Lex Fridman Podcast - #147 – Dmitri Dolgov: Waymo and the Future of Self-Driving Cars

Episode Date: December 21, 2020

Dmitri Dolgov is the CTO of Waymo, an autonomous vehicle company. Please support this podcast by checking out our sponsors: - Tryolabs: https://tryolabs.com/lex - Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium - BetterHelp: https://betterhelp.com/lex to get 10% off - Cash App: https://cash.app/ and use code LexPodcast to get $10 EPISODE LINKS: Waymo's Twitter: https://twitter.com/waymo Waymo's Website: https://waymo.com PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: - Check out the sponsors above, it's the best way to support this podcast - Support on Patreon: https://www.patreon.com/lexfridman - Twitter: https://twitter.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/LexFridmanPage - Medium: https://medium.com/@lexfridman OUTLINE: Here's the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) - Introduction (07:46) - Computer games (12:52) - Childhood (15:24) - Robotics (16:14) - Moscow Institute of Physics and Technology (18:26) - DARPA Urban Challenge (28:46) - Waymo origin story (44:27) - Waymo self-driving hardware (53:00) - Connected cars (58:53) - Waymo fully driverless service in Phoenix (1:03:14) - Getting feedback from riders (1:11:28) - Creating a product that people love (1:17:18) - Do self-driving cars need to break the rules like humans do? (1:24:03) - Waymo Trucks (1:29:41) - Future of Waymo (1:42:53) - Role of lidar in autonomous driving (1:55:53) - Machine learning is essential for autonomous driving (1:59:55) - Pedestrians (2:06:32) - Trolley problem (2:11:00) - Book recommendations (2:22:26) - Meaning of life

The following is a conversation with Dmitry Dalgov, the CTO of Weimo, which is an autonomous driving company that started as Google self-driving car project in 2009 and became Weimo in 2016. Dmitry was there all along. Weimo is currently leading in the fully autonomous vehicle space in that they actually have an at-scale deployment of publicly accessible autonomous vehicles driving passengers around with no safety driver with nobody in the driver's seat. This, to me, is an incredible accomplishment of engineering on one of the most difficult and exciting artificial intelligence challenges of the 21st century. Quick mention of a sponsor followed by some thoughts related to the episode.

Thank you to Trial Labs, a company that helps businesses apply machine learning to solve real world problems. Blinkist and App I Use for reading through summaries of books, better help, online therapy with a licensed professional and cash app. The app I use to send money to friends. Please check out the sponsors in the description to get a discount at the support this podcast. As a side note, let me say that autonomous and semi-autonomous driving was the focus of my work at MIT and is a problem space that I find fascinating and full of open questions from both robotics

and a human psychology perspective. There's quite a bit that I could say here about my experiences and academia on this topic. That revealed to me, let's say, the less admirable size of human beings. But I choose to focus on the positive on solutions. I'm brilliant engineers like Dmitry and the team at Waymo, who work tirelessly to innovate and to build amazing technology that will define our future. Because of Demetri and others like him, I'm excited for this future.

And who knows, perhaps I too, will help contribute something of value to it. If you enjoyed this thing, subscribe on YouTube, review it with 5 stars and up a podcast, follow on Spotify, support on Patreon, or connect with me on Twitter and Lex Friedman. As usual, I'll do a few minutes of ads now and no ads in the middle. I try to make these interesting, but I give you time stamps, so if you skip, please still check out the sponsors by clicking the links in the description. It is, in fact, the best way to support this podcast. This episode is brought to you by Trial Labs, a new sponsor

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made over the past five years. Anyway, all that to say is that trial labs is legit. Great engineers, if you own a business and want to see how I can help you, do check them out at triallabs.com slash Lex. That's triallabs.com slash Lex. This episode is also supported by Blinkist, my favorite app for learning new things. Blinkist takes the key ideas from thousands of nonfiction books and condenses them down into just 15 minutes that you can read or listen to. I'm a big believer in reading,

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to advance robotics, STEM education for young people around the world. And now, here's my conversation with Mithri Dahlguff. When did you first fall in love with robotics or even computer science more in general? Computer science first at a fairly young age. Robotics happened much later. I think my first interesting introduction to computers was in the late 80s when we got our first computer. I think it was an IBM IT. Remember those things that had a turbo button in the front? The radio precedent make the thing go faster.

Did they already have floppy disks? Yeah, the 5.4 inch ones. I think there was a bigger inch. So when something, then 5 inches and 3 inches. Yeah, I think that was the 5. I don't know, maybe that was before that was the giant plates. And I didn't get that. But it was definitely not the three inch ones.

Anyway, so that, you know, we got that computer. I spent the first few months just playing video games, as you would expect. I got bored of that. So I started messing around and trying to figure out how to make the thing do other stuff, got into exploring programming. And a couple of years later, I got to a point where I actually wrote a game,

a lot of games, and a game developer. A Japanese game developer actually offered to buy it for me for a few hundred bucks, but for a kid in Russia. It's a big deal. Yeah. I did not take the deal. Integrity. Yeah, I instead, I- It's a bit of a... Yes, there was not the most acute financial move that I made in my life. You know, looking back at it now. I instead put it, well, you know, I had a reason. I put it online.

It was, what do you call it back in the days? It was a free-ware thing, right? It was not open source, but you could upload the binaries, you could put the game online, and the idea was that people like it, and then they contribute in the senior little donations, right? So I did my quick math of like, you know, of course, you know, thousands and millions of people are gonna play my game,

send me a couple bucks a piece, you know, if you definitely do that. As I said, not the best find. You're already playing business models at that, yeah. Remember what language it was? What programmable it was a base. Oh, scale. Which, what?

Pascal. Pascal, and they had a graphical component was, it was a base. Oh, scale. What? Pascal. Pascal. And that graphical component, so it's a text-based. Yeah, yeah, it was like, I think there are 320 by 200, whatever it was. I think that kind of the earlier, that's the VGA resolution, right? And I actually think the reason why this company wanted to buy it is not like the fancy graphics or the implementation, it was maybe the idea the actual game. The idea of the game.

One of the things, it's so funny, I've used a place game called Golden X and the simplicity of the graphics and something about the simplicity of the music, like it still haunts me. I don't know if that's a childhood thing, I don't know if that's the same thing for college duty these days for young kids. But I still think that the one of the games are simple, that simple purity makes for, like, allows your imagination to take over and thereby creating a more magical experience. Like now, with better and better graphics, it feels like your imagination doesn't get to create worlds, which is kind of interesting. It could be just an old man on a porch, like waving at kids these days that have no respect, but I still think that graphics almost get

in the way of the experience. I don't know. Flippy bird. Yeah. That's what I don't know if the game is closed. That's more about games that, like, that's more like Tetris World where they optimally masterfully, like, create a fun, short-term, dopamine experience versus I'm more referring to, like, role-playing games where there's like a story you can live in it for months or years. Like there's an Elder Scroll series which is probably my favorite set of games. That was a magical experience and then the graphics were terrible. The characters were all randomly generated but they're I don't know.

It pulls you in. There's a story. It's like an interactive version of an Elder Scrolls Tolkien world. And you get to live in it. I don't know. I miss it. One of the things that suck about being an adult is there's no, you have to live in the real world as opposed to the Elder Scrolls world.

You know, whatever brings a joy, right? Minecraft, right? Minecraft is a great example. You create, and I could it's not the fancy graphics, but it's the creation of your own worlds. Yeah, that one is crazy. You know, one of the pitches for being a parent that people tell me is that you can like use the excuse of parenting to go back into the video game world. And like, like, that's like, you know, father son, father daughter time.

But really, you just get to play video games with your kids. So anyway, at that time, did you have any ridiculous ambitious dreams of where as a creator, you might go as an engineer? Did you, what, what did you think of yourself as as an engineer? As a tinker, or did you want to be like an astronaut? Or something like that? I'm tempted to make something up about robots, engineering, or mysteries of the universe, but that's not the actual memory

that pops into my mind when you ask me about childhood dreams. So I actually share the real thing. When I was maybe four or five years old, I, as we all do, I thought about no one I wanted to do when I grow up. And I had this dream of being a traffic control cup. They don't have those today, as I think, but back in the 80s and in Russia, you're probably familiar with that likes. They had these police officers that would stand in the middle of an intersectional day and they would have their likes striped back back and white batons that they would use to control the flow of traffic. And for whatever reason, I was strangely infatuated with this whole process and like that,

that was my dream. That's what I wanted to do when I grew up. And my parents, both physics profs, by the way, I think were a little concerned with that level of ambition coming from their child at that age. It's an interesting, I don't know if you can relate, but I very much love that idea. I have an OCD nature that I think lends itself very close to the engineering mindset, which is you want to kind of optimize, solve a problem by creating an automated solution, like a set of rules that set of rules you can follow, and then thereby make it ultra-efficient.

I don't know if that's, it was of that nature. I certainly have that. There's like, like SimCity and factory building games, all those kinds of things, kinda speak to that engineering mindset. Or did you just like the uniform? I think it was more of the letter. I think it was the uniform and the, you know,

the Stripe Baton that made cars go up. Right direction, that trophy. You know, the striped baton that made cars go up in the right direction. That drove it. But I guess, you know, it is, I did end up, I guess, you know, working on the transportation industry one one where none of you know, far enough, but that's right. That's the list of times. Maybe, maybe it was my, you know, deep inner infatuation with the, you know, traffic control batons that led to this career.

Okay, when was the leap from programming to robotics? That happened later. That was after grad school. And I actually, the most self-driving cars was I think my first real hands-on introductions to robotics, but I never really had that much hands-on experience in school and training, I worked on applied math and physics then in college. I did more have

Apps track computer science and it was After grad school that I really got involved in robotics It was actually self-driving cars and you know that was a big bit flip what grad school So into grad school in Michigan, and then I did a postdoc at Stanford, which was the postdoc where I got to play with Southern Driving Cars. Yeah, so we'll return there.

But let's go back to Moscow. So for episode 100, I talked to my dad, and also I grew up with my dad, I guess. So I had to put up with them for many years. And he went to the Fistia, or my PT. We were to say in English, because I've heard all of this in Russian. Moscow Institute of Physics and Technology. And to me, that was like...

I met some super interesting as a child, I met some super interesting characters. It felt to me like the greatest university in the world, the most elite university in the world. And just the people that I met that came out of there were like, not only brilliant, but also special humans. It seems like that place really tested the soul both like in terms of technically and like spiritually. So that could be just the romanticization of that place. I'm not sure, so maybe you can speak to it, but is it correct to say that you spent some time at Ciccia? Yeah, that's right. Six years. I got my bachelor's and masters and Yeah, that's right. Six years. I got my bachelor's and master's and physicist math there and it actually was interesting because my dad and actually both my parents won there and I think all the stories that I heard like just like you Alex growing up about the place and you know how interesting and special

and you know magical it was a thing that was a significant maybe the main reason I wanted to go there for college. Enough so that I actually went back to Russia from the US. I graduated high school in the US. And you went back there. I went back there. Yeah, that, wow. Exactly the reaction, most of my peers in college head, but perhaps a little bit stronger. Point me out as this crazy kid. Will your parents support of that? Yeah. Yeah. I think it was your previous question. They supported me and a lot of letting me pursue my passions and the things that it was. That's a bold move.

Wow. What was it like there? It was interesting. You know, definitely fairly hardcore on the fundamentals of math and physics and lots of good memories from those times. So, okay, so Stanford, how did you get into autonomous vehicles? I had the great fortune and great honor to join Stanford's DARPA Urban Challenge Team in 2006. This was a third in the sequence of the DARPA challenges, there were two grand challenges prior to that.

And then in 2007, they held the DARPA Urban Challenge. So, you know, I was doing my postdoc. I had, I joined the team and worked on motion planning for that competition. So okay, so for people who might not know, I know from a certain person, autonomous vehicles is a funny world. In a certain circle, people, everybody knows everything. And in a certain circle, nobody knows anything. I mean, in terms of general public. So it's interesting, it's a good question what to talk about, but I do think that the urban challenge is worth revisiting.

It's a fun little challenge. One that first of all, like sparked so much, so many incredible minds to focus on one of the hardest problems of our time in artificial intelligence. So that's a success from a perspective of a single little challenge. But can you talk about like what did the challenge involve? So were there pedestrians, were there other cars?

What was the goal? Who was on the team? How long did it take? Any fun sort of specs? Sure, sure, sure. So the way the challenge was constructed and just a little bit of backgrounding, as I mentioned, this was the third competition in that series.

The first two were at the Grand Challenge, the Grand Challenge. The goal there was to just drive in a completely static environment. You had to drive in a desert. That was very successful, so then DARPA followed with what they called the urban challenge, where the goal was to build vehicles that could operate in more dynamic environments and share them with other vehicles. There were no pedestrians there, but what DARPA did is they took over an abandoned air force base and it was kind of like a little fake city that they built out there. And they had a bunch of robots, cars, there were autonomous in there all at the same time mixed in with

other vehicles driven by professional drivers. And each car had a mission. And so there's a crude map that they received beginning and they had a mission, you know, go here and then there and over here. And they kind of all were sharing this environment at the same time they interact to interact with each other. They had to interact with the human drivers. So it's this very first, very rudimentary version of a self-driving car that could operate in an environment shared with other dynamic actors. That, as you said, you know, really, in many ways,

you know, kickstarted this whole industry. Okay. So who was on the team? And how did you do? I forget. I came in second. Perhaps that was my contribution to the team. I think the staff routine came in first and the DARPA challenge, but then I joined the team and, you know, you were the one with the bug in the code. I mean, do. Do you have memories of some particularly challenging things? One of the cool things, this isn't a product, this isn't the thing that... You have a little bit more freedom to experiment, so you can take risks, so you can make mistakes. Is there interesting mistakes?

Is there interesting challenges that stand out to you? Some like taught you a good technical lesson or a good philosophical lesson from that time? Yeah, definitely a very memorable time. Not really a challenge, but like one of the most vivid memories that I have from the time. And I think that was actually one of the days that really got me hooked on this whole field was

the first time I got to run my software on the car. And I was working on a part of our planning algorithm that had to navigate in parking lots. So it was something that called free space motion planning. So the very first version of that, we tried on the car. It was on Stanford's campus in the middle of the night. And I had this little course constructed with cones in the middle of a parking lot.

So we're there in like 3 a.m. By the time we got the code to compile and turn over. And it drove. I could actually did something quite reasonable. And, you know, I was, of course, very buggy at the time and had all kinds of problems, but it was pretty darn magical. I remember going back and, you know, later in my own trying to fall asleep and just, you know, being unable to fall asleep for, you know, the rest of the night. My mind was blown.

That's what I've been doing ever since for more than a decade. In terms of challenges and interesting memories, on the day of the competition, it was pretty nerve-wracking. I remember standing there with Mike Montemarrolo, who was the software lead and wrote most of the code. I got one little part of the planner. Mike incredibly did pretty much the rest of it, with a bunch of other incredible people. But I remember standing on the day of the competition, watching the car with Mike and cars are completely empty. They're all lined up in the beginning of the race and then, you know,

DARPA sends them, you know, on their mission, one by one, something leave and like, you just, they had these sirens, right? They all had their different sirens, right? Each siren had its own personality, if you will. So, you know, off the go and you don't see them, just kind of, and then every once in a while, they, you know, come a little bit closer to where, you know, the audiences and you can kind of hear, you know, the sound of your car and then, you know, come a little bit closer to where the audience is and you can kind of hear, you know, the sound of your car and it seems to be moving along so that gives you hope. And then, you know, it goes away and you can't hear it for too long. It's starting getting anxious, right? So it's a little bit like sending your kids to college and like, you know, kind of you invested in them.

You hope you, you build it properly, but it's still anxiety inducing. So that was an incredibly fun few days in terms of you know bugs as we mentioned you know one that there was my bug that caused us the loss of the first place I still a debate that you know occasionally have with people in the CMU team CMU came first I should mention Uh that you haven't heard of them, but yeah, it's something you know it's a small school It's it's yeah, it's really glitched that you know, they happen to succeed at something robotics related. Very scenic though. So most people go there for the scenery. Yeah, it's a beautiful campus. I like unlike Stanford. So for people, yeah, that's true. I'm like Stanford. For people who don't know,

CMU is one of the great robotics and sort of artificial intelligence universities in the world. CMU Carnegie Valley University. Okay, sorry, go ahead. Good, good PSA. So in the part that I contributed to, which was navigating parking lots, and the way, you know, that part of the mission work is you in a parking lot, you would get from DARPA, an outline of the map, you basically get this giant polygon that defined the perimeter of the parking lot, and there would be an entrance and maybe multiple entrances are access to it, and then you would get a goal within that open space, X, Y, heading, where the car had to park. It had no information about the optical, so the obstacle is that the car might encounter there.

So it had to navigate completely free space from the entrance to the parking lot into that parking space. And then once it had parked there, it had to exit the parking lot. And while of course, encountering and reasoning about all the obstacles that it encounters in real time. So our interpretation, or at least my interpretation

of the rules was that you had to reverse out of the parking spot. And that's what our cars did, even if there's no obstacle in front. That's not what Seam Use Car did. And it just kind of drove right through. So there's still a debate. And of course, you stop and then reverse out

and go out the different way. That cost you some time. So there's still a debate. And of course, you know, as you stop and then reverse out and go out the different way, that cost you some time. And so there's still a debate whether, you know, it was my poor implementation, the cost is extra time, or whether it was, you know, CMU violating the important rule of the competition. And you know, I have my own opinion here in terms of other bugs. And like I have to apologize to Mike Montemarra, for sharing this on air. But it is actually one of the more memorable ones. And it's something that's kind of become a bit of a metaphor, had an alike in the industry since then, I think, at least in some circles. It's called the Victory Circle, our Victory Lab.

And our cars did that. So in one of the missions in the urban challenge in one of the courses, there was this big oval right by the start and finish of the race. So the ARPA head, a lot of the missions would finish in that same location. And it was pretty cool because you could see the cars come by, you know, kind of finish that part

like of the trip, like that like of the mission, and then you know, go on and finish the rest of it. Other vehicles would come, hit their waypoint, exit the oval and off the would go. Our car in the hand would hit the checkpoint, and then it would do an extra lap around the oval, and only then leave and go on its merry way. Over the course of the full day, it accumulated some extra time. And the problem was that we had a bug where it wouldn't start reasoning about the next way point and plan a route to get to that

next point until a kid a previous one. And in that particular case, by the time you hit that one, it was too late for us to consider the next one and kind of make a lane change so that every time we would do like an extra lap. So, you know, that's the Stanford Victory lap. The Victory lap. Oh, I feel like there's something philosophical profound in there somehow,

but I mean, ultimately, everybody is a winner in that kind of competition. And it led to sort of famously to the creation of Google Self Driving Car Project and now Waymo. So can we give an overview of how is Waymo born, how is the Google Self Driving Car Project born, what is the mission, what is the hope, what is the engineering kind of set of milestones that it seeks to accomplish. There's a lot of questions in there.

Yeah. I mean, you're right. It kind of the DARPA-ITN-ORBAN challenge and the DARPA-ITN-ORBAN previous DARPA grand challenges kind of led, I think, to a very large degree to that next step. And then you know, Larian Sergei, Larian patient, you know, Sergei Bren,

Google Fondorscourt, you know, saw that competition and believed in the technology. So, the Google self-driving car project was born. You know, at that time, and we started in 2009, it was a pretty small group of us, about a dozen people who came together to work on this project at Google. At that time, we saw an incredible early result in the DARPA Urban Challenge. I think we're all incredibly excited about where we got to. And we believed in the future of the technology, but we still had a very

rudimentary understanding of the problem space. So the first goal of this project in 2009 was to really better understand what we're up against. And with that goal in mind, when we started the project, we created a few milestones for ourselves that maximized learnings. Well, the two milestones were, one was to drive 100,000 miles in autonomous mode, which was at that time, you know, orders of magnitude that more than anybody has ever done. And the second milestone was to drive 10 routes. Each one was 100 miles long. There were specifically chosen to be kind of extra spicy, you know, extra complicated and simple, the full complexity of that domain. And you had to drive each one from beginning to end with no intervention, no human

intervention. So you would get to the beginning of the course, you would press the button, that would engage in autonomy, and you had to go for 100 miles beginning to end with no interventions. And it sampled again, the full complexity of driving conditions. We're on freeways. We had one route that went all through all the freeways and all the bridges in the Bay Area. We had some that went around Lake Tahoe, and kind of mountains, roads.

We had some that drove through denser dense urban environments like downtown Palolta and through San Francisco. So it was incredibly interesting to work on and it took us just under two years of body, year and a half, a little more, to finish both of these milestones. And in that process, A, it was an incredible amount of fun, probably the most fun I had in my professional career. And you're just learning so much. You are, the goal here is to learn in prototype. You're not yet starting to build a production system.

So you just, you were, this is when you were kind of working 24, 7, and hacking things together. And you also don't know how hard this is when you're kind of, you know, working 24, seven and hacking things together. And you also don't know how hard this is. I mean, it's the point. Like, so, I mean, that's an ambitious, if I put myself in that mindset, even still, that's a really ambitious set of goals.

Like just those two, just picking, just picking 10 different, difficult, spicy challenges and then having zero interventions. So like not saying gradually we're going to like, you know, over a period of 10 years, we're going to have a bunch of roots and gradually reduce the number of interventions. You know, that literally says like, by as soon as possible, we want to have zero and unhard roads. So to me, if I was facing that, it's unclear whether that takes two years, or whether that takes 20 years. I mean, it took us under two.

I guess that speaks to a really big difference between doing something once and having a prototype where you are going after you know learning about the problem versus how you go about engineering a product that you know where you look at You know do properly do evaluation you look at metrics you know drive dog and you're confident that you can do that a honey and I guess that's the you know why it took a dozen people You know 16 months are a little bit more than that back in 2000, buying in 2010 with the technology of, you know, the more than a decade ago, that amount of time to achieve that milestone of 10 routes, 100 miles each in no interventions.

And, you know, it took us a little bit longer to get to a full driverless product, the customer's use. That's another really important moment. Is there some memories of technical lessons, or just one, like what did you learn about the problem of driving from that experience? I mean, we could now talk about what you learned

from modern day Waymo, but I feel like you may have learned some profound things in those early days, even more so, because it feels like what Waymo is now is to trying to, you know, how to do scale, how to make sure you create a product, how to make sure it's safe, you know, those things, which is all fascinating challenges. But like you were facing the more fundamental philosophical problem of driving in those early days. Like, what the hell is driving? As an autonomous, or maybe I'm again romanticizing it, but is there, uh, is there some valuable lessons you picked up over there at those two years?

A tonne. The most important one is probably that we believe that it's doable. And we've gotten far enough into the problem that we had, I think, only glimpse of the true complexity of that domain. It's a little bit like climbing them out and where you see the next peak and you think that's the summit, but then you get to that and you see that this is just the start of the journey. But we've tried, we've sampled enough of the problem space and we've made enough rapid

success, even with technology of 2009-2010 that it gave us confidence to then pursue this as a real product. So, okay, so the next step, you mentioned the milestones that you had in those two years, what are the next milestones that then led to the creation of Waymo and Beyond? Yeah, it was a really interesting journey. And Waymo came a little bit later. Then we completed those milestones in 2010 that was the pivot when we decided to focus on actually building a product using this technology.

The initial couple years after that, we were focused on a freeway, what you would call a driver assist, maybe on an L3 driver assist program. Then around 2013, we've learned enough about the space and the more deeply about the product that we wanted to build that we pivoted. We pivoted towards this vision of building a driver and deploying it fully

driverless vehicles without a person. And that's the path that we've been on since then. And it goes exactly the right decision for us. So there was a moment where you're also considered like what is the right trajectory here? What is the right role of automation in the task of driving? There was still... It wasn't from the early days obviously you want to go fully autonomous. From the early days it was not. I think it was around 2013 maybe, that we've...

That became very clear and we made that pivot and it also became very clear and we made the pivot and also became very clear and that it's the way you go building a driver assist system is You know fundamentally different from how you go building a fully driverless vehicle. So you know, we've Pivoted towards the latter and that's what we've been working on ever since and So that was around 2013 then there's sequence of really meaningful for us, really important defining milestones since then. And 2015, we had our first, actually the world's first,

fully driverless trade on public roads. It was in a custom-built vehicle that we had, I must have seen those, we called them the Firefly, that, you know, funny looking marshmallow looking thing. And we put a passenger, his name was Steve Mann, you know, great friend of our project from the early days, the man happens to be blind. So we put them

in that vehicle. The car had no steering wheel, no pedals. It was an uncontrolled environment, no lead or chase cars, no police escorts. And we did that trip a few times in Austin, Texas. So that was a really big milestone. But that was in Austin. Yeah. Okay. And we only, but at that time, it took a, we're only, it took a tremendous amount of engineering. It took a tremendous amount of validation to get to that point. But we only did it a few times. Maybe only did that. It was a fixed route. It was not kind of a controlled environment, but it was a fixed route. And we only did a few times. Then in 2016, end of 2016, beginning of 2017, was when we founded Weimel, the company that's when we,

that was the next phase of the project where I wanted, we believed in the commercial vision of this technology and it made sense to create an independent empty, within that alphabet umbrella, to pursue this product at scale. Beyond that, in 2017 later in 2017, there was another really huge step for us, really big milestone where we started,

I think it was October of 2017, where when we started regular driverless operations on public roads, that first day of operations we drove in one day and that first day a hundred miles in driverless fashion and then the most important thing about that milestone was not that you know, a hundred miles in one day, but that it was the start of kind of regular, driverless operations. When we say driverless, it means no driver. That's exactly right. So on that first day, we actually had a mix.

And we didn't want to be on YouTube and Twitter that same day. So in many of the rides, we had somebody in the driver's seat, but they could not disengage. Like the car, not disengage. But actually on that first day, some of the miles were driven and just completely empty driver's seat. And this is the key distinction that I think people don't realize is that oftentimes when you talk about autonomous vehicles, you're, there's often a driver in the seat that's ready to take over what's called a safety driver, and then Waymo is really one of the only companies

that I'm aware of, or at least as like boldly and carefully and all that, is actually has cases, and now we'll talk about more and more, where there's literally no driver. So that's another, in the interesting case of where the driver's not supposed to disengage that second nice middle ground, if they're still there, but they're not supposed to disengage, but really there's the case when there's no,

okay, there's something magical about there being nobody in the driver's seat. Like, just like to me, you mentioned the first time you wrote some code for free space navigation of the parking lot, that was like a magical moment. To me, just sort of an observer of robots, the first magical moment is seeing an autonomous vehicle turn, like make a left turn, like apply sufficient torque to the steering wheel to where it like there's a lot of rotation. And for some reason, and there's nobody in the driver's seat, for some reason that communicates that hears a being with power that makes a decision

There's something about like the steering wheel because we perhaps romanticize the notion of the steering wheel It's all essential to the our conception our 20th century conception of a car and it turning the steering wheel with nobody in Jehovah's seat that to me I think maybe to others, it's really powerful. Like this thing is in control. And then there's this leap of trust that you give. Like I'm going to put my life in the hands of this thing that's in control. So in that sense, when there's no driver in the driver's seat, that's a magical moment for robots.

So I got in a chance to last year to take a ride in a way more vehicle and that was the magical moment. There's like nobody in the driver's seat. It's like the little details. You would think it doesn't matter whether there's a driver or not, but like if there's no driver and the steering was turning on its own, I don't know, that's magical. It's absolutely magical. I've taken many of these rides and a completely empty car. No human in the car pulls up.

You call it on your cell phone, it pulls up. You get in, it takes you on its way. There's nobody in the car but you, right? That's something we call fully driverless. Our rider only mode of operation. Yeah, it is magical, it is transformative. This is what we hear from our writers. It kind of really changes your experience

and like that, that really is what unlocks the real potential of this technology. But coming back to our journey, that was 2017 when we started truly driverless operations. Then in 2018, we've launched our public commercial service, that we called Waymo1 in Phoenix. In 2019, we started offering truly driverless writer-only rights to our early writer population of users.

And then 2020 has also been a pretty interesting year. One of the first ones was less about technology, but more about the maturing and the growth of Waymo as a company. We raised our first round of external financing this year, and you'll be part of Alphabet. So obviously we have access to significant resources, but as kind of on the journey of Waymo, my sharing as a company it made sense for us to partially

go externally and this round. So we raised about $3.2 billion from that round. So, you know, we're raised about $3.2 billion with that round. We've also started putting our fifth generation of our driver, our hardware, that is on the new vehicle, but it's also a qualitatively different set of self-driving hardware. That's what is now in the JLR pace, so that was a very important step for us. The hardware specs, fifth generation, I think it would be fun to maybe... I apologize if I'm interrupting, but maybe talk about maybe the generations or the focus on what we're talking about in the fifth generation in terms of hardware aspects, like what's on this car?

Sure. So we separated out the actual car that we are driving from the self-driving hardware we put on it. Right now we have, so this is, as I mentioned, the fifth generation, we've gone through, we started building our own hardware many, many years ago, that Firefly vehicle also had the hardware suite that was mostly designed, engineering building house. Liders are one of the more important components that we designed and build from the ground up.

So on the fifth generation of our driveers of our South Island hardware that we're switching to right now, we have, as with previous generations, in terms of sensing, we have lighters, cameras, and radars. And we have a pretty beefy computer that processes all that information and makes decisions in real time on board the car. So in all of the, and it's really a qualitative jump forward in terms of the capabilities

and the various parameters and aspects of the hardware compared to what we had before and compared to what you can kind of get off the shelf in the market today. Meaning from fifth to fourth to from fifth to first. Definitely from first to fifth, but also from the world's dumbest question. Definitely from fourth to fifth, as well as the last stop is a big step forward. So everything's in house.

So like Lidar's built in house. And cameras are built in house. Of course, the different, we work with partners. There's some components that we get from our manufacturing and supply chain partners. What exactly is in-house is a bit different. We do a lot of custom design on all of our Sonsi Madenal. There's lighters, radars, cameras.

Exactly. There's lighters are almost exclusively in-house and some of the technologies that we have, some of the fundamental technologies there are completely unique to Weimo. That is also largely true about radars and cameras. It's a little bit more of a mix in terms of what we do ourselves versus

what we get from partners. Is there something super sexy about the computer that you can mention? It's not top secret. For people who enjoy computers. There's a lot of machine learning involved, but there's a lot of basic computers. You have to probably do a lot of signal processing on all the different sensors. If Ninja Grid everything has to be in real time, there's probably some kind of redundancy type of situation. Is there something interesting you can say about the computer for the people who love hardware? It does have all of the characteristics, all the properties that you just mentioned. Redundancy, very beefy compute for general processing, as well as inference and ML models.

It is some of the more sensitive stuff that I don't want to get into for IP reasons, but we've shared a little bit in terms of the specs of the sensors that we have on the car. You know, we actually shared some videos of what are lighter sees, lighters, see in the world. We have 29 cameras, we have five lighters, we have six radars on these vehicles,

and you can kind of get a feel for them out of data that they're producing that all has to be processed in real time to do perception to do complex reasoning. It kind of gives you some idea of how beefy those computers are, but I don't want to get into specifics of exactly how we build them. Okay, well, let me try some more questions that you can't get into the specifics of like GPU wise, is that something you can get into? You know, I know that Google works with GPUs and so on. I mean, for machine learning folks, it's kind of interesting. Or is there no, how do I ask it?

I've been talking to people in the government about UFOs and they don't answer any questions. So this is how I feel right now asking about GPUs. But is there something interesting that you could reveal? Or is it just, you know, or would leave it up to our imagination some of the computers? Is there any, I guess, is there any fun trickery? Like I talked to Chris Latner for a second time in years of key person about TPUs and there's a lot of fun stuff going on in Google in terms of hardware

that the optimizes for machine learning. Is there something you can reveal in terms of how much you match customization, how much customization there is for hardware for machine learning purposes? I'm going to be like that government. You've got a person to vote you, foes. But I guess I will say that it's really compute is really important. We have very data-hungry and compute-hungry ML models of all over our stack.

This is where both being part of alphabet as well as designing our own sensors and the entire hardware suite together, where on one hand, you get access to really rich raw sensor data that you can pipe from your sensors into your compute platform, and build the whole pipe from sensor, raw sensor data to the big compute, as then have the massive compute to process all that data. And this is where we're finding that having a lot of control of that hardware part of the stack is really advantageous. One of the fascinating magical places to me, again, might not be able to speak to the

details, but is the other compute, which is like, we're just talking about a single car. But the, you know, the driving experience is a source of a lot of fascinating data. And you have the huge amount of data coming in on the car, on the car. And, you know, the infrastructure of storing some of that data to then train or to analyze or so on, that's a fascinating like piece of it that I understand a single car. I don't understand how you pull it all together in a nice way.

Is that something that you could speak to in terms of the challenges of seeing the network of cars and then bringing the data back and analyzing things that like edge cases of driving be able to learn on them to improve the system, to see where things went wrong, where things went right, and analyze all that kind of stuff. Is there something interesting there

from an engineering perspective? Oh, there's an incredible amount of really interesting work that's happening there. Both in the real-time operation of the fleet of cars and the information that they exchange with each other in real-time to make better decisions, as well as on the kind of the off-board component where you have to deal with massive amounts of data

for training your ML models, evaluating the ML models for simulating the entire system and for evaluating your entire system. This is where being part of ELF that has become tremendously advantageous. And we consume an incredible amount of compute for ML infrastructure. We build a lot of custom frameworks to get good at, you know, on data mining, finding the interesting edge cases for training and for evaluation of the system for both training and evaluating some components

and sub parts of the system, and various ML models, as well as the evaluating the entire system and simulation. Okay, that first piece that you mentioned, that cars communicating to each other essentially, I mean, through perhaps through a centralized point, but what, that's fascinating too. How much does that help you? Like, if you imagine, like, you know, right now, the number of way more vehicles is whatever X, I don't know if you can talk to what that number is, but it's not in the hundreds of millions yet. And imagine if the whole world is way more vehicles. Like that changes potentially the power of connectivity. Like the more

cars you have, I guess actually, if you look at Phoenix, because there's enough vehicles, there's enough, when there's just like some level of density, you can start to probably do some really interesting stuff with the fact that cars can negotiate, can be, can communicate with each other and thereby make decisions. Is there something interesting there that you can talk to about like how does that help with the driving problem from as compared to just a single car solving the driving problem by itself? Yeah, it's a spectrum. I first say that it helps, and it helps in various ways, but it's not required. Right now, the way we build our system, and each car can operate independently, they can operate with no connectivity.

So I think it is important that you have a fully autonomous, fully capable driver that computerized driver that each car has. Then they do share information, and they share information real-time, and it really helps. So the way we do this today is whenever one car encounters something interesting in the world, whether it might be an accident or a new construction zone, that information immediately gets uploaded over the air and is propagated to the rest of the fleet. And that's how we think about maps as priors in terms of the knowledge of our fleet of drivers that is distributed across the fleet and it's updated in real time. So that's one use case.

You can imagine as the density of these vehicles go up that they can exchange more information in terms of what they're planning to do and start influencing how they interact with each other, as well as potentially sharing some observations, to help with enough density of these vehicles, where one car might be seeing something that is relevant to another car that is very dynamic. It's not part of your updating your static prior

of the map of the world, but it's more of a dynamic information that could be relevant to the decisions that another car is making real-time. so you can see them exchanging that information and you can build on that. But again, I see that as an advantage, but it's not a requirement. So what about the human and the loop? So when I got a chance to drive the ride in a way, ride in a way more, you know, there's customer service. So like there is somebody that's able to dynamically like tune in and help you out. What role does the human play in that picture? That's a fascinating. Like, you know, the idea of teleoperation, be able to remotely control a vehicle.

So here what we're talking about is like frictionless, like a human being able to in a frictionless way sort of help you out. I don't know if they're able to actually control the vehicle. Is that something you could talk to? Yes. To be clear, we don't do teleoperation. I got to believe in teleoperation for a reason, that's not what we have in our cars.

We do, as you mentioned, have a version of customer support. You know, a call of life health. In fact, we find it that it's very important for our right experience, especially if it's your first trip. You've never been in a fully driverless ride or only way more vehicle, you get in. There's nobody there. So you can imagine having all kinds of questions in your head like how this thing works.

So we've put a lot of thought in, so you're kind of guiding our writers, our customers through that experience, especially for the first time. They get some information on the phone. If the fully driverless vehicle is used to service their trip, when you get into the car, we have an in car screen and audio that kind of guides them and explains what to expect. They also have a button that they can push that will connect them to a real life human being that they can talk to about this whole process. So that's one aspect of it. There is, I should mention that there is another function that humans provide to our cars, but it's not teleoperation.

You can think of it a little bit more like fleet assistance, kind of like traffic control that you have, where our cars, again, they're responsible on their own for making all of the decisions, all of the driving decisions that don't require connectivity. They, you know, anything that a safety or latency critical is done, you know, purely autonomously by onboard, our onboard system, but there are situations where,

you know, connectivity is available, kinda car encounters a particularly challenging situation. You can imagine like a super hairy scene of an accident. The cars will do their best, they will recognize that it's an off-nominal situation. They will do their best to come up with the right interpretation and the best course of action in that scenario. But if connectivity is available, they can ask for confirmation from a human-assistner to confirm those actions and perhaps provide a little bit of contextual information and guidance.

October 8th was when you were talking about the Wemble launch, the fully self-public version of its fully driverless, that's the right term, I think service and Phoenix. Is that accurate? That's right. It's the introduction of fully driverless right or only vehicles into our public WAMO service. Okay, so that's amazing. So it's like anybody can get into WAMO in Phoenix?

That's right. So we previously had early people in our early writer program taking fully driverless rides in Phoenix. And just this a little while ago, we opened Nanato Reis. We opened that mode of operation to the public. So I can download the app and go on the write. There's a lot more demand right now for the service than we have capacity. So we're kind of managing that, but that's exactly the way we describe it. Yeah, well this is interesting. So there's more demand than you can handle. What has been the reception so far? Like what? Okay, so this is a product, right? I mean, okay, so, you know, this is a product, right? That's a whole other discussion of like how compelling of a product it is.

Great, but it's also like one of the most kind of transformational technologies of the 21st century. So it's also like a tourist attraction. Like it's fun to, you know, to be a part of it. So it'd be interesting to see like what do people say? What do people, what have been the feedback so far? You know, still early days, but so far the feedback has been incredible, incredibly positive. We asked them for feedback during the ride. We asked them for feedback

after the ride as part of their trip. We asked them some questions. We asked them to rate the performance of our driver, most by far, most of our drivers give us plus five stars in our app, which is absolutely great to see. And they're also giving us feedback on things we can improve. And that's one of the main reasons we're doing this as Phoenix and over the last couple of years. And every day today, we are just learning a tremendous amount of new stuff

from our users. There's no substitute for actually doing the real thing, actually having a fully driverless product out there in the field with users that are actually paying us money to get from point A to point B. So this is a legitimate, like, there's a paid service. That's right. And the idea is you use the app to go from

point A to point B. And then what are the A's? What are the what's the freedom of the starting and ending places? It's an area of geography where that service is enabled. It's a decent size of geography of territories. It's actually larger than the size of San Francisco. And within that, you have full freedom of selecting where you want to go. Of course, there's some on your app, you get a map, you tell the car where you want to be picked up, and where you want the car to pull over and pick you up,

and then you tell it where you want to be dropped off. And of course, there's some exclusions, right? You want to be where in terms of where the car is allowed to pull over and pick you up and then you tell it where you want to be dropped off. And of course, there are some exclusions where you want to be where in terms of where the card is allowed to pull over. So that you can't do, but besides that, it's amazing. It's not like a fixed, just what I guess I don't know, maybe that's what's the question behind your question, but it's not a pre-sub set of. So within the geographic constraints, within the area anywhere, it can be picked up and dropped off anywhere. That's right. And people use them on all kinds of trips. And we have incredible spectrum of riders. I think the youngest actually have car scenes to them.

And we have people taking their kids and rides. I think the youngest riders we get on cars are one or two years old. And the full spectrum of use cases, people can take them to schools, to go grocery shopping, to restaurants, to bars, run errands, go shopping, etc. etc. You can go to your office, right? Like the full spectrum of use cases, and people can use them in their daily lives to get around. And we see all kinds of really interesting use cases and that that's there's providing us incredibly valuable experience that we then used to improve our product. So as somebody who's been on, done a few long grants

with Joe Rogan and others about the toxicity of the internet and the comments and the negativity in the comments. I'm fascinated by feedback. I believe that most people are good and kind and intelligent and can provide like even in disagreement really fascinating ideas. So on a product side, it's fascinating to me. Like, how do you get the richest possible user feedback like to improve? What are the channels that you use to measure? Because like, you're no longer... It's one of the magical things about autonomous vehicles.

It's not, like, it's frictionless interaction with the human. So like, you don't get to, you know, it's just giving a ride. So how do you get feedback from people in order to improve? Oh yeah, great question. Various mechanisms. So it's part of the normal flow we ask people for feedback. As the car is driving around, we have on the phone and in the car, and we have a touchscreen in the car, you can actually click some buttons and provide real-time feedback on how the car is doing and how the car is handling a particular situation, both positive and negative.

So that's one channel. We have, as we discussed, customer support or life help, where if a customer wants to, it has a question, or he has some sort of concern, they can talk to a person in real time. So that is another mechanism that gives us feedback. At the end of a trip, we also ask them how things went. They give us comments and start rating.

And if we also ask them to explain what went well and what could be improved. And we, we have our, our writers are providing, you know, Vera Rich feedback there, you know, a lot, the large fraction is very passionate, very excited about this technology, so we get really good feedback. We also run UXR studies, right? And you know, specific, and that are kind of more, you know, go more in depth, and we will run both, kind of lateral and longitudinal studies, where we have a deeper engagement with our customers.

We have our user experience research team tracking over time, that's things that are a lot of the things that are cool. That's exactly right. And that's another really valuable feedback, source of feedback. And we're just covering a tremendous amount, right? If people go grocery-stroping and they want to load 20 bags of groceries in our cars, and that's one workflow

that you maybe don't think about getting just right when you're building the driverless product. I have people who bike as part of their trip. So they bike somewhere, then they get on our cars. They take a path, part of their bike that load into our vehicle, then they go, and that's where we want to pull over and how that get in and get out process works, provides a very useful feedback. In terms of what makes a good pickup and drop off location,

we get really valuable feedback. In terms of what makes a good pickup and drop off location, we get really valuable feedback. Yeah. In fact, we had to do some really interesting work with high definition maps and thinking about walking directions. If you imagine you're in a store, you're in some giant space and then, you know, you want to be picked up somewhere. If you just drop pin at a current location, which is maybe in the middle of a shopping mall, I quote, the best location for the car to come pick you up. And you can have simple heuristics where you're just going to take your cleaning distance

and find the nearest spot where the car can pull over. That's closest to you, but oftentimes, that's not the most convenient one. I have many anecdotes where that heuristic breaks in horrible ways. I want an example that, you know, I often mention is somebody wanted to be, you know, dropped off in Phoenix and, you know, weak car picked location that was close, the closest to there, you know, where the pin

was dropped on the map in terms of, you know. But it happened to be on the other side of a parking lot that had this row of cacti. And the poor person had to walk all around the parking lot to get to where they wanted to be in 110 degree heat. So that was a welcome. So then we took all of these, all of that feedback from our users and incorporated into our system and improve it. Yeah, I feel like that's like requires AGI to solve the problem of like,

when you're, which is a very common case, when you're in a big space of some kind, like apartment building, it doesn't matter. It's just out some large space. And then you call the, like, a Waymo from there, right? Like, and whatever that doesn't matter, right, chair, vehicle. And like, where is the pin supposed to drop? I feel like that's, you don't think,

I think that requires a GI, I'm gonna add the two. You know what I'm saying? Okay, the alternative, which I think the Google search engine is taught, is like, there's something really valuable about the perhaps slightly dumb answer, but a really powerful one, which is like there's something really valuable about the perhaps slightly dumb answer, but a really powerful one, which is like what was done in the past by others.

Like what was the choice made by others? That seems to be, like in terms of Google search, when you have like billions of searches, you could see which, like when they recommend what you might possibly mean, they suggest based on not some machine learning thing, which they also do, but like on what was successful for others in the past and finding a thing that they were happy with. Is that integrated at all, the way more like what pickups worked for others?

It is. I think you're exactly right. So there's, really, it's an interesting problem. It is. I think you're exactly right. So there's real, it's an interesting problem. naive solutions have interesting failure modes. So there's definitely lots of things that can be done to improve. And both learning from what works, but doesn't work in actual hail from getting richer data and getting more information about the environment and richer maps, but you're absolutely right that there's some properties of solutions that in terms of the effect that they have on users so much, much, much better than others, right?

And predictability and understandability is important. So you can have maybe something that is not quite as optimal, but is very natural and predictable to the user and kind of works the same way all the time. And that matters a lot for the user experience. And to get to the basics, the pretty fundamental property is that the car actually arrives where you told it to right. Like you can always change it to see it on the map and you can move it around if you don't like it.

And but like that property that the car actually shows up on the plane is critical, which you know, we're compared to some of the human driven analogs. I think you can have more predictability. It's actually the fact if I have a you know my dear little bit of a deets were here. I think the fact that it's you know your phone and the cars to computers talking to each other can lead to some really interesting things we can do in terms of the user interfaces you know both in terms of function like the car actually shows up exactly where you told it you want it to to be, but also some really interesting things on the user interface.

I think the car is driving as you call it, and it's on the way to come and pick you up. And of course, you get the position of the car and the route on the map, but and they actually follow that route, of course. But it can also share some really interesting information about what is doing. So our cars, as they are coming to pick you up,

if it's coming up for cars coming up to a stop sign, it will actually show you that it's there sitting because it's set a stop sign or a traffic light will show you that it's got a setting out of red light. So you know, there's like little things, right? But I find those little touches really interesting, really magical and it's just little things like that that you can do to delight your users. This makes me think of there's some products that I just love.

There's a company called Rev.com where I like for this podcast, for example, I like is drag and drop a video and Then they do all the captioning It's humans doing the captioning, but they connect you could they they automate automate everything of connecting you to the humans And they do the captioning and transcription. It's all effortless and it like I remember when I first started using them was like Life is good like because I was was so painful to figure that out earlier. The same thing with something called the Isotope RX. This company I use for cleaning up audio, like the sound cleanup they do. It's like drag and drop and it just cleans everything up.

Very nicely. Another experience like that I had with Amazon one click purchase first time. I mean, are there places to do that now, but just the effort, listeners of purchasing, making it frictionless. It kind of communicates to me, like, I'm a fan of design, I'm a fan of products that you can just create a really pleasant experience. The simplicity of it, the elegance just makes you fall in love with it.

So, I know, do you think about this kind of stuff? experience, the simplicity of it, the elegance just makes you fall in love with it. So do you think about this kind of stuff? I mean, it's exactly what we've been talking about. It's like the little details that somehow make you fall in love with the product. Is that we went from like urban challenge days where love was not part of the conversation probably. And to this point where there's human beings and you want them to fall in love with the experience, is that something you're trying to optimize for

or try to think about how do you create experience that people love? Absolutely. I think that's the vision is removing any friction or complexity from getting our users, our writers, to where they want to go. And making that as simple as possible, and then, you know, beyond that, just transportation, making things and, you know, goods get to their destination as seamlessly as possible.

And I talked about, you know, a drag and drop experience where I kind of express your intent and then it just magically happens. And for our writers, that's what we're trying to get to is you download an app and you can click and car shows up. It's the same car. It's very predictable. It's a safe and high quality experience and then it gets you in a very reliable, very convenient, frictionless way to where you want to be. And along the journey, I think

we also want to do a little things to delight our users. Like the ride sharing companies because they don't control the experience, I think. They can't make people fall in love necessarily with the experience, or maybe they haven't put in the effort, but I think if I would speak to the ride sharing experience I currently have, it's just very convenient. But there's a lot of room for falling in love with it. We can speak to car companies do this well. You can fall in love with a car, and be a loyal car person, like whatever. I like badass hot rods. I guess 69 Corvette.

At this point, cars are so only a only a car is so 20th century man, but is there something about the waymo experience where you hope that people will fall in love with it? Because is that part of it? Or is it just about making a convenient ride, not ride-sharing, I don't know what the right term is, but just a convenient A to be autonomous transport. Or do you want them to fall in love with Waymo?

Maybe elaborate a little bit. I mean, almost like from a business perspective, I'm curious. How do you want to be in the background invisible or do you want to be like a source of joy that's in very much in the foreground? I want to provide the best, most enjoyable transportation solution. And that means building it, building our product, building our service in a way that people do. And I've used in a very seamless, frictionless way in their day-to-day lives.

And I think that does mean, in some way, falling in love in that product, it just becomes part of your routine. It comes down my mind to safety, predictability of the experience, and privacy, I think, aspects of it, right? Our cars, you get the same car, you get very predictable behavior, and that is important. I think if you're going to use it in your daily life, privacy. And when you're in a car, you can do other things. You're spending a bunch just another space where you're spending a significant part of your life. And so not having to share it was other people who you don't want to share it was, I think, is a very nice

property. Maybe you want to take a phone caller, you caller, do something else in the vehicle. And safety on the quality of the driving, as well as the physical safety of not having to share that ride is important to a lot of people. What about the idea that when there's somebody like a human driving and they do a rolling stop on a stop sign, like sometimes like, you know, you get a new bar of lift or whatever, like human driver and, you know, they can be a little bit aggressive as drivers. It feels like there is not all aggression is bad. Now that may be a wrong, again again 20th century conception of driving,

maybe it's possible to create a driving experience. Like if you're in the back busy doing something, maybe aggression is not a good thing. It's a very different kind of experience perhaps, but it feels like in order to navigate this world, you need to, uh, how do I, uh, phrase this? You need to kind of bend the rules a little bit or at least I could test the rules. I don't know what language politicians use to discuss this, but, uh, whatever language they use, you like flirt with the rules. I don't know, but like you, uh, you sort of, uh, have a bit of an aggressive way of driving that asserts your presence in this world.

They're by making other vehicles and people respect your presence and thereby allowing you to sort of navigate through intersections in a timely fashion. I don't know if any of that made sense, but like how does that fit into the experience of driving autonomously? Is that- Thanks a lot of us. This is your hitting on a very important point of a number of behavioral components and parameters that make your driving feel assertive and natural and comfortable and predictable. Our cars will follow rules. They will do the safest thing possible in all situations. They'll

be clear on that. But if you think of really, really good drivers, think about professional limit drivers. They will follow the rules. They're very, very smooth, and yet they're very efficient, but they're sort of comfortable for the people in the vehicle. They're predictable for the other people outside the vehicle that they share the environment with. That's the kind of driver that we want to build. You just need to think, maybe there's a sport analogy there. You can do in very many sports, the true professionals are very

efficient in their movements. They don't do hectic flailing, they're smooth and precise. They get the best results. That's the driver that we want to build. In terms of aggressiveness, you can roll through the stop signs, you can do crazy lane changes. Typically, it doesn't get you to your destination faster. Typically, not the safest or most predictable, very most comfortable thing to do. But there is a way to do both. And that's what we're drawing, trying to build a driver that is safe, comfortable, smooth, and predictable. Yeah, that's a really interesting distinction. I think in the early days of autonomous vehicles,

the vehicles felt cautious as opposed to efficient. And I'm still probably, but when I rode in the waymo, I mean, it was quite assertive. It moved pretty quickly. Like, yeah, then one of the surprising feelings was that it actually went fast and it didn't feel like awkwardly cautious than autonomous vehicles. So I've also programmed autonomous vehicles and everything I've ever built was felt awkwardly either overly aggressive Okay, especially when it was my call code or

like awkwardly cautious as the way I would put it and the waymo's vehicle felt like assertive and I think efficient is like the right terminology here. It wasn't and I also like the professional limo driver because we often think like you know an Uber driver or a bus driver or a taxi. This is the funny thing. People think they track taxi drivers or professionals. I mean, it's like, that's like saying, I'm a professional

walker just because I've been walking all my life. I think there's an art to it, right? And if you take its seriously as an art form, then there's a certain way that mastery looks like. And it's interesting to think about what does mastery look like in driving. And perhaps what we associate with aggressiveness is unnecessary. It's not part of the experience of driving. It's like unnecessary fluff that efficiency, you can be, efficiency, you can be, you can create a good driving experience within the rules. I mean, you're the first person to tell me this, so it's kind of interesting. I need to think about this, but that's exactly what it felt like with Waymo. I kind of had this intuition, maybe it's the Russian thing, I don't know, that you have

to break the rules in life to get anywhere. But maybe, maybe it's possible that that's not the case in driving. I have to think about that. But it certainly felt that way on the streets of Phoenix when I was there in Weimo, that that that was a very pleasant experience and it wasn't frustrating in that like, come on and move already kind of feeling.

It wasn't, that wasn't there. Yeah, I mean, that's what, that's what we're going after. I don't think you have to pick one. I think truly good driving, it gives you both efficiency, assertiveness, but also comfort and predictability and safety. And, you know, it's, that's what fundamental improvements in the core capabilities truly unlock. And you can think of it as precision and recoil trade-off.

You have certain capabilities of your model, and then it's very easy when you have some curve of precision and recoil. You can move things around. You can choose your operating point, and you're trading off precision versus recoil, false positive versus false negatives. But then, you can tune things on that curve and be more cautious or more aggressive, but plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, plus, move the whole curve up. Before I forget, let's talk about trucks a little bit. So I also got a chance to check out some of the Waymo trucks. I'm not sure if we want to go too much into that space, but it's a fascinating one, so maybe we can mention it at least briefly. Waymo is also now an autonomous trucking and how different, like philosophically and technically,

is that whole space of problems? It's one of our two big products and commercial applications of our driver, right? Hailing and deliveries. We have Weimo one and Weimo via moving people and moving goods. Trucking is an example of moving goods. We've been working on trucking since 2017. It is a very interesting space and you're a question of how different is it. It has this really nice property that the first order challenges,

like the science, the hard engineering, whether it's hardware, or on-board software, or off-board software, all of the systems that you build for training your ML models for a value-oriented, entire system. Like those fundamentals carry over. The true challenges of driving, perception, semantic understanding, prediction, decision-making, planning, evaluation, the

simulator, ML infrastructure, those carry over. Like the data and the application and kind of the domains might be different, but the most difficult problems, all of that carries over between the domains. So that's very nice. So that's how we approach it. We're kind of build investing in the core, the technical core,

and then there's specialization of that core technology to different product lines, to different commercial applications. So on, just to tease it apart a little bit on trucks, so starting with the hardware. The configuration of the sensors is different, they're different physically, geometrically, different vehicles.

So for example, we have two of our main laser on the trucks on both sides, so that we have the blind spots, whereas on the J on both sides so that we have, you know, to have the blind spots, whereas on the JLR, I-PACE, we have, you know, one of it, setting at the very top, but the actual sensors are almost the same, or largely the same. So all of the investment that over the years,

we've put into building our custom lighters, custom radars, we're pulling the whole system together, that carries over very nicely. Then on the perception side, the fundamental challenges of seeing, understanding the world, whether it's object detection, classification, tracking, semantic understanding, all that carries over. Yes, there's some specialization.

When you're driving on freeways, range becomes more important. The domain is a little bit different. But again, the fundamentals carry over very, very nicely. Same, and you guess you get into prediction or decision-making. The fundamentals of what it takes to predict what other people are going to do, to find the long tail, to improve your system and not long tail of behavior, prediction, and response, that carries over, right? And so on and so on.

So I mean, that's pretty exciting. By the way, this way, MoVie include using the smaller vehicles for transportation of goods. That's an interesting distinction. So I'll say there's three interesting modes of operation. So one is moving humans, one is moving goods, and one is like moving nothing, zero occupancy, meaning like you're going to the destination. You're your MTV equal. I mean, it's the third is the last way of that's the entirety of it. It's the

less, you know, exciting from the commercial perspective. Well, I mean, in terms of like, if you think about what's inside a vehicle as it's moving, because it does, you know, some significant fraction of the vehicle's movement has to be empty. I mean, it's kind of fascinating. Maybe just on that small point is, is there different control and policies that are applied for zero occupancy vehicle, so vehicle with nothing in it, or is it just move as if there is a person inside what was with some subtle differences? As a first-order approximation, there are no differences. And if you think about safety and comfort and quality of driving, only part of it has

to do with the people or the goods inside of the vehicle. But you don't want to drive smoothly, as we discussed, not for the purely for the benefit of whatever you have inside the car. It's also for the benefit of the people outside, kind of feeding, naturally and predictably into the whole environment. So, yes, there are some second order things you can do. You can change your route and optimize maybe your fleet, things at the fleet scale, and you would take into account whether some of your cars are actually serving a useful trip whether with people or with goods or as you know other cars are driving completely empty to that next valuable trip that they're going to provide but those are mostly

second-order effects. Okay, cool. So Phoenix is an incredible place. And what you've announced in Phoenix is it's kind of amazing. But you know, that's just like one city. How do you take over the world? I mean, I'm asking for a friend. One step at a time. Okay. We've wanted to do that. Is that the cartoon, pinky and the brain? Okay. But gradually is a true answer. So I think the heart of your question is, what can you ask a better question than I ask?

A great question. Answer that one. I'm just gonna phrase it in the terms that I want to answer. Exactly right. Brilliant. Please. No, what, you know, where are we today? And, you know, what happens next? And what does it take to go beyond Phoenix and what does it take to get this technology to more places and more people around the world? get this technology to more places and more people around the world. All right. So our next big area of focus is exactly that larger scale commercialization and

scaling up. If I think about the main and your Phoenix gives us that platform, it gives us that foundation of upon which we can build. And there are a few really challenging aspects of this whole problem that you have to pull together in order to build a technology, in order to deploy it into the field, to go from a driverless car to a fleet of cars that are providing a service and then all the way to commercialization. So, and this is what we have in Phoenix, we've taken the technology from a proof point to an actual deployment, and have taken our driver from one cart to a fleet that can provide a service.

Beyond that, if I think about what it will take to scale up and deploy in more places with more customers. I tend to think about three main dimensions, three main axes of scale. One is the core technology, the hardware and software, core capabilities of our driver. The second dimension is evaluation and deployment. And the third one is the product, commercial, and operational excellence. So you can talk a bit about where we are along each one of those three dimensions, about where we are today, and what will happen next. On the core technology, the hardware and software, together, Comparison driver, we obviously have that foundation

that is providing fully driverless trips to our customers as we speak. In fact, and we've learned a tremendous amount from that. So now what we're doing is we are incorporating all those lessons into some pretty fundamental improvements in our core technology, both on the hardware side and on the software side, to build a more general, more robust solution that then will enable us to massively scale, you know, and be young Phoenix. So on the hardware side, all of those lessons are now incorporated

into this fifth generation hardware platform that is being deployed right now. And that's the platform, the fourth generation, the thing that we have right now, driving in Phoenix it's good enough to operate fully drive-eously, night and day, in various speeds and various conditions. But the fifth generation is the platform upon which we want to go to massive scale. We've really made quality of improvements in terms of the capability of the system, the simplicity of the architecture, the reliability of the redundancy.

It is designed to be manufacturable at very large scale and it provides the right unit economics. So that's the next big step for us on the hardware side. That's already there for scale. The version 5. That's right. Is that coincidence or should we look into a conspiracy theory that it's the same version as the Pixel phone?

Is that what's the hardware that you need or confirm? Okay. Denylex. All right, cool. So, sorry. So that's the, okay, that's that X-E's. What else?

So similarly, the hardware is a very discreet jump, but similar to that, to how we're making that change from the fourth generation hardware to the fifth, we're making that change from the four-generation hardware to the fifth, we're making similar improvements on the software side to make it more, robust and more general and allow us to quickly scale the young Phoenix. So, that's the first dimension of core technology. The second dimension is evaluation and deployment. Now, how do you measure your system? How do you evaluate it? How do you build a release and deployment process where you with confidence, you can regularly release new versions of your driver into a fleet?

How do you get good at it so that it is not a huge tax on your researchers and engineers that you can how do you build all these processes, the frameworks, the simulation, the evaluation, the data science, the validation, so that people can focus on improving the system and the releases just go out the door and get deployed across the fleet. So we've gotten really good at that in Phoenix.

That's been a tremendously difficult problem, but that's what we have in Phoenix right now that gives us that foundation. And now we're working on kind of incorporating all the lessons that we've learned to make it more efficient, to go to new places, scale up, and just kind of stamp things out. So that's that second dimension

of evaluation and deployment. And the third dimension is product, commercial and operational excellence. And again, Phoenix there is providing an incredibly valuable platform. That's why we're doing things end-to-end in Phoenix. We're learning as we discussed a little earlier today. Trimendism on really valuable lessons from our users getting really incredible feedback,

and we'll continue to iterate on that and incorporate all those lessons into making our product even better and more convenient for our users. So you're converting this whole process of Phoenix, in Phoenix, into something that could be copying pasted elsewhere. So like, perhaps you didn't think of it that way when you were doing the experimentation phoenix, but so how long did It basically then you can correct me, but you've I mean it's still early days, but you're taking the full journey in phoenix, right as you were saying Of like what it takes to basically automate. I mean, it's not the entirety of phoenix, right, but I imagine it can

encompass the entirety of ph, right? But I imagine it can encompass the entirety of Phoenix that's some near-term date. But that's not even perhaps important. As long as it's a large note, you're a graphic area. So what, how copy-pasteable is that processed currently? And how, like, when you copy and paste in Google Docs, I think, you know, in Word and Word, you can like apply source formatting or apply destination formatting.

So when you copy and paste the Phoenix into, like, say Boston, how do you apply the destination formatting? Like, how much of the core of the entire process of bringing an actual public transportation, autonomous transportation service to a city is there in Phoenix that you understand enough to copy and paste into Boston or wherever. So we're not quite there yet. We're not at a point where we're kind of massively copy and pasting all over the place. But Phoenix, we didn't Phoenix and we very intentionally have chosen Phoenix as our first full deployment area,

exactly for that reason, to kind of tease the problem apart. Look at each dimension, focus on the fundamentals of complexity and de-risking those dimensions, and then bringing the entire thing together to get all the way and force ourselves to learn all those hard lessons on technology, hardware and software, on the evaluation deployment, on operating a service, operating the business,

using actually serving our customers, all the way so that we're fully informed about the most difficult, most important challenges to get us to that next step of massive copy and pasting, as you said. That's what we're doing right now. We're incorporating all those things that we learned into that next system that then will allow us to copy and paste it all over the place and to massively scale to more

users and more locations. I mean, you know, just talked a little bit about, what does that mean along those different dimensions? So on the hardware side, for example, again, it's that switch from the fourth to the fifth generation. And the fifth generation is designed to kind of have that property. Can you say what other cities you're thinking about? Like, I'm thinking about, sorry, I'm going to San Francisco now. I thought I want to move to San Francisco, but I'm thinking about moving to Austin. I don't know why. People are not being very nice about San Francisco, currently, maybe it's a small, maybe it's in Vogue right now. But Austin seems, I visited there and it was

I was in a Walmart. It's funny, these moments, like, turn your life. There's this very nice these moments like turn your life. There's this very nice woman with kind eyes. Just like stopped and said he looks so handsome in that tie honey to me. This is never happening to me in my life, but just the sweetness of this woman. It's something I've never experienced. Certainly in the streets of Boston, but even in San Francisco where people wouldn't, it's just not how they speak or think. I don't know. There's a warmth to Austin that love. And since Waymo does have a little bit of a history there, is that a possibility?

Is this your version of asking the question of like, you know, Demetri, I know you can't share your commercial and deployment road map. But I'm thinking about moving to the San Francisco of Austin, like, you know, blink twice if you think I should move to. Yeah, that's true. That's true. You got me. We've been testing all over the place.

I think we've been testing more than 25 cities. We drive in San Francisco. We drive in Michigan for snow. We are doing significant amount of testing in the Bay Area and cleaning San Francisco. But it's not like, because we're talking about the various different things, which is like a full-on, large geographic area, public service. You can't share. Okay.

What about Moscow? When is that happening? Take on Yandex, not paying attention to those folks. They're doing, you know, there's a lot of fun. I mean, maybe as a way of a question, you didn't speak to sort of like policy or like, is there tricky things with government and so on? like Is there other friction that you've encountered except sort of technological friction of solving this very difficult problem? Is there other stuff that you have to overcome?

When when deploying a public service in a city? That's interesting. It's very important. So we put significant effort in when deploying a public service in a city. That's interesting. It's very important. So we put significant effort in creating those partnerships, and those relationships with governments at all levels, local governments, municipalities, state level, federal level, we've been engaged in very deep conversations from the earliest days of our projects

whenever at all of these levels, whenever we go to test or operate in a new area, we always lead with the conversation with the local officials. And but the result of that, that investment is that, no, it's not challenges we have to overcome, but it isn't very important that we continue to have this conversation. Oh, yeah. Hello politicians, too.

Okay. So, Mr. Elon Musk said that Lidar is a crutch. What are your thoughts? I wouldn't characterize it exactly that way. I know. I think think light is very important. It is a key sensor that we use just like other modalities. As we discussed, our cars use cameras, lighters and radars. They are all very important.

They are at the physical level. They are very different. I have very different physical characteristics. Cameras are passive, wide-reson, radars are active, use different wavelengths. So that means they complement each other very nicely. And they together combined, they can be used to build a much safer and much more capable system. So, to me, it's more of a question, why the heck would you hand to kept yourself and not use one or more of those sensing modalities when they undoubtedly just make your system more capable and safe for it. Now, it, you know, what might make sense for one product or one business might not make sense for another one. So, if you're talking about driver assist technologies, you make certain design decisions and you make certain trade-offs and you make different ones if you are, you know, building a driver that deploy and fully driverless vehicles.

And you know, and the lighters specifically when this question comes up, I, you know, typically the criticisms that I hear or, you know, the counterpoints that cost and aesthetics. And I don't find either of those, honestly, very compelling. So on the cost side, there's nothing fundamentally prohibitive about the cost of lighters. Radars used to be very expensive before people started,

before people made certain advances in technology and you started to manufacture them, massive scale and deployment vehicles, right? Similar with lighters. And this is where the lighters that we have on our cars, especially the fifth generation, we've been able to make some pretty qualitative discontinuous jumps in terms of the fundamental technology that allow us to manufacture those things at very significant scale and add a fraction of the cost of both our previous generation as well as a fraction of the cost of what might be available on the market off the shelf right now and that improvement will continue. So I think cost is not a real issue. Second one is aesthetics. I don't think that's a real issue either. You do this in the eye that beholder. You can make lidar sexy again.

I think you're exactly right. I think it's sexy. I think form, I always thought. It's function. Well, okay. You know, I always actually somebody brought this up to me. I mean, all forms of lidar, even like the ones that are big, you can make look beautiful.

There's no sense in which you can't integrate into design. There's all kinds of awesome designs. I don't think small and humble is beautiful. It could be brutalism or it could be harsh or like, it could be like harsh corn. I mean, like I said, like hot rods. Like I don't like, I don't necessarily like, like, oh man, I'm gonna start so much controversy with this. I don't like porches.

Okay, the Porsche 911, like everyone says the most beautiful, no, no, it's like a baby car. It doesn't make any sense. But everyone, it's beauty's a night that the holder, you're already looking at me like, what's this kid talking about? I'm happy to talk about. You're digging your own hole.

So the form and function and my take on the beauty of the hardware that we put on our vehicles, I will not comment on your Porsche monologue. Okay, all right, I will not comment on the Porsche monologue. Okay. All right. So, but aesthetics find, but there's an underlying like philosophical question behind the kind of lighter question is like, how much of the problem can be solved with computer vision with machine learning? So I think without sort of

the machine learning. So I think without sort of disagreements and so on, it's nice to put it on the spectrum because we're most doing a lot of machine learning as well. It's interesting to think how much of driving if we look at five years, 10 years, 50 years down the road, what can be learned in almost more and more and more end to end way. If you look at what Tesla is doing with as a machine learning problem, they're doing a multitask learning thing where it's just they break up driving into a bunch of learning tasks and they have one single neural network and they're just collecting huge amounts of data that's training that. Everything hung out with George Hots. I don't know if you know George.

I love him so much. He's just an entertaining human being. We were off mic talking about Hunter S. Thompson. He's the Hunter S. Thompson of a time of striding. Okay, so he, I didn't realize this with Kama AI, but they're like really trying to end to end. They're the machine, like looking at the machine learning problem. They're really not doing multitask learning,

but it's computing the drivable area as a machine learning task. And hoping that like down the line, this level two system that's driver assistance will eventually lead to allow you to have a fully autonomous vehicle. Okay, there's an underlying deep philosophical question, there are technical questions of how much of driving can be learned. So light is an effective tool today for actually deploying a successful service in Phoenix,

right, that's safe, that's reliable, etc. But the question, and I'm not saying you can't do machine learning in LiDAR, but the question is that how much of driving can be learned eventually? Can we do fully autonomous, that's learned? Learning is all over the place, and play is a key role in every part of our system. As you said, I would decouple the sensing modalities from the ML and the software parts of it. Lider, radar, cameras, it's all machine learning. All of the object detection classification, of course, like, well, that's what these modern deep nuts and kind of nuts are very good at.

You feed them raw data, massive amounts of raw data. And that's actually what our custom build lighters and radars are really good at. And radars, they don't just give you point estimates of objects in space, they give you raw physical observations. And then you take all of that raw information, there's colors of the pixels, whether it's lighters returns and some auxiliary information, it's not just distance, right?

And, you know, angle and distance, there's much richer information that you get from those returns, plus really rich information from the raiders. You fuse it all together, and you feed it into those massive ML models that then, you know, lead to the best results in terms of, you know, object detection,

classification, you know, state estimation. So there's a site to interrupt, but there is a fusion. I mean, that's something that people didn't do for a very long time, which is at the sensor fusion level, I guess, early on fusing the information together, whether so that the sensory information at the vehicle receives from the different modalities, or even from different different cameras is combined before it is fed into the machine learning models. Yeah, so I think this is one of the trends. You're seeing more of that. You mentioned N10. There's different interpretation of N10. There is kind of the purest interpretation. I'm going to like have one model that goes from raw sensor data to like,

you know, steering torque and, you know, gas breaks. That, you know, that's too much. I don't think that's the right way to do it. There's more, you know, smaller versions of N10, where you're, you know, kind of doing more N2N learning or core training or depropagation of kind of signals back and forth across the different stages of your system. There's, you know, really good ways, it gets into some pretty fairly complex design choices, where, on on one hand you want modularity and the composability, the composability of your system. But on the other hand, you don't want to create interfaces that are too narrow or too brittle to engineered, where you're giving up on the generality of a solution,

or you're unable to properly propagate signal, you know, reach signal forward, and losses, and you know, bass back so you can optimize the whole system jointly. So I would decouple and I guess what you're seeing in terms of the fusion of the sensing data from different modalities as well as kind of fusion at in the temporal level going more from, you know, frame by frame, where, you know, you would have one net that would do frame by frame detection camera and then, you know, something that does frame-by-frame in the lighter

and then the radar. And then you fuse it in a weaker, engineered way later. The field over the last decade has been evolving in more joined fusion, more into end models that are solving some of these tasks jointly. And there's tremendous power in that. And that's the progression that our stack has been on as well.

Now, so I your, you know, that's, so I would decouple the sensing and how that information is used from the role of ML and the entire stack. And, you know, I guess it's, I, there's trade-offs. And, you know, modularity and how do you inject inductive bias into your system? All right, this is, there's tremendous power in being able to do that.

So we have, there's no part of our system that does not heavily leverage data-driven development or state of the Artemel. But there's mapping, there's a simulator, there's perception, object level, perception, whether it's semantic understanding, there's perception, you know, object level, you know, perception, whether it's semantic understanding prediction decision making, you know, so for this on. It's, and of course, object detection and cleft specification, like in finding pedestrians and cars and cyclists and, you know, cones and signs and vegetation and being very good at estimating kind of detection classification and state estimation, there's just stable stakes. very good at estimating detection classification and state estimation, there's just stable stakes.

That's step zero of this whole stack. You can be incredibly good at that, whether you use cameras or light as a radar, but there's just that stable stakes. That's just step zero. Beyond that, you get into the really interesting challenges of semantic understanding, the perception level. You get into scene level reasoning. You get into very deep problems that have to do with prediction and joint prediction and

interaction, so do interaction between all of the actors in the environment, pedestrian, cyclists, all the cars, and get into decision making. Right? So how do you build a lot of systems? So we leverage ML very heavily in all of these components. I do believe that the best results you achieve by kind of using a hybrid approach and having different types of ML, having different models with different degrees of inductive bias that you can have. And combining model free approaches with some model-based approaches and some

rule-based, physics-based systems. So one example I can give you is traffic lights. There's problem of the detection of traffic lights state, and obviously that's a great problem for computer vision. Confidence, that's their bread and butter. That's how you build that. But then the interpretation of a traffic light, that you're going to need to learn that, right?

You don't need to build some, you know, complex ML model that, you know, infers with some, you know, precision and recall that red means stop. Like, it was a, it's a very clear engineer signal with very clear semantics, right? So you wanna induce that bias. Like, how you induce that bias and that, whether, you know, it's a constraint or a cost function in your stack, but it is

important to be able to inject that clear semantic signal into your stack. And that's what we do. But then the question of like, and that's when you apply it to yourself. When you are making decisions, whether you want to stop for a red light or not. But if you think about how other people treat traffic lights, we're back to the ML version of that. You know they're supposed to stop for a red light, but that doesn't mean that we'll.

So then you're back in the very heavy ML domain where you're picking up on very subtle keys about, they have to do with the behavior of objects, pedestrians, cyclists, cars, and the whole thing, you know, the entire configuration of the scene that allow you to make accurate predictions on whether they will in fact stop or run a red light. So it sounds like already for Waymo, like machine learning is a huge part of the stack. So it's a huge part of like, not just, so obviously the first level zero or whatever you said,

which is like just the object detection of things that, you know, with no machine learning can do, but also starting to do prediction, behavior, and so on to model the, what other parties in the scene, entities in the scene are gonna do. So machine learning is more and more playing a role in that as well. Of course, when.

Oh, absolutely. I think we've been going back to the earliest days, like DARPA Grand Challenge. And team was leveraging machine learning. I was like pre-image nut. And I was very different type of ML. And I think actually it was before my time, but the Stanford team during the Grand Challenge

had a very interesting machine-learned system that would use LiDAR and camera, when driving in the desert. And we had built the model where it would kind of extend the range of free space reasoning. So we get a clear signal from LiDAR, and then it had a model that, hey, like this stuff in camera, kind of sort of looks like this stuff in LiDAR. and I know this stuff that I've seen in LIDAR,

I'm very confident that it's free space. So let me extend that free space zone into the camera range that would allow the vehicle to drive faster. And then we've been building on top of that and kind of staying and pushing the state of the art in ML in all kinds of different ML over the years. And in fact, from the earliest days, I think, you know, 2010, it's probably the year where Google, maybe 2011, probably, got pretty heavily involved in machine learning,

kind of deep nuts. And at that time, it was probably the only company that was very heavily investing in state of the art, ML, and self-driving cars. And they go hand-in-hand. And we've been on that journey ever since. We're doing pushing a lot of these areas in terms of research at Waymo, and we're collaborating very heavily with the researchers in alphabet, and all kinds of ML. Supervised ML, unsupervised ML, published some interesting research papers in the space, especially

recently. It's just a super, super active learning as well. Yeah, super, super active. Of course, there is kind of the more mature stuff, like, you know, convenettes for, you know, object detection, but there's some really, really, really active work that's happening and kind of more, you know, and bigger models and, you know, models that have more structure to them, you know, not just large bitmaps and reason about temporal sequences. And some of the interesting breakthroughs that you've, you know, we've seen in language models, right, you know, transformers, you know, GPT, you know, three unfriends. There's some really interesting applications of some of the core breakthroughs to those problems

of behavior prediction as well as, you know, decision making and planning, right? You can think about it kind of the behavior, how, you know, the path of trajectories, the how people drive, they have kind of a share a lot of the fundamental structure, you know, this problem. There's, you know, sequential, you know, nature, there's a lot of structure. In this representation, there is a strong locality, kind of like in sentences, you know,

words that follow each other, they're strongly connected, but they're also kind of larger context that doesn't have that locality, and you also see that in driving, right? What, you know, it's happening in the scene as a whole has very strong implications on, you know, the kind of the next step in that sequence where whether you're predicting what other people are going to do, whether you're making your own decisions, or whether in the simulator, you're building generative models of, you know, humans walking, cyclists riding, the cars driving. That's all really fascinating.

Like how is fascinating to think that transform models and all the break-throughs and language, an NLP that might be applicable to like driving at the higher level, at the behavior level, that's kind of fascinating. Let me ask about pesky little creatures called pedestrians and cyclists. They seem so humans are a problem if we can get rid of them I would.

But unfortunately, they're all sort of a source of joy and love and beauty. So let's keep them around. They're also our customers for your perspective. Yes. Yes. For sure. There's some money. Very good.

But I don't even know where I was going. Oh, yes. Pedestrians and cyclists. I, you know, they're a fascinating injection into the system of uncertainty of, I was like a game theoretic dance of what to do. And also they have perceptions of their own and they can tweet about your product.

So you don't want to run them over from that perspective. I mean, I don't know. I'm joking a lot, but I think in seriousness, like pedestrians are complicated, computer vision problem, complicated behavioral problem, is there something interesting you could say about what you've learned from a machine learning perspective, from also an autonomous vehicle, and a product perspective about just interacting with the humans in this world? Yeah, just, you know, just stayed on record.

We cared deeply about the safety of pedestrians, you know, even the ones that don't have Twitter accounts. Thank you. All right. Cool. You know, not me. But, yes, I'm glad, I'm glad somebody does. Okay.

But in all seriousness, safety of vulnerable road users, pedestrians or cyclists is one of our highest priorities. We do a tremendous amount of testing and validation and put a very significant emphasis on the capabilities of our systems that have to do with safety or route those unprotected vulnerable road users. Cars just discussed earlier in Phoenix, we have completely empty cars, completely drive those cars, driving in this very large area and some people use people using them to go to schools. So they'll drive through school zones. Kids are the very special class of those vulnerable user users. You want to be super, super safe and super, super cautious around those. So we take a very, very, very seriously. And what does it take to be good at it. An incredible amount of performance across your whole stack. It starts

with hardware. Again, you want to use old sensing of modalities available to you. Imagine driving on a residential road at night and making a turn. You don't have headlights covering some part of the space. A kid you know, a kid might run out. And, you know, lighters are amazing at that. They see just as well in complete darkness as they do during the day, right? So just again, it gives you that extra margin in terms of capability and performance and safety and quality.

And in fact, we oftentimes, in these kinds of situations, we have our system detects something, in some cases, even earlier, than our trade operators in the car might do, especially in conditions like, very dark nights. So, starts with sensing, then, you know, perception has to be incredibly good. And you have to be very, very good at detecting pedestrians

in all kinds of situations and all kinds of environments including people in weird poses, people kind of running around and being partially occluded. So that's stuck number one. Then you have to have in very high accuracy and very low latency in terms of your reactions to what these actors might do. And we've put a tremendous amount of engineering and tremendous amount of validation into make sure our system performs properly. And oftentimes it does require a very strong reaction to do the safe thing.

And we actually see a lot of cases like that. The long tail of really rare, really crazy events that contribute to the safety around pedestrians. One one example that comes to mind that we actually happened in Phoenix where we were driving along and I think it was a 45 mile per hour road, so you're pretty high speed traffic and there was a sidewalk.

Next to it, and there was a cyclist on the sidewalk. And as we were in the right lane, right next to the sidewalk, it was a multi-lane road. So as we got close to the cyclist on the sidewalk, it was a woman, she tripped and fell. She just fell right into the path of our vehicle. And our car, this was actually a test driver, our test drivers, did exactly the right thing. They kind of reacted and came to stop. It requires both very strong steering

and strong application of the brake. And then we simulated what our system would have done in that situation. And it did exactly the same thing. And that speaks to all of those components of really good state estimation and tracking. And you can imagine a person on a bike and they're falling over and they're doing that right in front of you. So you have to be really like things of changing. The appearance of that whole thing is changing, right? And the person goes one way, they're falling on the road, they're being flat on the ground in front of you. The bike goes flying the other direction. Like the two objects that used to be one there now are splitting apart and the car has to detect all of that. And like milliseconds matter. And it doesn't, it's not good enough to just break.

You have to steer and break in their straffical round you. So like it all has to come together. And it was really great to see in this case and other cases like that that we're actually seeing in the wild. That our system is, you know, performing exactly the way that we would have liked and is able to, you know, avoid collisions like this. It's such an exciting space for robotics. Like in that split second to make decisions of life and death, I don't know. The stakes are high in the sense, but it's also beautiful that for somebody who loves artificial intelligence, the possibility that an AI system might be

able to save a human life, that's kind of exciting, as a problem like to wake up. It's terrifying probably for an engineer to wake up and to think about, but it's also exciting, because it's in your hands. Let me try to ask a question that's often brought up about autonomous vehicles. And it might be fun to see if you have anything interesting to say, which is about the trolley problem. So the trolley problem. So a trolley problem is

an interesting philosophical construct of that highlights and there's many others like it of the difficult ethical decisions that we humans have before us in this complicated world. So the specifically is the choice between if you were forced to choose to kill a group X of people versus a good Y of people like one person. If you didn't, if you did nothing, you would kill one person, but if you would kill five people and if you decide to swear by the way, you would only kill one person, do you do nothing or you choose to do something, and you can construct all kinds of sort of ethical experiments of this kind that I think at least on a positive note inspire you to think about like introspect

what are the physics of our morality. And there's usually not good answers there. I think people love it because it's just an exciting thing to think about. I think people who build a ton of most vehicles usually roll their eyes because this is not this one as constructed, this like literally never comes up in reality. You never have to choose between killing one or two groups of people. But I wonder if you can speak to, is there something interesting to use an engineer of autonomous vehicles that's within the trolley problem, or maybe more generally, are there difficult,

ethical decisions that you find that algorithm must make? On the specific version of the trolley problem, which one would you do? If you're driving. The question itself is a profound question because we humans ourselves cannot answer it, and that's the very point. I guess both's it.

I would kill both. Um, yeah, humans, I think you're exactly right. And that, you know, humans are not particularly good. I think they kind of phrased it. So like what would a computer do? But like humans are not very good. And not actually oftentimes think that, you know, freezing and kind of not doing anything because like you've taken

a few extra milliseconds to just process and then you end up by doing the worst of the loss of blowout comes. I do think that as you've pointed out, it can be a bit of a distraction and it can be a bit of a red herring. I think it's an interesting, fullest discussion in the realm of philosophy, but in terms of what, how that affects the actual engineering and deployment of self-driving vehicles,

it's not how you go about building a system. We've talked about how you engineer a system, how you go about evaluating the different components and the safety of the entire thing. How do you kind of inject the various model-based, safety-based arenas? And you're like, yes, you reason it parts the system. You reason about the probability of a collision, the severity of that collision, right? And that is incorporated. And there's, you know, you have to properly reason about uncertainty that flows through the system, right? So, you know, those, you know, factors definitely play a role in how the cars then behave, but they tend to be more of them, immersion behavior. And what you can see, you're absolutely right, that these clear

theoretical problems that they, you know, you don't occur that in system, and really kind of being back to our previous discussion of like, what, you know, which one do you choose? Well, you know, oftentimes, you made a mistake earlier. Like, it shouldn't be in that situation in the first place, right? And in reality, the system comes up. If you build a very good, safe and capable driver, you have enough clues in the environment that you drive defensively, so you don't put yourself in that situation, right? And again, you know, this, if you go back to that analogy of, you know, precision and recoil, like, okay, you can make it, you know, very hard trade-off off, do I want, but like, neither answer is really good. But what instead you focus on is kind of moving

the whole curve up, and then you focus on building the right capability and the right defensive driving, so that, you know, you don't put yourself in the situation like this. I don't know if you have a good answer for this, but people love it when I ask this question about books. Are there books in your life that you've enjoyed philosophical, fiction, technical that had a big impact on you as an engineer or as a human being? You know, everything from science fiction to a favorite textbook. Is there three books that stand out that you can think of? Three books. So I would, you know, that impacted me. I would say,

this one is, you probably know it well, but not generally well-known. I think in the US are kind of internationally the master and margarita. It's one of actually my favorite books. It is by Russian, so a novel by Russian author, you play a little gawk of, and it's just, it's a great book. It's one of those books that you can reread your entire life, and it's very accessible. You can read it as a kid, and it's you know it's you know the plot is interesting it's you know the the devil you know visiting the Soviet Union and you know but it it like you read it reread it at different stages of your life and you you enjoy it for different very different reasons and you keep finding like deeper and deeper meaning and you know kind of affected you know I hadn't definitely had an like imprint on me, mainly mostly from the, I've probably kind of the cultural stylistic aspect, like it makes you through one of those books that is good and makes you think, but also has like this really,

you know, silly quirky dark sense of humor. Hey, it captures the Russian soul. That's more than maybe perhaps many other books. And that slight note, just out of curiosity. One of the saddest things is I've read that book in English. Did you buy a chance we did in English or in Russian? In Russian, only in Russian. And actually, that is a question I had. I've posted myself every once in a while and I wonder how well it translates. If it translates at all, and there's the language aspect of it, and then there's the cultural aspect. So actually, I'm not sure if either of those would work well in English. Now, I forget their names, but so when the COVID lists a little bit, I'm traveling to Paris for several reasons. One is just that I've never been in Paris. I want to go to Paris, but there's the most famous translators of the St. Oscar Toly tolstoy of most of Russian literature lived there

There's a couple they're famous a man and woman and I'm gonna sort of have a series of conversations with them and In preparation for that I'm starting to read the St. Askiy in Russian so I'm really embarrassed to say that I read this everything I've read a Russian literature of like serious depth has been in English. Even though I can also read, I mean obviously in Russian, but for some reason it seemed in the optimization of life, it seemed the improper decision to do to read Russian. Like I don't need to think in English, not in Russian, but now I'm changing my mind on that. And so the question of how well it translate

is a really fun method one, like, even with Dusty Yasky. So for what I understand, the Steyevsky Translates is easier. Others don't as much. Obviously, the poetry doesn't translate as well. I'm also the music of a big fan of Vladimir Vosotsky. He doesn't obviously translate well. People have tried.

But Mastermind, I don't know about that one. I just know it in English. It was fun as hell in English. So, but it's a curious question and I wanna study rigorously from both a machine learning aspect and also because I want to study it rigorously from both a machine learning aspect and also because I want to do a couple of interviews in Russia that I'm still unsure of how to properly conduct an interview

across a language barrier. It's a fascinating question that ultimately communicates to an American audience. There's a few Russian people that I think are truly special human beings. And I feel like I sometimes encounter this with some incredible scientists, and maybe you encounter this as well at some point

in your life that it feels like because of the language barrier, their ideas are lost to history. It's a sad thing. I think about like Chinese scientists or even authors that like that we don't in English-speaking world don't get to appreciate some like the depth of the culture because it's lost in translation. And I feel like I would love to show that to the world. Like I'm just somebody yet, but because I have this, like at least some semblance of skill in speaking Russian, I feel like, and I know how to record stuff on a video camera. I feel like I wanna catch Gregor E. Perlman,

who's a mathematician, I'm not sure if he familiar with him. I wanna talk to, like he's a fascinating mind and to bring him to a wider audience in English speaking. It'll be fascinating, but that requires to be rigorous about this question of how well Bulgakov translates. I mean, I know it's a silly concept, but it's a fundamental one because how do you translate? And that's the thing that Google Translate is also facing as a more machine learning problem,

but I wonder as a more bigger problem for AI, how do we capture the magic that's there in the language? I think that's really interesting, really challenging problem. If you do read it, Master in Margarita in English, sorry, in Russian, I'm curious. You get your opinion. And I think part of it is language, but part of it is just centuries of culture. The cultures are different, so it's hard to connect that. Okay, so that was my first one. You had two more.

The second one I would probably pick the science fiction by the Strogoski brothers. It's up there with Isaac Asimov and Ray Bradbury and the company. The Strogoski brothers appealed more to me. I think more it made more of an impression on me growing up. Can you applaud as if I'm showing my complete ignorance? I'm so weak on sci-fi. What are they right? Oh, roadside picnic Hard to be a god

Beetle in an end hill Monday starts in Saturday like it's not just science fiction. It's also like has very interesting, you know, interpersonal and societal questions and some of the old languages just completely collerious. That's the one. That's all interesting Monday starts in Saturday. So I need to read, okay, boy,

you put that in a category of science fiction. That one is, I mean, this was more of a silly, you know, humorous work. I mean, there is good of profound too, right? Science fiction, right? It's about, you know, this research institute and like it's it, it has deep parallels to like serious research, but the setting, of course, is that they're working on magic. There's a lot of... And that's their style. Other books are very different.

Hard to be a god, it's about this higher society being injected into this primitive world and how they operate there. Some of the very deep ethical questions there. They've got this spectrum sum, more about more adventure style. I enjoy all of their books. There's probably a couple. Actually one, I think that they consider their most important work. I think it's the snail on a hill.

I don't know exactly how to show how to translate. I tried reading a couple of times. They still don't get it. But everything else I fully enjoyed. For one of my birthdays as a kid, I still don't get it, but everything else, I fully enjoyed. And for one of my births, this is a kid I got their entire collection, occupied a giant shelf in my room, and then over the holidays, my parents couldn't drag me out of the room, and I read the whole thing covered to cover.

And it really enjoyed it. And that's it one more. For the third one, maybe a little bit darker, but it comes to mind as Orwell's 1984. And you know, I asked what made an impression on me and the books that people should read. That one I think falls in the category of both.

And you know, definitely is one of those books that you read. And you just kind of put it down and you stare in space for a while. Yeah, that kind of work. I think there's lessons there. People should not ignore. And nowadays, everything that's happening in the world, I can't help it, but I have my mind jump to some, in apparelus, with what Orwell described.

And like this, this whole concept of double think and ignoring logic and holding completely contradictory opinions in your mind and not have that not bother you and stick into the party line at all costs like you know there's something there. And I'm a huge fan of animal farm, which is a kind of friendly as a friend of 1984 by Orwell. It's kind of another thought experiment of how our society may go in directions that we wouldn't like it to go. But if anything that's been kind of heartbreaking to an optimist about 2020, is that society's kind of fragile. Like we have this, this is a special little experiment we have going on, and it's not unbreakable. Like we should be careful to like preserve whatever the special thing we have going on.

I mean, I think 1984 and in these books, Brave New World, they're hopeful in thinking like, stuff can go wrong in non-obvious ways. And it's like, it's up to us to preserve it. And it's like, it's a responsibility. It's been weighing heavy on me because, like, for some reason, like, more than my mom follows me on Twitter and I feel like I have now some of our responsibility to do this world.

And it dawned on me that me and millions of others are the little ants that maintain this little colony, right? So we have a responsibility not to be, I don't know what the right analogy is, but put a flame throw over to the place. We want to not do that. And there's interesting complicated ways of doing that as 1984 shows. It could be through bureaucracy, it could be through incompetence, it could be through misinformation, it could be through division and toxicity. I'm a huge believer in that love will be somehow the solution. So love and robots. I think you're right.

Unfortunately, I think it's less of a flamed thrower type of next to it. I think it's more of a, in many cases, it can be more of a slow boil and that's the danger. Let me ask, it's a fun thing to make a world-class robot-assist engineer and leader uncomfortable with a ridiculous question about life. What is the meaning of life, to me, tree, from a robotics and a human perspective? You only have a couple of minutes or one minute to answer, so. I don't know.

I don't know if that makes it more difficult or easier, actually. Yeah. Yeah. You know, they're very tempted to quote one of the stories, stories by Isaac Asimov, actually, actually titled the properly titled the last question, short story, where the plot is that humans build the supercomputer, this AI intelligence, and once it gets powerful enough, they pose this question to it.

How can the entropy in the universe be reduced? So the computer replies, and as of yet, insufficient information to give a meaningful answer. And then thousands of years go by, and they keep posing the same question. And the computer gets more and more powerful, and keeps giving the same answer. As of yet, insufficient information

to give a meaningful answer are something along those lines. And then it keeps happening and happening you fast forward, like millions of years into the future, and billions of years. And like at some point it's just the only entity in the universe, it's like sub-sort all humanity and all knowledge in the universe. And it like keeps posing the same question to itself.

And finally it gets to the point where it is able to answer that question. But of course at that point, there's the heat death of the universe has occurred and that's the only entity. And there's nobody else to provide that answer to. So the only thing it can do is to, you know, answer it by demonstration. So it like, you know, recreates the big bang right and resets the clock, right? But I, you know, I can try to give kind of a, a different version of the answer, you

know, maybe not on the behalf of all humanity, I think that that might be a little presumptuous for me to speak about the meaning of life on behalf of all humans, but at least personally, it changes. I think if you think about it, kind of what gives you, you know, you and your life meaning and purpose

and kind of what drives you, it seems to change over time, right? And the lifespan of your existence, you know, when you just enter this world, it's all about kind of new experiences, right? You get like new smells, new sounds, new emotions, right? And like, that's what's driving you, right? You're experiencing new amazing things, right? And that's magical, right? That's pretty, pretty, pretty awesome, right? That gives you greater meaning.

Then you get a little bit older, you start more intentionally learning about things, right? I guess actually, before you start intentionally learning, it's probably fun. Fun is a thing that gives you a good meaning at purpose and purpose, and the thing you optimize for, right?'s probably fun. Fun is a thing that gives you a meaning and purpose and purpose and the thing you optimize for, right? And fun is good.

Then you get, you know, start learning. And I guess that this joy of comprehension and discovery is another thing that gives you meaning and purpose and drives you, right? Then, you know, you learn enough stuff enough stuff and you want to give some of the back, right? And so impact and contributions back to technology or society, people, local or more globally is, becomes a new thing that drives a lot of your behavior and something that gives you purpose and that you derive positive feedback from. Then you go, and so on and so forth.

You go three areas stages of life. If you have kids, that definitely changes your perspective on things. I have three that definitely flips some bits in your head in terms of what you care about and what you optimize for and what matters, what doesn't matter. So, and so on and so forth. And it seems to me that it's all of those things. And as you go through a live, you want these to be additive, new experiences, fun, learning, impact, like you want to be accumulating.

Although, you know, I don't want to, you know, stop having fun or, you know, experiencing new things. And I think it's important that, you know, just kind of becomes, uh, additive as opposed to a replacement or subtraction. But, you know, those few as prog as far as I got, but you know, ask me in a few years, I might have one or two more to add to the list. And before you know, it time is up, just like it is for this conversation. Uh, but hopefully

it was a fun ride. It was a huge honor to meet you as you know. I've been a fan of yours and a fan of Google self driving car and Waymo for a long time. I can't wait. I mean, it's one of the most exciting. If we look back in the 21st century, I truly believe it will be one of the most exciting things we descendants of Apes have created on this earth.

So I'm a huge fan and I can't wait to see what you do next. Thanks so much for talking to me. Thanks for having me. And it's also a huge fan. It doesn't work. Honestly, I really enjoyed it. Thank you. Thanks for listening to this conversation with me, Sri Dahlgav.

And thank you to our sponsors, Trial labs, a company that helps businesses apply machine learning to solve real world problems. Blinkist and App I Use for reading through summaries of books. Better help online therapy with a licensed professional and cash app. The App I Use said money to friends. Please check out these sponsors in the description to get a discount and to support this podcast. If you enjoyed this thing, subscribe on YouTube, review it with 5.000 on a podcast, follow on Spotify, support on Patreon, or connect with me on Twitter at Lex Friedman. And now, let me leave you with some words from Isaac Asimov. Science can amuse and fascinate us all, but it is engineering that changes the world.

Thank you for listening and hope to see you next time. Thank you.