The following is a conversation with Sebastian Thrun.

He's 1 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 1 of the first massive open online courses, or MOOCs, as they're commonly called.

That experience led him to Kofound Udacity, an online education platform.

If you haven't taken courses on it yet, I highly recommend it.

Their self-driving car program for example is excellent.

He's also the CEO of Kitty Hawk, a company working on building flying cars, or more technically, EVTOLs, which stands for Electric Vertical Takeoff and Landing Aircraft.

He has launched several revolutions and inspired millions of people, But also, as many know, he's just a really nice guy.

It was an honor and a pleasure to talk with him.

This is the Artificial Intelligence Podcast.

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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 a crazy philosophical question.

Do you think we're living in a simulation?

And in general, do you find the thought experiment interesting?

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

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

Universe is an information processing system.

It's a huge physical, biological, chemical computer.

I think it's just the world evolves the way it evolves and it's beautiful, it's unpredictable and I'm really, really grateful to be alive.

Well, in fact, this whole conversation is just a Turing test to see if indeed you are.

You've also said that 1 of the first programs or the first few programs you've written was a, wait for it, TI-57 calculator.

I don't wanna date calculators or anything.

Yeah, so if you were to place yourself back into that time, into the mindset you were in, Could you have predicted the evolution of computing, AI, the internet technology in the decades that followed?

I was super fascinated by Silicon Valley, which I'd seen on television once and thought, my God, this is so cool.

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 80s and 90s, that artificial intelligence is what fascinated me the most.

What's missing is that back in the day, the computers are really small.

The brains you could build were not any bigger than a cockroach.

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?

I felt it was super cool to build an artificial human.

And the best way to build an artificial human would have been a robot, because that's kind of the closest you could do.

The robots today are still very brittle, but it's fascinating to study intelligence from a constructive perspective where you build something.

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

I think the biggest innovation that we've seen is machine learning.

And it's the idea that the computers can basically teach themselves.

I'd say everybody pretty much knows how to walk.

And we learn how to walk in the first year or 2 of our lives.

But no scientist has ever been able to write on the rules of human gait.

We can't put, we have it 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.

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 to make for like 30, 40 years, is an idea that computers can find their own rules.

So they can learn from falling down and getting up the same way children can learn from falling down and getting up.

And that revolution has led to a capability that's completely unmatched.

Today's computers can watch experts do their jobs, whether you're a doctor or a lawyer, pick up the regularities, learn those rules, and then become as good as the best experts.

So the dream of in the 80s of expert systems, for example, had at its core the idea that humans could boil down their expertise on a sheet of paper.

So to sort of reduce, sort of be able to explain to machines how to do something explicitly.

So do you think, what's the use of human expertise into this whole picture?

Do you think most of the intelligence will come from machines learning from experience without human expertise input?

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 many different ways.

And I think the expert systems was our best attempt in AI to capture expertise and rules.

But someone sat down and said, here are the rules of human gait.

Here's when you put your big toe forward and your heel backwards and you stop 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 like subconscious numerical perceptual things that we don't even, that we're self-aware of.

Now, when a AI system watches an expert do their job and practice their job, it can pick up things that people can't even put into writing, into books or rules.

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 1 can express in words.

So you were a key person in launching 3 revolutions, online education, autonomous vehicles, and flying cars or VTOLs.

So high level, and I apologize for all the philosophical questions.

How do you choose what problems to try and solve?

What drives you to make those solutions a reality?

I wanna literally make the lives of others better.

Or as we often say, maybe jokingly, make the world a better place.

I don't want to be in a job I'm good at because if I'm in a job that I'm good at, the chances for me to learn something interesting is actually minimized.

So I built, for example, what people often call flying cars.

These are electrical, vertical, takeoff and landing vehicles.

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, they really 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, in my opinion, even more than communication.

And cities are different, workers are different, women's rights are different because of transportation.

And yet we still have a very suboptimal transportation solution where we kill 1.2 or so million people every year in traffic.

It's like the leading cause of death for young people in many countries, where we are extremely inefficient resource wise, just go to your average neighborhood city and look at the number of parked cars, that's a travesty in my opinion, or where we spend endless hours in traffic jams.

And very, very simple innovations like a self-driving car or what people call a flying car could completely change this.

I mean, the technology is basically there.

You have to close your eyes not to see it.

So lingering on autonomous vehicles, a fascinating space, some incredible work you've done throughout your career there.

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 Race of 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?

Painful were all these incredibly complicated, stupid bugs that had to be found.

We had a phase where the Stanley, our car that eventually won the Darwin Grand Challenge, would every 30 miles just commit suicide.

And it ended up to be that in the syncing of 2 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.

I'd say enlightening is the Stanford team immediately focused on machine learning and on software, whereas everybody else seemed to focus on building better hardware.

Our analysis had been a human being with an existing rental car can perfectly drive the course.

Why do I have to build a better rental car?

And the human being to me was a conjunction of 3 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.

The sensors are actually also easy to build.

What was missing was the brain, So we had to build a human brain.

And nothing clear then to me that the human brain is a learning machine.

So we would build massive machine learning into our machine.

And with that we're able to not just learn from human drivers, we had the entire speed control of the vehicle was copied from human driving.

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

You mentioned the pain point of software and clocks.

be a problem that continues with robotics.

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 exploring, it's not even sure that anybody can finish when we're talking about the race of the desert the year before, nobody finished.

What does it take to scramble and finish a product that actually, a system that actually works?

It was 4 because 5 couldn't comfortably sit inside a car, but 4 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.

We just wanted to see how far we can get.

What we did really, really well was time management.

We were done with everything a month before the race.

And we froze the entire software a month before the race.

And it turned out, looking at other teams, every other team complained if they had just 1 more week, they would have won.

And we decided, we're not going to fall into that mistake.

And we had an entire month to shake the system.

And we actually found 2 or 3 minor bugs in the last month that we had to fix, and we were completely prepared when the race occurred.

Okay, so first of all, that's such an incredibly rare achievement in terms of being able to be done on time or ahead of time.

What do you, how do you do that in your future work?

Because it seems to be so rare, especially in highly innovative projects like this.

Well, the nice thing about the DARPA Grant Challenge is that the problem was incredibly well-defined.

We were able for a while to drive the old DARPA Grant 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 this northern desert and we were able to drive desert trails just at the same time So there was never any debate about like what is actually the problem?

We didn't sit down and say hey should we build a car or a plane if we had to build a car?

That made it very very easy Then I studied my own life and life of others and realized that the typical mistake that people make is that there's this kind of crazy bug left that they haven't found yet.

And it's just, they regret it and the bug would have been trivial to fix, they just haven't fixed it yet.

We had a testing team that built a testing booklet of

pages of tests we had to go through just to make sure we shake out the system appropriately.

And the testing team was with us all the time and dictated to us today we do railroad crossings, tomorrow we do we practice the start of the event.

And in all of these we thought oh my god is long solve 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.

So it was really a continuous focus on improving the weakest part of the system.

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

To me as an engineer, it's just super exciting that you were thinking like that, especially at that stage.

That's brilliant That testing was such a core part of it.

It made me to linger on the point of leadership.

I think it's 1 of the first times you were really a leader and you've led many very successful teams since then.

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

That's very convenient, turns out, because I can't do all these things myself.

I'm an engineer at heart, so I care about engineering.

So 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.

And you could like in the middle of the night, wake up at 1 in the morning and switch on your computer.

And what you told you to do yesterday, it would still do.

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 stop doing it.

So you have to, so then the question really became how can you put yourself in the brain of people as opposed to computers?

And in terms of computers, they're super dumb.

They're 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.

And that's the thing where most of our leadership just fails because many, many engineers turn manager believe they can treat their team just the same way they can treat a computer and it just doesn't work this way.

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.

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.

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.

So how can we empower people and give them the right framework that they can accomplish this?

In moments when it works, it's magical because you'd see the confluence of people being able to make the world a better place and just having enormous confidence and pride out of this.

And that's when my environment works the best.

These are moments where I can disappear for a month and come back and things still work.

It's very hard to accomplish, but in Winnipeg it's amazing.

So 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.

We make this mistake, and this is a friend of mine, Alex Voda, told me this, that we judge ourselves by our intentions and others by their actions.

And I think that the biggest skill, I mean, here in Silicon Valley, we have fellow engineers who have very little empathy and are kind of befuddled by why it doesn't work for them.

The biggest skill I think that people should acquire is to put themselves into the position of the other and listen, and listen to what the other has to say.

And they'd be shocked how similar they are to themselves.

And they might even be shocked how their own actions don't reflect their intentions.

I often have conversations with engineers where I say, look, hey, I love you.

And by the way, what you just did Has the following effect are you aware of that and then people would say?

Oh my god, not I wasn't because my intention was that and say 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 people 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.

How to Make Friends and How to Influence Others, has the entire Bible.

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