Lex Fridman Podcast - Sebastian Thrun: Flying Cars, Autonomous Vehicles, and Education

Episode Date: December 21, 2019

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, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts or support it on Patreon. This episode is presented by Cash App. Download it (App Store, Google Play), 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

The following is a conversation with Sebastian Throon. He's one of the greatest roboticists, computer scientists, and educators of our time. He led the development of the autonomous vehicles of 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 is 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 EV stalls, 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 5 stars on Apple Podcasts, follow it on Spotify, support it on Patreon, or simply connect with me on Twitter, and Lex Friedman 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 of service being advertised. This show is presented by CashApp, the number one finance app in the App Store. I personally use CashApp to send money to friends, but you can also use it to buy, sell, and deposit big corn 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. Brokery 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, 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 and charity navigator, which means the donated money is used to maximum effectiveness. When you get Cash App from the App Store or Google Play and use code LEX Podcast, you'll get $10 and cash out but 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.

You've mentioned that the matrix may be your favorite movie. So let's start with the crazy philosophical question. Do you think we're living in a simulation and in general, do you find the thought experiment interesting? You find simulation, I would say, maybe VR, maybe you are not, but it's completely irrelevant to the way we should act. Right. 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's information processing system. The universe isn't information processing system. It is a huge physical, biological, chemical computer. There's no question. But I live here and now. I care about people, how care about us. What do you think is trying to compute? And I think there's an intention, I think,

that 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. Well, that in fact this whole conversation is just a touring test to see 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 a TI57 calculator. Yeah, maybe that's early 80s.

We don't want to date calculators or anything. The 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 of following. I was super fascinated by Silicon Valley, which I've 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 quarter students, 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, artificial intelligence was fascinating me the most. I was missing is that back in the day, the computers are really small. They like the brains you could build were not anywhere bigger as a cockroach. And cockroach is not 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? What did that seem possible? I always wondered to make robots smart. 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 study intelligence from a constructive perspective. I get built something. To understand, you build, what do you think it takes to build an intelligent system, 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 what to walk. And we learn how to walk in the first year, two of our lives. But no scientist has ever been able to write on the rules of human gate. We don't understand it. We can't put...

We have in our brains some, or we can't practice it. We understand it. But we cannot tickle it. We can't pass it on by language. And that to me is kind of the deficiency of today's computer programming. Even you could program a computer, they're so insanely dumb that you have to give them rules for every contingencies. They 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 has been in the make for like 30, 40 years,

is an idea that computers can find their own roles. 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 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 providing a book. You can express expertise by showing someone what you're doing. You can express expertise by applying it by many different ways. And I think

the expert systems was our best attempt in AI to capture expertise and rules. But someone said down and say, here are the rules of human gate. Here's when you put your big toe forward and your heel backwards and your heel stops stumbling. And as we now know, the set of rules, the set of language that we can command, is incredibly limited.

The majority of the human brain doesn't deal with language. It deals with that subconscious numerical perceptual things. If you don't even have to serve the way 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 dermatologist or radiologist, 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 fine cars or VTALs. So high level, and I apologize for all the philosophical questions. That's no apology, that's a say. How do you choose what problems to try and solve

that drives you to make those solutions a reality? 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. And second, I want to learn.

I want to get in the skill. So 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 chance 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 in a build for example, what people often call flying cars, these are electrical, vertical take-off and landing vehicles. I'm just no expert in any of this and it's so much

fun 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, the video focused on like what has the maximum impact on society. Like, transportation is something that has transformed the 21st or 20th century more than any other invention of happening even more than communication. And cities are different workers, different women's rights are different because of transportation. And and workers, 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. Let's go to your neighborhood city and look at the number of parked cars that's at Travis team, 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 yet to close your eyes not to see it. So lingering on autonomous vehicles, 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 call the autonomous cars, self-driving cars. So you led the development of Stanley, the autonomous car that won the race to the desert, the DARPA Challenge in 2005.

And junior, the car that I 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 hour, our card that eventually

wandered up and ran challenge would every 30 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 technology, 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 software, whereas everybody else seemed to focus on building better hardware.

Our analysis had been a human being within existing rental car can perfectly drive the course by having to, right off the bed, a better rental car, I just for sure, should be placed the human being. And the human being to me was a conjunction of three steps. We had a sensor, eyes and ears, mostly eyes. We had brains in the middle and then we had actuators or hands on our feet. Now, the actors 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 clear than to me that the human brain is a learning machine. So we're not just trainable, but so you would build a 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 learned 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.

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 or desert the air before nobody finish. What does it take to scramble and finish a product that actually a system that actually works? I mean they're lucky very, very, we need a small team, the core of the team of four people. It was four because five couldn't come through a sit inside a car but four could. And I as a team, my job was to get pizza for everybody and wash the car and stuff like this and repair the radiator and broke and debug the system. And we were very kind of open minded. We had like no egos involved. And we just wanted to

see how far we can get. Or 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, I'm 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 for the race of credit. 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 worked the last second. Well, the nice thing about the Topagvan challenges, that the problem was incredibly well-defined. We were able to drive the old Topagvan 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 deserts,

the snorren deserts, and we were able to drive desert trails, just at the same time. So there was never any debate about what was 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 we lost 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 regretted and that bug would have been trivial to fix it. It just haven't fixed it yet. And it 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, this long solved trivial and then we tested it out. Oh my god, it doesn't do a railroad crossing. Why not? Oh my god, it mistakes the rails for metal barriers. Yeah, we have to fix this. So it was 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

VD grade system. Let me just pause on that. To me as an engineer, it's 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

I just take credit for the work of others. That's very convenient in terms of, because I can't do all these things myself. I'm an engineer at heart, so I care about engineering. So I don't know what the chicken and the egg is, but as a kid, I love 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 you told you to do yesterday, I would still do there 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 they go a day later and they'll stop doing it. So they 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 is a computer that's 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 the of the worship just fails because many, many engineers turn manage or believe they can treat their team just the same way

can to 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 actually love you and their parents love you. And they give you all the credit for stuff you don't deserve. It turns out, all my students were smarter than me. All the great stuff invented. It's the most of the stuff, not my stuff. And they give me credit and say, oh, Sebastian, we're just making them figure it about themselves. So the question

of Elias, 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 and turns out every human being that I know has incredibly good intentions. I've really 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? If a moment's when it works, it's magical because you'd see the confluence of people being able to make the world a better place and desiring 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 to work. It's very hard to accomplish, but in winter books

it's amazing. So I grew up 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, not often heard. We make this mistake and this is a friend of mine, Aksuota, Tobinus, 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 follow engineers who have very little empathy and kind of be fattled by it, 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 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 they 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 they 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 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 a book, oh, Fail 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's just simple things, right? Like be positive. Remember people's name, smile.

And eventually have empathy. Like really think that the person that you will 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 Schwartzman. I'm not sure if you know who that is, but I do. And he said to my list, I know this. 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. 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. Yeah, I mean, that's my little history of engineering

that is when I was engineering with computers, I didn't care at all what the computer's problems were. I just told him to do it and do it. And it doesn't work. It doesn't work with me. If you come to me and say to do A, I do the opposite. But let's return to the comfortable world of engineering.

Can you tell me and broad strokes in how you see it? Because you're 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-Training Car and Waymo and the entire industry that sprung up of different kinds of approaches, debates and so on. With the idea of self-driving car, I was back to the 80s. There was a team in Germany, another team in Carnegie Mellon that did some very pioneering

work. 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 floated 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 going to read was a successful product of a research project. So we trained our researchers to produce

lots of paper. They all changed with the DARPA gun challenge. 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 a hundred hours. If you put in a thousand hours in, you could build a thousand 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 off 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 for 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 team, there was a team that was a server dude 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 you felt very banded to to get all these big multi million and billion dollar countries from the US government. And there was a great reset.

The universities moved in. I was very fortunate at Stanford that it just received 10 years, 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 they are covered 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 are mostly people who participate in those challenges. OK, 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, these 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 an assistance 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 sort of a component of a problem. Like with someone that feels like non-monitorial 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. Now, can 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's that that is much easier, but impressing my grandmother is very, very hard. So I would always thought if I 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 are so many problems like these, like there's a problem of queuing cancer or other, or there is, it lives twice as long.

Once a problem is defined, of course, I can't solve it in this 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 that's 10 for it. 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, it had do with the roles and that was really really beautiful. And it's most beautiful. You think what academia could aspire to is to build a prototype. That's the systems level that solves a 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. And I'm going to evaluate 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 we defundamentally 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 inner disciplinary viewpoint where your problem is dictated but 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, a given example, prior to me working on self-driving cars, I was a roboticist in a machine learning expert in a bookson of robotics, something called probabilistic robotics. It's a very methods-driven kind of view per another world. I read robots that acted in museums as tour guides, like let children around. It is 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 future robotics cars

are looked like as a gimmick, and they're not expressive enough. They can only put this bottle in 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. And among all robotic problems that I've seen in my life, I would say the self-loving car, transportation, is the one that has the most hope for the society. So how come the robotics community wasn't all over the place? And it was because we focused on methods,

on solutions, 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 and Silicon Valley is, Silicon Valley is full of smart people outside academia. So there's the Larry pages and Magzacca books in the world who are anywhere smarter than the 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 system. 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 there's kind of vertical integration between the real product, the real impact, and the real thought, the real ideas. That's actually working surprisingly well on 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 massive, the 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, 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 work for others, and I'm actually very optimistic, and people have been kind of worrying is it too fast, is too slow, why is it not there yet, and so on. It is actually quite an interesting heart 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. Do a 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've worked 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 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 being. They're able to deal with the couch on the highway, right? Or the deer in the headlight or the blown tire that we've never been trained for and all of a sudden have to handle it in an emergency situation and often do very, very successfully. It's amazing. From that perspective, our 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 that

technology is there and I've seen it. I've seen it in Waymore, I've seen it in Appter, I've seen it in Cruz, in a number of companies in Voyage where vehicles are not 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 get the right setting of your app, you'll be picked up by a driver's 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, 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 automatic grade standard yet. And then to read 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 the to 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 Raymond has just done this. So, you know, interesting, you mentioned gutsy. 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. And 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 in states has a hundred plus year history of doing this very successfully. Air travel is what a hundred times a save per mile, then ground travel, then 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 yellow noise and fuel consumption. So I think that those practices are prune and they actually work. We live in the world safer than ever before.

And yes, they 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 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 5, 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 4 autonomy. So perfect autonomy in certain kind of geographical regions.

What are your thoughts on these contrasting approaches? Well, suppose I'm a very proud Tesla owner and I literally use the autopilot every everyday and it literally has kept me safe. It's a beautiful technology specifically for highway driving when I'm slightly tired because then it turns me into a much safer driver and that I'm 100% confident it's the case. In terms of the right approach, I think the biggest change I've seen since I have in the way more team is, is this thing called deep learning.

The deep learning was not a hot topic when I started the 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 was not a hot topic. And now there's a shift of emphasis from a more geometric perspective, where you use geometric sensors.

They give you a full 3D view. You only do a geometric reasoning about all 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. And that has really put, I think, all these approaches on steroids. At Udacity, we teach the course in self-driving cars. In fact, I think we've graduated 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 the 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. If there's like wine lane markers and he's what white really means, this is not quite white enough. So it's all, 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, maybe it's not quite a straight line because of what is curved. And do we know that there's these six feet between lane markings on 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, as they say, let's take an hour 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, there's never done any programming before in this space, can write a perfect lane finder as good as the best commercial lane 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.

Yeah, and just as a side note the self-driving car nanodegree the fact that you launched that Many years ago now maybe four years ago three years ago is incredible that that's a great example of system level thinking Sort of just taking an entire course. I teach you teaches out a solid entire problem, I definitely recommend people. It's been a bit of a problem, it's become actually incredibly high quality, we've really been with Mercedes and various other companies in that space. We find that engineers from Tesla and Veymo are taking it today. The insight was that two things, one is existing universities will be very slow to move because the department lies and there's no department for self-loving cars. So between Mackey and

double E and computer science, getting these folks together into one woman's really, really hard. And every professor listening here will know, will probably agree to that. And secondly, even 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. 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 there any dhamma than we cars? Are there any dumber than we are? Are we 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 1000 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. It's kind of interesting to imagine impossible to quantify, but the number, you know, over a period of several decades, the impact that has, like a single course, like a ripple effect of society.

If you, I just recently talked to Andruin who was creator of Cosmos, so it's interesting to think about how many scientists that show launched. And so it's really, in terms of impact, I can't imagine a better course than the self-driving car course. There's other more specific disciplines like deep learning and so on that you've asked these also teaching, but self-driving cars, it's really, really interesting course. Yeah, and it came at the right moment it came in time when there were a bunch of

aquihires. Aquihires are acquisition of a company not for its technology or its products or business but for its people. So aquihire 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 aquihires like GM Cruz 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 low abound, I estimated the value of and self-driving car engine in these acquisitions to be at least $10 million. So think about this. You get to save a skill and you team up and build a company and your worth now is $10 million. That's kind of cool.

But what other thing could you do in life to be worth $10 million within a year? Yeah, amazing. But to come back for a moment on to deep learning and its application and autonomous vehicles, what are your thoughts on Elon Musk's statement, provocative statement, perhaps that light is a crutch. So this geometric way of thinking about the world, maybe holding us back if what we should instead be doing in this robotics 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

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 use some other perception of what our bodies, accelerations, occasionally our ears, so they're not our noses. So the existence proves 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, we would 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 people trying different hypotheses.

It's almost like an antelope. If an antelope tries to forge for food, but you can sit there as two hands and agree what the perfect path is. And then every single hand 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 ends get it wrong, and they're all moving the wrong direction, they're going to waste the day, and then they're going to discuss again for another week, whereas if all these ends go in a right direction, some is going to succeed, and they're going to come back and claim victory

and get the Nobel Prize or whatever the ant equivalence, and then're all matched in the same direction. And that's great about society. It's great about the Western society. We're not plan-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? You get investor money, go out and try it out. And who knows who's going to win? I like it. In your, 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. 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, in example, we did a study at Stanford two years ago where we applied machine learning to detecting skin cancer and images. And we harvested a built-in dataset of 129,000 skin photo shots that were all had been biopsyed for what the actual situation was. And those included melanomas and carcinomas, also included rashes and other skin conditions, lesions. 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, this thing was great in this one thing I'm finding skin cancer, but I couldn't drive a car. 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, so that's gonna be transform human labor. But it's not this almighty general intelligence. We have 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 machine superiority. I don't think that we want to see this in my lifetime. I don't think human suppression 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. And in doing so, it can be super impactful, super impactful. Let's take the impact of artificial intelligence on human work. We all know that it takes 7,000,000 hours to become an expert. If you're going to 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 observe people become experts and observe experts. And then extract those rules from experts in some interesting way that could go from law to sales, to 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 been done, it's been uncommarsely in many, many, in instantiations.

That means we can use machine learning to make people an expert on the very first day of their work. Like, think about the impact. If your doctor is still in their first 10,000 hours, you have a doctor who is 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 in lack of expertise for human doctors. That could save your life.

If you can link on that for a little bit, in which way do you hope machines in the medical field could help assist doctors? So 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 machines. How do you envision that assistance looking? So we built 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 doctor. So let me tell you a story. So there's a friend of mine, it's Colin Ben. Ben is a very famous venture capitalist. He goes to his doctor and the doctor looks at him all and says, hey, that mole is probably harmless. And for some very funny reason, he pulls out that foam with

OARP. He's a collaborator in our study. And the app says, no, no, no, no, this is a melanoma. And for background, melanomas are skin cancers the most common cancer in this country. Melanomas can go from stage zero to stage four within less than a year. Stage zero means you can basically cut it out yourself with a kitchen knife and be safe. And stage four means your chances of five more years

than 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 and said, I mean, but just to be safe, let's cut it out and biopsy it. That's the technical term for it. Let's get an in-depth diagnostics that is more

and 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 and he said, I'm talking to this woman, skin cancer. He said, oh, funny, my doctor just had an iPhone that found my cancer. 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? 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 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 pancreatic cancer might be a headache. And when you finally go to your doctor because of these headaches or your your back pain and you're being imaged, it's usually stage four plus. And that's the time when the curing chances might be dropped to a single

digital percentage. So if you could leverage AI to inspect your body on a regular basis without even a doctor in the womb, maybe when you take a shower over the heavy, I know that sounds creepy, but then we might be able to save millions and millions of lives. 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 in their jobs. Do you have a worry of people losing their jobs or the economy being affected by the improvements in AI?

Yeah, anybody concerned considerable job losses, please come to getacity.com. We teach contemporary tech skills and we have a kind of implicit job promise. We often, when we measure, we spend way over 50% of our graders in new jobs, they're very satisfied about it. And of course almost nothing,

cause like 1,500 max of something like that. And so there's a cool new program that you agree with the US government guaranteeing that you will help us give scholarships that educate people in this kind of situation. You've been working with the US government on the idea of basically rebuilding the American dream. So Udacity has just dedicated 100,000 scholarships

for citizens of America for various levels of courses that eventually will get you a job. And those courses all somewhat relate to the tech sector because the tech sector is kind of the hottest sector right now and they're range from into level digital marketing to very advanced self-driving car engineering. And we're doing this with the White House because he thinks it's bipartisan. It's an issue that is that if you want to really make America great, being able to be part 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. I've always had this American dream to send our kids to college. And now the American dream has to be to send ourselves to college. Very, very, very efficiently and very, very, very in Sweden and the evenings and

things to online. And at all ages, all ages. So our, our learners go from age 11 to age 80. I just travel Germany and the guy in train compartment next to me was one of my students. Wow, that's amazing. I don't think about impact. We've become the educator of choice for now. I believe officially six countries. Five countries is most in the Middle East, like Saudi Arabia and Egypt. In Egypt, we just had a cohort graduate where we had 1100 high school students

that went through programming skills, proficient at the level of computer science undergrad. And we had a 95% graduation rate, even though everything's online, it's kind of tough. But we 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 irate, how evolves, only for the rich people, for the parents

who might be even bright 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 are truthful to the human mission, if we are truthful to our values, we are going to open up education to everybody in the world. So, Udacity is pledge of 100,000 scholarships. I think it's the biggest pledge of scholarships ever in terms of numbers. And we're working as a separate white house and with very accomplished CEOs,

like Tim Cook from Apple and others, to really bring education to everywhere in the world. Not to ask you to pick the favorite of your children, but at this point, it's Jasper. Okay, I've wanted to know of. Okay, but at this point, it's Jasper. Okay, I want that to know of. Okay, good. In this particular moment, what nanogridigree, what set of courses are you most excited about at Udacity, or is that too impossible to pick?

I've been super excited about something we haven't launched yet in the building, which is when we talk to our partner companies, we have now a very strong footing in the enterprise world. In order 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 in the Darsity, and I would say most universities are doing very poorly as well, because we're so obsessed with individual test scores 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 text skills and they're going to be great, but if we teach people empathy, there's going to have the same impact. Maybe harder than self-driving cars. But 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, we literally went in, in school in, 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, making me the best leader. And it turns out not to be that 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 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 a fluent world where we constantly deal with other people and that's a beauty It's not a nuisance. It's a beauty

So if we somehow develop that muscle that we can do that well and empower others in the workplace I think we're going to be super successful and I know many fellow roboticists and computer scientists that I will insist to take this course. Not to be named. Many many years ago 1903 the Wright Brothers flew in Kitty Hawk for the first time and you've watched a company of the same name, Kitty Hawk, with the dream of building flying cars, EV stalls. So at the big picture, what are the big challenges of making this thing that actually have inspired generations of people about what the future looks like? What does it take? What are the biggest challenges? So flying cars has always been a dream. Every boy, every girl wants to fly.

Let's be honest. Yes. And let's go back in our history of your 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 Seal famously said, they promised us flying cars and they gave us 140 characters, pointing at Twitter at the time, limitedly message size to 140 characters. So we're coming back now to really go for these 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 helicopters than anything else. They take off vertically and they fly horizontally. But they have important differences. One difference is that they're much quieter.

We just released a vehicle called Project Heavy Side that can fly over you as low as a helicopter. And you basically can't hear. It's like 38 decibels. It's like that. If you were inside the library, you might be able to hear it, but anywhere outdoors, your ambient noise is higher.

Secondly, 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 helicopters. There's a bolt between the blades that's caused 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 these 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. Have you said, if it loses a motor, has eight of those, and we lose one of those eight motors, so it's seven left. You can take off just like before, and land just like before. We are now also moving into a technology that doesn't require commercial pilots. Because in some level, flight is actually easier than the ground transportation, like in self-driving

cars. The void is full of like children and bicycles and other cars and mailboxes and curbs 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 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 15 or 20, but not 10,000. So the sky is very ample and very empty and very free. 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 site can fly 100 miles in range with still 30% electric reserves. It can fly up to like 180 miles an hour. We know that that solution at scale

would make your ground transportation 10 times as fast as a car based on use census statistics data, which means we would take your 300 hours of daily commute down to 30 hours and give you 270 hours back. Who wouldn't want, I mean, who doesn't hate traffic? Like, I hate, give me the person that doesn't hate traffic. I hate who doesn't hate traffic? Like, I hate, give me the person who doesn't hate traffic, I hate traffic every time I'm in traffic, I hate it. And if we could free the word from traffic, we have technology, we can free the word

from traffic. We have technology. It's there. We have an existence proof. It's not a technological problem anymore. 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 Taws fill the sky? I absolutely believe this and there's obviously the societal acceptance is a major question and of course safety is. I believe we can in safety is. I believe in safety, we're going to see ground transportation safety. It 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 quietest electric V12 vehicle ever built. The nice thing about the sky is it's three dimensional. So any mathematician will immediately recognize the difference between 1D of a regular highway to 3D of a sky. But to make it clear for the laymen, say you want to make 100 vertical lanes of

highway 101 in San Francisco, because you believe building a height and vertical lanes is the right solution. Imagine how much it would cost to stack 100 vertical lanes physically onto 101. There would be prohibitive. There would be consuming the world's GDP for an entire year, just for one highway. It's amazing expensive, okay. In the sky, it would just be a recombilation of a piece of software because all these lanes are virtual. That means any vehicle that is in conflict with another vehicle, which just go to different altitudes, and then the conflict is gone. And if you don't believe this, that's exactly how commercial aviation works.

When you fly from New York to San Francisco, another plane flies from San Francisco, New York, they are different altitudes, so they don't hit each other. 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 Wing 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. Do you envision autonomy being a key part of it so that the flying vehicles are either semi-autonomous

or fully autonomous? I 100% autonomous. You don't want idiots like me flying the sky, I promise you. And if you have 10,000, watch the movie of Fifth Element to get a people to happen if it's not autonomous? 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 similar as we have in the current commercial aviation but scale it up to much more vehicles.

That's a really interesting optimization problem. It is very, mathematically, very, very straightforward. Like the gap we leave between jets is gargantrous. And part of the reason is there isn't that many jets. So it just feels like a good solution. Today, when you get vectored by air traffic control, someone talks to you, right? So an A-T-C controller might have up to maybe 20 planes

on the same frequency, and then they talk to you, you have to talk back. And it feels right because there have up to maybe 20 planes on the same frequency and then they talk to you have to talk back And it feels right because there isn't more than 20 planes around any hour so you can talk to everybody But if there's 20,000 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 some of us solve the scale problem for smartphones. 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 ad-fabric control. So instead of me as a pilot talking to a human being and in the middle of the conversation receiving a new frequency, like how ancient is that, we could digitize this stuff and digitally transmit the right flight coordinates,

and that solution will automatically scale to 10,000 vehicles. We talked about empathy a little bit. Do you think we'll one day build an AI system that a human being can love and that loves that human back. Like in the movie her. Look, I'm a pragmatist. For me, AI is a tool. It's like a shuttle. And the ethics of using the shuttle I always with us, the people. And it has to be this way. In terms of emotions, In terms of emotions, I would hate to come into my kitchen and see that my refrigerator spoiled all my food. Then I haven't explained to me that it fell in love with a 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. 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 just wanted to work. I wanted to compliment 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 run across the Atlantic at near the speed of sound at 36,000 feet today. That's kind of amazing. I can 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 super human? I would even argue my my flashing toilet makes me super human just think of the time before flashing 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 It's it makes me super human. So to me, what technology does it compliments me? It makes me stronger. Therefore words like love and compassion have very little, very little interest in this for machines. I have interest in many people. You don't think, first of all, beautifully put, beautifully argued, but do you think love has use in our tools? Compassion. I think

love is a beautiful human concept and if you think 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, most of us people. We want to trust our technology, the same way we trust people. In human interaction, standards have emerged, and feelings, emotions have emerged, maybe genetically, maybe virologically, that are able to convey sense of trust, sense of safety, sense of passion, of love, of dedication, 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 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. We made exceptional birds exceptional birds who are just flapping wings. In fact, there are many people in aviation

that flap wings to their arms and jump from cliffs. Most of them didn't survive. Then the interesting thing is that the technology solutions are very different. Like, in technologies, really easily build a wheel. And biology is super hard to build a wheel. There's very few perpetually rotating things in biology, and they usually oneself things.

And engineering, we can build wheels. And those wheels gave rise to cars. Similar wheels gave rise to aviation. Like there's no thing that flies. They wouldn't have something to rotate, like a's no thing that flies, they wouldn't have something to rotate, 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 you really believed that the solution to the agriculture evolution was a humanoid robot, you would still be waiting today. Again, beautifully put, you said that you don't take yourself too seriously. You want me to say that? You want me to say that. Maybe.

You don't take me seriously. I'm not. I'm sorry. Good. You're right. I don't want to. I just made that up. But, you know, you have a humor and a lightness about life that I think is

is beautiful and inspiring to a lot of people. Where does that come from? The smile, the humor, the lightness amidst all the chaos, the hard work that you are in. Where does that come from? I just love my life. I love the people around me. I love I'm just so glad to be alive. I'm what 52, how to believe. People say 52 is a new 51, so now if you better. But in looking around the world, looking, just go back to 200, 300 years. Humanity is what, 300,000 years old. But for the first 300,000 years, minus the last 100, our life expectancy would have been plus or minus 30 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. Like, why am I born today when so many of my ancestors died of horrible deaths, like famines, massive wars, that ravaged Europe for the last 1,000 years, mystically disappeared after World War II,

when the Americans and the Allies disdainning amazing to my country that didn't deserve it, to country of Germany. This is so amazing. And then when you're alive and feel this every day, then it is so amazing what we can accomplish, what we can do. We live in the world that is so incredibly vastly changing every day. Almost everything that we cherish from your smartphone to your flashing toilet to all these basic inventions, the new clothes you're wearing, your watch, your plane, penicillin, I don't know, anesthesia for surgery,

penicillin, happen invented in the last 150 years. So in the last 150 years, something magical happened. And 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, they were able to invent agriculture and nitrogen fertilization that made agriculture so much more potent that we didn't have

to work in farms anymore. And we could start reading and writing and we could become all these wonderful things we are today from the Ellen pilot to massage therapist to software engineer. This is amazing. Living in that time is such a blessing. We should sometimes really think about this. Stephen 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 create a guy named Karl Bosch. And I challenge anybody, have you ever heard of the name Karl Bosch? I hadn't. There's a Bosch corporation in Germany, but it's not associated with Karl Bosch. So I looked it up. Karl 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 2500 percent 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. Think about that impact and what that means to society. That's the way I look at the world. I mean, it's so amazing to be alive and to be part of this. And I'm so glad I lived after a call, but I'm not before. I don't think there's a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, a better way to end this, the Sebastian's in honor to talk to you, to have had the chance to learn from you. Thank you so much for talking. Thanks for coming out.

Thanks for your pleasure. Thank you for listening to this conversation with Sebastian Thrun. And thank you to our presenting sponsor, CashApp. Download it, use code LEX Podcast, 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 5 stars on Apple Podcast, support it on Patreon or connect with me on Twitter. And now let me leave you with some words of wisdom from Sebastian Thrun. Support 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 something. Then you realize you have no fear.

And when your fear goes away, you can move the world. Thank you for listening and hope to see you next time. you