back to indexSebastian Thrun: Flying Cars, Autonomous Vehicles, and Education | Lex Fridman Podcast #59
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The following is a conversation with Sebastian Thrun.
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He's one of the greatest roboticists, computer scientists,
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and educators of our time.
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He led the development of the autonomous vehicles
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at Stanford that won the 2005 DARPA Grand Challenge
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and placed second in the 2007 DARPA Urban Challenge.
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He then led the Google Self Driving Car Program, which
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launched the Self Driving Car Revolution.
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He taught the popular Stanford course
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on artificial intelligence in 2011,
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which was one of the first massive open online courses,
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or MOOCs, as they're commonly called.
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That experience led him to cofound Udacity,
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an online education platform.
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If you haven't taken courses on it yet,
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I highly recommend it.
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Their Self Driving Car Program, for example, is excellent.
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He's also the CEO of Kitty Hawk, a company working
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on building flying cars, or more technically,
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EV TALLS, which stands for Electric Vertical Takeoff
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and Landing Aircraft.
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He has launched several revolutions
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and inspired millions of people.
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But also, as many know, he's just a really nice guy.
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It was an honor and a pleasure to talk with him.
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This is the Artificial Intelligence Podcast.
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If you enjoy it, subscribe on YouTube, give it five stars
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to dream of engineering a better world.
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And now here's my conversation with Sebastian Thrun.
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You mentioned that The Matrix may be your favorite movie.
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So let's start with a crazy philosophical question.
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Do you think we're living in a simulation
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and in general, do you find the thought experiment interesting?
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Define simulation, I would say.
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Maybe we are, maybe we are not,
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but it's completely irrelevant to the way we should act.
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Putting aside for a moment the fact
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that it might not have any impact
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on how we should act as human beings,
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for people studying theoretical physics,
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these kinds of questions might be kind of interesting,
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looking at the universe's information processing system.
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The universe is an information processing system.
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It is. It's a huge physical, biological,
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chemical computer. There's no question.
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But I live here and now.
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I care about people. I care about us.
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What do you think is trying to compute?
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I don't think there's an intention.
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I think the world evolves the way it evolves.
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And it's beautiful. It's unpredictable.
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And I'm really, really grateful to be alive.
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I've spoken like a true human.
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Which last time I checked I was.
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Well, in fact, this whole conversation
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is just a touring test to see if indeed you are.
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You've also said that one of the first programs
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or the first few programs you've written
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was a wait for a TI 57 calculator.
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Maybe that's early 80s.
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I don't want to date calculators or anything.
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That's early 80s, correct.
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So if you were to place yourself back into that time,
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into the mindset you were in,
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because you have predicted the evolution of computing,
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AI, the internet, technology in the decades that followed.
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I was super fascinated by Silicon Valley,
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which I'd seen on television once and thought, my God,
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They built like DRAMs there and CPUs.
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And as a college student, a few years later,
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I decided to study intelligence and study human beings
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and found that even back then in the 80s and 90s,
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artificial intelligence is what fascinated me the most.
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What's missing is that back in the day,
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the computers are really small.
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They're like the brains you could build
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were not anywhere bigger as a cockroach.
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And cockroaches aren't very smart.
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So we weren't at the scale yet where we are today.
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Did you dream at that time to achieve
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the kind of scale we have today?
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Did that seem possible?
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I always wanted to make robots smart.
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I felt it was super cool to build an artificial human.
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And the best way to build an artificial human
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would be to build a robot,
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because that's kind of the closest we could do.
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Unfortunately, we aren't there yet.
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The robots today are still very brittle,
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but it's fascinating to study intelligence
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from a constructive perspective where you build something.
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To understand you build,
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what do you think it takes to build an intelligent system
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and an intelligent robot?
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I think the biggest innovation that we've seen
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is machine learning.
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And it's the idea that their computers
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can basically teach themselves.
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Let's give an example.
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I'd say everybody pretty much knows how to walk.
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And we learn how to walk in the first year,
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But no scientist has ever been able
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to write on the rules of human gait.
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We don't understand it.
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We have it in our brains some, or we can practice it.
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We understand it, but we can't articulate it.
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We can't pass it on by language.
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And that to me is kind of the deficiency
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of today's computer programming.
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Even you program a computer,
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they're so insanely dumb
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that you have to give them rules for every contingencies.
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Very unlike the way people learn,
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but learn from data and experience,
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computers are being instructed.
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And because it's so hard to get this instruction set right,
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we pay software engineers $200,000 a year.
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Now, the most recent innovation,
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which has been in the make for like 30, 40 years,
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is an idea that computers can find their own rules.
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So they can learn from falling down and getting up
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the same way children can learn
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from falling down and getting up.
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And that revolution has led to a capability
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that's completely unmatched.
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Today's computers can watch experts do their jobs,
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whether you're a doctor or a lawyer,
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pick up the regularities, learn those rules,
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and then become as good as the best experts.
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So the dream of in the 80s of expert systems, for example,
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had at its core the idea that humans could boil down
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their expertise on a sheet of paper.
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So to sort of reduce,
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sort of be able to explain to machines
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how to do something explicitly.
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what's the use of human expertise into this whole picture?
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Do you think most of the intelligence
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will come from machines learning from experience
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without human expertise input?
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So the question for me is much more,
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how do you express expertise?
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You can express expertise by writing a book.
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You can express expertise by showing someone
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what you're doing.
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You can express expertise by applying it
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by many different ways.
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And I think the expert systems was our best attempt in AI
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to capture expertise and rules.
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But someone sat down and say,
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here are the rules of human gate.
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Here's when you put your big toe forward
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and your heel backwards and here how it stops stumbling.
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And as we now know, the set of rules,
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the set of language that we can command
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is incredibly limited.
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The majority of the human brain doesn't deal with language.
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It deals with like subconscious numerical,
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perceptual things that we don't even have self aware of.
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Now, when a AI system watches an expert do their job
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and practice their job,
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it can pick up things that people can't even put into writing
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into books or rules.
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And that's what the real power is.
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We now have AI systems that, for example,
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look over the shoulders of highly paid human doctors
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like dermatologists or radiologists
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and they can somehow pick up those skills
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that no one can express in words.
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So you were a key person in launching three revolutions,
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online education, autonomous vehicles
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and fine cars or vtals.
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So high level and I apologize
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for all the philosophical questions.
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That's no apology necessary.
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How do you choose what problems to try and solve?
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What drives you to make those solutions a reality?
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I have two desires in life.
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I want to literally make the lives of others better
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or as we often say,
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maybe jokingly make the world a better place.
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I actually believe in this.
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It's as funny as it sounds.
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And second, I want to learn.
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I want to get in the skills.
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I don't want to be in the drop I'm good at
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because if I'm in a job that I'm good at,
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the chance for me to learn something interesting
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is actually minimized.
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So when I be in a job, I'm bad at.
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That's really important to me.
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So in a build, for example,
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what people often call flying cars,
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these are electrical, vertical takeoff and landing vehicles.
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I'm just no expert in any of this.
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And it's so much fun to learn on the job
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what it actually means to build something like this.
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Now I'd say the stuff that I done lately
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after I finished my professorship at Stanford,
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they really focused on
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like what has the maximum impact on society.
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Like transportation is something that has transformed
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the 21st or 20th century
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more than any other invention I'm happy in
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and even more than communication.
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And cities are different workers, different women's rights
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are different because of transportation.
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And yet we still have a very suboptimal transportation
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solution where we kill 1.2 or so million people
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every year in traffic.
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It's like the leading cause of death
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for young people in many countries
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where we are extremely inefficient resource wise,
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just go to your other neighborhood city
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and look at the number of parked cars
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that's a travesty in my opinion,
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or where we spend endless hours in traffic jams.
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And very, very simple innovations
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like a self driving car or what people call a flying car
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could completely change this and it's there.
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I mean, the technology is basically there.
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You have to close your eyes not to see it.
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So lingering on autonomous vehicles,
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a fascinating space, some incredible work
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you've done throughout your career there.
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So let's start with DARPA.
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I think the DARPA challenge through the desert
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and then urban to the streets.
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I think that inspired an entire generation of roboticists
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and obviously sprung this whole excitement
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about this particular kind of four wheeled robots
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who called autonomous cars, self driving cars.
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So you led the development of Stanley,
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the autonomous car that won the race of the desert,
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the DARPA challenge in 2005.
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And junior, the car that finished second
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in the DARPA urban challenge also did incredibly well
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What are some painful inspiring or enlightening experiences
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from that time that stand out to you?
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Oh my God, painful were all these incredibly complicated
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stupid bugs that had to be found.
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We had a phase where the Stanley,
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our car that eventually won the DARPA run challenge
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would every 30 miles just commit suicide.
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And we didn't know why.
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And it ended up to be that in the sinking
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of two computer clocks, occasionally a clock went backwards
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and that negative time elapsed, screwed up
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the entire internal logic, but it took ages to find this.
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It was like bugs like that.
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I'd say enlightening is the Stanford team
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immediately focused on machine learning and on software
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whereas everybody else seemed to focus
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on building better hardware.
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Our analysis had been a human being
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with an existing rental car can perfectly drive the course
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by having to build a better rental car.
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I just thought it should replace the human being.
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And the human being to me was a conjunction of three steps.
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We had like sensors, eyes and ears,
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mostly eyes, we had brains in the middle
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and then we had actuators, our hands and our feet.
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Now the extras are easy to build.
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The sensors are actually also easy to build
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what was missing was the brain.
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So we had to build a human brain
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and nothing clearer than to me
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that the human brain is a learning machine.
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So why not just train our robot?
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So you would build a massive machine learning into our machine
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and with that we're able to not just learn from human drivers.
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We had the entire speed control of the vehicle
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was copied from human driving
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but also have the robot learn from experience
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where it made a mistake and go to recover from it
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and learn from it.
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You mentioned the pain point of software and clocks.
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Synchronization seems to be a problem
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that continues with robotics.
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It's a tricky one with drones and so on.
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So what does it take to build a thing,
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a system with so many constraints?
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You have a deadline, no time,
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you're unsure about anything really.
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It's the first time that people really even explain.
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It's not even sure that anybody can finish
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when we're talking about the race or the desert
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the year before nobody finished.
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What does it take to scramble and finish a product
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that actually, a system that actually works?
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We were lucky, we were a really small team.
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The core of the team were four people.
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It was four because five couldn't comfortably sit
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inside a car but four could.
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And I as a team leader, my job was to get pizza
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for everybody and wash the car and stuff like this
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and repair the radiator and it broke
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and debug the system.
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And we were very kind of open minded.
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We had like no egos involved.
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We just wanted to see how far we can get.
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What we did really, really well was time management.
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We were done with everything a month before the race
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and we froze the entire software a month before the race.
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And it turned out, looking at other teams,
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every other team complained if they had just one more week
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they would have won.
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And we decided that's gonna fall into that mistake.
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We're gonna be early.
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And we had an entire month to shake the system.
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And we actually found two or three minor bucks
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in the last month that we had to fix
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and we were completely prepared when the race occurred.
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Okay, so first of all, that's such an incredibly rare
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achievement in terms of being able to be done on time
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What do you, how do you do that in your future work?
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What advice do you have in general?
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Because it seems to be so rare,
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especially in highly innovative projects like this.
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People worked till the last second.
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Well, the nice thing about the Topper Grand Challenge
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is that the problem was incredibly well defined.
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We were able for a while to drive
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the old Topper Grand Challenge course
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which had been used the year before.
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And then at some reason we were kicked out of the region.
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So we had to go to different deserts,
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the snorren deserts,
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and we were able to drive desert trails
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just of the same type.
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So there was never any debate about like,
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what's actually the problem?
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We didn't sit down and say,
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hey, should we build a car or a plane
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that we had to build a car?
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That made it very, very easy.
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Then I studied my own life and life of others.
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And we asked that the typical mistake that people make
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is that there is this kind of crazy bug left
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that they haven't found yet.
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And it's just, they regret it
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and the bug would have been trivial to fix.
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They just haven't fixed it yet.
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And they didn't want to fall into that trap.
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So I built a testing team.
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We had a testing team that built a testing booklet
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of 160 pages of tests we had to go through
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just to make sure we shake out the system appropriately.
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And the testing team was with us all the time
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and dictated to us today,
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we do railroad crossings.
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Tomorrow we do, we practice the start of the event.
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And in all of these, we thought, oh my God,
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this long solved trivial and then we tested it out.
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Oh my God, it doesn't do a railroad crossing, why not?
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Oh my God, it mistakes the rails for metal barriers.
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We have to fix this.
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So it was really a continuous focus
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on improving the weakest part of the system.
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And as long as you focus on improving
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the weakest part of the system,
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you eventually build a really great system.
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Let me just pause in that, to me as an engineer,
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it's just super exciting that you were thinking like that,
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especially at that stage as brilliant
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that testing was such a core part of it.
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It may be to linger on the point of leadership.
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I think it's one of the first times
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you were really a leader
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and you've led many very successful teams since then.
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What does it take to be a good leader?
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I would say most of them just take credit.
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Yeah, for the work of others.
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That's very convenient, turns out,
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because I can't do all these things myself.
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I'm an engineer at heart, so I care about engineering.
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So I don't know what the chicken and the egg is,
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but as a kid, I loved computers
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because you could tell them to do something.
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And they actually did it.
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And you could, like, in the middle of the night,
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wake up at one in the morning and switch on your computer.
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And what you told you to do yesterday would still do.
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That was really cool.
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Unfortunately, they didn't quite work with people.
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So you go to people and tell them what to do
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and they don't do it and they hate you for it.
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Or you do it today and then they go a day later
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and they'll stop doing it, so you have to.
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So then the question really became,
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how can you put yourself in the brain of people
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as opposed to computers?
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And in terms of computers, it's super dumb.
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They're just so dumb.
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If people were as dumb as computers,
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I wouldn't want to work with them.
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But people are smart and people are emotional
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and people have pride and people have aspirations.
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So how can I connect to that?
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And that's the thing where most of leadership just fails
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because many, many engineers, turn manager,
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believe they can treat their team just the same way
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they can treat your computer and it just doesn't work this way.
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It's just really bad.
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So how can I connect to people?
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And it turns out, as a college professor,
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the wonderful thing you do all the time
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is to empower other people.
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Like, your job is to make your students look great.
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That's all you do.
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You're the best coach.
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And it turns out, if you do a fantastic job
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with making your students look great,
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they actually love you and their parents love you.
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And they give you all the credit for stuff you don't deserve.
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Turns out, all my students were smarter than me.
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All the great stuff invented at Stanford
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was their stuff, not my stuff.
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And they give me credit and say, oh, Sebastian,
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we're just making them feel good about themselves.
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So the question really is, can you take a team of people
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and what does it take to make them,
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to connect to what they actually want in life
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and turn this into productive action?
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It turns out, every human being that I know
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has incredibly good intentions.
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I've really rarely met a person with bad intentions.
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I believe every person wants to contribute.
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I think every person I've met wants to help others.
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It's amazing how much of an urge we have not to just help
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ourselves, but to help others.
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So how can we empower people and give them
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the right framework that they can accomplish this?
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In moments when it works, it's magical
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because you'd see the confluence of people
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being able to make the world a better place
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and deriving enormous confidence and pride out of this.
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And that's when my environment works the best.
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These are moments where I can disappear for a month
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and come back and things still work.
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It's very hard to accomplish, but when it works, it's amazing.
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So I agree with you very much, and it's not often
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heard that most people in the world have good intentions.
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At the core, their intentions are good,
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and they're good people.
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That's a beautiful message.
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It's not often heard.
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We make this mistake, and this is a friend of mine,
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Alex Zorta, talking this, that we judge ourselves
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by our intentions and others by their actions.
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And I think the biggest skill, I mean, here in Silicon Valley,
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we're full of engineers who have very little empathy
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and are kind of befuddled by why it doesn't work for them.
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The biggest skill, I think, that people should acquire
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is to put themselves into the position of the other
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and listen, and listen to what the other has to say.
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And they'd be shocked how similar they are to themselves.
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And they might even be shocked how their own actions don't
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reflect their intentions.
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I often have conversations with engineers where I say, look,
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hey, I love you, you're doing a great job.
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And by the way, what you just did has the following effect.
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Are you aware of that?
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And then people would say, oh, my god, not I wasn't,
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because my intention was that.
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And they say, yeah, I trust your intention,
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you're a good human being.
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But just to help you in the future,
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if you keep expressing it that way,
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then people will just hate you.
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And I've had many instances where you would say, oh, my god,
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thank you for telling me this, because it wasn't my intention
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to look like an idiot.
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It wasn't my intention to help other people.
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I just didn't know how to do it.
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Very simple, by the way.
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There's a book, Dale Carnegie, 1936,
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How to Make Friends and How to Influence Others,
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has the entire Bible.
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You just read it, and you're done,
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and you apply it every day.
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And I wish I was good enough to apply it every day.
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But it's just simple things, right?
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Be positive, remember people's names, smile,
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and eventually have empathy.
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Really think that the person that you hate
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and you think is an idiot is actually just like yourself.
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It's a person who's struggling, who
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means well, and who might need help.
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I've recently spoken with Stephen Schwartzman.
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I'm not sure if you know who that is.
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But he said to expand on what you're saying,
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that one of the biggest things you can do
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is hear people when they tell you what their problem is,
link |
and then help them with that problem.
link |
He says it's surprising how few people actually
link |
listen to what troubles others.
link |
And because it's right there in front of you,
link |
and you can benefit the world the most.
link |
And in fact, yourself and everybody around you
link |
by just hearing the problems and solving them.
link |
I mean, that's my little history of engineering.
link |
That is, while I was engineering with computers,
link |
I didn't care at all what their computer's problems were.
link |
I just told them what to do and they do it.
link |
And it just doesn't work this way with people.
link |
It doesn't work with me.
link |
If you come to me and say, do A, I do the opposite.
link |
But let's return to the comfortable world of engineering.
link |
And can you tell me in broad strokes
link |
in how you see it?
link |
Because you're at the core of starting it,
link |
the core of driving it, the technical evolution
link |
of autonomous vehicles from the first DARPA Grand Challenge
link |
to the incredible success we see with the program you started
link |
with Google Self Driving Car and Waymo
link |
and the entire industry that sprung up
link |
of different kinds of approaches, debates, and so on.
link |
Well, the idea of self driving car goes back to the 80s.
link |
There was a team in Germany and another team in Carnegie Mellon
link |
that did some very pioneering work.
link |
But back in the day, I'd say the computers
link |
were so deficient that even the best professors
link |
and engineers in the world basically stood no chance.
link |
It then folded into a phase where the US government spent
link |
at least half a billion dollars that I could count
link |
on research projects.
link |
But the way the procurement works,
link |
a successful stack of paper describing lots of stuff
link |
that no one's ever going to read was a successful product
link |
of a research project.
link |
So we trained our researchers to produce lots of paper.
link |
That all changed with the DARPA Grand Challenge.
link |
And I really got a credit the ingenious people at DARPA
link |
and the US government in Congress
link |
that took a complete new funding model where they said,
link |
let's not fund effort, let's fund outcomes.
link |
And it sounds very trivial, but there was no tax code
link |
that allowed the use of congressional tax money for a price.
link |
It was all effort based.
link |
So if you put in 100 hours in, you could charge 100 hours.
link |
If you put in 1,000 hours in, you could build 1,000 hours.
link |
By changing the focus and so you're making the price,
link |
we don't pay you for development, we pay you for the accomplishment.
link |
They automatically drew out all these contractors
link |
who are used to the drug of getting money per hour.
link |
And they drew in a whole bunch of new people.
link |
And these people are mostly crazy people.
link |
There were people who had a car and a computer
link |
and they wanted to make a million bucks.
link |
The million bucks was their visual price money,
link |
it was then doubled.
link |
And they felt if I put my computer in my car and program it,
link |
And that was so awesome.
link |
Like half the teams, there was a team that was surfer dudes
link |
and they had like two surfboards on their vehicle
link |
and brought like these fashion girls, super cute girls,
link |
like twin sisters.
link |
And you could tell these guys were not your common felt way
link |
bandit who gets all these big, multi million
link |
and billion dollar countries from the US government.
link |
And there was a great reset.
link |
Universities moved in.
link |
I was very fortunate at Stanford that I just received 10 year.
link |
So I couldn't get fired, no matter what I do.
link |
Otherwise I wouldn't have done it.
link |
And I had enough money to finance this thing.
link |
And I was able to attract a lot of money from third parties.
link |
And even car companies moved in.
link |
They kind of moved in very quietly
link |
because they were super scared to be embarrassed
link |
that their car would flip over.
link |
But Ford was there and Volkswagen was there
link |
and a few others and GM was there.
link |
So it kind of reset the entire landscape of people.
link |
And if you look at who's a big name
link |
in self living cars today,
link |
these are mostly people who participated in those challenges.
link |
OK, that's incredible.
link |
Can you just comment quickly on your sense of lessons
link |
learned from that kind of funding model
link |
and the research that's going on in academia
link |
in terms of producing papers.
link |
Is there something to be learned and scaled up bigger,
link |
having these kinds of grand challenges
link |
that could improve outcomes?
link |
So I'm a big believer in focusing on kind of an end
link |
I'm a really big believer in systems building.
link |
I've always built systems in my academic career,
link |
even though I do a lot of math and abstract stuff.
link |
But it's all derived from the idea of let's solve a real problem.
link |
And it's very hard for me to be in academic and say,
link |
let me solve a component of a problem.
link |
Like if someone feels like non monetary logic
link |
or AI planning systems where people believe
link |
that a certain style of problem solving
link |
is the ultimate end objective.
link |
And I would always turn around and say, hey, what problem
link |
would my grandmother care about that
link |
doesn't understand computer technology
link |
and doesn't want to understand?
link |
And how can I make her love what I do?
link |
Because only then do I have an impact on the world.
link |
I can easily impress my colleagues.
link |
That is much easier.
link |
But impressing my grandmother is very, very hard.
link |
So I would always thought if I can build a self driving car
link |
and my grandmother can use it even after she loses
link |
her driving privileges or children can use it
link |
or we save maybe a million lives a year,
link |
that would be very impressive.
link |
And then there's so many problems like these.
link |
Like there's a problem with Q and cancer or whatever it is.
link |
Live twice as long.
link |
Once a problem is defined, of course,
link |
I can't solve it in its entirety.
link |
Like it takes sometimes tens of thousands of people
link |
to find a solution.
link |
There's no way you can fund an army of 10,000 at Stanford.
link |
So you've got to build a prototype.
link |
Let's build a meaningful prototype.
link |
And the DARPA Grand Challenge was beautiful because it
link |
told me what this prototype had to do.
link |
I didn't even think about what it had to do.
link |
It just had to read the rules.
link |
And it was really, really beautiful.
link |
And it's most beautiful, you think,
link |
what academia could aspire to is to build a prototype that's
link |
the systems level that solves or gives you an inkling
link |
that this problem could be solved with this prototype.
link |
First of all, I want to emphasize what academia really is.
link |
And I think people misunderstand it.
link |
First and foremost, academia is a way to educate young people.
link |
First and foremost, a professor is an educator,
link |
no matter where you are at a small suburban college
link |
or whether you are a Harvard or Stanford professor.
link |
That's not the way most people think of themselves
link |
in academia because we have this kind of competition going
link |
on for citations and publication.
link |
That's a measurable thing.
link |
But that is secondary to the primary purpose
link |
of educating people to think.
link |
Now, in terms of research, most of the great science,
link |
the great research comes out of universities.
link |
You can trace almost everything back, including Google,
link |
So there's nothing really fundamentally broken here.
link |
It's a good system.
link |
And I think America has the finest university system
link |
We can talk about reach and how to reach people outside
link |
It's a different topic.
link |
But the system itself is a good system.
link |
If I had one wish, I would say it'd be really great
link |
if there was more debate about what the great big problems
link |
are in society and focus on those.
link |
And most of them are interdisciplinary.
link |
Unfortunately, it's very easy to fall
link |
into an interdisciplinary viewpoint where
link |
your problem is dictated, but your closest colleagues
link |
believe the problem is.
link |
It's very hard to break out and say,
link |
well, there's an entire new field of problems.
link |
So give an example.
link |
Prior to me working on self driving cars,
link |
I was a roboticist and a machine learning expert.
link |
And I wrote books on robotics, something called
link |
probabilistic robotics.
link |
It's a very methods driven kind of view point of the world.
link |
I built robots that acted in museums as tour guides that
link |
led children around.
link |
It is something that at the time was moderately challenging.
link |
When I started working on cars, several colleagues
link |
told me, Sebastian, you're destroying your career
link |
because in our field of robotics,
link |
cars are looked like as a gimmick.
link |
And they're not expressive enough.
link |
They can only put this bottle in the brakes.
link |
There's no dexterity.
link |
There's no complexity.
link |
It's just too simple.
link |
And no one came to me and said, wow,
link |
if you solve that problem, you can save a million lives.
link |
Among all robotic problems that I've seen in my life,
link |
I would say the self living car transportation
link |
is the one that has the most hope for society.
link |
So how come the robotics community wasn't all over the place?
link |
And it was become because we focused on methods and solutions
link |
and not on problems.
link |
Like if you go around today and ask your grandmother,
link |
What really makes you upset?
link |
I challenge any academic to do this
link |
and then realize how far your research is probably
link |
away from that today.
link |
At the very least, that's a good thing for academics
link |
The other thing that's really nice in Silicon Valley
link |
is Silicon Valley is full of smart people outside academia.
link |
So there's the Larry Pages and Mark Zuckerbergs
link |
in the world who are anywhere smarter, smarter
link |
than the best academics I've met in my life.
link |
And what they do is they are at a different level.
link |
They build the systems.
link |
They build the customer facing systems.
link |
They build things that people can use
link |
without technical education.
link |
And they are inspired by research.
link |
They're inspired by scientists.
link |
They hire the best PhDs from the best universities
link |
So I think this kind of vertical integration
link |
that between the real product, the real impact,
link |
and the real thought, the real ideas,
link |
that's actually working surprisingly well in Silicon
link |
It did not work as well in other places in this nation.
link |
So when I worked at Carnegie Mellon,
link |
we had the world's finest computer science university.
link |
But there wasn't those people in Pittsburgh
link |
that would be able to take these very fine computer science
link |
ideas and turn them into massively impactful products.
link |
That symbiosis seemed to exist pretty much only in Silicon
link |
Valley and maybe a bit in Boston and Austin.
link |
With Stanford, that's really interesting.
link |
So if we look a little bit further on
link |
from the DARPA Grand Challenge and the launch
link |
of the Google self driving car, what do you see
link |
as the state, the challenges of autonomous vehicles
link |
as they are now, is actually achieving that huge scale
link |
and having a huge impact on society?
link |
I'm extremely proud of what has been accomplished.
link |
And again, I'm taking a lot of quality for the work for others.
link |
And I'm actually very optimistic.
link |
And people have been kind of worrying,
link |
is it too fast, is it slow, why is it not there yet, and so on.
link |
It is actually quite an interesting hard problem
link |
in that a self driving car, to build one that manages 90%
link |
of the problems encountered in everyday driving is easy.
link |
We can literally do this over a weekend.
link |
To do 99% might take a month, then there's 1% left.
link |
So 1% would mean that you still have a fatal accident every week.
link |
Very unacceptable.
link |
So now you work on this 1%.
link |
And the 99% of that, the remaining 1%,
link |
is actually still relatively easy.
link |
But now you're down to like a 100% or 1%.
link |
And it's still completely unacceptable in terms of safety.
link |
So the variety of things you encounter are just enormous.
link |
And that gives me enormous respect for a human being
link |
that we're able to deal with the couch on the highway,
link |
or the deer in the headlight, or the blown tire
link |
that we've never been trained for.
link |
And all of a sudden, I have to handle an emergency situation
link |
and often do very, very successfully.
link |
From that perspective, how safe driving actually is,
link |
given how many millions of miles we drive every year
link |
We are now at a point where I believe the technology is there.
link |
I've seen it in way more.
link |
I've seen it in app, I've seen it in cruise,
link |
and in a number of companies and in voyage,
link |
where vehicles not driving around and basically
link |
flawlessly are able to drive people around
link |
in limited scenarios.
link |
In fact, you can go to Vegas today
link |
and order a summoner lift.
link |
And if you get the right setting off your app,
link |
you'll be picked up by a driverless car.
link |
Now, there's still safety drivers in there.
link |
But that's a fantastic way to kind of learn
link |
what the limits are of technology today.
link |
And there's still some glitches.
link |
But the glitches have become very, very rare.
link |
I think the next step is going to be to down cost it,
link |
The entrapment, the sensors are not quite
link |
an automotive grade standard yet.
link |
And then to really build the business models,
link |
to really kind of go somewhere and make the business case.
link |
And the business case is hard work.
link |
It's not just, oh my god, we have this capability.
link |
People are just going to buy it.
link |
You have to make it affordable.
link |
You have to find the social acceptance
link |
None of the teams yet has been able to, or gutsy enough,
link |
to drive around without a person inside the car.
link |
And that's the next magical hurdle.
link |
We'll be able to send these vehicles around completely
link |
And I think, I mean, I wait every day,
link |
wait for the news that Waymo has just done this.
link |
So interesting, you mentioned gutsy.
link |
Let me ask some maybe unanswerable question,
link |
maybe edgy questions.
link |
But in terms of how much risk is required,
link |
some guts, in terms of leadership style,
link |
it would be good to contrast approaches.
link |
And I don't think anyone knows what's right.
link |
But if we compare Tesla and Waymo, for example,
link |
Elon Musk and the Waymo team, there's slight differences
link |
So on the Elon side, there's more,
link |
I don't know what the right word to use,
link |
but aggression in terms of innovation.
link |
And on Waymo's side, there's more cautious, safety focused
link |
approach to the problem.
link |
What do you think it takes?
link |
What's leadership?
link |
But which moment is right?
link |
Which approach is right?
link |
Look, I don't sit in either of those teams.
link |
So I'm unable to even verify, like somebody says, correct.
link |
In the end of the day, every innovator in that space
link |
will face a fundamental dilemma.
link |
And I would say you could put aerospace tightens
link |
into the same bucket, which is you
link |
have to balance public safety with your drive to innovate.
link |
And this country in particular in the States
link |
has a 100 plus year history of doing this very successfully.
link |
Air travel is what 100 times is safe per mile,
link |
then ground travel, then cars.
link |
And there's a reason for it, because people
link |
have found ways to be very methodological about ensuring
link |
public safety while still being able to make progress
link |
on important aspects, for example, like yell and noise
link |
and fuel consumption.
link |
So I think that those practices are proven,
link |
and they actually work.
link |
We live in a world safer than ever before.
link |
And yes, there will always be the provision
link |
that something goes wrong.
link |
There's always the possibility that someone makes a mistake,
link |
or there's an unexpected failure.
link |
We can never guarantee to 100% absolute safety
link |
other than just not doing it.
link |
But I think I'm very proud of the history of the United States.
link |
I mean, we've dealt with much more dangerous technology,
link |
like nuclear energy, and kept that safe too.
link |
We have nuclear weapons, and we keep those safe.
link |
So we have methods and procedures
link |
that really balance these two things very, very successfully.
link |
You've mentioned a lot of great autonomous vehicle companies
link |
that are taking sort of the level four, level five.
link |
They jump in full autonomy with a safety driver
link |
and take that kind of approach, and also
link |
through simulation and so on.
link |
There's also the approach that Tesla autopilot is doing,
link |
which is kind of incrementally taking a level two vehicle
link |
and using machine learning and learning from the driving
link |
of human beings, and trying to creep up,
link |
trying to incrementally improve the system
link |
until it's able to achieve level four autonomy.
link |
So perfect autonomy in certain kind of geographical regions.
link |
What are your thoughts on these contrasting approaches?
link |
Well, first of all, I'm a very proud Tesla owner,
link |
and I literally use the autopilot every day,
link |
and it literally has kept me safe.
link |
It is a beautiful technology specifically
link |
for highway driving when I'm slightly tired,
link |
because then it turns me into a much safer driver,
link |
and I'm 100% confident that's the case.
link |
In terms of the right approach, I think the biggest change
link |
I've seen since I went in the Waymo team
link |
is this thing called deep learning.
link |
Deep learning was not a hot topic when I started Waymo,
link |
or Google self having cars.
link |
It was there, but in fact, we started Google Brain
link |
at the same time in Google X, so I invested in deep learning,
link |
but people didn't talk about it.
link |
It wasn't a hot topic.
link |
There's a shift of emphasis from a more geometric
link |
perspective, where you use geometric sensors.
link |
They give you a full 3D view, and you
link |
do a geometric reasoning about, oh, this box over here
link |
Towards a more human like, oh, let's just learn about it.
link |
This looks like the thing I've seen 10,000 times before,
link |
so maybe it's the same thing.
link |
Machine learning perspective.
link |
And that has really put, I think,
link |
all these approaches on steroids.
link |
At Udacity, we teach a course in self driving cars.
link |
In fact, I think we've credited over 20,000 or so people
link |
on self driving cars skills.
link |
So every self driving car team in the world
link |
now uses our engineers.
link |
And in this course, the very first homework assignment
link |
is to do lane finding on images.
link |
And lane finding images, for laymen, what this means
link |
is you put a camera into your car, or you open your eyes,
link |
and you wouldn't know where the lane is.
link |
So you can stay inside the lane with your car.
link |
Humans can do this super easily.
link |
You just look and you know where the lane is.
link |
For machines, for a long time, it was super hard
link |
because people would write these kind of crazy rules.
link |
If there's like wine lane markers,
link |
and these are what white really means,
link |
this is not quite white enough, so let's all, it's not white.
link |
Or maybe the sun is shining, so when the sun shines,
link |
and this is white, and this is a straight line.
link |
Or maybe it's not quite a straight line
link |
because the road is curved.
link |
And do we know that there's a 6 feet between lane markings
link |
or not, or 12 feet, whatever it is?
link |
And now, what the students are doing,
link |
they would take machine learning.
link |
Instead of like writing these crazy rules
link |
for the lane marker, they would say,
link |
hey, let's take an hour of driving,
link |
and label it and tell the vehicle,
link |
this is actually the lane by hand.
link |
And then these are examples,
link |
and have the machine find its own rules
link |
what lane markings are.
link |
And within 24 hours, now every student
link |
that's never done any programming before in this space
link |
can write a perfect lane finder
link |
as good as the best commercial lane finders.
link |
And that's completely amazing to me.
link |
We've seen progress using machine learning
link |
that completely dwarfs anything
link |
that I saw 10 years ago.
link |
Yeah, and just as a side note,
link |
the self driving car nanodegree,
link |
the fact that you launched that many years ago now,
link |
maybe four years ago, three years ago,
link |
is incredible that that's a great example
link |
of system level thinking.
link |
Sort of just taking an entire course
link |
that teaches how to solve the entire problem.
link |
I definitely recommend people.
link |
It's been super popular,
link |
and it's become actually incredibly high quality
link |
related with Mercedes and various other companies
link |
And we find that engineers from Tesla
link |
and Waymo are taking it today.
link |
The insight was that two things.
link |
One is existing universities will be very slow to move
link |
because the departmentalized
link |
and there's no department for self driving cars.
link |
So between Mackey and EE and computer science,
link |
getting those folks together into one room
link |
is really, really hard.
link |
And every professor listening here will know,
link |
we'll probably agree to that.
link |
And secondly, even if all the great universities
link |
just did this, which none so far
link |
has developed a curriculum in this field,
link |
it is just a few thousand students that can partake
link |
because all the great universities are super selective.
link |
So how about people in India?
link |
How about people in China or in the Middle East
link |
or Indonesia or Africa?
link |
Why should those be excluded
link |
from the skill of building self driving cars?
link |
Are they any dumber than we are?
link |
Are they any less privileged?
link |
And the answer is we should just give everybody the skill
link |
to build a self driving car.
link |
Because if we do this,
link |
then we have like a thousand self driving car startups.
link |
And if 10% succeed, that's like a hundred,
link |
that means a hundred countries now
link |
will have self driving cars and be safer.
link |
It's kind of interesting to imagine impossible to quantify,
link |
but the number, over a period of several decades,
link |
the impact that has, like a single course,
link |
like a ripple effect to society.
link |
If you, I just recently talked to Andrew
link |
and who was creator of Cosmos, so it's a show.
link |
It's interesting to think about
link |
how many scientists that show launched.
link |
And so it's really, in terms of impact,
link |
I can't imagine a better course
link |
than the self driving car course.
link |
That's, you know, there's other more specific disciplines
link |
like deep learning and so on
link |
that Udacity is also teaching,
link |
but self driving cars, it's really, really interesting course.
link |
In the end, it came at the right moment.
link |
It came at a time when there were a bunch of acquires.
link |
Acquire is acquisition of a company,
link |
not for its technology or its products or business,
link |
but for its people.
link |
So acquire means maybe the company of 70 people,
link |
they have no product yet,
link |
but they're super smart people
link |
and they pay a certain amount of money.
link |
So I took acquires like GM Cruise and Uber and others
link |
and did the math and said,
link |
hey, how many people are there
link |
and how much money was paid?
link |
And as a lower bound,
link |
I estimated the value of a self driving car engineer
link |
in these acquisitions to be at least $10 million, right?
link |
So think about this, you get yourself a skill
link |
and you team up and build a company
link |
and your worth now is $10 million.
link |
I mean, that's kind of cool.
link |
I mean, what other thing could you do in life
link |
to be worth $10 million within a year?
link |
But to come back for a moment onto deep learning
link |
and its application in autonomous vehicles,
link |
you know, what are your thoughts on Elon Musk's statement,
link |
provocative statement, perhaps that light air is a crutch.
link |
So there's geometric way of thinking about the world,
link |
maybe holding us back if what we should instead be doing
link |
in this robotics space,
link |
in this particular space of autonomous vehicles
link |
is using camera as a primary sensor
link |
and using computer vision and machine learning
link |
as the primary way to...
link |
Okay, I have two comments.
link |
I think first of all, we all know that people can drive cars
link |
without lighters in their heads
link |
because we only have eyes
link |
and we mostly just use eyes for driving.
link |
Maybe we use some other perception about our bodies,
link |
accelerations, occasionally our ears,
link |
certainly not our noses.
link |
So the existence proof is there,
link |
that eyes must be sufficient.
link |
In fact, we could even drive a car
link |
if someone put a camera out
link |
and then gave us the camera image with no latency,
link |
we would be able to drive a car that way the same way.
link |
So a camera is also sufficient.
link |
Secondly, I really love the idea that in the Western world,
link |
we have many, many different people
link |
trying different hypotheses.
link |
It's almost like an anthill,
link |
like if an anthill tries to forge for food, right?
link |
You can sit there as two ants
link |
and agree what the perfect path is
link |
and then every single ant marches
link |
for the most likely location of food is,
link |
or you can even just spread out.
link |
And I promise you the spread out solution will be better
link |
because if they're discussing
link |
philosophical intellectual ants get it wrong
link |
and they're all moving the wrong direction,
link |
they're gonna waste the day
link |
and then they're gonna discuss again for another week.
link |
Whereas if all these ants go in the right direction,
link |
someone's gonna succeed and they're gonna come back
link |
and claim victory and get the Nobel Prize
link |
or whatever the ant equivalent is.
link |
And then they all march in the same direction.
link |
And that's great about society.
link |
That's great about the Western society.
link |
We're not plant based, we're not central based,
link |
we don't have a Soviet Union style central government
link |
that tells us where to forge.
link |
We just forge, we started in the C Corp.
link |
We get investor money, go out and try it out.
link |
And who knows who's gonna win?
link |
When you look at the longterm vision of autonomous vehicles,
link |
do you see machine learning as fundamentally
link |
being able to solve most of the problems?
link |
So learning from experience.
link |
I'd say we should be very clear
link |
about what machine learning is and is not.
link |
And I think there's a lot of confusion.
link |
What is today is a technology
link |
that can go through large databases
link |
of repetitive patterns and find those patterns.
link |
So in example, we did a study at Stanford two years ago
link |
where we applied machine learning
link |
to detecting skin cancer in images.
link |
And we harvested or built a data set of 129,000 skin
link |
photo shots that were all had been biopsied
link |
for what the actual situation was.
link |
And those included melanomas and carcinomas,
link |
also included rashes and other skin conditions, lesions.
link |
And then we had a network find those patterns
link |
and it was by and large able to then detect skin cancer
link |
with an iPhone as accurately as the best board certified
link |
Stanford level dermatologist.
link |
Now this thing was great in this one thing
link |
and finding skin cancer, but it couldn't drive a car.
link |
So the difference to human intelligence
link |
is we do all these many, many things
link |
and we can often learn from a very small data set
link |
of experiences where as machines still need
link |
very large data sets and things that will be very repetitive.
link |
Now that's still super impactful
link |
because almost everything we do is repetitive.
link |
So that's gonna really transform human labor.
link |
But it's not this almighty general intelligence.
link |
We're really far away from a system
link |
that would exhibit general intelligence.
link |
To that end, I actually commiserate the naming a little bit
link |
because artificial intelligence, if you believe Hollywood,
link |
is immediately mixed into the idea of human suppression
link |
and machine superiority.
link |
I don't think that we're gonna see this in my lifetime.
link |
I don't think human suppression is a good idea.
link |
I don't see it coming.
link |
I don't see the technology being there.
link |
What I see instead is a very pointed,
link |
focused pattern recognition technology
link |
that's able to extract patterns from large data sets.
link |
And in doing so, it can be super impactful.
link |
Let's take the impact of artificial intelligence
link |
We all know that it takes something like 10,000 hours
link |
to become an expert.
link |
If you're gonna be a doctor or a lawyer
link |
or even a really good driver,
link |
it takes a certain amount of time to become experts.
link |
Machines now are able and have been shown
link |
to observe people become experts and observe experts
link |
and then extract those rules from experts
link |
in some interesting way.
link |
They could go from law to sales,
link |
to driving cars, to diagnosing cancer
link |
and then giving that capability
link |
to people who are completely new in their job.
link |
We now can, and that's been done.
link |
It's been done commercially in many, many instantiations.
link |
That means we can use machine learning
link |
to make people an expert
link |
on their very first day of their work.
link |
Like think about the impact.
link |
If your doctor is still in their first 10,000 hours,
link |
you have a doctor who's not quite an expert yet.
link |
Who would not want a doctor
link |
who's the world's best expert?
link |
And now we can leverage machines
link |
to really eradicate error in decision making,
link |
error in lack of expertise for human doctors.
link |
That could save your life.
link |
If we can link on that for a little bit,
link |
in which way do you hope machines in the medical field
link |
could help assist doctors?
link |
You mentioned this sort of accelerating the learning curve
link |
or people, if they start a job
link |
or in the first 10,000 hours can be assisted by machines.
link |
How do you envision that assistance looking?
link |
So we built this app for an iPhone
link |
that can detect and classify and diagnose skin cancer.
link |
And we proved two years ago
link |
that it does pretty much as good
link |
or better than the best human doctor.
link |
So let me tell you a story.
link |
So there's a friend of mine that's called Ben.
link |
Ben is a very famous venture capitalist.
link |
He goes to his doctor and the doctor looks at a mole
link |
and says, hey, that mole is probably harmless.
link |
And for some very funny reason,
link |
he pulls out that phone with our app.
link |
He's a collaborator in our study.
link |
And the app says, no, no, no, no.
link |
This is a melanoma.
link |
And for background,
link |
melanomas are skin cancer is the most common cancer
link |
Melanomas can go from stage zero to stage four
link |
within less than a year.
link |
Stage zero means you can basically cut it out yourself
link |
with a kitchen knife and be safe.
link |
And stage four means your chances
link |
of leaving five more years than less than 20%.
link |
So it's a very serious, serious, serious condition.
link |
So this doctor who took out the iPhone
link |
looked at the iPhone and was a little bit puzzled.
link |
He said, I mean, what, just to be safe,
link |
let's cut it out and biopsy it.
link |
That's the technical term for let's get an in depth diagnostics
link |
that is more than just looking at it.
link |
And it came back as cancerous as a melanoma.
link |
And it was then removed.
link |
And my friend Ben, I was hiking with him
link |
and we were talking about AI.
link |
And he said, I'm talking to this vocal skin cancer.
link |
And he said, oh, funny.
link |
My doctor just had an iPhone that found my cancer.
link |
So I was like completely intrigued.
link |
I didn't even know about this.
link |
So here's a person.
link |
I mean, this is a real human life, right?
link |
Now, who doesn't know somebody who has been affected
link |
Cancer is cause of death number two.
link |
Cancer is this kind of disease that is mean.
link |
And in the following way, most cancers
link |
can actually be cured relatively easily
link |
if we catch them early.
link |
And the reason why we don't tend to catch them early
link |
is because they have no symptoms.
link |
Like your very first symptom of a gallbladder cancer
link |
or a pancreate cancer might be a headache.
link |
And when you finally go to your doctor
link |
because of these headaches or your back pain
link |
and you're being imaged, it's usually stage four plus.
link |
And that's the time when your curing chances
link |
might be dropped to a single digital percentage.
link |
So if you could leverage AI to inspect your body
link |
on a regular basis without even a doctor in the room,
link |
maybe when you take a shower or what have you,
link |
I know this sounds creepy, but then we might be able
link |
to save millions and millions of lives.
link |
You've mentioned there's a concern that people have
link |
about near term impacts of AI in terms of job loss.
link |
So you've mentioned being able to assist doctors,
link |
being able to assist people in their jobs.
link |
Do you have a worry of people losing their jobs
link |
or the economy being affected by the improvements in AI?
link |
Yeah, anybody concerned about job losses,
link |
please come to Udacity.com, we teach contemporary tech skills
link |
and we have a kind of implicit job promise.
link |
We often, when we measure, we spend way over 50%
link |
of our graduates in new jobs and they're very satisfied
link |
about it and it costs almost nothing
link |
cause like 1,500 max or something like that.
link |
And I saw there's a cool new program
link |
that you agreed with the U.S. government
link |
guaranteeing that you will help give scholarships
link |
that educate people in this kind of situation.
link |
Yeah, we're working with the U.S. government
link |
on the idea of basically rebuilding the American dream.
link |
So Udacity has just dedicated 100,000 scholarships
link |
for citizens of America for various levels of courses
link |
that eventually will get you a job.
link |
And those courses all somewhat relate to the tech sector
link |
because the tech sector is kind of the hottest sector
link |
right now and they range from interlevel digital marketing
link |
to very advanced self driving car engineering.
link |
And we're doing this with the White House
link |
because we think it's bipartisan.
link |
It's an issue that is that if you wanna really make
link |
America great, being able to be part of the solution
link |
and live the American dream requires us to be proactive
link |
about our education and our skillset.
link |
It's just the way it is today.
link |
And it's always been this way.
link |
And we always had this American dream
link |
to send our kids to college
link |
and now the American dream has to be
link |
to send ourselves to college.
link |
We can do this very, very efficiently
link |
and very, very, we can squeeze in in the evenings
link |
and things to online at all ages.
link |
So our learners go from age 11 to age 80.
link |
I just traveled Germany and the guy
link |
in the train compartment next to me was one of my students.
link |
It's like, wow, that's amazing.
link |
I don't think about impact.
link |
We've become the educator of choice
link |
for now, I believe officially six countries
link |
or five countries is the most in the Middle East
link |
like Saudi Arabia and in Egypt.
link |
In Egypt, we just had a cohort graduate
link |
where we had 1100 high school students
link |
that went through programming skills,
link |
proficient at the level of a computer science undergrad.
link |
And we had a 95% graduation rate
link |
even though everything's online.
link |
It's kind of tough, but we kind of trying to figure out
link |
how to make this effective.
link |
The vision is, the vision is very, very simple.
link |
The vision is education ought to be a basic human right.
link |
It cannot be locked up behind ivory tower walls
link |
only for the rich people, for the parents
link |
who might be bright themselves into the system
link |
and only for young people and only for people
link |
from the right demographics and the right geography
link |
and possibly even the right race.
link |
It has to be opened up to everybody.
link |
If we are truthful to the human mission,
link |
if we are truthful to our values,
link |
we're gonna open up education to everybody in the world.
link |
So Udacity's pledge of 100,000 scholarships,
link |
I think is the biggest pledge of scholarships ever
link |
in terms of numbers.
link |
And we're working, as I said, with the White House
link |
and with very accomplished CEOs like Tim Cook
link |
from Apple and others to really bring education
link |
to everywhere in the world.
link |
Not to ask you to pick the favorite of your children,
link |
but at this point. Oh, that's Jasper.
link |
I only have one that I know of.
link |
In this particular moment, what nano good degree,
link |
what set of courses are you most excited about Udacity
link |
or is that too impossible to pick?
link |
I've been super excited about something
link |
we haven't launched yet in the building,
link |
which is when we talk to our partner companies,
link |
we have now a very strong footing in the enterprise world.
link |
In order to our students,
link |
we've kind of always focused on these hard skills
link |
like the programming skills or math skills
link |
or building skills or design skills.
link |
And a very common ask is soft skills.
link |
Like how do you behave in your work?
link |
How do you develop empathy?
link |
How do you work in a team?
link |
What are the very basics of management?
link |
How do you do time management?
link |
How do you advance your career
link |
in the context of a broader community?
link |
And that's something that we haven't done
link |
very well at Udacity, and I would say most universities
link |
are doing very poorly as well
link |
because we're so obsessed with individual test scores
link |
pays a little attention to teamwork in education.
link |
So that's something I see us moving into as a company
link |
because I'm excited about this.
link |
And I think, look, we can teach people tech skills
link |
and they're gonna be great.
link |
But if you teach people empathy,
link |
that's gonna have the same impact.
link |
Maybe harder than self driving cars, but I don't think so.
link |
I think the rules are really simple.
link |
You just have to, you have to want to engage.
link |
It's, we literally went in school and in K through 12,
link |
we teach kids like get the highest math score.
link |
And if you are a rational human being,
link |
you might evolve from this education, say,
link |
having the best math score and the best English scores,
link |
making me the best leader.
link |
And it turns out not to be the case.
link |
It's actually really wrong because making the,
link |
first of all, in terms of math scores,
link |
I think it's perfectly fine to hire somebody
link |
with great math skills.
link |
You don't have to do it yourself.
link |
You can't hire some of the great empathy for you.
link |
That's much harder,
link |
but it can always hire some of the great math skills.
link |
But we live in a fluent world
link |
where we constantly deal with other people
link |
and that's a beauty.
link |
It's not a nuisance, it's a beauty.
link |
So if we somehow develop that muscle
link |
that we can do that well and empower others
link |
I think we're gonna be super successful.
link |
And I know many fellow robot assistant computer scientists
link |
that I will insist to take this course.
link |
Not to be named you.
link |
Many, many years ago, 1903,
link |
the Wright Brothers flew in Kitty Hawk for the first time.
link |
And you've launched a company of the same name, Kitty Hawk,
link |
with the dream of building flying cars, EV Talls.
link |
So at the big picture,
link |
what are the big challenges of making this thing
link |
that actually inspired generations of people
link |
about what the future looks like?
link |
What does it take?
link |
What are the biggest challenges?
link |
So flying cars has always been a dream.
link |
Every boy, every girl wants to fly.
link |
And let's go back in our history
link |
of your dreaming of flying.
link |
I think honestly, my single most remembered childhood dream
link |
has been a dream where I was sitting on a pillow
link |
I was like five years old.
link |
I remember like maybe three dreams of my childhood,
link |
but that's the one that we remember most vividly.
link |
And then Peter Thiel famously said,
link |
they promised us flying cars
link |
and they gave us 140 characters,
link |
pointing as Twitter at the time,
link |
limited message size to 140 characters.
link |
So we're coming back now to really go
link |
for these super impactful stuff like flying cars.
link |
And to be precise, they're not really cars.
link |
They don't have wheels.
link |
They're actually much closer to a helicopter
link |
than anything else.
link |
They take off vertically in their flight horizontally,
link |
but they have important differences.
link |
One difference is that they are much quieter.
link |
We just released a vehicle called Project Heavy Sight
link |
that can fly over you as low as a helicopter
link |
and you basically can't hear.
link |
It's like 38 decibels.
link |
It's like, if you were inside the library,
link |
you might be able to hear it,
link |
but anywhere outdoors, your ambient noise is higher.
link |
Secondly, they're much more affordable.
link |
They're much more affordable than helicopters.
link |
And the reason is helicopters are expensive
link |
There's lots of single point of figures in a helicopter.
link |
There's a bolt between the blades
link |
that's caused Jesus bolt.
link |
And the reason why it's called Jesus bolt is
link |
if this bolt breaks, you will die.
link |
There is no second solution in helicopter flight.
link |
Whereas we have these distributed mechanism.
link |
When you go from gasoline to electric,
link |
you can now have many, many, many small motors
link |
as opposed to one big motor.
link |
And that means if you lose one of those motors,
link |
Heavy Sight, if it loses a motor,
link |
it has eight of those.
link |
If it loses one of those eight motors,
link |
so it's seven left,
link |
you can take off just like before
link |
and land just like before.
link |
We are now also moving into a technology
link |
that doesn't require a commercial pilot.
link |
Because in some level,
link |
flight is actually easier than ground transportation.
link |
Like in self driving cars,
link |
the world is full of like children and bicycles
link |
and other cars and mailboxes and curbs and shrubs
link |
all these things you have to avoid.
link |
When you go above the buildings and tree lines,
link |
there's nothing there.
link |
I mean, you can do the test right now,
link |
look outside and count the number of things you see flying.
link |
I'd be shocked if you could see more than two things.
link |
It's probably just zero.
link |
In the Bay Area, the most I've ever seen was six.
link |
And maybe it's 15 or 20, but not 10,000.
link |
So the sky is very ample and very empty and very free.
link |
can we build a socially acceptable mass transit solution
link |
for daily transportation that is affordable?
link |
And we have an existence proof.
link |
Heavy sites can fly 100 miles in range
link |
with still 30% electric reserves.
link |
It can fly up to like 180 miles an hour.
link |
We know that that solution at scale
link |
would make your ground transportation 10 times as fast
link |
as a car based on use sensors or statistics data,
link |
which means we would take your 300 hours of daily commute
link |
down to 30 hours and give you 270 hours back.
link |
Who wouldn't want, I mean, who doesn't hate traffic?
link |
Like I hate, give me the person who doesn't hate traffic.
link |
I hate traffic every time I'm in traffic, I hate it.
link |
And if we could free the world from traffic,
link |
we have technology, we can free the world from traffic.
link |
We have the technology.
link |
It's there, we have an existence proof.
link |
It's not a technological problem anymore.
link |
Do you think there is a future where tens of thousands,
link |
maybe hundreds of thousands of both delivery drones
link |
and flying cars of this kind, EV talls, fill the sky?
link |
I absolutely believe this.
link |
And there's obviously the societal acceptance
link |
is a major question and of course safety is.
link |
I believe in safety, we only exceed ground transportation
link |
safety, as has happened for aviation already,
link |
commercial aviation.
link |
And in terms of acceptance, I think
link |
one of the key things is noise.
link |
That's why we are focusing relentlessly on noise
link |
and we built perhaps the crisis electric V tall vehicle
link |
The nice thing about the sky is it's three dimensional.
link |
So any mathematician will immediately
link |
recognize the difference between 1D
link |
of like a regular highway to 3D of a sky.
link |
But to make it clear for the layman,
link |
say you want to make 100 vertical lanes of highway 101
link |
in San Francisco, because you believe building
link |
a hundred vertical lanes is the right solution.
link |
Imagine how much it would cost to stack
link |
100 vertical lanes physically onto 101.
link |
They would be prohibitive.
link |
They would be consuming the world's GDP for an entire year
link |
just for one highway.
link |
It's amazingly expensive.
link |
In the sky, it would just be a recompilation
link |
of a piece of software because all these lanes are virtual.
link |
That means any vehicle that is in conflict with another vehicle
link |
would just go to different altitudes
link |
and the conflict is gone.
link |
And if you don't believe this, that's
link |
exactly how commercial aviation works.
link |
When you fly from New York to San Francisco,
link |
another plane flies from San Francisco to New York,
link |
there are different altitudes so they don't hit each other.
link |
It's a solved problem for the jet space.
link |
And it will be a solved problem for the urban space.
link |
There's companies like Google Wing and Amazon
link |
working on very innovative solutions.
link |
How do we have space management?
link |
They use exactly the same principles
link |
as we use today to route today's jets.
link |
There's nothing hard about this.
link |
Do you envision autonomy being a key part of it
link |
so that the flying vehicles are either semi autonomous
link |
or fully autonomous?
link |
You don't want idiots like me flying in the sky.
link |
And if you have 10,000, watch the movie, The Fifth Element,
link |
to get a pee for what would happen if it's not autonomous.
link |
And a centralized, that's a really interesting idea
link |
of a centralized management system for lanes and so on.
link |
So actually just being able to have
link |
similar as we have in the current commercial aviation,
link |
but scale it up to much more vehicles.
link |
That's a really interesting optimization problem.
link |
It is mathematically very, very straightforward.
link |
Like the gap we leave between jets is gargantuanous.
link |
And part of the reason is there isn't that many jets.
link |
So it just feels like a good solution.
link |
Today, when you get vectored by air traffic control,
link |
someone talks to you.
link |
So an ATC controller might have up to maybe 20 planes
link |
on the same frequency.
link |
And then they talk to you.
link |
You have to talk back.
link |
And it feels right because there isn't more than 20 planes
link |
around anyhow, so you can talk to everybody.
link |
But if there's 20,000 things around,
link |
you can't talk to everybody anymore.
link |
So we have to do something that's called digital,
link |
like text messaging.
link |
We do have solutions.
link |
Like we have what, four, five billion smartphones
link |
in the world now, and they're all connected.
link |
And some of us solve the scale problem for smartphones.
link |
We know where they all are.
link |
They can talk to somebody.
link |
And they're very reliable.
link |
They're amazingly reliable.
link |
We could use the same system, the same scale,
link |
for air traffic control.
link |
So instead of me as a pilot talking to a human being
link |
in the middle of the conversation receiving
link |
a new frequency, like how ancient is that,
link |
we could digitize the stuff and digitally transmit
link |
the right flight coordinates.
link |
And that solution will automatically scale to 10,000
link |
We talked about empathy a little bit.
link |
Do you think we'll one day build an AI system
link |
that a human being can love and that
link |
loves that human back, like in the movie Her?
link |
Look, I'm a pragmatist.
link |
For me, AI is a tool.
link |
It's like a shovel.
link |
And the ethics of using the shovel
link |
are always with us, the people.
link |
And it has to be this way.
link |
In terms of emotions, I would hate to come into my kitchen
link |
and see that my refrigerator spoiled all my food,
link |
then have it explained to me that it fell in love
link |
with a dishwasher.
link |
And I wasn't as nice as the dishwasher.
link |
So as a result, it neglected me.
link |
That would just be a bad experience.
link |
And it would be a bad product.
link |
I would probably not recommend this refrigerator
link |
And that's where I draw the line.
link |
To me, technology has to be reliable
link |
and has to be predictable.
link |
I want my car to work.
link |
I don't want to fall in love with my car.
link |
I just want it to work.
link |
I want it to complement me, not to replace me.
link |
I have very unique human properties.
link |
And I want the machines to make me turn me into a superhuman.
link |
Like, I'm already a superhuman today,
link |
thanks to the machines that surround me.
link |
And I'll give you examples.
link |
I can run across the Atlantic near the speed of sound
link |
at 36,000 feet today.
link |
That's kind of amazing.
link |
I can, my voice now carries me all the way to Australia
link |
using a smartphone today.
link |
And it's not the speed of sound, which would take hours.
link |
It's the speed of light.
link |
My voice travels at the speed of light.
link |
That makes me superhuman.
link |
I would even argue my flushing toilet makes me superhuman.
link |
Just think of the time before flushing toilets.
link |
And maybe you have a very old person in your family
link |
that you can ask about this.
link |
Or take a trip to rural India to experience it.
link |
It makes me superhuman.
link |
So to me, what technology does, it complements me.
link |
It makes me stronger.
link |
Therefore, words like love and compassion have very little interest
link |
in this for machines.
link |
I have interest in people.
link |
You don't think, first of all, beautifully put,
link |
beautifully argued.
link |
But do you think love has use in our tools, compassion?
link |
I think love is a beautiful human concept.
link |
And if you think of what love really is,
link |
love is a means to convey safety, to convey trust.
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I think trust has a huge need in technology as well,
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We want to trust our technology in a similar way
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In human interaction, standards have emerged.
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And feelings, emotions have emerged, maybe genetically,
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maybe ideologically, that are able to convey
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a sense of trust, sense of safety,
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a sense of passion, of love, of dedication.
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That makes the human fabric.
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And I'm a big slacker for love.
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I want to be loved.
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I want to be trusted.
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I want to be admired.
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All these wonderful things.
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And because all of us, we have this beautiful system,
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I wouldn't just blindly copy this to the machines.
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When you look at, say, transportation,
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you could have observed that up to the end of the 19th century,
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almost all transportation used any number of legs,
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from one leg to two legs to 1,000 legs.
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And you could have concluded that is the right way
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to move about the environment.
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We've made the exception of birds who is flapping wings.
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In fact, there are many people in aviation
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that flap wings to their arms and jump from cliffs.
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Most of them didn't survive.
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Then the interesting thing is that the technology solutions
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are very different.
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Like, in technology, it's really easy to build a wheel.
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In biology, it's super hard to build a wheel.
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There's very few perpetually rotating things in biology.
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And they usually run cells and things.
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In engineering, we can build wheels.
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And those wheels gave rise to cars.
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Similar wheels gave rise to aviation.
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Like, there's no thing that flies that
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wouldn't have something that rotates,
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like a jet engine or helicopter blades.
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So the solutions have used very different physical laws
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So for me to be too much focused on, oh, this
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is how nature does it, let's just replicate it,
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if you really believed that the solution to the agricultural
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evolution was a humanoid robot, you would still
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Again, beautifully put, you said that you don't take yourself
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You don't say that?
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You want me to say that?
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You don't take me seriously.
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Yeah, that's right.
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I just made that up.
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But you have a humor and a likeness about life
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that I think is beautiful and inspiring to a lot of people.
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Where does that come from?
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The smile, the humor, the likeness
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amidst all the chaos of the hard work that you're in.
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Where does that come from?
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I just love my life.
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I love the people around me.
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I love, I'm just so glad to be alive.
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Like, I'm, what, 52?
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People say 52 is a new 51.
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So now I feel better.
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But in looking around the world, looking,
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just go back 200, 300 years.
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Humanity is what, 300,000 years old.
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But for the first 300,000 years minus the last 100,
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our life expectancy would have been plus or minus 30 years,
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roughly, give or take.
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So I would be long dead now.
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Like, that makes me just enjoy every single day of my life.
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Because I don't deserve this.
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Like, why am I born today when so many of my ancestors
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died of horrible deaths?
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Like, famines, massive wars that ravaged Europe
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for the last 1,000 years, mystically disappeared
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after World War II when the Americans and the Allies
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did something amazing to my country
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that didn't deserve it, the country of Germany.
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This is so amazing.
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And then when you're alive and feel this every day,
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then it's just so amazing what we can accomplish,
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We live in a world that is so incredibly
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changing every day, almost everything
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that we cherish from your smartphone
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to your flushing toilet, to all these basic inventions,
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your new clothes you're wearing, your watch, your plane,
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penicillin, I don't know, anesthesia for surgery,
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penicillin, have been invented in the last 150 years.
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So in the last 150 years, something magical happened.
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And I would trace it back to Gutenberg and the printing
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press that has been able to disseminate information
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more efficiently than before, that all of a sudden we
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were able to invent agriculture and nitrogen
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fertilization that made agriculture so much more
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potent that we didn't have to work with farms anymore.
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And we could start reading and writing,
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and we could become all these wonderful things we are today,
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from airline pilot to massage therapist to software engineer.
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It's just amazing, living in that time is such a blessing.
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We should sometimes really think about this.
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Steven Pinker, who is a very famous author and philosopher
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whom I really adore, wrote a great book called Enlightenment
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Now, and that's maybe the one book I would recommend.
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And he asked the question, if there
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was only a single article written in the 20th century,
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only one article, what would it be?
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What's the most important innovation,
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the most important thing that happened?
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And he would say this article would credit a guy named Carl Bosch.
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And I'd challenge anybody, have you ever heard of the name Carl
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There's a Bosch corporation in Germany,
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but it's not associated with Carl Bosch.
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So I looked it up.
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Carl Bosch invented nitrogen fertilization.
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And in doing so, together with an older invention of irrigation,
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was able to increase the yield per agricultural land
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by a factor of 26, so a 2,500% increase in fertility of land.
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And that, so Steve Pinker argues,
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saved over 2 billion lives today, 2 billion people
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who would be dead if this man hadn't done what he had done.
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Think about that impact and what that means to society.
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That's the way I look at the world.
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I mean, it's so amazing to be alive and to be part of this.
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And I'm so glad I lived after Carl Bosch and not before.
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I don't think there's a better way to end this.
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Sebastian, it's an honor to talk to you,
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to have had the chance to learn from you.
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Thank you so much for talking to us.
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Thanks for coming out.
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It's a real pleasure.
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Thank you for listening to this conversation with Sebastian Thrun.
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And thank you to our presenting sponsor, Cash App.
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Download it, use code LEX Podcast.
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And $10 will go to first, a STEM education nonprofit
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that inspires hundreds of thousands of young minds
link |
to learn and to dream of engineering our future.
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If you enjoy this podcast, subscribe on YouTube,
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get 5 stars on Apple Podcast, support on Patreon,
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or connect with me on Twitter.
link |
And now, let me leave you with some words of wisdom
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from Sebastian Thrun.
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It's important to celebrate your failures
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as much as your successes.
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If you celebrate your failures really well,
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if you say, wow, I failed, I tried, I was wrong,
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but I learned something,
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then you realize you have no fear.
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And when your fear goes away, you can move the world.
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Thank you for listening and hope to see you next time.