Dream Machines: Physical AI, Robotics, Being Human
00:18:49 · Evan Helda, Charles Wong, Aravind Kandiah
[00:18:49] S1: Mhmm. [00:18:50] S2: You know, Aravind, you wanna give a quick intro? [00:18:52] S0: Yeah. Yeah. Charles didn't mention this, but he was also building race cars. [00:18:58] S1: Oh, yeah. That's cool. [00:19:00] S0: He built a race car from scratch. He's he's underselling himself for sure, but he did a lot of cool stuff. He inhaled a lot of carbon fiber as well during that time. So his lifespan is significantly short creativity. [00:19:12] S2: Indeed. Indeed. I will I will outlive Charles. So
00:19:17 · Aravind Kandiah
[00:19:17] S0: my journey was I was doing a lot of AI research back then. So this is, pre chat GPT. Right? And my main thing was I wasn't quite fulfilled with academia because you would publish like this. You will publish the approach of, oh, this is possible, but then you wouldn't actually take it to fruition. Right? So I said, okay. I wanna build an AI system from zero all the way to a 100, and I wanna see what it takes to deploy an AI system into the wild. Right? So what I found was there were a lot of people going blind in India at that point of time. And the main reason they were going blind was because they are... They were going undiagnosed because of, like, diabetes. So it seemed like a tractable problem. So what I built was using a smartphone, you could take a picture of your eye, and it would tell you a level of diabetes and whether you would go blind or not. Right? And, you know, a mobile phone is so a big... Ubiquitous that you could scale it up really quickly. And the models, you can just send it as an app as well. We actually started building it, and we actually put it through clinical trials and hit, like, a 99% accuracy in clinical trials. Wow. Right? And the cool part is just using a low cost device, you could, I don't wanna say replace a doctor, but you could augment a doctor, and you could just do screens. So it's like... There was like that real epiphany moments like, oh my god. This technology is so powerful. Like, what else could it do? Right? And I think for me and Charles, you know, he came from the self driving space. I came from that, like, medical side of things. We were like, this is such foundational technology that, sure, it will make impacts, like, you know, it's chatbots, image generators, and all that in the digital world, but what would they... What would this look like if it crossed into, like, the physical world? Like, how would manufacturing look different? How would transport look different? How would indoor household tasks look different? Yeah. And we tried pitching this to a lot of VCs at the time, and we had to really, really paint this picture of, like, hey. We are here now. You know, there's this small little AI model that can do this, but imagine what it could do. Right? And self driving cars also weren't a thing back then. Right? They were just only taking off. Yeah. Back to you, Charles.
00:21:32 · Charles Wong, Aravind Kandiah
[00:21:32] S2: I I remember, like, you know, like, in Newtonomy, I was working on, like, feed management as well as perception systems. Right? And so when me and Aravind came together, like, the the key thesis we're trying to figure out was what is the most scalable path to get AI into the real world. Right? What is the enabling... The the best enabling tool or technology that that we could work on? Right? I think simulations and data very much came to the fore for us. And coming from the industry, it just felt so obvious to us at the time. Like, why wouldn't you tap on an unlimited source of data or, you know, a real world, you know, stimuli for robots to learn from. Right? This makes more sense. And, you know, the funny thing is that when we when we actually got together and started trying to find the company, tried to raise our first round of pre seed, that was that was a challenge. Right? Mhmm. We were going to all these VCs and trying to explain a, what AI was, because nobody really understood what it what it meant at the time. Right? Everybody was just thinking Terminator. Like, you trying to build Terminator? What's going on? [00:22:32] S0: Right? And
00:22:32 · Aravind Kandiah, Charles Wong, Evan Helda
[00:22:32] S0: Right? And [00:22:33] S2: then eventually, we moved over to we've been at the pitch evolved into like, we're actually working on autonomous systems and robotics. Right? And then when investors heard the word robotics, they were like, oh, robotics, terrible ROI, not investable, economics don't make sense. Right? And to add, you know, fuel to the, you know, to add, you know, insult to injury, we we told them we're not building robots. We're building dev tools for robots. Right? Which is like, how many layers of abstraction do we have to go before we become totally uninvestible? But, you know, we had a bit of a break, me and Aravind. So we did... We got connected to folks at NASA JPL pretty early in our journey. Right? And this was... We got connected to... Which is a warm intro just to one of the planetary scientists working on the Mars rover. Right? [00:23:26] S1: Cool. [00:23:27] S0: And I [00:23:27] S2: still remember it. Like, this was in the middle of COVID. Like, we like, we were staying at our parents' houses. Right? Like, we were in Singapore at the time because we were all locked down. Right? Mhmm. And we were like, man, there's a... We we we when we got the meeting, we we over prepared. Right? We were [00:23:45] S0: like, okay. [00:23:46] S2: One... We have this one shot. We have to nail it, and Aravind went away and created these beautiful, like, lunar terrain simulations and everything. [00:23:53] S1: That's awesome.
00:23:53 · Evan Helda, Charles Wong
[00:23:53] S1: That's awesome. [00:23:54] S2: Then when we got there, you know, we did the we did the call. He was so impressed. He then said, I'm gonna organize a seminar for all my fellow planetary scientists to come in and listen to you guys. [00:24:04] S1: Wow. That's cool. [00:24:05] S2: They're like, well, shit. Shit just hit them. [00:24:08] S1: Talk about a tail, as they say. [00:24:10] S2: Exactly. We're still faking it till we make it. Right? And so we got in the room there and, like, I like, this was like, you know, 3AM in Singapore. Right? We were pitching these NASA scientists, you know, in our parents' homes. Right? And I remember like, at some point during the the meeting, like, my mom, like, came into the room and wanted to give me, like, a drink. And I was like, no, mom. I'm on the I'm on the phone with NASA right now. Casual. What did come out of that was that we... NASA became our very first pilot project. Right? And it was for validating martian landing systems. So, like, the train you see behind me is from from one of those, you know, like, terrain generations. Right? Nice. Essentially, that the new the new Mars helicopter at the time, they were trying to validate, you know, where it's safe to land. Right? You know, regular debris, all that stuff. And they used our data to validate those systems. And I think that gave us a lot of street cred when later on we talked to more customers and, you know, the pitch ball from there.
00:24:10 · Charles Wong, Evan Helda
[00:24:10] S2: Exactly. We're still faking it till we make it. Right? And so we got in the room there and, like, I like, this was like, you know, 3AM in Singapore. Right? We were pitching these NASA scientists, you know, in our parents' homes. Right? And I remember like, at some point during the the meeting, like, my mom, like, came into the room and wanted to give me, like, a drink. And I was like, no, mom. I'm on the I'm on the phone with NASA right now. Casual. What did come out of that was that we... NASA became our very first pilot project. Right? And it was for validating martian landing systems. So, like, the train you see behind me is from from one of those, you know, like, terrain generations. Right? Nice. Essentially, that the new the new Mars helicopter at the time, they were trying to validate, you know, where it's safe to land. Right? You know, regular debris, all that stuff. And they used our data to validate those systems. And I think that gave us a lot of street cred when later on we talked to more customers and, you know, the pitch ball from there. [00:25:10] S1: That's amazing. So you have this Lighthouse customer. What did... So how did you go from creating some data and something in a very custom way? What came out of that? And then what was required to turn it into a product? And, like, what what does the actual product look like today? And from, like, a user experience perspective for someone who's just getting started using using Bifrost?