Sebastian Thrun: Flying Cars, Autonomous Vehicles, and Education | Lex Fridman Podcast #59

The following is a conversation with Sebastian Thrun. He's one of the greatest roboticists, computer scientists and educators of our time. He led the development of the autonomous vehicles at Stanford that won the 2005 DARPA Grand Challenge and placed second in the 2007 DARPA Urban Challenge. He then led the Google Self Driving Car program which launched the self Driving Car revolution. He taught the popular Stanford course on artificial intelligence in 2011, which was one of the first massive open online courses, or MOOCs as they're commonly called. That experience led him to co found Udacity, an online education platform. If you haven't taken courses on it yet, I highly recommend it. Their self Driving Car program, for example, is excellent. He's also the CEO of Kitty Hawk, a company working on building flying cars or more technically, EVTOLs, which stands for electric vertical takeoff and landing aircraft. He has launched several revolutions and inspired millions of people. But also, as many know, he's just a really nice guy. It was an honor and a pleasure to talk with him. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it five stars on Apple Podcasts, follow it on Spotify, support on Patreon, or simply connect with me on Twitter lexfriedman spelled F R I D M A n. If you leave a review on Apple Podcasts or YouTube or Twitter, consider mentioning ideas, people, topics you find interesting. It helps guide the future of this podcast, but in general I just love comments with kindness and thoughtfulness in them. This podcast is a side project for me, as many people know, but I still put a lot of effort into it. So the positive words of support from an amazing community from you really help. I recently started doing ads at the end of the introduction. I'll do one or two minutes after introducing the episode and never any ads in the middle that can break the flow of the conversation. I hope that works for you and doesn't hurt the listening experience. I provide timestamps for the start of the conversation that you can skip to, but it helps if you listen to the ad and support this podcast by trying out the product or service being advertised. This show is presented by Cash App, the number one finance app in the App Store. I personally use Cash App to send money to friends, but you can also use it to buy, sell and deposit Bitcoin in just seconds. Cash App also has a new investing feature. You can buy fractions of a stock, say $1 worth no matter what the stock price is. Brokerage services are provided by Cash App Investing subsidiary of Square and Member sipc. I'm excited to be working with Cash App to support one of my favorite organizations called first.

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Do you think we're living in a simulation? And in general, do you find the thought experiment interesting?

Define simulation. I would say maybe we are, maybe we are not, but it's completely irrelevant to the way we should act.

Putting aside for a moment the. The fact that it might not have any impact on how we should act as human beings, for people studying theoretical physics, these kinds of questions might be kind of interesting. Looking at the universe's information processing system.

Universe is an information processing system. It's a huge physical, biological, chemical, computer. There's no question. But I live here and now I care about people or care about us.

What do you think is trying to compute?

I don't think there's an intention. I think the world evolves the way it evolves, and it's beautiful, it's unpredictable, and I'm really, really grateful to be alive.

Spoken like a true human.

Which, last time I checked, I was.

Or that in fact this whole conversation is just a Turing test to see if, if indeed, if indeed you are. You've also said that one of the first programs of the first few programs you've written was a wait for a TI57 calculator. Yeah, maybe that's early 80s. We don't want to date calculators or anything.

Early 80s, correct.

Yeah. So if you were to place yourself back into that time, into the mindset you were in, could you have predicted the evolution of computing, AI, the Internet technology in the decades that followed?

I was super fascinated by Silicon Valley, which I'd seen on television once and thought, my God, this is so cool. They build like drams there and CPUs, how cool is that? And as a college student a few years later, a few years later, I decided to study intelligence and study human beings and found that even back then, in the 80s and 90s, that artificial intelligence is what fascinated me the most. What's missing is that back in the day, the computers are really small. They're like the brains we could build were not anywhere bigger as a cockroach, and cockroaches aren't very smart. So we weren't at the scale yet where we are today.

Did you dream at that time to achieve the kind of scale we have today, or did that seem possible?

I always wanted to make robots smart, and I felt it was super cool to build an artificial human. And the best way to build an artificial human will to build a robot, because that's kind of the closest we could do. Unfortunately, we aren't there yet. The robots today are still very brittle. But it's fascinating to study intelligence from a constructive perspective, where you build something.

To understand, you build. What do you think it takes to build an intelligent system and an intelligent robot?

I think the biggest innovation that we've seen is machine learning. And it's the idea that their computers can basically teach themselves. Let's give an example. I'd say everybody pretty much knows how to walk, and we learn how to walk in the first year or two of our lives. But no scientist has ever been able to write down the rules of human gait. We don't understand that we have in our brain somehow we can practice it, we understand it, but we can't articulate it. We can't pass it on by language. And that, to me, is kind of the deficiency of today's computer programming. When you program a computer, they're so insanely dumb that you have to give them rules for every contingencies. Very unlike the way people learn, but learn from data and experience. Computers are being instructed. And because it's so hard to get this instruction set right, we Pay software engineers $200,000 a year. Now, the most recent innovation, which is actually in the make for like 30, 40 years, is an idea that computers can find their own rules so they can learn from falling down and getting up, the same way children can learn from falling down and getting up. And that revolution has led to a capability that's completely unmatched. Today's computers can watch experts do their jobs, whether you're a doctor or a lawyer, pick up the regularities, learn those rules, and then become as good as the best experts.

So the dream of in the 80s of expert systems, for example, had at its core the idea that humans could boil down their expertise on a sheet of paper. So to sort of reduce Sort of be able to explain to machines how to do something explicitly. So do you think what's the use of human expertise into this whole picture? Do you think most of the intelligence will come from machines learning from experience without human expertise input?

So the question for me is much more, how do you express expertise? You can express expertise by writing a book. You can express expertise by showing, showing someone what you're doing. You can express expertise by applying it by, by many different ways. And I think the expert systems was our best attempt in AI to capture expertise and rules, where someone sat down and say, here are the rules of human gait. Here's when you put your big toe forward and your heel backwards, and here how you stop stumbling. And as we now know, the set of rules, a set of language that we can command, is incredibly limited. The majority of the human brain doesn't deal with language. It deals with subconscious, numerical, perceptual things that we don't even self aware of. Now when a AI system watches an expert do their job and practice their job, it can pick up things that people can't even put into writing into books or rules. And that's where the real power is. We now have AI systems that for example, look over the shoulders of highly paid human doctors like dermatologists or radiologists, and they can somehow pick up those skills that no one can express in words.

So you were a key person in launching three revolutions, online education, autonomous vehicles and flying cars or VTOLs. So high level. And I apologize for all the philosophical.

Questions, no apology necessary.

How do you choose what problem to try and solve? What drives you to make those solutions a reality?

I have two, two desires in life. I want to literally make the lives of others better, or as we often say, maybe jokingly make the world a better place. I actually believe in this. It's as funny as it sounds. And second, I want to learn, I want to get in the skills. I don't want to be in a job I'm good at because if I'm in a job that I'm good at, the chances for me to learn something interesting is actually minimized. So I want to be in a job I'm bad at. That's really important to me. So, and I build, for example, what people often call flying cars. These are electrical vertical takeoff and landing vehicles. I'm just no expert in any of this. And it's so much fun to, to learn on the job what it actually means to build something like this. Now I'd say the stuff that I done lately After I finished my professorship at Stanford, they really focused on what has the maximum impact on society. Transportation is something that has transformed the 21st or 20th century more than any other invention, in my opinion. Even more than communication. And cities are different, work is different, women's rights are different because of transportation. And yet we still have a very suboptimal transportation solution where we kill 1.2 or so million people every year in traffic. It's like the leading cause of death for young people in many countries where we are extremely inefficient resource wise. Just go to your average neighborhood city and look at the number of parked cars. That's a travesty, in my opinion. Or where we spend endless hours in traffic jams. And very, very simple innovations like a self driving car, or what people call a flying car could completely change this. And it's there. I mean, the technology is basically there. You have to close your eyes not to see it.

So lingering on autonomous vehicles, a fascinating space, some incredible work you've done throughout your career there. So let's start with darpa. I think the DARPA Challenge, the desert and then urban to the streets. I think that inspired an entire generation of roboticists and obviously sprung this whole excitement about this particular kind of four wheeled robots we called autonomous cars, self driving cars. So you led the development of Stanley, the autonomous car that won the, the race to the desert, the Darpa Challenge in 2005. And Junior, the car that finished second in the Darpa Urban Challenge, also did incredibly well in 2007. I think what are some painful, inspiring or enlightening experiences from that time that stand out to you?

Oh my God. Painful were all these incredibly complicated stupid bugs that had to be found. We had a phase where the Stanley, our car that eventually won the Diaper grand challenge, would every 30 miles just commit suicide. And we didn't know why. And it ended up to be that in the sinking of two computer clocks, occasionally a clock went backwards and that negative time elapsed, screwed up the entire internal logic. But it took ages to find this. There were like bugs like that. I'd say enlightening is. The Stanford team immediately focused on machine learning and on software, whereas everybody else seemed to focus on building better hardware. Our analysis had been, look, a human being with an existing rental car can perfectly drive the course. Why do I have to build a better rental car? I just should replace the human being. And the human being to me was a conjunction of three steps. We had sensors, eyes and ears, mostly eyes. We had brains in the middle and then we had actuators, our hands and our feet. Now, the actuators are easy to build. The sensors are actually also easy to build. What was missing was the brain. So we had to build a human brain. And nothing clear than to me that the human brain is a learning machine. So why not just train our robots? We would build massive machine learning into our machine. And with that, we're able to not just learn from human drivers. We had the entire speed control of the vehicle was copied from human driving, but also have the robot learn from experience where it made a mistake and got to recover from it and learn from it.

You mentioned the pain point of software and clocks. Synchronization seems to be a problem that continues with robotics. It's a tricky one with drones and so on. What does it take to build a thing, a system with so many constraints? You have a deadline, no time, you're unsure about anything really. It's the first time that people are really even exploring. It's not even sure that anybody can finish. When we're talking about the race of the desert the year before, nobody finished, what does it take to scramble and finish a product that actually, a system that actually works?

I mean, we were very lucky. We were really small team. The core of the team were four people. It was four because five couldn't comfortably sit inside a car, but four could. And I as a team leader, my job was to get pizza for everybody and wash the car and stuff like this, and repair the radiator when it broke and debug the system. And we were very kind of open minded. We had no egos involved in this. We just wanted to see how far we can get. What we did really, really well was time management. We were done with everything a month before the race, and we froze the entire software a month before the race. And it turned out, looking at other teams, every other team complained if they had just one more week, they would have won. And we decided we're not going to fall into that mistake. We're going to be early. And we had an entire month to shake the system. And we actually found two or three minor bugs in the last month that we had to fix. And we were completely prepared when the race occurred.

Okay, so first of all, that's such an incredibly rare achievement in terms of being able to be done on time or ahead of time. How do you do that in your future work? What advice do you have in general? Because it seems to be so rare, especially in highly innovative projects like this, people work to the last second well.

The nice thing about the DARPA Gwen challenge is that the problem was incredibly well defined. Were able for a while to drive the old DARPA Challenge course, which had been used the year before. And then at some reason, we were kicked out of the region. So we had to go to different desert, the Sonoran Desert, and were able to drive desert trails just of the same type. So there was never any debate about, like, what is actually the problem. We didn't sit down and say, hey, should we build a car or a plane? We had to build a car. That made it very, very easy. Then I studied my own life and life of others and realized that the typical mistake that people make is that there's this kind of crazy bug left that they haven't found yet. And it's just they regret it. And the bug would have been trivial to fix. They just haven't fixed it yet. They didn't want to fall into that trap. So I built a testing team. We had a testing team that built a testing booklet of 160 pages of tests we had to go through just to make sure we shake out the system appropriately.

Wow.

And the testing team was with us all the time and dictated to us, today we do railroad crossings, tomorrow we do. We practice the start of the event. And in all of these, we thought, oh, my God, is long solved trivial. And then we tested it out. Oh, my God, it doesn't do railroad crossing. Why not? Oh, my God. It mistakes the rails for metal barriers. We have to fix this. Yes. So it was really a continuous focus on improving the weakest part of the system. And as long as you focus on improving the weakest part of the system, you eventually build a really great system.

Let me just pause on that. It's. To me, as an engineer, it's just super exciting that you were thinking like that, especially at that stage, as brilliant that testing was such a core part of it. Maybe to linger on the point of leadership. I think it's one of the first times you were really a leader, and you've led many very successful teams since then. What does it take to be a good leader?

I would say most of us just take credit for the work of others. That's very convenient. Turns out, because I can't do all these things myself, I'm an engineer at heart, so I care about engineering. I don't know what the chicken and the egg is, but as a kid, I loved computers because you could tell them to do something and they actually did it. It was very cool. And you could, in the Middle of the night, wake up at one in the morning and switch on your computer and what you told you to do yesterday, it would still do. That was really cool. Unfortunately, that didn't quite work with people. So you go to people and tell them what to do and they don't do it and they hate you for it, or you do it today, and then you go a day later and they stop doing it. So you have to. So then the question really became, how can you put yourself in the brain of people as opposed to computers? And in terms of computers are super dumb. That's so dumb. If people were as dumb as computers, I wouldn't want to work with them. But people are smart and people are emotional and people have pride and people have aspirations. So how can I connect to that? And that's the thing where most of leadership just fails. Because many, many engineers turn manager believe they can treat their team just the same way they can treat a computer. And it just doesn't work this way. It's just really bad. So how can I connect to people? And it turns out, as a college professor, the wonderful thing you do all the time is to empower other people. Your job is to make your students look great. That's all you do. You're the best coach. And it turns out if you do a fantastic job with making your students look great, they actually love you and their parents love you and they give you all the credit for stuff you don't deserve. All my students were smarter than me. All the great stuff invented at Stanford was their stuff, not my stuff. And they give me credit and say, oh, Sebastian. By just making them feel good about themselves. So the question really is, can you take a team of people and what does it take to make them to connect to what they actually want in life and turn this into productive action? It turns out every human being that I know has incredibly good intentions. I've really rarely met a person with bad intentions. I believe every person wants to contribute. I think every person I've met wants to help others. It's amazing how much of a urge we have not to just help ourselves, but to help others. So how can we empower people and give them the right framework that they can accomplish this in moments? When it works, it's magical because you'd see the confluence of people being able to make the world a better place and driving enormous confidence and pride out of this. And that's when my environment works the best. These are moments where I can disappear for a month and come back and things still Work, it's very hard to accomplish, but when it works, it's amazing.

So I agree with you very much. It's not often heard that most people in the world have good intentions at the core, their intentions are good and they're good people. That's a beautiful message. It's not often heard.

We make this mistake, and this is a friend of mine, Alex Voda, told me this, that we judge ourselves by our intentions and others by their actions. And I think the biggest skill, I mean, here in Silicon Valley, we're full of engineers who have very little empathy and are kind of befuddled why it doesn't work for them. The biggest skill, I think, that people should acquire is to put themselves into the position of the other and listen and listen to what the other has to say, and they'd be shocked how similar they are to themselves. And they might even be shocked how their own actions don't reflect their intentions. I often have conversations with engineers where I say, look, hey, I love you. You're doing a great job. And by the way, what you just did has the following effect. Are you aware of that? And then people would say, oh, my God. Not, I wasn't, because my intention was that. And say, yup, I trust your intention. You're a good human being. But just to help you in the future, if you keep expressing it that way, then people will just hate you. And I've had many instances where you say, oh, my God. Thank you for telling me this, because it wasn't my intention to look like an idiot. It wasn't my intention to help other people. I just didn't know how to do it. Very simple. By the way, There's Dale Carnegie, 1936. How to Make Friends and How to Influence Others has the entire Bible. You just read it and you're done. And you apply it every day. And I wish I could. I was good enough to apply it every day. But it says simple things, right? Like, be positive, remember people's names, smile, and eventually have empathy. Really think that the person that you hate and you think is an idiot is actually just like yourself. It's a person who's struggling, who means well and who might need help. And guess what? You need help.

I've recently spoken with Stephen Schwarzman. I'm not sure if you know who that is, but I do, so.

And he said it's on my list.

But he. He said sort of to expand on what you're saying, that one of the biggest things you can do is hear people when they tell you what their problem is and then help them with that problem. He says it's surprising how few people like actually listen to what troubles others.

Yeah.

And because it's right there in front of you and you can benefit the world the most and in fact, yourself and everybody around you by just hearing the problems and solving them.

I mean, that's my, my little history of, of engineering. That is, while I was engineering with computers, I didn't care at all what the computer's problems were. I just told them what to do and to do it. And it just doesn't work this way with people. It doesn't work with me. If you come to me and say do a, I do the opposite.

But let's return to the comfortable world of engineering. And can you, can you tell me in broad strokes in how you see it? Because you're at the core of starting it, the core of driving it. The technical evolution of autonomous vehicles from the first DARPA grand challenge to the incredible success we see with the program. You started with Google self driving car and Waymo and the entire industry that sprung up of different kinds of approaches, debates and so on.

Well, the idea of self driving car goes back to the 80s. There was a team in Germany, another team at Carnegie Mellon that did some very pioneering work. But back in the day, I'd say the computers were so deficient that even the best professors and engineers in the world basically stood no chance. It then folded into a phase where the US Government spent at least half a billion dollars that I could count on research projects. But the way the procurement works, a successful stack of paper describing lots of stuff that no one's ever going to read was a successful product of a research project. So we trained our researchers to produce lots of paper. That all changed with the DARPA grant challenge. And I really got to credit the ingenious people at DARPA and the US Government and Congress that took a complete new funding model where they said, let's not fund effort, let's fund outcomes. It sounds very trivial. But there was no tax code that allowed the use of congressional tax money for a price. It was all effort based. So if you put in 100 hours in, you could charge 100 hours. If you put in $1,000 in, you could bill 1,000 hours by changing the focus and say, making the price. We don't pay you for development, we pay you for the accomplishment. They drew in, they automatically drew out all these contractors who are used to the drug of getting money per hour, and they drew in a whole bunch of new People, and these people are mostly crazy people. They were people who had a car and a computer and they wanted to make a million bucks. The million bucks was the official price. Money was then doubled. And they felt if I put my computer in my car and program it, I can be rich. And that was so awesome. Like half the teams, there was a team that was surfer dudes and they had like two surfboards on their vehicle and brought like these fashion girls, super cute girls, like twin sisters. And you could tell these guys were not your common beltway bandit who gets all these big multimillion and billion dollar contracts from the US government. And there was a great reset. Universities moved in. I was very fortunate at Stanford that I'd just received tenure so I couldn't get fired whenever I wanted to, otherwise I wouldn't have done it. And I had enough money to finance this thing and I was able to attract a lot of money from third parties. And even car companies moved in, they kind of moved in very quietly because they were super scared to be embarrassed that their car would flip over. But Ford was there and Volkswagen was there and a few others and GM was there. So it kind of reset the entire landscape of people. And if you look at who's a big name in self driving cars today, these were mostly people who participated in those challenges.

Okay, that's incredible. Can you just comment quickly on your sense of lessons learned from that kind of funding model and the research that's going on in academia in terms of producing papers? Is there something to be learned and scaled up bigger, having these kinds of grand challenges that could improve outcomes?

So I'm a big believer in focusing on kind of an end to end system. I'm a really big believer in systems building. I've always built systems in my academic career, even though I do a lot of math and abstract stuff. But it's all derived from the idea of let's solve a real problem. And it's very hard for me to be an academic and say let me solve a component of a problem like with someone. There's fields like non monetary logic or AI planning systems where people believe that a certain style of problem solving is the ultimate end objective. And I would always turn it around and say, hey, what problem would my grandmother care about that doesn't understand computer technology and doesn't want to understand? And how could I make her love what I do because only then do I have an impact on the world. I can easily impress my colleagues, that is much easier. But impressing my grandmother is very, very hard So I always thought if I can build a self driving car and my grandmother can use it even after she loses her driving privileges, or children can use it, or we save maybe a million lives a year, that would be very impressive. And there's so many problems like these. There's a problem of curing cancer, whatever, live twice as long as. Once a problem is defined, of course I can't solve it in its entirety. It takes sometimes tens of thousands of people to find a solution. There's no way you can fund an army of 10,000 at Stanford. So you're going to build a prototype. Let's build a meaningful prototype. And the DARPA Grand Challenge was beautiful because it told me what this prototype had to do. I didn't have to think about what it had to do, I just had to read the rules. And that was really beautiful at its most beautiful.

You think what academia could esper aspire to is to build a prototype. That's the systems level that solves, gives you an inkling that this problem could be solved with this prototype.

First of all, I want to emphasize what academia really is and I think people misunderstand it. First and foremost, academia is a way to educate young people. First and foremost, a professor is an educator. No matter where you are at a small suburban college or whether you are a Harvard or Stanford professor. That's not the way most people think of themselves in academia because we have this kind of competition going on for citations and publication. That's a measurable thing. But that is secondary to the primary purpose of educating people to think now in terms of research. Most of the great science, the great research, comes out of universities. You can trace almost everything back, including Google, to universities. So there's nothing really fundamentally broken here. It's a good system. And I think America has the finest university system on the planet. We can talk about reach and how to reach people outside the system. It's a different topic, but the system itself is a good system. If I had one wish, I would say it'd be really great if there was more debate about what the great big problems are in society and focus on those. And most of them are interdisciplinary. Unfortunately, it's very easy to fall into a inner disciplinary viewpoint where your problem is dictated by what your closest colleagues believe the problem is. It's very hard to break out and say, well, there's an entire new field of problems. So give an example. Prior to me working on self driving cars, I was a roboticist and a machine learning expert and I wrote books on robotics, something called probabilistic robotics. It's a very methods driven kind of viewpoint of the world. I built robots that acted in museums as tour guides, that led children around. It's something that at the time was moderately challenging. When I started working on cars, several colleagues told me, sebastian, you're destroying your career. Because in our field of robotics, cars are looked like as a gimmick and they're not expressive enough. They can only push the throttle and the brakes. There's no dexterity, there's no complexity, it's just too simple. And no one came to me and said, wow, if you solve that problem, you can save a million lives. Among all robotic problems that I've seen in my life, I would say the self driving car transportation is the one that has the most hope for society. So how come the robotics community wasn't all over the place? And it was because we focus on methods, on solutions and not on problems. Like if you go around today and ask your grandmother what bugs you, what really makes you upset? I challenge any academic to do this and then realize how far your research is probably away from that today.

At the very least, that's a good thing for academics to deliberate on.

The other thing that's really nice in Silicon Valley is Silicon Valley is full of smart people outside academia. So there's the Larry Pages and Mark Zuckerbergs in the world who are anywhere smart or smarter than the best academics I've met in my life. And what they do is they are at a different level. They build the systems, they build the customer facing systems, they build things that people can use without technical education. And they are inspired by research, they're inspired by scientists, they hire the best PhDs from the best universities for a reason. So I think this vertical integration that between the real product, the real impact and the real thought, the real ideas, that's actually working surprisingly well in Silicon Valley. It did not work as well in other places in this nation. So when I worked at Carnegie Mellon, we had the world's finest computer science university. But there wasn't those people in Pittsburgh that would be able to take these very fine computer science ideas and turn them into massively impactful products. That symbiosis seemed to exist pretty much only in Silicon Valley and maybe a bit in Boston and Austin.

Yeah, with Stanford. That's really interesting. So if we look a little bit further on from the DARPA Grand Challenge and the launch of the Google self driving car, what do you see as the state the challenges of autonomous vehicles as they are now is Actually achieving that huge scale and having a huge impact on society.

I'm extremely proud of what has been accomplished and again, I'm taking a lot of credit for the workflows and I'm actually very optimistic. And people have been kind of worrying is it too fast, is it too slow, why is it not there yet? And so on. It is actually quite an interesting hard problem in that a self driving car to build, one that manages 90% of the problems encountered in everyday driving is easy. We can literally do this over a weekend. To do 99% might take a month, then there's 1% left. So 1% would mean that you still have a fatal accident every week. Very unacceptable. So now you work on this 1% and the 99% of that, the remaining 1% is actually still relatively easy, but now you're down to like a hundredth of 1% and that's still completely unacceptable in terms of safety. So the variety of things you encounter are just enormous. And that gives me enormous respect for human beings that we're able to deal with the couch on the highway, right, or the deer in the headlight, or the blown tire that we never been trained for and all of a sudden have to handle in an emergency situation and often do very, very successfully. It's amazing from that perspective how safe driving actually is given how many millions of miles we drive every year in this country. We are now at a point where I believe the technology is there. And I've seen it. I've seen it in Waymo, I've seen it in Aptif, I've seen it in Cruise, in a number of companies in Voyage, where vehicles now driving around and basically flawlessly are able to drive people around in limited scenarios. In fact, you can go to Vegas today and order a Summon and Lyft. And if you got the right setting of your app, you'll be picked up by a driverless car. Now there's still safety drivers in there, but that's a fantastic way to kind of learn what the limits are of technology today. And there's still some glitches, but the glitches have become very, very rare. I think the next step is going to be to down cost it, to harden it. The entrapment, the sensors are not quite an automotive grade standard yet. And then to really build the business models to really kind of go somewhere and make the business case. And the business case is hard work. It's not just oh my God, we have this capability. People are just going to buy it. You have to make it Affordable. You have to give people, find the social acceptance of people. None of the teams yet has been able to or gutsy enough to drive around without a person inside the car. And that's the next magical hurdle. We'll be able to send these vehicles around completely empty in traffic. And I think, I mean I wait every day wait for the news that Waymo has just done this.

So you know, interesting you mentioned gutsy. Let me, let me ask some, maybe unanswerable question, maybe edgy questions, but in terms of how much risk is required, some guts. In terms of leadership style, it would be good to contrast approaches and I don't think anyone knows what's right, but if we compare Tesla and Waymo, for example, Elon Musk and the Waymo team, there's slight differences in approach. So on the Elon side there's more, I don't know what the right word to use but aggression in terms of innovation. On Waymo side there's more sort of cautious, safety focused approach to the problem. What do you think it takes? What leadership at which moment is right? Which approach is right?

Look, I don't sit in either of those teams so I'm unable to even verify like somebody says. Correct. In the end of the day, every innovator in that space will face a fundamental dilemma. And I would say you could put aerospace titans into the same bucket, which is you have to balance public safety with your drive to innovate. And this country in particular states has a hundred plus year history of doing this very successfully. Air Travel is what, 100 times as safe per mile than ground travel than cars. And there's a reason for it, because people have found ways to be very methodological about ensuring public safety while still being able to make progress on important aspects, for example like yell and noise and fuel consumption. So I think that those practices are proven and they actually work. We live in a world safer than ever before. And yes, there will always be the provision that something goes wrong. There's always the possibility that someone makes a mistake or there's an unexpected failure. We can Never guarantee to 100% absolute safety other than just not doing it. But I think I'm very proud of the history of, of the United States. I mean we've, we've dealt with much more dangerous technology like nuclear energy and kept that safe too. We have nuclear weapons and we keep those safe. So we have methods and procedures that really balance these two things very, very successfully.

You've mentioned a lot of great autonomous vehicle companies that are taking sort of the level four, level five, they jump in full autonomy with a safety driver and take that kind of approach and also through simulation and so on. There's also the approach that Tesla Autopilot is doing, which is kind of incrementally taking a Level 2 vehicle and using machine learning and learning from the driving of human beings and trying to creep up, trying to incrementally improve the system until it's able to achieve level four autonomy. So perfect autonomy in certain kind of geographical regions. What are your thoughts on these contrasting approaches?

First of all, I'm a very proud Tesla owner and I literally use the Autopilot every day and it literally has kept me safe. It is a beautiful technology specifically for highway driving when I'm slightly tired because then it turns me into a much safer driver and I'm 100% confident that's the case in terms of the right approach. I think the biggest change I've seen since I ran the Waymo team is there's this thing called deep learning. I think deep learning was not a hot topic when I started way more or Google self driving cars. It was there. In fact, we started Google Brain at the same time in Google X. So I invested in deep learning but people didn't talk about it. It wasn't a hot topic and now it is. There's a shift of emphasis from a more geometric perspective where you use geometric sensors that give you a full 3D view and you do geometric reasoning about, oh, this box over here might be a car towards a more human like, oh, let's just learn about this look.