Sebastian Thrun: Flying Cars, Autonomous Vehicles, and Education

Sebastian Thrun is one of the greatest roboticists, computer scientists, and educators of our time. He led 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 revolution. He taught the popular Stanford course on Artificial Intelligence in 2011 which was one of the first MOOCs. That experience led him to co-found Udacity, an online education platform. He is also the CEO of Kitty Hawk, a company working on building flying cars or more technically eVTOLS which stands for electric vertical take-off and landing aircraft. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter https://twitter.com/lexfridman, LinkedIn https://www.linkedin.com/in/lexfridman/, Facebook https://www.facebook.com/lexfridman, Medium https://medium.com/@lexfridman, or YouTube https://www.youtube.com/lexfridman where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts https://podcasts.apple.com/us/podcast/artificial-intelligence/id1434243584 or support it on Patreon https://www.patreon.com/lexfridman. This episode is presented by Cash App. Download it (App Store https://apps.apple.com/us/app/cash-app/id711923939, Google Play https://play.google.com/store/apps/details?id=com.squareup.cash&hl=en_US), use code “LexPodcast”. Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 – Introduction 03:24 – The Matrix 04:39 – Predicting the future 30+ years ago 06:14 – Machine learning and expert systems 09:18 – How to pick what ideas to work on 11:27 – DARPA Grand Challenges 17:33 – What does it take to be a good leader? 23:44 – Autonomous vehicles 38:42 – Waymo and Tesla Autopilot 42:11 – Self-Driving Car Nanodegree 47:29 – Machine learning 51:10 – AI in medical applications 54:06 – AI-related job loss and education 57:51 – Teaching soft skills 1:00:13 – Kitty Hawk and flying cars 1:08:22 – Love and AI 1:13:12 – Life

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  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.

• 0:43Lex Fridman?

  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, EV TALS, 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 it on Patreon, or simply connect with me on Twitter at LexFriedman, spelled F-R-I-D-M-A-N.

• 1:25Lex Fridman?

  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

• 2:09Lex Fridman?

  helps if you listen to the ad and support this podcast by trying out the product or service being advertised.

• 2:16Lex Fridman?

  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, a subsidiary of Square and member SIPC. I'm excited to be working with Cash App to support one of my favorite organizations called FIRST, best known for their first robotics and Lego competitions. They educate and inspire hundreds of thousands of students in over 110 countries and have a perfect rating on Charity Navigator,

• 2:58Lex Fridman?

  which means the donated money is used to maximize effectiveness. When you get Cash App from the App Store or Google Play and use code LETSPODCAST, you'll get $10 and Cash App will also donate $10 to FIRST, which again is an organization that I've personally seen inspire girls and boys to dream of engineering a better world. And now here's my conversation with Sebastian Thrun.

• 3:41Lex Fridman?

  You've mentioned that The Matrix may be your favorite movie. So let's start with a crazy philosophical question. Do you think we're living in a simulation? And in general, do you find the thought experiment interesting?

• 3:56Speaker 01

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

• 4:03Lex Fridman?

  Putting aside for a moment 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 as an information processing system.

• 4:20Speaker 01

  Universe is an information processing system?

• 4:22Lex Fridman?

  It is.

• 4:22Speaker 01

  It's a huge physical, biological, chemical computer. There's no question.

• 4:27Speaker 01

  But I live here and now. I care what people, I care what us.

• 4:32Lex Fridman?

  What do you think it's trying to compute?

• 4:34Speaker 01

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

• 4:44Lex Fridman?

  Spoken like a true human.

• 4:46Speaker 01

  Which last time I checked, I was.

• 4:49Lex Fridman?

  Well, that, in fact, this whole conversation is just a touring test to see if, if indeed, if indeed you are. You've also said that one of the first programs, or the first few programs you've written, was a, wait for it, TI 57 calculator.

• 5:05Speaker 01

  Yep.

• 5:06Lex Fridman?

  Maybe that's early eighties? I don't want to date calculators or anything.

• 5:10Speaker 01

  Early eighties, correct.

• 5:12Lex Fridman?

  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, in decades that followed?

• 5:27Speaker 01

  I was super fascinated by Silicon Valley, which I'd seen on television once and thought, my God, this is so cool. They built like DRAMs there and CPUs. How cool is that? And as a college student, a few years later, I decided to really study intelligence and study human beings and found that even back then in the eighties and nineties, 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 than a cockroach. And cockroaches aren't very smart. So we weren't at the scale yet where we are today.

• 6:02Lex Fridman?

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

• 6:09Speaker 01

  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 was 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, to study intelligence from a constructive perspective, if you build something.

• 6:29Lex Fridman?

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

• 6:37Speaker 01

  I think the biggest innovation that we've seen is machine learning and it's the idea that the 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 on the rules of human gate. We don't understand that we can't put, we have in our brains 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.

• 7:21Speaker 01

  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 has been 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.

• 7:55Lex Fridman?

  So the dream of in the eighties of expert systems, for example, had at its core the idea that humans could boil down their expertise on a sheet of paper. So 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?

• 8:23Speaker 01

  So the, 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 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, the set of language that we can command isn't incredibly limited. The majority of the human brain doesn't deal with language. It deals with like subconscious numerical perceptual things that we don't even self-aware of.

• 9:07Speaker 01

  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.

• 9:34Lex Fridman?

  So you were a key person in launching three revolutions. Online education, autonomous vehicles and flying cars or VTOLs. So, high level.

• 9:48Lex Fridman?

  And I apologize for all the philosophical questions.

• 9:51Speaker 01

  There's no apology necessary.

• 9:53Lex Fridman?

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

• 9:59Speaker 01

  I have two desires in life. I wanna 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.

• 10:14Speaker 01

  And second, I wanna learn. I wanna get in the skills. I don't wanna 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 wanna be in a job I'm bad at. That's really important to me. So in a 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 the stuff that I done lately, after I finished my professorship at Stanford, they really focused on like what has a maximum impact on society. Like, transportation is something that has transformed the twenty first or twentieth century more than any other invention, in my opinion, and even more than communication.

• 10:59Speaker 01

  And cities are different, workers different, women's rights are different because of transportation. And yet we still have a very suboptimal transportation solution where we kill one point two 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 is basically there. You have to close your eyes not to see it.

• 11:43Lex Fridman?

  So lingering on autonomous vehicles, fascinating space. Some incredible work you've done throughout your career there. So let's start start with DARPA, I think. The DARPA Challenge, through 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 of the Desert, the DARPA Challenge in two thousand and five. And Junior, the car that finished second in the DARPA Urban Challenge,

• 12:25Lex Fridman?

  also did incredibly well in two thousand and seven, I think. What are some painful, inspiring or enlightening experiences from that time that stand out to you?

• 12:37Speaker 01

  Oh my god, painful were all these incredibly complicated stupid bugs that had to be found. We had a phase where the Stanley hour, our car that eventually won the upper grand challenge, would every thirty miles just commit suicide and we didn't know why. And it ended up to be that in the syncing 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. It was 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.

• 13:21Speaker 01

  Our analysis had been a human being with an existing rental car can perfectly drive the course. Why do you have to build a better rental car? It 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, nothing clearer than to me that the human brain is a learning machine. So we're not just training our robots. We would build massive machine learning into our machine. And with that, we were able to not just learn from human drivers. We had the entire speed control of the vehicle was copied from human driving,

• 14:06Speaker 01

  but also have the robot learn from experience where it made a mistake and go recover from it and learn from it.

• 14:12Lex Fridman?

  You mentioned the pain point of software and clocks. Synchronization seems to, 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 really even explain. 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?

• 14:52Speaker 01

  We were very lucky, we were a 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 like no egos involved, we just wanted to see how far we can get. But we did really, really well with 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.

• 15:36Speaker 01

  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.

• 15:46Lex Fridman?

  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. What do you, 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 till the last second.

• 16:07Speaker 01

  Well, the nice thing about the Dapak Grand Challenge is that the problem was incredibly well-defined. We were able for a while to drive the old Dapak Grand 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 a different desert, the Snorn Desert, and were able to drive desert trails just of the same type. So there was never any debate about like, what's 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 I studied my own life and life of others and realized that the typical mistake that people make is that there is this kind of crazy bug left that they haven't found yet um and and it's just they regret it and the bug would have been trivial to fix

• 16:52Speaker 01

  it just haven't fixed it yet and 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 hundred sixty pages of tests we had to go through just to make sure we shake out the system appropriately.

• 17:05Lex Fridman?

  Wow.

• 17:06Speaker 01

  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, um, we we we thought, oh, my God, this long solve trivial and then we tested it out. Oh, my God, it doesn't do railroad crossing, why not? Oh, my God, it mistakes the the rails for metal barriers.

• 17:25Lex Fridman?

  Yeah.

• 17:25Speaker 01

  Should we have to fix this?

• 17:27Lex Fridman?

  Yes.

• 17:28Speaker 01

  So it was really a continuous focus on improving the weakest part of the system. And as long as you you focus on improving the weakest part of the system, you eventually build a really great system.

• 17:38Lex Fridman?

  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. It may be 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?

• 18:04Speaker 01

  I would say, um, most of us just take credit for the work of others.

• 18:10Lex Fridman?

  Right.

• 18:11Speaker 01

  Um, that's that's very convenient, turns out, because I can't do all these things myself. I'm an engineer at heart, so I I care about engineering. So so I 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 like in the middle of the night, wake up at one in the morning and switch on your computer and what he told you to yesterday, I would still do. That was really cool. Unfortunately, they 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 they 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 the people, of people as opposed to computers? And it turns out, computers are super dumb. They're so dumb.

• 18:54Speaker 01

  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 our leadership just fails because many, many engineers turned manager believe they can treat their team just the same way they can treat your computer and it just doesn't work this way. It's just really bad. So how can I, 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. Like, 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

• 19:37Speaker 01

  actually love you and their parents love you and they give you all the credit for stuff you don't deserve. Turns out, 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. But 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.

• 20:20Speaker 01

  So how can we empower people and give them the right framework that they can accomplish this? If, 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 to have enormous confidence and pride out of this. And that's when, 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.

• 20:51Lex Fridman?

  So I, I agree with you very much and 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 that's not often heard.

• 21:06Speaker 01

  We make this mistake, and this is a friend of mine, Alex Wada, 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 have fellow engineers who have very little empathy and are kind of befuddled by 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.

• 21:49Speaker 01

  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, no, I wasn't because my intention was that." And I say, "Yeah, 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 they would 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 a book, 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.

• 22:33Speaker 01

  But it's just simple things, right? Like be positive, remember people's names, smile, and eventually have empathy. Like 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.

• 22:53Lex Fridman?

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

• 22:58Speaker 01

  I do.

• 22:59Lex Fridman?

  So, and he said

• 23:00Speaker 01

  It's on my list.

• 23:02Lex Fridman?

  On the list. But 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 actually listen to what troubles others.

• 23:25Speaker 01

  Yeah.

• 23:25Lex Fridman?

  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.

• 23:37Speaker 01

  Yeah, I mean, that's my little history of engineering. That is, while I was engineering with computers, I didn't care at all what the computers' 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.

• 24:00Lex Fridman?

  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's self-driving car and Waymo and the entire industry that sprung up of different kinds of approaches, debates and so on.

• 24:27Speaker 01

  Well, the idea of self-driving cargo was back to the 80s. There was a team in Germany and another team in 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.

• 25:09Speaker 01

  That all changed with the DARPA Gun Challenge. And I really got to credit the ingenious people at DARPA and the US government in Congress that took a completely new funding model, where they said, "Let's not fund effort, let's fund outcomes." And 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 hours in, you could bill 1,000 hours. By changing the focus, instead of 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.

• 25:54Speaker 01

  There were people who had a car and a computer, and they wanted to make a million bucks. The million bucks was their initial price money, it 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 bended who gets all these big multi-million 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,

• 26:38Speaker 01

  so I couldn't get fired, no matter what I do, 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.

• 27:09Lex Fridman?

  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?

• 27:30Speaker 01

  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 that feels like non-monotony 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?

• 28:15Speaker 01

  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 would 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, like there's a problem of curing cancer, or it's life twice as long. Once a problem is defined, of course, I can't solve it in its entirety. Like 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've got to build a prototype. Let's build a meaningful prototype.

• 28:59Speaker 01

  And the DABAR 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 it was really, really beautiful.

• 29:06Lex Fridman?

  And it's most beautiful, you think, what academia could 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.

• 29:20Speaker 01

  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, 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.

• 30:05Speaker 01

  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 an interdisciplinary viewpoint, where your problem is dictated by what your closest colleagues believe the problem is.

• 30:50Speaker 01

  It's very hard to break out and say, well, there's an entire new field of problems. So to 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 this throttle and the brakes, there's no dexterity, there's no complexity. It's just too simple.

• 31:36Speaker 01

  And no one came to me and said, wow, if you solve that problem, you can save a million lives. Right? Among all robotic problems that I've seen in my life, I would say the self-driving car, transportation is the one has the most hope for society. So how come the robotics community wasn't all over the place? And it was become because we focused on methods and 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.

• 32:11Lex Fridman?

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

• 32:15Speaker 01

  The other thing that's really nice in Silicon Valley is Silicon Valley is full of smart people outside academia, right? So there's the Larry Pages and Mark Zuckerberg's in the world who are anywhere smarter, 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 kind of 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.

• 32:59Speaker 01

  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.

• 33:23Lex Fridman?

  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?

• 33:48Speaker 01

  I'm extremely proud of what has been accomplished. And again, I'm taking a lot of credit for the work for us. 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. And 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.

• 34:32Speaker 01

  But now you're down to like a hundredth of 1%. And it'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 headlights, or the blown tire that we've 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.

• 35:04Speaker 01

  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 Aptiv, I've seen it in Cruise, and 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 summoner lift. 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 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.

• 35:47Speaker 01

  The entrainment, the sensors are not quite an automotive great 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 gonna buy it. You have to make it affordable. You have to give people the, 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.

• 36:27Lex Fridman?

  So, you know, interesting, you mentioned gutsy. Let me, let me ask some, uh, 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, the, 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.

• 37:10Lex Fridman?

  And on Waymo's 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?

• 37:27Speaker 01

  Look, I'm, I don't sit in either of those teams, so I'm unable to even verify, like somebody says, correct.

• 37:34Lex Fridman?

  Right.

• 37:35Speaker 01

  In the end of the day, every innovator in, in that space will, will face a fundamental dilemma. And I would say you could put aerospace titans into the same bucket.

• 37:44Lex Fridman?

  Yes.

• 37:45Speaker 01

  Which is you have to balance public safety with your drive to innovate. And this country in particular, in the States, has a hundred-plus year history of doing this very successfully. Air travel is what, a hundred times as safe per mile than ground travel, than, than cars.

• 38:04Speaker 01

  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 yellow 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't never guarantee to 100% absolute safety other than just not doing it. But I think I'm very proud of the history of the United States. I mean, we've dealt with much more dangerous technology like nuclear energy and kept that

• 38:48Speaker 01

  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.

• 38:58Lex Fridman?

  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 two 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?

• 39:39Speaker 01

  Well, 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.

• 40:03Speaker 01

  In terms of the right approach, I think the biggest change I've seen since I ran the Waymo team is this thing called deep learning. The deep learning was not a hot topic when I started Waymo or Google's 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 when 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 it. This looks like the thing I've seen 10,000 times before, so maybe it's the same thing. Machine learning perspective.

• 40:46Speaker 01

  And that has really put, I think, all these approaches on steroids. At Udacity, we teach a course on self-driving cars. In fact, I think we've credited over 20,000 or so people on self-driving car skills. So every self-driving car team in the world now uses our engineers. And in this course, the very first homework assignment is to do lane finding on images. And lane finding images for laymen, what this means is you put a camera into your car or you open your eyes and you would know where the lane is, right? So you can stay inside the lane with your car. Humans can do this super easily. You just look and you know where the lane is, just intuitively. For machines, for a long time, it was super hard because people would write these kind of crazy rules.

• 41:31Speaker 01

  If there's like wine lane markers and he's what white really means, this is not quite white enough, so it's not white. Or maybe the sun is shining, so when the sun shines, and this is white, and this is a straight line, or maybe it's not quite a straight line because the road is curved. And do we know that there's six feet between lane markings or not, or 12 feet, whatever it is? And now, what the students are doing, they would take machine learning. So instead of like writing these crazy rules for the lane marker, they'll say, hey, let's take an hour of driving and label it and tell the vehicle, this is actually the lane by hand. And then these are examples and have the machine find its own rules, what lane markings are. And within 24 hours, now every student that's never done any programming before in this

• 42:11Speaker 01

  space can write a perfect lane finder, as good as the best commercial line finders. And that's completely amazing to me. We've seen progress using machine learning that completely dwarfs anything that I saw 10 years ago.

• 42:27Lex Fridman?

  Yeah, and just as a side note, the self-driving car nano degree, the fact that you launched that many years ago now, maybe four years ago.

• 42:36Speaker 01

  Three years ago.

• 42:37Lex Fridman?

  Three years ago is incredible that that's a great example of system-level thinking. Sort of just taking an entire course that teaches how to solve the entire problem. I definitely recommend people.

• 42:47Speaker 01

  It's been become super popular and it's become actually incredibly high-quality. We've worked with Mercedes and various other companies in that space. And we find that engineers from Tesla and Vaymo are taking it today. The insight was that, two things. One is existing universities will be very slow to move because the departmentalized and there's no department for self-driving cars. So between MECI and double E and computer science, getting those folks together into one room is really, really hard. And every professor listening here will know, will probably agree to that. And secondly, even if if all the great universities did this, which none so far has developed a curriculum in this field, it is just a few thousand students that can partake because all the great universities are super selective.

• 43:32Speaker 01

  So how about people in India? How about people in China or in the Middle East or Indonesia or Africa? Why should those be excluded from the skill of building self-driving cars? Are they any dumber than we are? Are they any less privileged? And the answer is, we should just give everybody the skill to build a self-driving car. Because if we do this, then we have like a thousand self-driving car startups. And if 10% succeed, that's like 100, that means 100 countries now will have self-driving cars and be safer.

• 44:03Lex Fridman?

  It's kind of interesting to imagine, impossible to quantify, but the number, the, you know, over a period of several decades, the impact that has, like a single course, like a ripple effect to society. If you, I just recently talked to Andrew and who was creator of Cosmos, so show, it's interesting to think about how many scientists that show launched. And so it's really.

• 44:30Lex Fridman?

  In terms of impact, I can't imagine a better course than the self-driving car course. That's, you know, there's other more specific disciplines like deep learning and so on that Udacity is also teaching, but self-driving cars, it's really, really interesting course.

• 44:43Speaker 01

  Yeah, and it came at the right moment. It came at a time when there were a bunch of Acquihires. Acquihire is an acquisition of a company not for its technology or its products or business, but for its people. So Acquihire means maybe the company of 70 people, they have no product yet, but they're super smart people and they pay a certain amount of money. So I took Acquihires like GM Cruise and Uber and others and did the math and said, hey, how many people are there and how much money was paid? And as a lower bound, I estimated the value of a self-driving car engineer in these acquisitions to be at least $10 million, right? So think about this, you get yourself a skill and you team up and build a company and your worth now is $10 million. I mean, that's kind of cool. I mean, but what other

• 45:28Speaker 01

  thing could you do in life to be worth $10 million within a year?

• 45:32Lex Fridman?

  Yeah, amazing. But to come back for a moment on to deep learning and its application in autonomous vehicles, you know, what are your thoughts on Elon Musk's statement, provocative statement perhaps, that lighters are crutch? So this geometric way of thinking about the world may be holding us back if what we should instead be doing in this robotic space, in this particular space of autonomous vehicles, is using camera as a primary sensor and using computer vision and machine learning as the primary way to?

• 46:06Speaker 01

  I have two comments. I think first of all, we all know that people can drive cars without lighters in their heads because we only have eyes and we mostly just use eyes for driving. Maybe we use some other perception of our bodies, accelerations, occasionally our ears. Certainly not our noses. So the existence proof is there, that eyes must be sufficient. In fact, we could even drive a car if someone put a camera out and then gave us the camera image with no latency, you'll be able to drive a car that way the same way. So a camera is also sufficient. Secondly, I really love the idea that in the Western world, we have many, many different

• 46:49Speaker 01

  people trying different hypotheses. It's almost like an ant hill, like if an ant hill tries to forge for food, but you can sit there as two ants and agree what the perfect path is, and then every single ant marches for the most likely location of food is, or you can even just spread out. And I promise you, the spread out solution will be better because if the discussing philosophical intellectual ants get it wrong, and they're all moving in the wrong direction, they're going to waste a day, and then they're going to discuss again for another week. Whereas if all these ants go in a random direction, someone's going to succeed, and they're going to come back and claim victory and get the Nobel Prize or whatever the ant equivalent is, and then they all march in the same direction. And that's great about society. That's great about the Western society. We're not plan

• 47:30Speaker 01

  based, we're not central based, we don't have a Soviet Union style central government that tells us where to forge. We just forge. We start in C corp. We get investor money, go out and try it out, and who knows who's going to win?

• 47:45Lex Fridman?

  I like it. When you look at the long term vision of autonomous vehicles, do you see machine learning as fundamentally being able to solve most of the problems? So, learning from experience?

• 47:58Speaker 01

  I'd say we should be very clear about what machine learning is and is not, and I think there's a lot of confusion. What it is today is a technology that can go through large databases of repetitive patterns and find those patterns. So, for example, we did a study at Stanford two years ago where we applied machine learning to detecting skin cancer in images, and we harvested or built a dataset of 129,000 skin photo shots that all had been biopsied for what the actual situation was. And those included melanomas and carcinomas, also included rashes and other skin conditions, lesions.

• 48:43Speaker 01

  And then we had a network find those patterns and it was by and large able to then detect skin cancer with an iPhone as accurately as the best board certified Stanford level dermatologist. We proved that. Now, now this thing was great in this one thing, I'm finding skin cancer, but I couldn't drive a car. So, so the difference to human intelligence is we do all these many, many things and we can often learn from a very small data set of experiences. Whereas machines still need very large data sets and things that will be very repetitive. Now, that's still super impactful because almost everything we do is repetitive.

• 49:22Speaker 01

  So that's gonna be transform human labor, but it's not this almighty general intelligence. We're really far away from a system that will exhibit general intelligence. To that end, I actually commiserate the naming a little bit because artificial intelligence, if you believe Hollywood, is immediately mixed into the idea of human suppression and, and machine superiority. I don't think that we're gonna see this in my lifetime. I don't think human suppression is a, is a good idea. I don't see it coming. I don't see the technology being there. What I see instead is a very pointed focused pattern recognition technology that's able to extract patterns from large data sets.

• 50:04Speaker 01

  And in doing so, it can be super impactful, right? Super impactful. Let's take the impact of artificial intelligence on, on human work. We all know that it takes something like 10,000 hours to become an expert. If you wanna be a doctor or a lawyer or even a really good driver, it takes a certain amount of time to become experts. Machines now are able and have been shown to, to observe people become experts and observe experts and then extract those rules from experts in some interesting way. They could go from law to sales to...

• 50:41Speaker 01

  Driving cars to diagnosing cancer and then giving that capability to people who are completely new in their job. We now can, and that's, that's been done, it's been done commercially in many, many instances. So that means we can use machine learning to make people expert on their very first day of their work. Like, think about the impact. If, if your doctor is still in their first 10,000 hours, you have a doctor who's not quite an expert yet, who would not want a doctor who is the world's best expert? And now we can leverage machines to really eradicate error in decision making, error and lack of expertise for human doctors. That could save your life.

• 51:24Lex Fridman?

  If I can link on that for a little bit, in which way do you hope machines in the medical, in the medical field could help assist doctors? You mentioned this sort of accelerating the learning curve or people, if they start a job or in the first 10,000 hours can be assisted by a machine. How do you envision that assistance looking?

• 51:45Speaker 01

  So we built this, this app for an iPhone that can detect and classify and diagnose skin cancer. And we proved two years ago that it does pretty much as good or better than the best human doctors. So let me tell you a story. So there's a friend of mine, let's call him Ben. Ben is a very famous venture capitalist. He goes to his doctor and the doctor looks at a mole and, and says, hey, that mole's probably harmless. And for some very funny reason, he pulls out that phone with our app. He's a collaborator in our study. And the app says, no, no, no, no, this is a melanoma. And, and for background, melanomas are, and skin cancer is the most common cancer in this country.

• 52:29Speaker 01

  Melanomas can go from, from stage zero to stage four within less than a year. Stage zero means you can basically cut it out yourself with, with a kitchen knife and be safe. And stage four means your chances of living five more years is less than 20%. So it's a very serious, serious, serious condition. So. This doctor who took out the iPhone, looked at the iPhone and was a little bit puzzled, saying, you know what? Just to be safe, let's cut it out and biopsy it. That's the technical term for let's get an in-depth diagnostics. That is more than just looking at it. And it came back as cancerous, as a melanoma. And it was then removed. And my friend Ben, I was hiking with him and we were talking about AI.

• 53:12Speaker 01

  And I told him, I do this work on skin cancer. And he said, oh, funny. My doctor just had an iPhone that found my cancer. Wow. So I was like completely intrigued. I didn't even know about this. So here's a person, I mean, this is a real human life, right?

• 53:27Lex Fridman?

  Yes.

• 53:28Speaker 01

  Like, who doesn't know somebody who has been affected by cancer? Cancer is cause of death number two. Cancer is this kind of disease that that is mean in the following way. Most cancers can actually be cured relatively easily if we catch them early. And the reason why we don't tend to catch them early is because they have no symptoms. Like your very first symptom of a gallbladder cancer or a pancreas cancer might be a headache. And when you finally go to your doctor because of these headaches or your back pain and you're being imaged, it's usually stage four plus. And that's the time when your curing chances might be dropped to a single-digit percentage. So if we could leverage AI to inspect your body on a regular basis

• 54:12Speaker 01

  without even a doctor in the room, maybe when you take a shower or what have you, I know that sounds creepy, but then we might be able to save millions and millions of lives.

• 54:22Lex Fridman?

  You've mentioned there's a concern that people have about near-term impacts of AI in terms of job loss. So you've mentioned being able to assist doctors, being able to assist people on their jobs. Do you have a worry of people losing their jobs? Or the economy being affected by the improvements in AI?

• 54:41Speaker 01

  Yeah, anybody concerned about job losses, please come to getacity.com. We teach contemporary tech skills and we have our kind of implicit job promise. We often, when we measure, we spend way over 50% of our graduates in new jobs and they're very satisfied about it. And it costs almost nothing, costs like 1,500 max or something like that.

• 55:03Lex Fridman?

  And so there's a cool new program that you agree with the with the US government, guaranteeing that you will help give scholarships that educate people in this kind of situation.

• 55:14Speaker 01

  Yeah, we're working with the US government. On an idea of basically rebuilding the American dream. So Yerazi has just dedicated one hundred thousand scholarships for citizens of America for various levels of courses that eventually will get you a job. And those courses are somewhat related to the tech sector because the tech sector is kind of the hottest sector right now and they range from inter-level digital marketing to very advanced self-driving car engineering. And we're doing this with the White House because we think it's bipartisan. It's an issue that is, if you want to really make America great, being able to be a part

• 55:55Speaker 01

  of the solution and live the American dream requires us to be proactive about our education and our skill set. It's just the way it is today. And it's always been this way. And we always had this American dream to send our kids to college. And now the American dream has to be to send ourselves to college. We can do this very, very, very efficiently and very, very, we can squeeze in the evenings and things to online. At all ages. All ages. So our, our learners go from age eleven to age eighty. I just traveled Germany and, and the guy in the train compartment next to me was one of my students. It's like, wow, that's amazing. I'm thinking about impact.

• 56:37Speaker 01

  We, we've become the educator of choice for now, I believe officially six countries or five countries, mostly in the Middle East, like Saudi Arabia and in Egypt. In Egypt, we just had a cohort graduate where we had eleven, a hundred high school students that went through programming skills proficient at the level of computer science undergrad. And we had a ninety-five percent graduation rate, even though everything's online. It's kind of tough, but we're kind of trying to figure out how to make this effective. The vision is, the vision is very, very simple. The vision is education ought to be a basic human right. It cannot be locked up behind ivory tower walls only for the rich people, for the parents

• 57:20Speaker 01

  who might be bribed themselves into the system, and only for young people, and only for people from the right demographics and the right geography, and possibly even the right race. It has to be opened up to everybody. If we if we are truthful to the human mission, if we truthful to our values, we're gonna open up education to everybody in the world. So Udacity's pledge of a hundred thousand scholarships, I think is the biggest pledge of scholarships ever in terms of numbers. And we're working, as I said, with the White House and with very accomplished CEOs like Tim Cook from Apple and others to really bring education to everywhere in the world.

• 57:57Lex Fridman?

  Not to ask you to pick the favorite of your children, but at this

• 58:02Speaker 01

  Oh, that's Jasper. I only have one that I know of.

• 58:06Lex Fridman?

  Okay, good.

• 58:09Lex Fridman?

  In this particular moment, what Nanog degree, what set of courses are you most excited about at Udacity? Or is that too impossible to pick?

• 58:18Speaker 01

  I I've been super excited about something we haven't launched yet and we're building, which is when we talk to our partner companies, we have now a very strong footing in the enterprise world. And also to our students, we've kind of always focused on these hard skills, like the programming skills or math skills or building skills or design skills. And a very common ask is soft skills, like how do you behave in your work? How do you develop empathy? How do you work on a team? What are the very basics of management? How do you do time management? How do you advance your career in the context of a broader community? And that's something that we haven't done very well at Udacity. And I would say most universities are doing very poorly as well

• 59:01Speaker 01

  because we're so obsessed with individual test scores and and so little pays so little attention to teamwork in education. So that's something I see us moving into as a company because I'm excited about this. And I think, look, we can teach people tech skills and they're going to be great. But if we teach people empathy, that's going to have the same impact.

• 59:21Lex Fridman?

  Maybe harder than self-driving cars, but

• 59:24Speaker 01

  I don't think so. I think the rules are really simple. You just have to, you have to, you have to want to engage. It's, it's, we, we literally, when in, in school and in K through 12, we teach kids like, get the highest math score. And if you are a rational human being, you might evolve from this education, say, having the best math score and the best English scores make me the best leader. And it turns out not to be the case. It's actually really wrong. Because making, first of all, in terms of math scores, I think it's perfectly fine to hire someone with great math skills. You don't have to do it yourself. You can't hire someone with great empathy for you, that's much harder. But you can always hire someone with great math skills. But we live in, in a fluent world where we constantly deal with other people and that's a beauty. It's not a nuisance, it's a beauty.

• 1:00:09Speaker 01

  So if we somewhat develop that muscle, that we can do that well and empower others in the workplace, I think we're going to be super successful.

• 1:00:19Lex Fridman?

  And I know many fellow roboticists and computer scientists that I will insist to take this course.

• 1:00:27Speaker 01

  Not to be named here.

• 1:00:28Lex Fridman?

  Not to be named. Many, many years ago, 1903, the Wright Brothers flew in Kitty Hawk for the first time. And you've launched a company of the same name, Kitty Hawk, with the dream of building flying cars, EV dolls. So at the big picture, what are the big challenges of making this thing that actually you've inspired generations of people about what the future looks like? What does it take? What are the biggest challenges?

• 1:01:00Speaker 01

  So, so flying cars has always been a dream. Every boy, every girl wants to fly. Let's be honest.

• 1:01:06Lex Fridman?

  Yes.

• 1:01:07Speaker 01

  And let's go back in our history when we are dreaming of flying. I think my, honestly, my single most remembered childhood dream has been a dream where I was sitting on a pillow and I could fly. I was like five years old. I remember like maybe three dreams of my childhood, but that's the one I dream remember most vividly. And then Peter Seid famously said, they promised us flying cars and they give us 140 characters, pointing at Twitter at the time, limited message size to 140 characters. So we're coming back now to really go for this super impactful stuff like flying cars. And to be precise, they're not really cars. They don't have wheels. They're actually much closer to a helicopter than than anything else. They take off vertically and they fly horizontally. But they have important differences.

• 1:01:50Speaker 01

  One difference is that they are much quieter. We just released a vehicle called Project Heavy Side. They can fly over you as low as a helicopter and you basically can't hear. It's like thirty eight decibels. It's like, if you were inside the library, you might be able to hear it. But anywhere outdoors, your ambient noise is higher.

• 1:02:11Speaker 01

  Secondly, they're they're much more affordable. They're much more affordable than helicopters. And the reason is helicopters are expensive for many reasons. There's lots of single point of figures in a helicopter. There's a bolt between the blades. That's called Jesus bolt. And the reason why it's called Jesus bolt is that if this bolt breaks, you will die. There is no second solution in helicopter flight. Whereas we have this distributed mechanism. When you go from gasoline to electric, you can now have many, many, many small motors as opposed to one big motor. And that means if you lose one of those motors, not a big deal. Heavy Side, if it loses a motor, has eight of those. If it loses one of those eight motors, so it's seven left, it can take off just like before and land just like before.

• 1:02:56Speaker 01

  We are now also moving into a technology that doesn't require a commercial pilot. Because at some level, flight is actually easier than than ground transportation, like in in self-driving cars. The world is full of like children and bicycles and other cars and mailboxes and kerbs and and shrubs and whatever you. All these things you have to avoid. When you go above the buildings and tree lines, there's nothing there. I mean, you can do the test right now. Look outside and count the number of things you see flying. I'd be shocked if you could see more than two things. It's probably just zero. In the Bay Area, the most I've ever seen was six. And maybe it's fifteen or twenty, but not ten thousand. So the sky is very ample and very empty and very free.

• 1:03:40Speaker 01

  So the vision is, can we build a socially acceptable mass transit solution for daily transportation that is affordable? And we have an existence proof. Heavy side can fly a hundred miles in range with still thirty percent electric reserves. It can fly up to like a hundred eighty miles an hour. We know that that solution at scale would make your ground transportation ten times as fast as a car, based on your sensors or statistics data, which means it would take your three hundred hours of daily commute down to thirty hours and give you two hundred seventy hours back. Who wouldn't want, I mean, who doesn't hate traffic?

• 1:04:24Speaker 01

  Like, I hate, give me the person who doesn't hate traffic. I hate traffic every day. Every time I'm in traffic, I hate it. And if we could free the world from traffic, we have technology. We can free the world from traffic. We have the technology. It's there. We have an existence proof. It's not a technological problem anymore.

• 1:04:41Lex Fridman?

  Do you think there is a future where tens of thousands, maybe hundreds of thousands of both delivery drones and flying cars of this kind, EV tolls fill the sky?

• 1:04:56Speaker 01

  I absolutely believe this. And there's obviously the societal acceptance is a major question. And of course, safety is. I believe in safety, we will exceed ground transportation safety, as has happened for aviation already, commercial aviation. And in terms of acceptance, I think one of the key things is noise. That's why we are focusing relentlessly on noise and we build perhaps the quickest electric vehicle ever built. The nice thing about the sky is it's three dimensional. So any mathematician will immediately recognize the difference between one D of like a regular highway to three D of a sky. But to make it clear for the layman, say you want to make a hundred vertical lanes

• 1:05:39Speaker 01

  of Highway one oh one in San Francisco, because you believe building a hundred vertical lanes is the right solution. Imagine how much it would cost to stack a hundred vertical lanes physically onto one on one. That would be prohibitive. That would be consuming the world's GDP for an entire year just for one highway. It's amazingly expensive, okay? In the sky, it would just be a recompilation of a piece of software because all these lanes are virtual. That means any vehicle that is in conflict with another vehicle would just go to different altitudes. And then the conflict is gone. And if you don't believe this, that's exactly how how commercial aviation works. When you fly from New York to San Francisco, another plane flies from San Francisco to New York. They're at different altitudes, so they don't hit each other.

• 1:06:23Speaker 01

  It's a solved problem for the jet space, and it will be a solved problem for the urban space. There's companies like Google Ring and Amazon working on very innovative solutions. How do we have space management? They use exactly the same principles as we use today to route today's jets. There's nothing hard about this.

• 1:06:42Lex Fridman?

  Do you envision autonomy being a key part of it? So that that the the flying vehicles are either semi-autonomous or fully autonomous?

• 1:06:53Speaker 01

  A hundred percent autonomous. You don't want idiots like me flying in the sky, I promise you. And if you have ten thousand, watch the the movie A Fifth Element to get a pee for what it happened. If if it's not autonomous.

• 1:07:06Lex Fridman?

  And a centralized, that's a really interesting idea of a centralized sort of management system for lanes and so on. So actually, just being able to have a similar as we have in the current commercial aviation, but scale it up to much, much more vehicles. It's a really interesting optimization problem.

• 1:07:24Speaker 01

  It is very mathematically, very, very straightforward. Like, the gap we leave between jets is gargantous. And part of the reason is there isn't that many jets, so this feels like a good solution. Today, when you get vectored by air traffic control, someone talks to you, right? So any ATC controller might have up to maybe twenty planes on the same frequency. And then they talk to you, you have to talk back. And it feels right because there isn't more than twenty planes around anyhow, so you can talk to everybody. But if there's twenty thousand things around, you can't talk to everybody anymore. So we have to do. Something that's called digital, like text messaging, like we do have solutions like we have what four or five billion smartphones in the world now, right? And they're all connected and somehow we solve the scale problem for smartphones.

• 1:08:07Speaker 01

  We know where they all are, they can talk to somebody and they're very reliable, they're amazingly reliable. We could use the same system, the same scale for air traffic control. So instead of me as a pilot talking to a human being in the middle of the the conversation, receiving a new frequency, like how ancient is that? We could digitize the stuff and and digitally transmit the right flight coordinates. And that's the solution will automatically scale to ten thousand vehicles.

• 1:08:36Lex Fridman?

  We talked about empathy a little bit. Do you think we will one day build an A I system that a human being can love? And that loves that human back, like in the movie Her.

• 1:08:47Speaker 01

  Look, I'm I'm a pragmatist. For me, A I is a is a tool. It's like a shovel and the ethics of using the shovel are always with us, the people. And and it has to be this way. In terms of of emotions, I would hate to come into my kitchen and see that my refrigerator spoiled all my food. Then have it explain to me that it fell in love with the dishwasher and I wasn't as nice as the dishwasher. So as a result, it neglected me. That would just be a bad experience and it would be a bad product. I would probably not recommend this refrigerator to my friends.

• 1:09:28Speaker 01

  And that's where I draw the line. I think to me, technology has to be reliable and has to be predictable. I want my car to work. I don't want to fall in love with my car. I I I just want it to work. It wanted to complement me, not to replace me. I have very unique human properties and I want the machines to make me, turn me into a superhuman. Like I'm already a superhuman today, thanks to the machines that surround me. And I give you examples, I can.

• 1:09:59Speaker 01

  Run across the Atlantic at near the speed of sound at thirty six thousand feet today. That's kind of amazing. I can, uh, my voice now carries me all the way to Australia. Using a smartphone today and it's not, not the speed of sound, which would take hours. It's the speed of light. My voice travels at the speed of light. How cool is that? That makes me superhuman. I would even argue my, my flushing toilet makes me superhuman. Just think of the time before flushing toilets. And, and maybe you have a very old person in your family that you can ask about this. Or take a trip to rural India to experience it.

• 1:10:39Speaker 01

  It's, it's, it makes me superhuman. So to me, what technology does, it complements me. It, it, it makes me stronger. Therefore, words like love and compassion have very little.

• 1:10:53Speaker 01

  I have very little interest in this for machines. I have interest in people.

• 1:10:57Lex Fridman?

  You don't think, uh, first of all, beautifully put, beautifully argued. But do you think love has use in our tools, compassion?

• 1:11:06Speaker 01

  I think love is a beautiful human concept. And if you think of what love really is. Love is a means to convey safety, to convey trust. I think trust has a huge need in technology as well, not just people. We want to trust our technology the same way we, or in a similar way we trust people.

• 1:11:32Speaker 01

  In, in human interaction, standards have emerged and, and feelings, emotions have emerged, maybe genetically, maybe biologically. That are able to convey sense of trust, sense of safety, sense of passion, of love, of dedication. That, that makes the human fabric and I'm a big slacker for love. I want to be loved. I want to be trusted. I want to be admired. All these wonderful things. And because all of us, we have this beautiful system. I wouldn't just blindly copy this to the machines. Here's why. When you look at, say, transportation, you could have observed that up to the end of the 19th century, almost all transportation used any number of legs,

• 1:12:14Speaker 01

  from one leg to two legs to a thousand legs. And you could have concluded that is the right way to move about the environment. With the exception of birds, who use flapping wings. In fact, there were many people in aviation that flapped wings to their arms and jumped from cliffs. Most of them didn't survive. Then the interesting thing is that the technology solutions are very different. Like in technology, it's really easy to build a wheel. In biology, it's super hard to build a wheel. There's very few perpetually rotating things in biology. And they're usually one cells things. In engineering, we can build wheels. And those wheels gave rise to cars.

• 1:12:57Speaker 01

  Similar wheels gave rise to aviation. Like, there's no thing that flies. They wouldn't have something that rotates, like a jet engine or helicopter blades. So the solutions have used very different physical laws than nature. And that's great. So for me, to be too much focused on, oh, this is how nature does it, let's just replicate it. If we really believed that the solution to the agricultural evolution was a humanoid robot, we would still be waiting today.

• 1:13:27Lex Fridman?

  Again, beautifully put. You said that you don't take yourself too seriously.

• 1:13:32Speaker 01

  You didn't say that? You want me to say that? You don't take me seriously.

• 1:13:37Lex Fridman?

  I'm not, yeah, that's right.

• 1:13:39Speaker 01

  Good, you're right, I don't wanna.

• 1:13:41Lex Fridman?

  I just made that up. But, you know, you have a humor and a lightness about life that I think is beautiful and inspiring to a lot of people. Where does that come from? The smile, the humor, the lightness amidst all the chaos and the hard work that you're in? Where does that come from?

• 1:14:00Speaker 01

  I just love my life. I love the people around me. I love. I'm just so glad to be alive. Like, I'm, what, fifty-two, hard to believe. People say fifty-two, so no fifty-one, so no, feel better. But in, in, in, in, in looking around the world, looking, um, just go back two hundred, three hundred years. Humanity is, what, three hundred thousand years old. But for the first three hundred thousand years, minus the last one hundred, our life expectancy would have been plus or minus thirty years, roughly, give or take. So I would be long dead now. Like, that makes me just enjoy every single day of my life. Because I don't deserve this.

• 1:14:44Speaker 01

  Like, why am I born today when so many of my ancestors died of horrible deaths? Like famines, massive wars, uh, that ravaged Europe for the last one thousand years, mystically disappeared after World War Two, when the Americans and the Allies descending amazing to my country that didn't deserve it, um, the country of Germany. This is so amazing. And then, when you're, when you're alive and feel this every day, then it's just so amazing what, what we can accomplish, uh, what we can do. We live in a world that is so incredibly vastly changing every day. Almost everything that we cherish, from your smartphone

• 1:15:29Speaker 01

  to your flushing toilet, um, to all these basic inventions, your new clothes you're wearing, your watch, your plane, penicillin, uh, I don't know, uh, anesthesia for surgery, um, penicillin have been invented in the last one hundred fifty years. So in the last one hundred fifty years, something magical happened. And I, I would trace it back to Gutenberg and the printing press that has been able to disseminate information more efficiently than before, that all of a sudden we were able to invent agriculture and nitrogen fertilization that made agriculture so much more potent, that we didn't have to work in the farms anymore, and we could start reading and writing, and we could become all these wonderful things we are today, from airline pilot to massage therapist to software engineer.

• 1:16:13Speaker 01

  This is amazing. Like, living in that time is such a blessing. We should sometimes really think about this. Steven Pinker, who is a very famous author and philosopher, whom I really adore, wrote a great book called Enlightenment Now, and that's maybe the one book I would recommend. And he asked the question, if there was only a single article written in the 20th century, only one article, what would it be? What's the most important innovation, the most important thing that happened? And he would say this article would credit a guy named Carl Bosch. And I challenge anybody, have you ever heard of the name Carl Bosch? I hadn't, okay? There's a Bosch Corporation in Germany, but it's

• 1:16:52Speaker 01

  not associated with Carl Bosch. So I looked it up. Carl Bosch invented nitrogen fertilization. And in doing so, together with an older invention of irrigation, was able to increase the yield per agricultural land by a factor of 26. So a 2,500% increase in fertility of land. And that, so Steve Pinker argues, saved over 2 billion lives today. 2 billion people who would be dead if this man hadn't done what he had done. Okay? Think about that impact and what that means to society. That's the way I look at the world. I mean, it's just so amazing to be alive and be part of this. And I'm so glad I lived after Carl Bosch and not before.

• 1:17:36Lex Fridman?

  I don't think there's a better way to end it, Sebastian. It's an honor to talk to you, to have had the chance to learn from you. Thank you so much for talking to me.

• 1:17:44Speaker 01

  Thanks for coming out. It's a real pleasure.

• 1:17:46Lex Fridman?

  Thank you for listening to this conversation with Sebastian Thrun. And thank you to our presenting sponsor, Cash App. Download it, use code LEGSPODCAST, you'll get $10, and $10 will go to First, a STEM education nonprofit that inspires hundreds of thousands of young minds to learn and to dream of engineering our future. If you enjoy this podcast, subscribe on YouTube, get five stars on Apple Podcasts, support it on Patreon, or connect with me on Twitter. And now, let me leave you with some words of wisdom from Sebastian Thrun. It's important to celebrate your failures as much as your successes. If you celebrate your failures really well, if you say, wow, I failed, I tried, I was wrong, but I learned

• 1:18:30Lex Fridman?

  something, then you realize you have no fear. And when your fear goes away, you can move the world.

• 1:18:39Lex Fridman?

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