The following is a conversation with Sebastian Thrun.
He's one of the greatest roboticists, computer scientists,
and educators of our time.
He led the development of the autonomous vehicles
at Stanford that won the 2005 DARPA Grand Challenge
and placed second in the 2007 DARPA Urban Challenge.
He then led the Google self-driving car program,
which launched the self-driving car revolution.
He taught the popular Stanford course
on artificial intelligence in 2011,
which was one of the first massive open online courses,
or MOOCs, as they're commonly called.
That experience led him to co-found Udacity,
an online education platform.
If you haven't taken courses on it yet,
I highly recommend it.
Their self-driving car program, for example, is excellent.
He's also the CEO of Kitty Hawk,
a company working on building flying cars,
or more technically EV-talls,
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 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.
The universe is an information processing system.
It is. It's a huge physical, biological,
chemical computer, there's no question.
But I live here and now.
I care about people, I care about us.
What do you think is trying to compute?
I don't think there's an intention, I think,
to just the world evolves the way it evolves
and it's beautiful, it's unpredictable,
and I'm really, really grateful to be alive.
I've 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 TI-57 calculator.
Yeah.
Maybe that's early 80s.
I don't wanna date calculators or anything.
That's early 80s, correct.
Yeah.
So if you were to place yourself back into that time,
into the mindset you were in,
could you have predicted the evolution of computing,
AI, the internet, technology in the decades that followed?
I was super fascinated by Silicon Valley,
which I'd seen on television once and thought,
my God, this is so cool.
They built like DRAMs there and CPUs.
How cool is that?
And as a college student, 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 is what fascinated me the most.
What's missing is that back in the day,
the computers are really small.
They're like the brains you could build
were not anywhere bigger than a cockroach.
And cockroaches aren't very smart.
So we weren't at the scale yet where we are today.
Did you dream at that time
to achieve the kind of scale we have today?
Did that seem possible?
I always wanted to make robots smart.
I felt it was super cool to build an artificial human.
And the best way to build an artificial human
would be to build a robot
because that's kind of the closest we could do.
Unfortunately, we aren't there yet.
The robots today are still very brittle,
but it's fascinating to study intelligence
from a constructive perspective where you build something.
To understand you build,
what do you think it takes to build an intelligent system
and an intelligent robot?
I think the biggest innovation
that we've seen is machine learning.
And it's the idea that their computers
can basically teach themselves.
Let's give an example.
I'd say everybody pretty much knows how to walk.
And we learn how to walk in the first year, two of our lives.
But no scientist has ever been able
to write on the rules of human gait.
We don't understand it.
We have it in our brains some or 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.
Even you program a computer,
they're so insanely dumb
that you have to give them rules for every contingency.
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 in the make for like 30, 40 years,
is an idea that computers can find their own rules.
So they can learn from falling down
and getting up the same way children can learn
from falling down and getting up.
And that revolution has led to a capability
that's completely unmatched.
Today's computers can watch experts do their jobs,
whether you're a doctor or a lawyer,
pick up the regularities, learn those rules,
and then become as good as the best experts.
So the dream of in the 80s of expert systems,
for example, had at its core the idea
that humans could boil down their expertise
on a sheet of paper, sort of reduce,
sort of be able to explain to machines
how to do something explicitly.
So do you think, what's the use of human expertise
into this whole picture?
Do you think most of the intelligence
will come from machines learning from experience
without human expertise input?
So the question for me is much more,
how do you express expertise?
You can express expertise by writing a book.
You can express expertise by showing 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 here how it 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 subconscious numerical, perceptual things
that we don't even have to be self aware of.
Now, when an 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 what the real power is.
We now have AI systems that, for example,
look over the shoulders of highly-paid human doctors
like dermatologists or radiologists.
And they can somehow pick up those skills
that no one can express in words.
So you were a key person in launching three revolutions,
online education, autonomous vehicles,
and flying cars or VTOLs.
So high level.
And I apologize for all the philosophical questions.
No apology necessary.
How do you choose what problems to try and solve?
What drives you to make those solutions a reality?
I have two desires in life.
I want to literally make the lives of others better.
Or as we often say, maybe jokingly,
make the world a better place.
I actually believe in this.
It's as funny as it sounds.
And second, I want to learn.
I want to get in the skill set.
And I want to be in the 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 takeoff
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've done lately
after I finished my professorship at Stanford,
they really focused on what has the maximum impact on society.
Transportation is something that has transformed
the 21st or 20th century more than any other invention,
in my opinion, and even more than communication.
And cities are different, workers different.
Women's rights are different because of transportation.
And yet, we still have a very suboptimal transportation
solution where we kill 1.2 or so million people every year
in traffic.
It's like the leading cause of death
for young people in many countries
where we are extremely inefficient resource-wise.
Just go to your average neighborhood city
and look at the number of parked cars.
That's a travesty, in my opinion.
Or where we spend endless hours in traffic jams.
And very, very simple innovations
like a self-driving car or what people call a flying car
could completely change this.
And it's there.
I mean, the technology is basically there.
You have to close your eyes not to see it.
So lingering on autonomous vehicles,
a fascinating space, some incredible work
you've done throughout your career there.
So let's start with DARPA.
I think the DARPA challenge 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
who 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 finished second
in the DARPA urban challenge,
also did incredibly well in 2007, I think.
What are some painful, inspiring,
or enlightening experiences from that time
that stand out to you?
Oh my God, painful were all these incredibly complicated
stupid bugs that had to be found.
We had a phase where the Stanley,
our car that eventually won the DARPA run challenge
would every 30 miles just commit suicide.
And we didn't know why.
And it ended up to be that in the sinking
of two computer clocks, occasionally a clock went
backwards and that negative time elapsed,
screwed up the entire internal logic,
but it took ages to find this.
It were like bugs like that.
I'd say enlightening is the Stanford team
immediately focused on machine learning and on software,
whereas everybody else seemed to focus
on building better hardware.
Our analysis had been a human being
with an existing rental car can perfectly drive the course
by having to build a better rental car.
I just should replace the human being.
And the human being to me was a conjunction of three steps.
We had like sensors, eyes and ears,
mostly eyes, we had brains in the middle
and then we had actuators, our hands and our feet.
Now the extras are easy to build.
The sensors actually also easy to build
what was missing was the brain.
So we had to build a human brain
and nothing clear than to me
that the human brain is a learning machine.
So why not just train our robot?
So you would build a massive machine learning
into our machine.
And with that we were able to not just learn
from human drivers, we had the entire speed control
of the vehicle was copied from human driving,
but also have the robot learn from experience
where it made a mistake and got recovered 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.
So 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 the desert
the year before nobody finished.
What does it take to scramble and finish a product
that actually, a system that actually works?
We were lucky, we were a really small team.
The core of the team were four people.
It was four because five couldn't comfortably sit
inside a car, but four could.
And I as a team leader, my job was to get pizza
for everybody and wash the car and stuff like this
and repair the radiator and it broke
and debug the system.
And we were very kind of open-minded.
We had like no ego as involved in this.
We just wanted to see how far we can get.
What we did really, really well was time management.
We were done with everything a month before the race.
And we froze the entire software a month before the race.
And it turned out, looking at other teams,
every other team complained if they had just one more week,
they would have won.
And we decided, that's gonna fall into that mistake.
We're gonna be early.
And we had an entire month to shake the system.
And we actually found two or three minor bucks
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?
What advice do you have in general?
Because it seems to be so rare,
especially in highly innovative projects like this.
People worked till the last second.
But the nice thing about the Topper Grand Challenge
is that the problem was incredibly well defined.
We were able for a while to drive
the old Topper Grand Challenge course,
which had been used the year before.
And then at some reason, we were kicked out of the region.
So we had to go to different deserts,
the snorren deserts,
and we were able to drive desert trails
just of the same type.
So there was never any debate about,
like what's actually the problem.
We didn't sit down and say,
hey, should we build a car or a plane?
We had to build a car.
That made it very, very easy.
Then I studied my own life and life of others.
And we asked that the typical mistake that people make is
there's this kind of crazy bug left
that they haven't found yet.
And they just, they regret it
and the bug would have been trivial to fix.
They just haven't fixed it yet.
They didn't want to fall into that trap.
So I built a testing team.
We had a testing team that built a testing booklet
of 160 pages of tests we had to go through
just to make sure we shake out the system appropriately.
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.
We have to fix this.
So it was really a continuous focus
on improving the weakest part of the system.
And as long as you focus on improving
the weakest part of the system,
you eventually build a really great system.
Let me just pause on that.
To me, as an engineer, it's just super exciting
that you were thinking like that,
especially at that stage as brilliant.
That testing was such a core part of it.
It may be to linger on the point of leadership.
I think it's one of the first times
you were really a leader
and you've led many very successful teams since then.
What does it take to be a good leader?
I would say most of them 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.
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 would still do.
That was really cool.
Unfortunately, they didn't quite work with people.
So you go to people and tell them what to do
and they don't do it and they hate you for it.
Or you do it today and then they go a day later
and they'll 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, it's super dumb.
They're just so dumb.
If people were as dumb as computers,
I wouldn't want to work with them.
But people are smart and people are emotional
and people have pride and people have aspirations.
So how can I connect to that?
And that's the thing where most of leadership just fails
because many, many engineers, turn manager,
believe they can treat their team just the same way
they can treat your computer.
And it just doesn't work this way.
It's just really bad.
So how can I connect to people?
And it turns out, as a college professor,
the wonderful thing you do all the time
is to empower other people.
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.
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.
We're 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 an 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 deriving enormous confidence and pride
out of this.
And that's when my environment works the best.
These are moments where I can disappear for a month
and come back and things still work.
It's very hard to accomplish, but when it works, it's amazing.
So I agree 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.
It's not often heard.
We make this mistake, and this is a friend of mine,
Alxota, talking this, that we judge ourselves
by our intentions and others by their actions.
And I think the biggest skill, I mean, here in Silicon Valley,
we have a lot of 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, 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 I say, yeah, I trust your intention,
you're a good human being.
But just to help you in the future,
if you keep expressing it that way,
then people will just hate you.
And I've had many instances where you would say, oh, my god,
thank you for telling me this, because it wasn't my intention
to look like an idiot.
It wasn't my intention to help other people.
I just didn't know how to do it.
It's very simple, by the way.
There's a book, Dale Carnegie, 1936,
How to Make Friends and How to Influence Others,
has the entire Bible.
You just read it, and you're done,
and you apply it every day.
And I wish I was good enough to apply it every day.
But it's just simple things, right?
Like, be positive, remember people's names, smile,
and eventually have empathy.
Like, really think that the person that you hate
and you think is an idiot is actually just like yourself.
It's a person who's struggling, who means well,
and who might need help.
And guess what?
You need help.
I've recently spoken with Stephen Schwartzman.
I'm not sure if you know who that is, but.
I do.
So, and he said.
It's on 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.
I mean, that's my little history of engineering.
That is, while I was engineering with computers,
I didn't care at all what their computer's problems were.
Because I just told them what to do and to do it.
And it just doesn't work this way with people.
It doesn't work with me.
If you come to me and say to A, I do the opposite.
But let's return to the comfortable world of engineering.
And can you tell me in broad strokes
in how you see it?
Because you're at the core of starting it,
the core of driving it, the technical evolution
of autonomous vehicles from the first DARPA Grand Challenge
to the incredible success we see with the program you started
with Google self-driving car and Waymo
and the entire industry that sprung up
of different kinds of approaches, debates, and so on.
Well, the idea of self-driving car
goes back to the 80s.
There was a team in Germany, another team in Carnegie Mellon
that did some very pioneering work.
But back in the day, I'd say the computers
were so deficient that even the best professors
and engineers in the world basically stood no chance.
It then folded into a phase where the US government spent
at least half a billion dollars that I could count
on research projects.
But the way the procurement works,
a successful stack of paper describing lots of stuff
that no one's ever going to read was a successful product
of a research project.
So we trained our researchers to produce lots of paper.
That all changed with the DARPA Grand Challenge.
And I really got a credit the ingenious people at DARPA
and the US government in Congress
that took a complete new funding model where they said,
let's not fund effort, let's fund outcomes.
And it sounds very trivial, but there
was no tax code that allowed the use of congressional tax
money for a price.
It was all effort-based.
So if you put in 100 hours in, you could charge 100 hours.
If you put in 1,000 hours in, you could build 1,000 hours.
By changing the focus and so you're making the price,
we don't pay you for development, we pay you for the accomplishment.
They automatically drew out all these contractors
who are used to the drug of getting money per hour.
And they drew in a whole bunch of new people.
And these people are mostly crazy people.
There were people who had a car and a computer
and they wanted to make a million bucks.
The million bucks was the original price money.
It was then doubled.
And they felt if I put my computer in my car and program it,
I can be rich.
And that was so awesome.
Like half the teams, there was a team that was surfer dudes
and they had like two surfboards on their vehicle
and brought like these fashion girls, super cute girls,
like twin sisters.
And you could tell these guys were not your common felt way
bandit who gets all these big, multi-million and billion
dollar countries from the US government.
And there was a great reset.
Universities moved in.
I was very fortunate at Stanford that I just received 10 year.
So I couldn't get fired, no matter what I do.
Otherwise I wouldn't have done it.
And I had enough money to finance this thing.
And I was able to attract a lot of money from third parties.
And even car companies moved in.
They kind of moved in very quietly
because they were super scared to be embarrassed
that their car would flip over.
But Ford was there and Volkswagen was there
and a few others and GM was there.
So it kind of reset the entire landscape of people.
And if you look at who's a big name in self-driving cars
today, these were mostly people who participated
in those challenges.
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,
having these kinds of grand challenges that
could improve outcomes?
So I'm a big believer in focusing on kind of an end
to end system.
I'm a really big believer in systems building.
I've always built systems in my academic career,
even though I do a lot of math and abstract stuff.
But it's all derived from the idea of let's solve a real problem.
And it's very hard for me to be in academic and say,
let me solve a component of a problem.
Like with someone that feels like non-monotonic 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 around and say, hey, what problem
would my grandmother care about that
doesn't understand computer technology
and doesn't want to understand?
And how 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 is much easier.
But impressing my grandmother is very, very hard.
So I would always thought if I can build a self-driving car
and my grandmother can use it even after she loses her driving
privileges or children can use it or we save maybe
a million lives a year, that would be very impressive.
And then there's so many problems like these.
Like there's a problem with Q and cancer or whatever it is.
Live twice as long.
Once a problem is defined, of course,
I can't solve it in its entirety.
Like it takes sometimes tens of thousands of people
to find a solution.
There's no way you can fund an army of 10,000 at Stanford.
So you've got to build a prototype.
Let's build a meaningful prototype.
And the DARPA Grand Challenge was beautiful
because it told me what this prototype had to do.
I didn't even think about what it had to do.
It just had to read the rules.
And it 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 or gives you an inkling
that this problem could be solved with this prototype.
First of all, I want to emphasize what academia really is.
And I think people misunderstand it.
First and foremost, academia is a way to educate young people.
First and foremost, a professor is an educator,
no matter where you are at a small sub-urban college
or whether you are a Harvard or Stanford professor.
That's not the way most people think of themselves
in academia because we have this kind of competition going
on for citations and publication.
That's a measurable thing.
But that is secondary to the primary purpose
of educating people to think.
Now, in terms of research, most of the great science,
the great research comes out of universities.
You can trace almost everything back,
including Google, to universities.
So there's nothing really fundamentally broken here.
It's a good system.
And I think America has the finest university system
on the planet.
We can talk about reach and how to reach people
outside the system.
It's a different topic.
But the system itself is a good system.
If I had one wish, I would say it would be really great
if there was more debate about what the great big problems
are in society and focus on those.
And most of them are interdisciplinary.
Unfortunately, it's very easy to fall
into an interdisciplinary viewpoint
where your problem is dictated, 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 to give an example, prior to me
working on self-driving cars, I was a roboticist
and a machine learning expert.
And I wrote books on robotics, something called
probabilistic robotics.
It's a very methods-driven kind of view point of the world.
I built robots that acted in museums as tour guides that
led 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 field of robotics,
cars are looked like as a gimmick.
And they're not expressive enough.
They can only put this bottle in the brakes.
There's no dexterity.
There's no complexity.
It's just too simple.
And no one came to me and said, wow,
if you solve that problem, you can save a million lives.
Among all robotic problems that I've seen in my life,
I would say the self-driving car transportation
is the one that has the most hope for society.
So how come the robotics community wasn't all over the place?
And it was become because we focused
on methods and solutions and not on problems.
If you go around today and ask your grandmother, what bugs you?
What really makes you upset?
I challenge any academic to do this
and then realize how far your research is probably
away from that today.
At the very least, that's a good thing for academics
to deliberate on.
The other thing that's really nice in Silicon Valley
is Silicon Valley is full of smart people outside academia.
So there's the Larry Pages and Mark Zuckerbergs
in the world who are anywhere smarter, smarter
than the best academics I've met in my life.
And what they do is they are at a different level.
They build the systems.
They build the customer-facing systems.
They build things that people can use
without technical education.
And they are inspired by research.
They're inspired by scientists.
They hire the best PhDs from the best universities for a reason.
So I think 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 in Silicon Valley.
It did not work as well in other places in this nation.
So when I worked at Carnegie Mellon,
we had the world's finest computer science university.
But there wasn't those people in Pittsburgh
that would be able to take these very fine computer science
ideas and turn them into massively impactful products.
That symbiosis seemed to exist pretty much only
in Silicon Valley and maybe a bit in Boston and Austin.
Yeah.
With Stanford, that's really interesting.
So if we look a little bit further on
from the DARPA Grand Challenge and the launch
of the Google self-driving car, what
do you see as the state, the challenges of autonomous vehicles
as they are now?
Is actually achieving that huge scale
and having a huge impact on society?
I'm extremely proud of what has been accomplished.
And again, I'm taking a lot of quality for the work for others.
And I'm actually very optimistic.
And people have been kind of worrying,
is it too fast, is it too slow, why is it not there yet,
and so on.
It is actually quite an interesting hard problem.
And in that, a self-driving car, to build one that
manages 90% of the problems encountered in everyday driving
is easy.
We can literally do this over a weekend.
To do 99% might take a month, then there's 1% left.
So 1% would mean that you still have a fatal accident every week.
Very unacceptable.
So now you work on this 1%.
And the 99% of that left, the remaining 1%
is actually still relatively easy.
But now you're down to like a 100% or 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
that we're able to deal with the couch on the highway, right?
Or the deer in the headlight, or the blown tire
that we'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 how safe driving
actually is, given how many millions of miles we drive
every year in this country.
We are now at a point where I believe the technology is there.
And I've seen it.
I've seen it in way more.
I've seen it in Aptif, I've seen it in Cruz,
in a number of companies and in Voyage
where vehicles 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 off your app,
you'll be picked up by a driverless car.
Now, there's still safety drivers in there.
But that's a fantastic way to kind of learn
what the limits are of technology today.
And there's still some glitches, but the glitches
have become very, very rare.
I think the next step is going to be to down-cost it,
to harden it.
The entrapment, the sensors are not quite
an automotive grade standard yet.
And then to really build the business models,
to really kind of go somewhere and make the business case.
And the business case is hard work.
It's not just, oh my god, we have this capability.
People are just going to buy it.
You have to make it affordable.
You have to find the social acceptance of people.
None of the teams yet has been able to, or gutsy enough,
to drive around without a person inside the car.
And that's the next magical hurdle.
We'll be able to send these vehicles around completely empty
in traffic.
And I think, I mean, I wait every day,
wait for the news that Waymo has just done this.
So it's 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 cautious, safety-focused
approach to the problem.
What do you think it takes?
What's leadership, but 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 what it says is 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 tightens
into the same bucket, which is you
have to balance public safety with your drive to innovate.
And this country, in particular in the States,
has a 100-plus year history of doing this very successfully.
Air travel is what 100 times is safe 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 yell and noise
and fuel consumption.
So I think that those practices are proven,
and they actually work.
We live in a world safer than ever before.
And yes, there will always be the provision
that something goes wrong.
There's always the possibility that someone makes a mistake,
or there's an unexpected failure.
We can never guarantee to 100% absolute safety
other than just not doing it.
But I think I'm very proud of the history of 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 five.
They jump in full autonomy with a safety driver
and take that kind of approach and also
through simulation and so on.
There's also the approach that Tesla autopilot is doing,
which is kind of incrementally taking a level two vehicle
and using machine learning and learning from the driving
of human beings and trying to creep up,
trying to incrementally improve the system
until it's able to achieve level four autonomy.
So perfect autonomy in certain kind of geographical regions.
What are your thoughts on these contrasting approaches?
Well, first of all, I'm a very proud Tesla owner,
and I literally use the autopilot every day,
and it literally has kept me safe.
It is a beautiful technology specifically
for highway driving when I'm slightly tired,
because then it turns me into a much safer driver.
And I'm 100% confident that's the case.
In terms of the right approach, I think the biggest change
I've seen since I went in the Waymo team
is this thing called deep learning.
Deep learning was not a hot topic when I started Waymo,
or Google self-driving cars.
It was there.
In fact, we started Google Brain at the same time
in Google X, so I invested in deep learning,
but people didn't talk about it.
It wasn't a hot topic.
And now it is.
There's a shift of emphasis from a more geometric
perspective, where you use geometric sensors.
They give you a full 3D view, and you
do a geometric reasoning about, oh, this box over here
might be a car.
Towards a more human-like, oh, let's just learn about it.
This looks like the thing I've seen 10,000 times before,
so maybe it's the same thing.
Machine learning perspective.
And that has really put, I think,
all these approaches on steroids.
And he does that if you teach a course in self-driving cars.
In fact, I think we've credited over 20,000 or so people
on self-driving cars skills.
So every self-driving car team in the world
now uses our engineers.
And in this course, the very first homework assignment
is to do lane finding on images.
And lane finding images, for laymen, what this means
is you put a camera into your car, or you open your eyes,
and you wouldn't know where the lane is.
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.
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 for white really means, this is not quite white enough.
So let'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.
Or maybe it's not quite a straight line,
because the road is curved.
And do we know that there's a 6 feet between lane markings
on north or 12 feet, whatever it is?
And now, what the students are doing,
they would take machine learning.
So instead of like writing these crazy rules
for the lane marker, they would say,
hey, let's take an hour of driving,
and label it, and tell the vehicle,
this is actually the lane by hand.
And then these are examples, and have the machine
find its own rules, what lane markings are.
And within 24 hours, now every student
that's never done any programming before in this 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,
it teaches how to solve the entire problem.
I definitely recommend people.
It's been super popular, and it's become actually
incredibly high quality, really with Mercedes,
and various other companies in that space.
And we find that engineers from Tesla
and Waymo 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-driving cars.
So between Mackey, and Double E, and Computer Science,
getting those folks together into one room is really,
really hard, and every professor listening here
will know, will probably agree to that.
And secondly, even if all the great universities
just 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 they any dumber than we are?
Are they any less privileged?
And the answer is we should just give everybody the skill
to build a self-driving car, because if we do this,
then we have like a thousand self-driving car startups.
And if 10% succeed, that's like a hundred,
that means a hundred countries now
will have self-driving cars and be safer.
It's kind of interesting to imagine impossible to quantify,
but the number, over a period of several decades,
the impact that has, like a single course,
like a ripple effect to society.
If you, I just recently talked to Andrew
and who was creator of Cosmos, so it's a show.
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.
That's, you know, there's other more specific disciplines
like deep learning and so on that Udasi is also teaching,
but self-driving cars, it's really, really interesting course.
In the end, it came at the right moment.
It came at a time when there were a bunch of acquires.
Acquire is acquisition of a company,
not for its technology or its products or business,
but for its people.
So acquire 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 acquires like GM Cruise and Uber and others
and did the math and said,
hey, how many people are there
and how much money was paid?
And as a lower bound, I estimated the value
of a self-driving car engineer in these acquisitions
to be at least $10 million, right?
So think about this, you get yourself a skill
and you team up and build a company
and your worth now is $10 million.
I mean, that's kind of cool.
I mean, 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 onto deep learning
and its application in autonomous vehicles,
what are your thoughts on Elon Musk's statement,
provocative statement, perhaps, that light air is a crutch?
So this geometric way of thinking about the world
may be holding us back
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 I have two comments, I think first of all,
we all know that people can drive cars
without lighters in their heads
because we only have eyes
and we mostly just use eyes for driving.
Maybe we use some other perception about our bodies,
accelerations, occasionally our ears,
certainly not our noses.
So the existence proof is there,
that eyes must be sufficient.
In fact, we could even drive a car
if someone put a camera out
and then gave us the camera image with no latency,
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.
An antelope tries to forge for food, right?
You can sit there as two ants
and agree what the perfect path is
and then every single ant marches
for the most likely location of food is
or you can even just spread out.
And I promise you the spread out solution will be better
because if the discussing philosophical intellectual ants
get it wrong and they're all moving the wrong direction,
they're gonna waste the day
and then they're gonna discuss again for another week.
Whereas if all these ants go in the right direction,
someone's gonna succeed and they're gonna come back
and claim victory and get the Nobel Prize
or whatever the ant equivalent is.
And then they all march in the same direction.
And that's great about society.
That's great about the Western society.
We're not plant-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 started in the C-Corp.
We get investor money, go out and try it out.
And who knows who's gonna 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 in images.
And we harvested or built a data set of 129,000
skin photo shots that all had been biopsied
for what the actual situation was.
And those included melanomas and carcinomas,
also included rashes and other skin conditions, lesions.
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.
And we proved that.
Now, this thing was great in this one thing,
I'm finding skin cancer, but it 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 really transform human labor.
But it's not this almighty general intelligence.
We're really far away from a system
that would 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're gonna 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 something like 10,000 hours
to become an expert.
If you're gonna 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 done commercially in many, many instantiations.
So that means we can use machine learning
to make people an expert on their 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's not quite an expert yet.
Who would not want a doctor who's 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 we can link on that for a little bit,
in which way do you hope machines in the medical field
could help assist doctors?
You mentioned this sort of accelerating the learning curve
or people if they start a job
or in the first 10,000 hours can be assisted by 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 that's called Ben.
Ben is a very famous venture capitalist.
He goes to his doctor and the doctor looks at a mole
and says, hey, that mole is probably harmless.
And for some very funny reason,
he pulls out that phone with our app.
He's a collaborator in our study.
And the app says, no, no, no, no.
This is a melanoma.
And for background,
melanomas are skin cancer is 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 leaving 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.
He said, I mean, what, just to be safe,
let's cut it out and biopsy it.
That's the technical term for let's get an in-depth diagnostics
that is more than just looking at it.
And it came back as cancerous as a melanoma
and it was then removed.
And my friend Ben, I was hiking with him
and we were talking about AI
and he said, I'm talking to do this work on skin cancer.
And he said, oh, funny.
My doctor just had an iPhone that found my cancer.
Wow.
So I was like completely intrigued.
I didn't even know about this.
So here's a person, I mean, this is a real human life, right?
Now, 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.
And 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 pancreate cancer might be a headache.
And when you finally go to your doctor
because of these headaches or your back pain
and you're being imaged, it's usually stage four plus.
And that's the time when your curing chances
might be dropped to a single digital percentage.
So if you could leverage AI to inspect your body
on a regular basis without even a doctor in the room,
maybe when you take a shower or what have you,
I know this 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 about job losses,
please come to Udacity.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 graduates in new jobs and they're very satisfied
about it and it costs almost nothing,
it costs like 1,500 max or something like that.
And I saw there's a cool new program that you agreed
with the US government guaranteeing that you will help
give scholarships that educate people
in this kind of situation.
Yeah, we're 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 range from inter-level digital marketing
to very advanced self-driving car engineering.
And we're doing this with the White House
because we think it's bipartisan.
It's an issue that is that if you wanna really make
America great, being able to be a part of the solution
and live the American dream requires us to be proactive
about our education and our skillset.
It's just the way it is today.
And it's always been this way.
And we always had this American dream
to send our kids to college
and now the American dream has to be
to send ourselves to college.
We can do this very, very efficiently
and very, very, we can squeeze in in the evenings
and things to online at all ages.
All ages.
So our learners go from age 11 to age 80.
I just traveled Germany and the guy in the train compartment
next to me was one of my students.
It's like, wow, that's amazing.
I don't think about impact.
We've become the educator of choice
for now I believe officially six countries
or five countries and most in the Middle East
like Saudi Arabia and in 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 a 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 very, very simple.
The vision is education ought to be a basic human right.
It cannot be locked up behind ivory tower walls
only for the rich people, for the parents
who might be 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 truthful to our values,
we're gonna open up education to everybody in the world.
So Udacity's pledge of 100,000 scholarships,
I think is the biggest pledge of scholarships ever
in terms of numbers.
And we're working, as I said, with the White House
and with very accomplished CEOs
like Tim Cook from Apple and others
to really bring education to everywhere in the world.
Not to ask you to pick the favorite of your children,
but at this point-
Oh, that's Jasper.
I only have one that I don't know of.
Okay, good.
In this particular moment, what nano-good degree,
what set of courses are you most excited about, 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.
And also to our students,
we've kind of always focused on these hard skills
like the programming skills or math skills
or building skills or design skills.
And a very common ask is soft skills.
Like, how do you behave in your work?
How do you develop empathy?
How do you work in a team?
What are the very basics of management?
How do you do time management?
How do you advance your career
in the context of a broader community?
And that's something that we haven't done
very well at Udacity.
And I would say most universities
are doing very poorly as well
because we're so obsessed with individual test scores
and so little,
pays a little attention to teamwork in education.
So that's something I see us moving into as a company
because I'm excited about this.
And I think, look, we can teach people tech skills
and they're gonna be great.
But if you teach people empathy,
that's gonna 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 want to engage.
We literally went in school and in K through 12,
we teach kids like get the highest math score.
And if you are a rational human being,
you might evolve from this education, say,
having the best math score and the best English scores,
making me the best leader.
And it turns out not to be the case.
It's actually really wrong.
Because first of all, in terms of math scores,
I think it's perfectly fine to hire somebody
with great math skills.
You don't have to do it yourself.
You can hire some of the great empathy for you
that's much harder,
but you can always hire some of the 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 gonna be super successful.
And I know many fellow robot assistant computer scientists
that I will insist to take this course.
Not to be named you.
Not to be named.
Many, many years ago, 1903,
the Wright Brothers flew in Kitty Hawk for the first time.
And you've launched a company of the same name, Kitty Hawk,
with the dream of building flying cars, EV Talls.
So at the big picture,
what are the big challenges of making this thing
that actually 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 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 that we remember most vividly.
And then Peter Thiel famously said,
they promised us flying cars
and they gave us 140 characters,
pointing as Twitter at the time,
limited 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 a helicopter
than anything else.
They take off vertically in their flight horizontally,
but they have important differences.
One difference is that they are much quieter.
We just released a vehicle called Project Heavy Sight
that can fly over you as low as a helicopter
and you basically can't hear.
It's like 38 decibels.
It's like, if you were inside the library,
you might be able to hear it,
but anywhere outdoors, your ambient noise is higher.
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 a helicopter.
There's a bolt between the blades
that's caused Jesus bolt.
And the reason why it's called Jesus bolt is
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.
Heavy Sight, if it loses a motor,
if it has eight of those,
if it loses 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 a commercial pilot
because in some level,
flight is actually easier than ground transportation.
Like in self-driving cars,
the world 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 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 sites 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 sensors or 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 who doesn't hate traffic.
I hate traffic every time I'm in traffic, I hate it.
And if we could free the world from traffic,
we have technology, we can free the world from traffic.
We have the technology.
It's there, we have an existence proof.
It's not a technological problem anymore.
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 TALLs, 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 in safety, we're going to exceed
ground transportation safety as has happened
for aviation already, commercial aviation.
And in terms of acceptance, I think
one of the key things is noise.
That's why we are focusing relentlessly on noise
and we've built perhaps the crisis electric VTALL 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 like a regular highway to 3D of a sky.
But to make it clear for the layman,
say you want to make 100 vertical lanes of highway 101
in San Francisco because you believe building
a hundred vertical lanes is the right solution.
Imagine how much it would cost to stack
100 vertical lanes physically onto 101.
They would be prohibitive.
They would be consuming the world's GDP for an entire year
just for one highway.
It's amazingly expensive.
In the sky, it would just be a recompilation
of a piece of software because all these lanes are virtual.
That means any vehicle that is in conflict with another vehicle
would just go to different altitudes
and 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 to New York,
there 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?
100% autonomous.
You don't want idiots like me flying in the sky.
I promise you.
And if you have 10,000, watch the movie, The Fifth Element,
to get a pee for what would happen if it's not autonomous.
And that's a really interesting idea
of a centralized 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
is a really interesting optimization problem.
It is mathematically very, very straightforward.
Like the gap we leave between jets is gargantuanous
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.
So an ATC 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 isn't more than 20 planes
around anyhow, 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.
We do have solutions.
We have about four or five billion smartphones in the world
now, 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 air traffic control.
So instead of me as a pilot talking to a human being
in the middle of the conversation receiving
a new frequency, like how ancient is that,
we could digitize the 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 will 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 shovel.
And the ethics of using the shovel
are always with us, the people.
And it has to be this way.
In terms of emotions, I would hate to come into my kitchen
and see that my refrigerator spoiled all my food,
then have it explained to me that it fell in love
with the dishwasher.
And I wasn't as nice as the dishwasher,
so as a result, it neglected me.
That would just be a bad experience,
and it would be a bad product.
I would probably not recommend this refrigerator
to my friends.
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 want it to work.
I want it to complement me, not to replace me.
I have very unique human properties,
and I want the machines to make me turn me into a superhuman.
I'm already a superhuman today, thanks to the machines that
surround me.
And I'll give you examples.
I can run across the Atlantic near the speed of sound
at 36,000 feet today.
That's kind of amazing.
My voice now carries me all the way to Australia
using a smartphone today.
And it's not the speed of sound which would take hours.
It's the speed of light.
My voice travels at the speed of light.
How cool is that?
That makes me superhuman.
I would even argue my flushing toilet makes me superhuman.
Just think of the time before flushing toilets.
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 makes me superhuman.
So to me, what technology does, it complements me.
It makes me stronger.
Therefore, words like love and compassion
have very little interest in this for machines.
I have interest in 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 of what love really is,
love is a means to convey safety, to convey trust.
I think trust has a huge need in technology as well,
not just people.
We want to trust our technology in a similar way
we trust people.
In human interaction, standards have emerged.
And feelings, emotions have emerged, maybe genetically,
maybe ideologically, 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 1,000 legs.
And you could have concluded that is the right way
to move about the environment.
We've made the exception of birds who is 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 technology, it's really easy to build a wheel.
In biology, it's super hard to build a wheel.
There's very few perpetually rotating things in biology.
And they usually run cells and things.
In 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 that
wouldn't have something that rotates,
like a jet engine or helicopter blades.
So the solutions have used very different physical laws
than nature.
And that's great.
So for me to be too much focused on, oh, this
is how nature does it, let's just replicate it,
if you really believed that the solution to the agricultural
evolution was a humanoid robot, you would still
be waiting today.
Again, beautifully put, you said that you
don't take yourself too seriously.
Did I say that?
You want me to say that?
Maybe.
You don't take me seriously.
I'm not.
Yeah, that's right.
Good.
You're right.
I don't want to.
I just made that up.
But you have a humor and a likeness about life
that I think is beautiful and inspiring to a lot of people.
Where does that come from?
The smile, the humor, the likeness
amidst all the chaos of the hard work that you're 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.
Like, I'm, what, 52?
How to believe?
People say 52 is a new 51.
So now I feel better.
But in looking around the world, looking,
just go back 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
did something amazing to my country that didn't deserve it,
my country of Germany.
This is so amazing.
And then when you're alive and feel this every day,
then it's just so amazing what we can accomplish,
what we can do.
We live in a world that is so incredibly vastly changing
every day, almost everything that we cherish from your smartphone
to your flushing toilet, to all these basic inventions,
your new clothes you're wearing, your watch, your plane,
penicillin, I don't know, anesthesia for surgery,
penicillin, have been 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 we
were able to invent agriculture and nitrogen
fertilization that made agriculture so much more
potent that we didn't have to work with farms anymore,
and we could start reading and writing,
and we could become all these wonderful things we are today,
from airline pilot to massage therapist to software engineer.
It's just 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, it's only one article,
what would it be?
What's the most important innovation, the most important
thing that happened?
And he would say this article would credit a guy named
Karl Bosch.
And I challenge anybody, have you ever heard of the name
Karl Bosch?
I haven't, OK.
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 2,500% increase in fertility of land.
And that, so Steve Pinker argues,
saved over 2 billion lives today.
2 billion people who would be dead if this man hadn't done
what he had done.
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 Karl Bosch and not before.
I don't think there's a better way to end it.
Sebastian, it's an honor to talk to you,
to have had the chance to learn from you.
Thank you so much for talking to us.
Thanks for coming out.
It's a real pleasure.
Thank you for listening to this conversation with Sebastian
Thrun.
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And now, let me leave you with some words of wisdom
from Sebastian Thrun.
It's important to celebrate your failures as much
as your successes.
If you celebrate your failures really well,
if you say, wow, I failed, I tried, I was wrong,
I had to learn 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.