feel like a Bosch. That's not a joke. That was their actual tagline from the last time around. Well, I know like a lot of the models for robots and otherwise, we also talked to the CEO of Rivian recently. They talk about how they don't actually program rules of any kind. They just gather more data. Is that similar with you guys as well with the Glove? Absolutely. [00:28:31] S0: Yeah. It's just that I think if you want robots in the real world, in the open world, like you cannot, if else, all the things that can happen. And trying to engineer that is kind of... You'll be kinda cursed if you try to enumerate all the things that can go wrong. But the nice thing about data is that all of these if and else are somewhat included inside the data. So if we have... Get very diverse high quality data and enough quantity of them, we don't actually need to do much like real coding, but the neural network will learn from data and and that ends up being better. [00:29:10] S1: As someone who's very close to this stuff, are you more or less optimistic than the average person about riding in a Waymo or Tesla self driving? [00:29:20] S0: I own Tesla. I self drive to to office every day. [00:29:26] S2: You used to work at Tesla. Right?
00:29:26 · Alex Heath, Tony Zhao
[00:29:26] S2: You used to work at Tesla. Right? [00:29:28] S0: Yes. I I did. I spent a summer there back in 2022. That was actually before the whole, like, end to end, scheme happening there, back when things are not working so well. Like, these days, work very well. I feel like I get more optimistic when... Of, like, going... When seeing things that doesn't work and believing that it will go to, like, 99. But going from that to robotaxi that is fully, fully autonomous and can scalably be deployed in any place, I think there... People might have, like, underestimated how much work there. So I think I'm more optimistic for always the the first chunk of it, and the second lag is, like, more challenging. [00:30:13] S2: If we could get a little more technical for a second... And and robotics are in the brain for me, Tony, because today, I interviewed Demis Hassabis. He used to work at DeepMind. Right? Yep. So... Yeah. So Demis and then, the CEO of Skild AI, which just raised a big round and is doing robotics in the enterprise. And everyone is approaching it differently, but Demis and and Pathak from Skilled were saying, like, they think enterprise is the right way to attack this problem from a data perspective. And it's interesting to see you do it from the home, you know, going towards the consumer route. And I'm curious, like, how you landed on that as the way that you all were gonna go versus just doing the more kind of boring but stable, safe enterprise route.
00:31:02 · Tony Zhao
[00:31:02] S0: Yeah. So I I think this is maybe a hot take, but I think there is a myth around, like, industrial automation or, like, in factories, it's an easier, like, thing for the robot to do. And that might be intuitive for people. Right? There's more constraints. Like, there's clearly well defined things that you should do. But I think the broader context here is more like if you want to do robotics, like, pretty much everything is hard. Like, a 100% of the things will be hard. And putting in that context, I think the relative... Like, the hardness difference between going for the industrial automation or going for, like, homes, I think it's small in that perspective. And then I think the question becomes like, hey. Are we really asking the right question of, like, trying to find things that are easy? Because everything is hard. And what we end up with there is, like, I think we would just want to work on something that is really inspiring and exciting to people, and that will help us build the best team. And that will help us gather people who are, like, truly excited about the mission and, like, putting the actual work, putting, like, all the ingenuities into making that a reality. So I think that's the... Maybe the hot take here is in a grand scheme of things, I think being, like, 20%, 30% harder or easier really doesn't matter that much. And I think there is also a a a different viewpoint here when it comes to home versus, like like, say, factories. Right? One of the big thesis we have is we believe robots will win on the availability before they win on the sheer, like, productivity or, like, the speed. So if you think about, like, factory automations, you're essentially competing with hourly wage workers and essentially humans. And human hands are so perfect after, like, millions years of revolution, and we're so intelligent. And it's actually really hard to beat the throughput and the precision that human has. But these are absolutely critical in factories because if you're slow, like, the whole line will be blocked by you. And if your success rate is not high enough, maybe you'll be damaging some parts, and that's, like, causing real economic damages.