Transcript
00:00:00 The following is a conversation with Sebastian Thrun.
00:00:03 He’s one of the greatest roboticists, computer scientists, and educators of our time.
00:00:08 He led the development of the autonomous vehicles at Stanford
00:00:11 that won the 2005 DARPA Grand Challenge and placed second in the 2007 DARPA Urban Challenge.
00:00:18 He then led the Google self driving car program, which launched the self driving car revolution.
00:00:24 He taught the popular Stanford course on artificial intelligence in 2011,
00:00:29 which was one of the first massive open online courses, or MOOCs as they’re commonly called.
00:00:35 That experience led him to co found Udacity, an online education platform.
00:00:39 If you haven’t taken courses on it yet, I highly recommend it.
00:00:43 Their self driving car program, for example, is excellent.
00:00:47 He’s also the CEO of Kitty Hawk, a company working on building flying cars,
00:00:52 or more technically, EVTOLs, which stands for electric vertical takeoff and landing aircraft.
00:00:58 He has launched several revolutions and inspired millions of people.
00:01:02 But also, as many know, he’s just a really nice guy.
00:01:06 It was an honor and a pleasure to talk with him.
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00:03:19 And now, here’s my conversation with Sebastian Thrun.
00:03:24 You mentioned that The Matrix may be your favorite movie.
00:03:28 So let’s start with a crazy philosophical question.
00:03:32 Do you think we’re living in a simulation?
00:03:34 And in general, do you find the thought experiment interesting?
00:03:40 Define simulation, I would say.
00:03:42 Maybe we are, maybe we are not,
00:03:43 but it’s completely irrelevant to the way we should act.
00:03:47 Putting aside, for a moment,
00:03:49 the fact that it might not have any impact on how we should act as human beings,
00:03:55 for people studying theoretical physics,
00:03:57 these kinds of questions might be kind of interesting,
00:03:59 looking at the universe as an information processing system.
00:04:03 The universe is an information processing system.
00:04:05 It’s a huge physical, biological, chemical computer, there’s no question.
00:04:10 But I live here and now.
00:04:12 I care about people, I care about us.
00:04:15 What do you think is trying to compute?
00:04:17 I don’t think there’s an intention.
00:04:18 I think the world evolves the way it evolves.
00:04:22 And it’s beautiful, it’s unpredictable.
00:04:25 And I’m really, really grateful to be alive.
00:04:28 Spoken like a true human.
00:04:30 Which last time I checked, I was.
00:04:33 Or that, in fact, this whole conversation is just a touring test
00:04:36 to see if indeed you are.
00:04:40 You’ve also said that one of the first programs,
00:04:42 or the first few programs you’ve written was a, wait for it, TI57 calculator.
00:04:49 Yeah.
00:04:50 Maybe that’s early 80s.
00:04:52 We don’t want to date calculators or anything.
00:04:54 That’s early 80s, correct.
00:04:55 Yeah.
00:04:56 So if you were to place yourself back into that time, into the mindset you were in,
00:05:02 could you have predicted the evolution of computing, AI,
00:05:06 the internet technology in the decades that followed?
00:05:10 I was super fascinated by Silicon Valley, which I’d seen on television once
00:05:14 and thought, my god, this is so cool.
00:05:16 They build like DRAMs there and CPUs.
00:05:19 How cool is that?
00:05:20 And as a college student a few years later, I decided to really study
00:05:25 intelligence and study human beings.
00:05:26 And found that even back then in the 80s and 90s,
00:05:30 artificial intelligence is what fascinated me the most.
00:05:33 What’s missing is that back in the day, the computers are really small.
00:05:38 The brains we could build were not anywhere bigger than a cockroach.
00:05:41 And cockroaches aren’t very smart.
00:05:43 So we weren’t at the scale yet where we are today.
00:05:46 Did you dream at that time to achieve the kind of scale we have today?
00:05:51 Or did that seem possible?
00:05:52 I always wanted to make robots smart.
00:05:54 And I felt it was super cool to build an artificial human.
00:05:57 And the best way to build an artificial human was to build a robot,
00:06:00 because that’s kind of the closest we could do.
00:06:03 Unfortunately, we aren’t there yet.
00:06:04 The robots today are still very brittle.
00:06:07 But it’s fascinating to study intelligence from a constructive
00:06:10 perspective when you build something.
00:06:12 To understand you build, what do you think it takes to build an intelligent
00:06:18 system, an intelligent robot?
00:06:20 I think the biggest innovation that we’ve seen is machine learning.
00:06:23 And it’s the idea that the computers can basically teach themselves.
00:06:28 Let’s give an example.
00:06:29 I’d say everybody pretty much knows how to walk.
00:06:33 And we learn how to walk in the first year or two of our lives.
00:06:36 But no scientist has ever been able to write down the rules of human gait.
00:06:41 We don’t understand it.
00:06:42 We have it in our brains somehow.
00:06:43 We can practice it.
00:06:45 We understand it.
00:06:46 But we can’t articulate it.
00:06:47 We can’t pass it on by language.
00:06:50 And that, to me, is kind of the deficiency of today’s computer programming.
00:06:53 When you program a computer, they’re so insanely dumb that you have to give them
00:06:57 rules for every contingencies.
00:06:59 Very unlike the way people learn from data and experience,
00:07:03 computers are being instructed.
00:07:05 And because it’s so hard to get this instruction set right,
00:07:07 we pay software engineers $200,000 a year.
00:07:11 Now, the most recent innovation, which has been in the make for 30,
00:07:14 40 years, is an idea that computers can find their own rules.
00:07:18 So they can learn from falling down and getting up the same way children can
00:07:21 learn from falling down and getting up.
00:07:23 And that revolution has led to a capability that’s completely unmatched.
00:07:28 Today’s computers can watch experts do their jobs, whether you’re
00:07:32 a doctor or a lawyer, pick up the regularities, learn those rules,
00:07:36 and then become as good as the best experts.
00:07:39 So the dream of in the 80s of expert systems, for example, had at its core
00:07:44 the idea that humans could boil down their expertise on a sheet of paper,
00:07:49 so to sort of reduce, sort of be able to explain to machines
00:07:53 how to do something explicitly.
00:07:55 So do you think, what’s the use of human expertise into this whole picture?
00:08:00 Do you think most of the intelligence will come from machines learning
00:08:03 from experience without human expertise input?
00:08:06 So the question for me is much more how do you express expertise?
00:08:10 You can express expertise by writing a book.
00:08:12 You can express expertise by showing someone what you’re doing.
00:08:16 You can express expertise by applying it by many different ways.
00:08:20 And I think the expert systems was our best attempt in AI
00:08:23 to capture expertise and rules.
00:08:25 But someone sat down and said, here are the rules of human gait.
00:08:28 Here’s when you put your big toe forward and your heel backwards
00:08:32 and you always stop stumbling.
00:08:34 And as we now know, the set of rules, the set of language that we can command
00:08:39 is incredibly limited.
00:08:41 The majority of the human brain doesn’t deal with language.
00:08:43 It deals with subconscious, numerical, perceptual things
00:08:48 that we don’t even self aware of.
00:08:51 Now, when an AI system watches an expert do their job and practice their job,
00:08:57 it can pick up things that people can’t even put into writing,
00:09:01 into books or rules.
00:09:03 And that’s where the real power is.
00:09:04 We now have AI systems that, for example, look over the shoulders
00:09:08 of highly paid human doctors like dermatologists or radiologists,
00:09:12 and they can somehow pick up those skills that no one can express in words.
00:09:18 So you were a key person in launching three revolutions,
00:09:22 online education, autonomous vehicles, and flying cars or VTOLs.
00:09:28 So high level, and I apologize for all the philosophical questions.
00:09:34 There’s no apology necessary.
00:09:37 How do you choose what problems to try and solve?
00:09:40 What drives you to make those solutions a reality?
00:09:43 I have two desires in life.
00:09:44 I want to literally make the lives of others better.
00:09:48 Or as we often say, maybe jokingly, make the world a better place.
00:09:52 I actually believe in this.
00:09:54 It’s as funny as it sounds.
00:09:57 And second, I want to learn.
00:09:59 I want to get new skills.
00:10:00 I don’t want to be in a job I’m good at, because if I’m in a job
00:10:02 that I’m good at, the chances for me to learn something interesting
00:10:05 is actually minimized.
00:10:06 So I want to be in a job I’m bad at.
00:10:09 That’s really important to me.
00:10:10 So in a bill, for example, what people often
00:10:12 call flying cars, these are electrical, vertical, takeoff,
00:10:15 and landing vehicles.
00:10:17 I’m just no expert in any of this.
00:10:19 And it’s so much fun to learn on the job what it actually means
00:10:23 to build something like this.
00:10:24 Now, I’d say the stuff that I’ve done lately
00:10:27 after I finished my professorship at Stanford,
00:10:31 they really focused on what has the maximum impact on society.
00:10:35 Transportation is something that has transformed the 21st
00:10:38 or 20th century more than any other invention,
00:10:40 in my opinion, even more than communication.
00:10:42 And cities are different.
00:10:43 Workers are different.
00:10:45 Women’s rights are different because of transportation.
00:10:47 And yet, we still have a very suboptimal transportation
00:10:51 solution where we kill 1.2 or so million people every year
00:10:56 in traffic.
00:10:57 It’s like the leading cause of death for young people
00:10:59 in many countries, where we are extremely inefficient
00:11:02 resource wise.
00:11:03 Just go to your average neighborhood city
00:11:06 and look at the number of parked cars.
00:11:08 That’s a travesty, in my opinion.
00:11:10 Or where we spend endless hours in traffic jams.
00:11:13 And very, very simple innovations,
00:11:15 like a self driving car or what people call a flying car,
00:11:18 could completely change this.
00:11:20 And it’s there.
00:11:21 I mean, the technology is basically there.
00:11:23 You have to close your eyes not to see it.
00:11:26 So lingering on autonomous vehicles, a fascinating space,
00:11:30 some incredible work you’ve done throughout your career there.
00:11:33 So let’s start with DARPA, I think, the DARPA challenge,
00:11:39 through the desert and then urban to the streets.
00:11:42 I think that inspired an entire generation of roboticists
00:11:45 and obviously sprung this whole excitement
00:11:49 about this particular kind of four wheeled robots
00:11:52 we called autonomous cars, self driving cars.
00:11:55 So you led the development of Stanley, the autonomous car
00:11:58 that won the race to the desert, the DARPA challenge in 2005.
00:12:03 And Junior, the car that finished second
00:12:07 in the DARPA urban challenge, also did incredibly well
00:12:11 in 2007, I think.
00:12:14 What are some painful, inspiring, or enlightening
00:12:17 experiences from that time that stand out to you?
00:12:20 Oh my god.
00:12:22 Painful were all these incredibly complicated,
00:12:28 stupid bugs that had to be found.
00:12:30 We had a phase where Stanley, our car that eventually
00:12:35 won the DARPA grand challenge, would every 30 miles
00:12:38 just commit suicide.
00:12:39 And we didn’t know why.
00:12:40 And it ended up to be that in the sinking of two computer
00:12:44 clocks, occasionally a clock went backwards
00:12:47 and that negative time elapsed, screwed up
00:12:50 the entire internal logic.
00:12:51 But it took ages to find this.
00:12:54 There were bugs like that.
00:12:56 I’d say enlightening is the Stanford team immediately
00:12:59 focused on machine learning and on software,
00:13:02 whereas everybody else seemed to focus on building better hardware.
00:13:05 Our analysis had been a human being with an existing rental
00:13:08 car can perfectly drive the course
00:13:10 but why do I have to build a better rental car?
00:13:12 I just should replace the human being.
00:13:15 And the human being, to me, was a conjunction of three steps.
00:13:18 We had sensors, eyes and ears, mostly eyes.
00:13:22 We had brains in the middle.
00:13:23 And then we had actuators, our hands and our feet.
00:13:26 Now, the actuators are easy to build.
00:13:28 The sensors are actually also easy to build.
00:13:29 What was missing was the brain.
00:13:30 So we had to build a human brain.
00:13:32 And nothing clearer than to me that the human brain
00:13:36 is a learning machine.
00:13:37 So why not just train our robot?
00:13:38 So we would build massive machine learning
00:13:40 into our machine.
00:13:42 And with that, we were able to not just learn
00:13:44 from human drivers.
00:13:45 We had the entire speed control of the vehicle
00:13:47 was copied from human driving.
00:13:49 But also have the robot learn from experience
00:13:51 where it made a mistake and recover from it
00:13:53 and learn from it.
00:13:55 You mentioned the pain point of software and clocks.
00:14:00 Synchronization seems to be a problem that
00:14:04 continues with robotics.
00:14:06 It’s a tricky one with drones and so on.
00:14:09 What does it take to build a thing, a system
00:14:14 with so many constraints?
00:14:16 You have a deadline, no time.
00:14:20 You’re unsure about anything really.
00:14:22 It’s the first time that people really even exploring.
00:14:24 It’s not even sure that anybody can finish
00:14:26 when we’re talking about the race to the desert
00:14:28 the year before nobody finish.
00:14:30 What does it take to scramble and finish
00:14:32 a product that actually, a system that actually works?
00:14:35 We were very lucky.
00:14:36 We were a really small team.
00:14:38 The core of the team were four people.
00:14:40 It was four because five couldn’t comfortably sit
00:14:43 inside a car, but four could.
00:14:45 And I, as a team leader, my job was
00:14:47 to get pizza for everybody and wash the car and stuff
00:14:50 like this and repair the radiator when it broke
00:14:52 and debug the system.
00:14:55 And we were very open minded.
00:14:56 We had no egos involved.
00:14:58 We just wanted to see how far we can get.
00:15:00 What we did really, really well was time management.
00:15:03 We were done with everything a month before the race.
00:15:06 And we froze the entire software a month before the race.
00:15:08 And it turned out, looking at other teams,
00:15:11 every other team complained if they had just one more week,
00:15:14 they would have won.
00:15:15 And we decided we’re not going to fall into that mistake.
00:15:18 We’re going to be early.
00:15:19 And we had an entire month to shake the system.
00:15:22 And we actually found two or three minor bugs
00:15:24 in the last month that we had to fix.
00:15:27 And we were completely prepared when the race occurred.
00:15:30 Okay, so first of all, that’s such an incredibly rare
00:15:33 achievement in terms of being able to be done on time
00:15:37 or ahead of time.
00:15:39 What do you, how do you do that in your future work?
00:15:43 What advice do you have in general?
00:15:44 Because it seems to be so rare,
00:15:46 especially in highly innovative projects like this.
00:15:49 People work till the last second.
00:15:50 Well, the nice thing about the DARPA Grand Challenge
00:15:52 is that the problem was incredibly well defined.
00:15:55 We were able for a while to drive
00:15:57 the old DARPA Grand Challenge course,
00:15:58 which had been used the year before.
00:16:00 And then at some reason we were kicked out of the region.
00:16:04 So we had to go to a different desert, the Snorran Desert,
00:16:06 and we were able to drive desert trails
00:16:08 just of the same type.
00:16:10 So there was never any debate about like,
00:16:12 what is actually the problem?
00:16:13 We didn’t sit down and say,
00:16:14 hey, should we build a car or a plane?
00:16:16 We had to build a car.
00:16:18 That made it very, very easy.
00:16:20 Then I studied my own life and life of others.
00:16:23 And we realized that the typical mistake that people make
00:16:26 is that there’s this kind of crazy bug left
00:16:29 that they haven’t found yet.
00:16:32 And it’s just, they regret it.
00:16:34 And that bug would have been trivial to fix.
00:16:36 They just haven’t fixed it yet.
00:16:37 They didn’t want to fall into that trap.
00:16:39 So I built a testing team.
00:16:41 We had a testing team that built a testing booklet
00:16:43 of 160 pages of tests we had to go through
00:16:46 just to make sure we shake out the system appropriately.
00:16:49 And the testing team was with us all the time
00:16:51 and dictated to us today, we do railroad crossings.
00:16:55 Tomorrow we do, we practice the start of the event.
00:16:58 And in all of these, we thought,
00:17:00 oh my God, it’s long solved trivial.
00:17:02 And then we tested it out.
00:17:03 Oh my God, it doesn’t do a railroad crossing.
00:17:04 Why not?
00:17:05 Oh my God, it mistakes the rails for metal barriers.
00:17:09 We have to fix this.
00:17:11 So it was really a continuous focus
00:17:14 on improving the weakest part of the system.
00:17:16 And as long as you focus on improving
00:17:19 the weakest part of the system,
00:17:20 you eventually build a really great system.
00:17:23 Let me just pause on that, to me as an engineer,
00:17:25 it’s just super exciting that you were thinking like that,
00:17:28 especially at that stage as brilliant,
00:17:30 that testing was such a core part of it.
00:17:33 It may be to linger on the point of leadership.
00:17:36 I think it’s one of the first times
00:17:39 you were really a leader
00:17:41 and you’ve led many very successful teams since then.
00:17:46 What does it take to be a good leader?
00:17:48 I would say most of all, I just take credit.
00:17:51 I put the work of others, right?
00:17:55 That’s very convenient turns out
00:17:57 because I can’t do all these things myself.
00:18:00 I’m an engineer at heart.
00:18:01 So I care about engineering.
00:18:03 So I don’t know what the chicken and the egg is,
00:18:06 but as a kid, I loved computers
00:18:07 because you could tell them to do something
00:18:09 and they actually did it.
00:18:10 It was very cool.
00:18:11 And you could like in the middle of the night,
00:18:12 wake up at one in the morning and switch on your computer.
00:18:15 And what he told you to yesterday, it would still do.
00:18:18 That was really cool.
00:18:19 Unfortunately, that didn’t quite work with people.
00:18:21 So you go to people and tell them what to do
00:18:22 and they don’t do it.
00:18:24 And they hate you for it, or you do it today
00:18:26 and then you go a day later and they stop doing it.
00:18:29 So you have to…
00:18:30 So then the question really became,
00:18:31 how can you put yourself in the brain of people
00:18:34 as opposed to computers?
00:18:35 And in terms of computers, it’s super dumb.
00:18:37 That’s so dumb.
00:18:38 If people were as dumb as computers,
00:18:39 I wouldn’t want to work with them.
00:18:41 But people are smart and people are emotional
00:18:43 and people have pride and people have aspirations.
00:18:45 So how can I connect to that?
00:18:49 And that’s the thing that most of our leadership just fails
00:18:52 because many, many engineers turn manager
00:18:56 believe they can treat their team just the same way
00:18:58 it can treat your computer.
00:18:59 And it just doesn’t work this way.
00:19:00 It’s just really bad.
00:19:02 So how can I connect to people?
00:19:05 And it turns out as a college professor,
00:19:07 the wonderful thing you do all the time
00:19:10 is to empower other people.
00:19:11 Like your job is to make your students look great.
00:19:14 That’s all you do.
00:19:15 You’re the best coach.
00:19:16 And it turns out if you do a fantastic job with making
00:19:19 your students look great, they actually love you
00:19:21 and their parents love you.
00:19:22 And they give you all the credit for stuff you don’t deserve.
00:19:25 All my students were smarter than me.
00:19:27 All the great stuff invented at Stanford
00:19:28 was their stuff, not my stuff.
00:19:30 And they give me credit and say, oh, Sebastian.
00:19:32 We’re just making them feel good about themselves.
00:19:35 So the question really is, can you take a team of people
00:19:38 and what does it take to make them
00:19:40 to connect to what they actually want in life
00:19:43 and turn this into productive action?
00:19:45 It turns out every human being that I know
00:19:48 has incredibly good intentions.
00:19:50 I’ve really rarely met a person with bad intentions.
00:19:54 I believe every person wants to contribute.
00:19:55 I think every person I’ve met wants to help others.
00:19:59 It’s amazing how much of an urge we have
00:20:01 not to just help ourselves, but to help others.
00:20:04 So how can we empower people and give them
00:20:06 the right framework that they can accomplish this?
00:20:10 In moments when it works, it’s magical.
00:20:12 Because you’d see the confluence of people
00:20:17 being able to make the world a better place
00:20:19 and deriving enormous confidence and pride out of this.
00:20:22 And that’s when my environment works the best.
00:20:27 These are moments where I can disappear for a month
00:20:29 and come back and things still work.
00:20:31 It’s very hard to accomplish.
00:20:32 But when it works, it’s amazing.
00:20:35 So I agree with you very much.
00:20:37 It’s not often heard that most people in the world
00:20:42 have good intentions.
00:20:43 At the core, their intentions are good
00:20:45 and they’re good people.
00:20:47 That’s a beautiful message, it’s not often heard.
00:20:50 We make this mistake, and this is a friend of mine,
00:20:52 Alex Werder, talking to us, that we judge ourselves
00:20:56 by our intentions and others by their actions.
00:21:00 And I think that the biggest skill,
00:21:01 I mean, here in Silicon Valley, we follow engineers
00:21:03 who have very little empathy and are kind of befuddled
00:21:06 by why it doesn’t work for them.
00:21:09 The biggest skill, I think, that people should acquire
00:21:13 is to put themselves into the position of the other
00:21:16 and listen, and listen to what the other has to say.
00:21:20 And they’d be shocked how similar they are to themselves.
00:21:23 And they might even be shocked how their own actions
00:21:26 don’t reflect their intentions.
00:21:28 I often have conversations with engineers
00:21:30 where I say, look, hey, I love you, you’re doing a great job.
00:21:33 And by the way, what you just did has the following effect.
00:21:37 Are you aware of that?
00:21:38 And then people would say, oh my God, not I wasn’t,
00:21:41 because my intention was that.
00:21:43 And I say, yeah, I trust your intention.
00:21:45 You’re a good human being.
00:21:46 But just to help you in the future,
00:21:48 if you keep expressing it that way,
00:21:51 then people will just hate you.
00:21:53 And I’ve had many instances where people say,
00:21:55 oh my God, thank you for telling me this,
00:21:56 because it wasn’t my intention to look like an idiot.
00:21:59 It wasn’t my intention to help other people.
00:22:00 I just didn’t know how to do it.
00:22:02 Very simple, by the way.
00:22:04 There’s a book, Dale Carnegie, 1936,
00:22:07 how to make friends and how to influence others.
00:22:10 Has the entire Bible, you just read it and you’re done
00:22:12 and you apply it every day.
00:22:13 And I wish I was good enough to apply it every day.
00:22:16 But it’s just simple things, right?
00:22:18 Like be positive, remember people’s name, smile,
00:22:22 and eventually have empathy.
00:22:24 Really think that the person that you hate
00:22:27 and you think is an idiot,
00:22:28 is actually just like yourself.
00:22:30 It’s a person who’s struggling, who means well,
00:22:33 and who might need help, and guess what, you need help.
00:22:36 I’ve recently spoken with Stephen Schwarzman.
00:22:39 I’m not sure if you know who that is, but.
00:22:41 I do.
00:22:42 So, and he said.
00:22:44 It’s on my list.
00:22:45 On the list.
00:22:47 But he said, sort of to expand on what you’re saying,
00:22:52 that one of the biggest things you can do
00:22:56 is hear people when they tell you what their problem is
00:23:00 and then help them with that problem.
00:23:02 He says, it’s surprising how few people
00:23:06 actually listen to what troubles others.
00:23:09 And because it’s right there in front of you
00:23:12 and you can benefit the world the most.
00:23:15 And in fact, yourself and everybody around you
00:23:18 by just hearing the problems and solving them.
00:23:20 I mean, that’s my little history of engineering.
00:23:23 That is, while I was engineering with computers,
00:23:28 I didn’t care at all what the computer’s problems were.
00:23:32 I just told them what to do and to do it.
00:23:34 And it just doesn’t work this way with people.
00:23:37 It doesn’t work with me.
00:23:38 If you come to me and say, do A, I do the opposite.
00:23:43 But let’s return to the comfortable world of engineering.
00:23:47 And can you tell me in broad strokes in how you see it?
00:23:52 Because you’re the core of starting it,
00:23:53 the core of driving it,
00:23:55 the technical evolution of autonomous vehicles
00:23:58 from the first DARPA Grand Challenge
00:24:00 to the incredible success we see with the program
00:24:03 you started with Google self driving car
00:24:05 and Waymo and the entire industry that sprung up
00:24:08 of different kinds of approaches, debates and so on.
00:24:11 Well, the idea of self driving car goes back to the 80s.
00:24:14 There was a team in Germany and another team
00:24:15 at Carnegie Mellon that did some very pioneering work.
00:24:18 But back in the day, I’d say the computers were so deficient
00:24:21 that even the best professors and engineers in the world
00:24:25 basically stood no chance.
00:24:28 It then folded into a phase where the US government
00:24:31 spent at least half a billion dollars
00:24:33 that I could count on research projects.
00:24:36 But the way the procurement works,
00:24:38 a successful stack of paper describing lots of stuff
00:24:42 that no one’s ever gonna read
00:24:43 was a successful product of a research project.
00:24:47 So we trained our researchers to produce lots of paper.
00:24:52 That all changed with the DARPA Grand Challenge.
00:24:54 And I really gotta credit the ingenious people at DARPA
00:24:58 and the US government and Congress
00:25:00 that took a complete new funding model where they said,
00:25:03 let’s not fund effort, let’s fund outcomes.
00:25:05 And it sounds very trivial,
00:25:06 but there was no tax code that allowed
00:25:09 the use of congressional tax money for a price.
00:25:13 It was all effort based.
00:25:15 So if you put in a hundred hours in,
00:25:16 you could charge a hundred hours.
00:25:17 If you put in a thousand hours in,
00:25:18 you could build a thousand hours.
00:25:20 By changing the focus instead of making the price,
00:25:22 we don’t pay you for development,
00:25:24 we pay for the accomplishment.
00:25:26 They drew in, they automatically drew out
00:25:28 all these contractors who are used to the drug
00:25:31 of getting money per hour.
00:25:33 And they drew in a whole bunch of new people.
00:25:35 And these people are mostly crazy people.
00:25:37 They were people who had a car and a computer
00:25:40 and they wanted to make a million bucks.
00:25:42 The million bucks was their visual price money,
00:25:43 it was then doubled.
00:25:45 And they felt if I put my computer in my car
00:25:48 and program it, I can be rich.
00:25:50 And that was so awesome.
00:25:52 Like half the teams, there was a team that was surfer dudes
00:25:55 and they had like two surfboards on their vehicle
00:25:58 and brought like these fashion girls, super cute girls,
00:26:01 like twin sisters.
00:26:03 And you could tell these guys were not your common
00:26:06 beltway bandit who gets all these big multimillion
00:26:10 and billion dollar countries from the US government.
00:26:13 And there was a great reset.
00:26:16 Universities moved in.
00:26:18 I was very fortunate at Stanford that I just received tenure
00:26:21 so I couldn’t get fired no matter what I do,
00:26:23 otherwise I wouldn’t have done it.
00:26:25 And I had enough money to finance this thing
00:26:28 and I was able to attract a lot of money from third parties.
00:26:31 And even car companies moved in.
00:26:32 They kind of moved in very quietly
00:26:34 because they were super scared to be embarrassed
00:26:36 that their car would flip over.
00:26:38 But Ford was there and Volkswagen was there
00:26:40 and a few others and GM was there.
00:26:43 So it kind of reset the entire landscape of people.
00:26:46 And if you look at who’s a big name
00:26:48 in self driving cars today,
00:26:49 these were mostly people who participated
00:26:51 in those challenges.
00:26:53 Okay, that’s incredible.
00:26:54 Can you just comment quickly on your sense of lessons learned
00:26:59 from that kind of funding model
00:27:01 and the research that’s going on in academia
00:27:04 in terms of producing papers,
00:27:06 is there something to be learned and scaled up bigger,
00:27:10 having these kinds of grand challenges
00:27:11 that could improve outcomes?
00:27:14 So I’m a big believer in focusing
00:27:16 on kind of an end to end system.
00:27:19 I’m a really big believer in systems building.
00:27:21 I’ve always built systems in my academic career,
00:27:23 even though I do a lot of math and abstract stuff,
00:27:27 but it’s all derived from the idea
00:27:28 of let’s solve a real problem.
00:27:29 And it’s very hard for me to be an academic
00:27:33 and say, let me solve a component of a problem.
00:27:35 Like with someone there’s fields like nonmonetary logic
00:27:38 or AI planning systems where people believe
00:27:41 that a certain style of problem solving
00:27:44 is the ultimate end objective.
00:27:47 And I would always turn it around and say,
00:27:49 hey, what problem would my grandmother care about
00:27:52 that doesn’t understand computer technology
00:27:54 and doesn’t wanna understand?
00:27:56 And how could I make her love what I do?
00:27:58 Because only then do I have an impact on the world.
00:28:01 I can easily impress my colleagues.
00:28:02 That is much easier,
00:28:04 but impressing my grandmother is very, very hard.
00:28:07 So I would always thought if I can build a self driving car
00:28:10 and my grandmother can use it
00:28:12 even after she loses her driving privileges
00:28:14 or children can use it,
00:28:16 or we save maybe a million lives a year,
00:28:20 that would be very impressive.
00:28:22 And then there’s so many problems like these,
00:28:23 like there’s a problem with curing cancer,
00:28:25 or whatever it is, live twice as long.
00:28:27 Once a problem is defined,
00:28:29 of course I can’t solve it in its entirety.
00:28:31 Like it takes sometimes tens of thousands of people
00:28:34 to find a solution.
00:28:35 There’s no way you can fund an army of 10,000 at Stanford.
00:28:39 So you gotta build a prototype.
00:28:41 Let’s build a meaningful prototype.
00:28:42 And the DARPA Grand Challenge was beautiful
00:28:43 because it told me what this prototype had to do.
00:28:46 I didn’t have to think about what it had to do,
00:28:47 I just had to read the rules.
00:28:48 And that was really beautiful.
00:28:51 And it’s most beautiful,
00:28:52 you think what academia could aspire to
00:28:54 is to build a prototype that’s the systems level,
00:28:58 that solves or gives you an inkling
00:29:01 that this problem could be solved with this prototype.
00:29:03 First of all, I wanna emphasize what academia really is.
00:29:06 And I think people misunderstand it.
00:29:08 First and foremost, academia is a way
00:29:11 to educate young people.
00:29:13 First and foremost, a professor is an educator.
00:29:15 No matter where you are at,
00:29:17 a small suburban college,
00:29:18 or whether you are a Harvard or Stanford professor,
00:29:21 that’s not the way most people think of themselves
00:29:25 in academia because we have this kind of competition
00:29:28 going on for citations and publication.
00:29:31 That’s a measurable thing,
00:29:32 but that is secondary to the primary purpose
00:29:35 of educating people to think.
00:29:37 Now, in terms of research,
00:29:39 most of the great science,
00:29:42 the great research comes out of universities.
00:29:45 You can trace almost everything back,
00:29:46 including Google, to universities.
00:29:48 So there’s nothing really fundamentally broken here.
00:29:52 It’s a good system.
00:29:53 And I think America has the finest university system
00:29:55 on the planet.
00:29:57 We can talk about reach
00:29:59 and how to reach people outside the system.
00:30:01 It’s a different topic,
00:30:02 but the system itself is a good system.
00:30:04 If I had one wish, I would say it’d be really great
00:30:08 if there was more debate about
00:30:11 what the great big problems are in society
00:30:15 and focus on those.
00:30:18 And most of them are interdisciplinary.
00:30:21 Unfortunately, it’s very easy to fall
00:30:24 into an interdisciplinary viewpoint
00:30:28 where your problem is dictated
00:30:30 by what your closest colleagues believe the problem is.
00:30:33 It’s very hard to break out and say,
00:30:35 well, there’s an entire new field of problems.
00:30:37 So to give an example,
00:30:39 prior to me working on self driving cars,
00:30:41 I was a roboticist and a machine learning expert.
00:30:44 And I wrote books on robotics,
00:30:46 something called probabilistic robotics.
00:30:48 It’s a very methods driven kind of viewpoint of the world.
00:30:51 I built robots that acted in museums as tour guides,
00:30:54 that let children around.
00:30:55 It is something that at the time was moderately challenging.
00:31:00 When I started working on cars,
00:31:02 several colleagues told me,
00:31:03 Sebastian, you’re destroying your career
00:31:06 because in our field of robotics,
00:31:08 cars are looked like as a gimmick
00:31:10 and they’re not expressive enough.
00:31:11 They can only push the throttle and the brakes.
00:31:15 There’s no dexterity.
00:31:16 There’s no complexity.
00:31:18 It’s just too simple.
00:31:19 And no one came to me and said,
00:31:21 wow, if you solve that problem,
00:31:22 you can save a million lives, right?
00:31:25 Among all robotic problems that I’ve seen in my life,
00:31:27 I would say the self driving car, transportation,
00:31:29 is the one that has the most hope for society.
00:31:32 So how come the robotics community wasn’t all over the place?
00:31:35 And it was because we focused on methods and solutions
00:31:37 and not on problems.
00:31:39 Like if you go around today and ask your grandmother,
00:31:42 what bugs you?
00:31:43 What really makes you upset?
00:31:45 I challenge any academic to do this
00:31:48 and then realize how far your research
00:31:51 is probably away from that today.
00:31:54 At the very least, that’s a good thing
00:31:56 for academics to deliberate on.
00:31:59 The other thing that’s really nice in Silicon Valley is,
00:32:01 Silicon Valley is full of smart people outside academia.
00:32:04 So there’s the Larry Pages and Mark Zuckerbergs in the world
00:32:06 who are anywhere smarter, smarter
00:32:09 than the best academics I’ve met in my life.
00:32:11 And what they do is they are at a different level.
00:32:15 They build the systems,
00:32:16 they build the customer facing systems,
00:32:19 they build things that people can use
00:32:21 without technical education.
00:32:23 And they are inspired by research.
00:32:25 They’re inspired by scientists.
00:32:27 They hire the best PhDs from the best universities
00:32:30 for a reason.
00:32:31 So I think this kind of vertical integration
00:32:35 between the real product, the real impact
00:32:37 and the real thought, the real ideas,
00:32:39 that’s actually working surprisingly well in Silicon Valley.
00:32:42 It did not work as well in other places in this nation.
00:32:44 So when I worked at Carnegie Mellon,
00:32:46 we had the world’s finest computer science university,
00:32:49 but there wasn’t those people in Pittsburgh
00:32:52 that would be able to take these
00:32:54 very fine computer science ideas
00:32:56 and turn them into massive, impactful products.
00:33:00 That symbiosis seemed to exist
00:33:02 pretty much only in Silicon Valley
00:33:04 and maybe a bit in Boston and Austin.
00:33:06 Yeah, with Stanford, that’s really interesting.
00:33:11 So if we look a little bit further on
00:33:14 from the DARPA Grand Challenge
00:33:17 and the launch of the Google self driving car,
00:33:20 what do you see as the state,
00:33:22 the challenges of autonomous vehicles as they are now
00:33:25 is actually achieving that huge scale
00:33:29 and having a huge impact on society?
00:33:31 I’m extremely proud of what has been accomplished.
00:33:35 And again, I’m taking a lot of credit for the work of others.
00:33:38 And I’m actually very optimistic.
00:33:40 And people have been kind of worrying,
00:33:42 is it too fast? Is it too slow?
00:33:43 Why is it not there yet? And so on.
00:33:45 It is actually quite an interesting, hard problem.
00:33:48 And in that a self driving car,
00:33:51 to build one that manages 90% of the problems
00:33:55 encountered in everyday driving is easy.
00:33:57 We can literally do this over a weekend.
00:33:59 To do 99% might take a month.
00:34:02 Then there’s 1% left.
00:34:03 So 1% would mean that you still have a fatal accident
00:34:06 every week, very unacceptable.
00:34:08 So now you work on this 1%
00:34:10 and the 99% of that, the remaining 1%
00:34:13 is actually still relatively easy,
00:34:15 but now you’re down to like a hundredth of 1%.
00:34:18 And it’s still completely unacceptable in terms of safety.
00:34:21 So the variety of things you encounter are just enormous.
00:34:24 And that gives me enormous respect for human being
00:34:26 that we’re able to deal with the couch on the highway,
00:34:30 or the deer in the headlights, or the blown tire
00:34:33 that we’ve never been trained for.
00:34:34 And all of a sudden have to handle it
00:34:35 in an emergency situation
00:34:37 and often do very, very successfully.
00:34:38 It’s amazing from that perspective,
00:34:40 how safe driving actually is given how many millions
00:34:43 of miles we drive every year in this country.
00:34:47 We are now at a point where I believe the technology
00:34:49 is there and I’ve seen it.
00:34:51 I’ve seen it in Waymo, I’ve seen it in Aptiv,
00:34:53 I’ve seen it in Cruise and in a number of companies
00:34:56 and in Voyage where vehicles now driving around
00:35:00 and basically flawlessly are able to drive people around
00:35:04 in limited scenarios.
00:35:06 In fact, you can go to Vegas today
00:35:07 and order a Summon and Lift.
00:35:09 And if you get the right setting of your app,
00:35:13 you’ll be picked up by a driverless car.
00:35:15 Now there’s still safety drivers in there,
00:35:18 but that’s a fantastic way to kind of learn
00:35:21 what the limits are of technology today.
00:35:22 And there’s still some glitches,
00:35:24 but the glitches have become very, very rare.
00:35:26 I think the next step is gonna be to down cost it,
00:35:29 to harden it, the entrapment, the sensors
00:35:33 are not quite an automotive grade standard yet.
00:35:36 And then to really build the business models,
00:35:37 to really kind of go somewhere and make the business case.
00:35:40 And the business case is hard work.
00:35:42 It’s not just, oh my God, we have this capability,
00:35:44 people are just gonna buy it.
00:35:45 You have to make it affordable.
00:35:46 You have to find the social acceptance of people.
00:35:52 None of the teams yet has been able to or gutsy enough
00:35:55 to drive around without a person inside the car.
00:35:59 And that’s the next magical hurdle.
00:36:01 We’ll be able to send these vehicles around
00:36:03 completely empty in traffic.
00:36:05 And I think, I mean, I wait every day,
00:36:08 wait for the news that Waymo has just done this.
00:36:11 So, interesting you mentioned gutsy.
00:36:15 Let me ask some maybe unanswerable question,
00:36:20 maybe edgy questions.
00:36:21 But in terms of how much risk is required,
00:36:26 some guts in terms of leadership style,
00:36:30 it would be good to contrast approaches.
00:36:32 And I don’t think anyone knows what’s right.
00:36:34 But if we compare Tesla and Waymo, for example,
00:36:38 Elon Musk and the Waymo team,
00:36:43 there’s slight differences in approach.
00:36:45 So on the Elon side, there’s more,
00:36:49 I don’t know what the right word to use,
00:36:50 but aggression in terms of innovation.
00:36:53 And on Waymo side, there’s more sort of cautious,
00:36:59 safety focused approach to the problem.
00:37:03 What do you think it takes?
00:37:06 What leadership at which moment is right?
00:37:09 Which approach is right?
00:37:11 Look, I don’t sit in either of those teams.
00:37:13 So I’m unable to even verify like somebody says correct.
00:37:18 In the end of the day, every innovator in that space
00:37:21 will face a fundamental dilemma.
00:37:23 And I would say you could put aerospace titans
00:37:27 into the same bucket,
00:37:28 which is you have to balance public safety
00:37:31 with your drive to innovate.
00:37:34 And this country in particular in the States
00:37:36 has a hundred plus year history
00:37:38 of doing this very successfully.
00:37:40 Air travel is what a hundred times a safe per mile
00:37:43 than ground travel, than cars.
00:37:46 And there’s a reason for it because people have found ways
00:37:50 to be very methodological about ensuring public safety
00:37:55 while still being able to make progress
00:37:56 on important aspects, for example,
00:37:59 like air and noise and fuel consumption.
00:38:03 So I think that those practices are proven
00:38:06 and they actually work.
00:38:07 We live in a world safer than ever before.
00:38:09 And yes, there will always be the provision
00:38:11 that something goes wrong.
00:38:12 There’s always the possibility
00:38:14 that someone makes a mistake
00:38:15 or there’s an unexpected failure.
00:38:17 We can never guarantee to a hundred percent
00:38:19 absolute safety other than just not doing it.
00:38:23 But I think I’m very proud of the history of the United States.
00:38:27 I mean, we’ve dealt with much more dangerous technology
00:38:30 like nuclear energy and kept that safe too.
00:38:33 We have nuclear weapons and we keep those safe.
00:38:36 So we have methods and procedures
00:38:39 that really balance these two things very, very successfully.
00:38:42 You’ve mentioned a lot of great autonomous vehicle companies
00:38:46 that are taking sort of the level four, level five,
00:38:48 they jump in full autonomy with a safety driver
00:38:51 and take that kind of approach
00:38:53 and also through simulation and so on.
00:38:55 There’s also the approach that Tesla Autopilot is doing,
00:38:59 which is kind of incrementally taking a level two vehicle
00:39:03 and using machine learning
00:39:04 and learning from the driving of human beings
00:39:08 and trying to creep up,
00:39:10 trying to incrementally improve the system
00:39:12 until it’s able to achieve level four autonomy.
00:39:15 So perfect autonomy in certain kind of geographical regions.
00:39:19 What are your thoughts on these contrasting approaches?
00:39:23 Well, so first of all, I’m a very proud Tesla owner
00:39:25 and I literally use the Autopilot every day
00:39:27 and it literally has kept me safe.
00:39:30 It is a beautiful technology specifically
00:39:33 for highway driving when I’m slightly tired
00:39:37 because then it turns me into a much safer driver.
00:39:42 And I’m 100% confident that’s the case.
00:39:46 In terms of the right approach,
00:39:47 I think the biggest change I’ve seen
00:39:49 since I went to Waymo team is this thing called deep learning.
00:39:54 I think deep learning was not a hot topic
00:39:56 when I started Waymo or Google self driving cars.
00:39:59 It was there, in fact, we started Google Brain
00:40:01 at the same time in Google X.
00:40:02 So I invested in deep learning,
00:40:04 but people didn’t talk about it, it wasn’t a hot topic.
00:40:07 And now it is, there’s a shift of emphasis
00:40:10 from a more geometric perspective
00:40:12 where you use geometric sensors
00:40:14 that give you a full 3D view
00:40:15 when you do a geometric reasoning about,
00:40:17 oh, this box over here might be a car
00:40:19 towards a more human like, oh, let’s just learn about it.
00:40:24 This looks like the thing I’ve seen 10,000 times before.
00:40:26 So maybe it’s the same thing, machine learning perspective.
00:40:30 And that has really put, I think,
00:40:32 all these approaches on steroids.
00:40:36 At Udacity, we teach a course in self driving cars.
00:40:38 In fact, I think we’ve graduated over 20,000 or so people
00:40:43 on self driving car skills.
00:40:45 So every self driving car team in the world
00:40:47 now uses our engineers.
00:40:49 And in this course, the very first homework assignment
00:40:51 is to do lane finding on images.
00:40:54 And lane finding images for layman,
00:40:56 what this means is you put a camera into your car
00:40:59 or you open your eyes and you would know where the lane is.
00:41:02 So you can stay inside the lane with your car.
00:41:05 Humans can do this super easily.
00:41:06 You just look and you know where the lane is,
00:41:08 just intuitively.
00:41:10 For machines, for a long time, it was super hard
00:41:12 because people would write these kind of crazy rules.
00:41:14 If there’s like wine lane markers
00:41:16 and here’s what white really means,
00:41:17 this is not quite white enough.
00:41:19 So let’s, oh, it’s not white.
00:41:20 Or maybe the sun is shining.
00:41:21 So when the sun shines and this is white
00:41:23 and this is a straight line,
00:41:24 I mean, it’s not quite a straight line
00:41:25 because the road is curved.
00:41:27 And do we know that there’s really six feet
00:41:29 between lane markings or not or 12 feet, whatever it is.
00:41:34 And now what the students are doing,
00:41:36 they would take machine learning.
00:41:37 So instead of like writing these crazy rules
00:41:39 for the lane marker,
00:41:40 they’ll say, hey, let’s take an hour of driving
00:41:42 and label it and tell the vehicle,
00:41:44 this is actually the lane by hand.
00:41:45 And then these are examples
00:41:47 and have the machine find its own rules,
00:41:49 what lane markings are.
00:41:51 And within 24 hours, now every student
00:41:53 that’s never done any programming before in this space
00:41:56 can write a perfect lane finder
00:41:58 as good as the best commercial lane finders.
00:42:00 And that’s completely amazing to me.
00:42:02 We’ve seen progress using machine learning
00:42:05 that completely dwarfs anything
00:42:08 that I saw 10 years ago.
00:42:10 Yeah, and just as a side note,
00:42:12 the self driving car nanodegree,
00:42:15 the fact that you launched that many years ago now,
00:42:18 maybe four years ago, three years ago is incredible
00:42:22 that that’s a great example of system level thinking
00:42:24 sort of just taking an entire course
00:42:27 that teaches you how to solve the entire problem.
00:42:29 I definitely recommend people.
00:42:31 It’s become super popular
00:42:32 and it’s become actually incredibly high quality
00:42:34 really with Mercedes and various other companies
00:42:37 in that space.
00:42:38 And we find that engineers from Tesla and Waymo
00:42:40 are taking it today.
00:42:43 The insight was that two things,
00:42:45 one is existing universities will be very slow to move
00:42:49 because they’re departmentalized
00:42:50 and there’s no department for self driving cars.
00:42:52 So between Mac E and double E and computer science,
00:42:56 getting those folks together
00:42:57 into one room is really, really hard.
00:42:59 And every professor listening here will know,
00:43:01 they’ll probably agree to that.
00:43:02 And secondly, even if all the great universities
00:43:06 just did this, which none so far has developed
00:43:09 a curriculum in this field,
00:43:11 it is just a few thousand students that can partake
00:43:13 because all the great universities are super selective.
00:43:16 So how about people in India?
00:43:18 How about people in China or in the Middle East
00:43:20 or Indonesia or Africa?
00:43:23 Why should those be excluded
00:43:25 from the skill of building self driving cars?
00:43:27 Are they any dumber than we are?
00:43:28 Are we any less privileged?
00:43:30 And the answer is we should just give everybody the skill
00:43:34 to build a self driving car.
00:43:35 Because if we do this,
00:43:37 then we have like a thousand self driving car startups.
00:43:40 And if 10% succeed, that’s like a hundred,
00:43:42 that means hundred countries now
00:43:44 will have self driving cars and be safer.
00:43:46 It’s kind of interesting to imagine impossible to quantify,
00:43:50 but the number, the, you know,
00:43:53 over a period of several decades,
00:43:55 the impact that has like a single course,
00:43:57 like a ripple effect of society.
00:44:00 If you, I just recently talked to Andrew
00:44:03 who was creator of Cosmos show.
00:44:06 It’s interesting to think about
00:44:08 how many scientists that show launched.
00:44:10 And so it’s really, in terms of impact,
00:44:15 I can’t imagine a better course
00:44:17 than the self driving car course.
00:44:18 That’s, you know, there’s other more specific disciplines
00:44:21 like deep learning and so on that Udacity is also teaching,
00:44:24 but self driving cars,
00:44:25 it’s really, really interesting course.
00:44:26 And then it came at the right moment.
00:44:28 It came at a time when there were a bunch of Acqui hires.
00:44:31 Acqui hire is a acquisition of a company,
00:44:34 not for its technology or its products or business,
00:44:36 but for its people.
00:44:38 So Acqui hire means maybe that a company of 70 people,
00:44:40 they have no product yet, but they’re super smart people
00:44:43 and they pay a certain amount of money.
00:44:44 So I took Acqui hires like GM Cruise and Uber and others,
00:44:48 and did the math and said,
00:44:50 hey, how many people are there and how much money was paid?
00:44:53 And as a lower bound,
00:44:55 I estimated the value of a self driving car engineer
00:44:58 in these acquisitions to be at least $10 million, right?
00:45:02 So think about this, you get yourself a skill
00:45:05 and you team up and build a company
00:45:06 and your worth now is $10 million.
00:45:09 I mean, that’s kind of cool.
00:45:10 I mean, what other thing could you do in life
00:45:13 to be worth $10 million within a year?
00:45:15 Yeah, amazing.
00:45:17 But to come back for a moment on to deep learning
00:45:21 and its application in autonomous vehicles,
00:45:23 what are your thoughts on Elon Musk’s statement,
00:45:28 provocative statement, perhaps that light air is a crutch.
00:45:31 So this geometric way of thinking about the world
00:45:34 may be holding us back if what we should instead be doing
00:45:38 in this robotic space,
00:45:39 in this particular space of autonomous vehicles
00:45:42 is using camera as a primary sensor
00:45:46 and using computer vision and machine learning
00:45:48 as the primary way to…
00:45:49 Look, I have two comments.
00:45:50 I think first of all, we all know
00:45:52 that people can drive cars without lighters in their heads
00:45:56 because we only have eyes
00:45:59 and we mostly just use eyes for driving.
00:46:02 Maybe we use some other perception about our bodies,
00:46:04 accelerations, occasionally our ears,
00:46:08 certainly not our noses.
00:46:10 So the existence proof is there,
00:46:12 that eyes must be sufficient.
00:46:15 In fact, we could even drive a car
00:46:17 if someone put a camera out
00:46:19 and then gave us the camera image with no latency,
00:46:23 we would be able to drive a car that way the same way.
00:46:26 So a camera is also sufficient.
00:46:28 Secondly, I really love the idea that in the Western world,
00:46:31 we have many, many different people
00:46:33 trying different hypotheses.
00:46:35 It’s almost like an anthill,
00:46:36 like if an anthill tries to forge for food,
00:46:39 you can sit there as two ants
00:46:41 and agree what the perfect path is
00:46:42 and then every single ant marches
00:46:44 for the most likely location of food is,
00:46:46 or you can even just spread out.
00:46:47 And I promise you the spread out solution will be better
00:46:50 because if the discussing philosophical,
00:46:53 intellectual ants get it wrong
00:46:55 and they’re all moving the wrong direction,
00:46:56 they’re going to waste a day
00:46:58 and then they’re going to discuss again for another week.
00:47:00 Whereas if all these ants go in a random direction,
00:47:02 someone’s going to succeed
00:47:03 and they’re going to come back and claim victory
00:47:05 and get the Nobel prize or whatever the ant equivalent is.
00:47:08 And then they all march in the same direction.
00:47:10 And that’s great about society.
00:47:11 That’s great about the Western society.
00:47:13 We’re not plan based, we’re not central based.
00:47:15 We don’t have a Soviet Union style central government
00:47:19 that tells us where to forge.
00:47:20 We just forge.
00:47:21 We started in C Corp.
00:47:24 We get investor money, go out and try it out.
00:47:25 And who knows who’s going to win.
00:47:28 I like it.
00:47:30 In your, when you look at the longterm vision
00:47:33 of autonomous vehicles,
00:47:35 do you see machine learning
00:47:36 as fundamentally being able to solve most of the problems?
00:47:39 So learning from experience.
00:47:42 I’d say we should be very clear
00:47:44 about what machine learning is and is not.
00:47:46 And I think there’s a lot of confusion.
00:47:48 What it is today is a technology
00:47:50 that can go through large databases
00:47:54 of repetitive patterns and find those patterns.
00:48:00 So in example, we did a study at Stanford two years ago
00:48:03 where we applied machine learning
00:48:05 to detecting skin cancer in images.
00:48:07 And we harvested or built a data set
00:48:10 of 129,000 skin photo shots
00:48:15 that were all had been biopsied
00:48:17 for what the actual situation was.
00:48:19 And those included melanomas and carcinomas,
00:48:22 also included rashes and other skin conditions, lesions.
00:48:27 And then we had a network find those patterns.
00:48:30 And it was by and large able to then detect skin cancer
00:48:34 with an iPhone as accurately
00:48:36 as the best board certified Stanford level dermatologist.
00:48:41 We proved that.
00:48:42 Now this thing was great in this one thing
00:48:45 and finding skin cancer, but it couldn’t drive a car.
00:48:49 So the difference to human intelligence
00:48:51 is we do all these many, many things
00:48:53 and we can often learn from a very small data set
00:48:56 of experiences.
00:48:58 Whereas machines still need very large data sets
00:49:01 and things that will be very repetitive.
00:49:03 Now that’s still super impactful
00:49:04 because almost everything we do is repetitive.
00:49:06 So that’s gonna really transform human labor
00:49:10 but it’s not this almighty general intelligence.
00:49:13 We’re really far away from a system
00:49:15 that will exhibit general intelligence.
00:49:18 To that end, I actually commiserate the naming a little bit
00:49:21 because artificial intelligence, if you believe Hollywood
00:49:24 is immediately mixed into the idea of human suppression
00:49:27 and machine superiority.
00:49:30 I don’t think that we’re gonna see this in my lifetime.
00:49:32 I don’t think human suppression is a good idea.
00:49:36 I don’t see it coming.
00:49:37 I don’t see the technology being there.
00:49:39 What I see instead is a very pointed focused
00:49:42 pattern recognition technology that’s able to
00:49:45 extract patterns from large data sets.
00:49:48 And in doing so, it can be super impactful.
00:49:51 Super impactful.
00:49:53 Let’s take the impact of artificial intelligence
00:49:55 on human work.
00:49:57 We all know that it takes something like 10,000 hours
00:50:00 to become an expert.
00:50:01 If you’re gonna be a doctor or a lawyer
00:50:03 or even a really good driver,
00:50:05 it takes a certain amount of time to become experts.
00:50:08 Machines now are able and have been shown
00:50:11 to observe people become experts and observe experts
00:50:15 and then extract those rules from experts
00:50:17 in some interesting way.
00:50:18 They could go from law to sales to driving cars
00:50:25 to diagnosing cancer.
00:50:28 And then giving that capability to people who are
00:50:30 completely new in their job.
00:50:32 We now can, and that’s been done.
00:50:34 It’s been done commercially in many, many instantiations.
00:50:37 So that means we can use machine learning
00:50:40 to make people expert on the very first day of their work.
00:50:44 Like think about the impact.
00:50:45 If your doctor is still in their first 10,000 hours,
00:50:50 you have a doctor who is not quite an expert yet.
00:50:53 Who would not want a doctor who is the world’s best expert?
00:50:56 And now we can leverage machines to really eradicate
00:51:00 the error in decision making,
00:51:02 error and lack of expertise for human doctors.
00:51:06 That could save your life.
00:51:08 If we can link on that for a little bit,
00:51:10 in which way do you hope machines in the medical field
00:51:14 could help assist doctors?
00:51:16 You mentioned this sort of accelerating the learning curve
00:51:21 or people, if they start a job or in the first 10,000 hours
00:51:26 can be assisted by machines.
00:51:27 How do you envision that assistance looking?
00:51:29 So we built this app for an iPhone that can detect
00:51:33 and classify and diagnose skin cancer.
00:51:36 And we proved two years ago that it does pretty much
00:51:40 as good or better than the best human doctors.
00:51:42 So let me tell you a story.
00:51:43 So there’s a friend of mine, let’s call him Ben.
00:51:45 Ben is a very famous venture capitalist.
00:51:47 He goes to his doctor and the doctor looks at a mole
00:51:50 and says, hey, that mole is probably harmless.
00:51:55 And for some very funny reason, he pulls out that phone
00:51:59 with our app.
00:52:00 He’s a collaborator in our study.
00:52:02 And the app says, no, no, no, no, this is a melanoma.
00:52:06 And for background, melanomas are,
00:52:08 and skin cancer is the most common cancer in this country.
00:52:12 Melanomas can go from stage zero to stage four
00:52:16 within less than a year.
00:52:18 Stage zero means you can basically cut it out yourself
00:52:20 with a kitchen knife and be safe.
00:52:23 And stage four means your chances of living
00:52:25 five more years in less than 20%.
00:52:28 So it’s a very serious, serious, serious condition.
00:52:31 So this doctor who took out the iPhone,
00:52:36 looked at the iPhone and was a little bit puzzled.
00:52:37 He said, I mean, but just to be safe,
00:52:39 let’s cut it out and biopsy it.
00:52:41 That’s the technical term for let’s get
00:52:43 an in depth diagnostics that is more than just looking at it.
00:52:47 And it came back as cancerous, as a melanoma.
00:52:50 And it was then removed.
00:52:52 And my friend, Ben, I was hiking with him
00:52:54 and we were talking about AI.
00:52:56 And I told him I do this work on skin cancer.
00:52:58 And he said, oh, funny.
00:53:00 My doctor just had an iPhone that found my cancer.
00:53:05 So I was like completely intrigued.
00:53:06 I didn’t even know about this.
00:53:08 So here’s a person, I mean, this is a real human life, right?
00:53:11 Like who doesn’t know somebody
00:53:12 who has been affected by cancer.
00:53:14 Cancer is cause of death number two.
00:53:16 Cancer is this kind of disease that is mean
00:53:19 in the following way.
00:53:21 Most cancers can actually be cured relatively easily
00:53:24 if we catch them early.
00:53:25 And the reason why we don’t tend to catch them early
00:53:28 is because they have no symptoms.
00:53:30 Like your very first symptom of a gallbladder cancer
00:53:33 or a pancreas cancer might be a headache.
00:53:37 And when you finally go to your doctor
00:53:38 because of these headaches or your back pain
00:53:41 and you’re being imaged, it’s usually stage four plus.
00:53:45 And that’s the time when the occurring chances
00:53:48 might be dropped to a single digit percentage.
00:53:50 So if we could leverage AI to inspect your body
00:53:54 on a regular basis without even a doctor in the room,
00:53:58 maybe when you take a shower or what have you,
00:54:00 I know this sounds creepy,
00:54:01 but then we might be able to save millions
00:54:03 and millions of lives.
00:54:06 You’ve mentioned there’s a concern that people have
00:54:09 about near term impacts of AI in terms of job loss.
00:54:12 So you’ve mentioned being able to assist doctors,
00:54:15 being able to assist people in their jobs.
00:54:17 Do you have a worry of people losing their jobs
00:54:22 or the economy being affected by the improvements in AI?
00:54:25 Yeah, anybody concerned about job losses,
00:54:27 please come to Gdacity.com.
00:54:30 We teach contemporary tech skills
00:54:32 and we have a kind of implicit job promise.
00:54:36 We often, when we measure,
00:54:38 we spend way over 50% of our graders in new jobs
00:54:41 and they’re very satisfied about it.
00:54:43 And it costs almost nothing,
00:54:44 costs like 1,500 max or something like that.
00:54:47 And so there’s a cool new program
00:54:48 that you agree with the U.S. government,
00:54:51 guaranteeing that you will help us give scholarships
00:54:54 that educate people in this kind of situation.
00:54:57 Yeah, we’re working with the U.S. government
00:54:59 on the idea of basically rebuilding the American dream.
00:55:03 So Gdacity has just dedicated 100,000 scholarships
00:55:07 for citizens of America for various levels of courses
00:55:12 that eventually will get you a job.
00:55:15 And those courses are all somewhat related
00:55:18 to the tech sector because the tech sector
00:55:20 is kind of the hottest sector right now.
00:55:22 And they range from interlevel digital marketing
00:55:24 to very advanced self diving car engineering.
00:55:28 And we’re doing this with the White House
00:55:29 because we think it’s bipartisan.
00:55:30 It’s an issue that if you wanna really make America great,
00:55:36 being able to be a part of the solution
00:55:40 and live the American dream requires us to be proactive
00:55:43 about our education and our skillset.
00:55:45 It’s just the way it is today.
00:55:47 And it’s always been this way.
00:55:48 And we always had this American dream
00:55:49 to send our kids to college.
00:55:51 And now the American dream has to be
00:55:53 to send ourselves to college.
00:55:54 We can do this very, very, very efficiently
00:55:58 and very, very, we can squeeze in in the evenings
00:56:00 and things to online.
00:56:01 So at all ages.
00:56:03 All ages.
00:56:03 So our learners go from age 11 to age 80.
00:56:08 I just traveled Germany and the guy in the train compartment
00:56:15 next to me was one of my students.
00:56:17 It’s like, wow, that’s amazing.
00:56:19 Think about impact.
00:56:21 We’ve become the educator of choice for now,
00:56:24 I believe officially six countries or five countries.
00:56:26 Most in the Middle East, like Saudi Arabia and in Egypt.
00:56:30 In Egypt, we just had a cohort graduate
00:56:33 where we had 1100 high school students
00:56:37 that went through programming skills,
00:56:39 proficient at the level of a computer science undergrad.
00:56:42 And we had a 95% graduation rate,
00:56:45 even though everything’s online, it’s kind of tough,
00:56:46 but we kind of trying to figure out
00:56:48 how to make this effective.
00:56:50 The vision is very, very simple.
00:56:52 The vision is education ought to be a basic human right.
00:56:58 It cannot be locked up behind ivory tower walls
00:57:02 only for the rich people, for the parents
00:57:04 who might be bribe themselves into the system.
00:57:06 And only for young people and only for people
00:57:09 from the right demographics and the right geography
00:57:11 and possibly even the right race.
00:57:14 It has to be opened up to everybody.
00:57:15 If we are truthful to the human mission,
00:57:18 if we are truthful to our values,
00:57:20 we’re gonna open up education to everybody in the world.
00:57:23 So Udacity’s pledge of 100,000 scholarships,
00:57:27 I think is the biggest pledge of scholarships ever
00:57:29 in terms of numbers.
00:57:30 And we’re working, as I said, with the White House
00:57:33 and with very accomplished CEOs like Tim Cook
00:57:36 from Apple and others to really bring education
00:57:39 to everywhere in the world.
00:57:40 Not to ask you to pick the favorite of your children,
00:57:44 but at this point.
00:57:45 Oh, that’s Jasper.
00:57:46 I only have one that I know of.
00:57:49 Okay, good.
00:57:52 In this particular moment, what nano degree,
00:57:55 what set of courses are you most excited about at Udacity
00:58:00 or is that too impossible to pick?
00:58:02 I’ve been super excited about something
00:58:03 we haven’t launched yet in the building,
00:58:05 which is when we talk to our partner companies,
00:58:09 we have now a very strong footing in the enterprise world.
00:58:12 And also to our students,
00:58:14 we’ve kind of always focused on these hard skills,
00:58:17 like the programming skills or math skills
00:58:19 or building skills or design skills.
00:58:22 And a very common ask is soft skills.
00:58:25 Like how do you behave in your work?
00:58:26 How do you develop empathy?
00:58:28 How do you work on a team?
00:58:30 What are the very basics of management?
00:58:32 How do you do time management?
00:58:33 How do you advance your career
00:58:36 in the context of a broader community?
00:58:39 And that’s something that we haven’t done very well
00:58:41 at Udacity and I would say most universities
00:58:43 are doing very poorly as well
00:58:45 because we are so obsessed with individual test scores
00:58:47 and pays a little attention to teamwork in education.
00:58:52 So that’s something I see us moving into as a company
00:58:55 because I’m excited about this.
00:58:56 And I think, look, we can teach people tech skills
00:59:00 and they’re gonna be great.
00:59:00 But if you teach people empathy,
00:59:02 that’s gonna have the same impact.
00:59:04 Maybe harder than self driving cars, but.
00:59:08 I don’t think so.
00:59:08 I think the rules are really simple.
00:59:11 You just have to, you have to want to engage.
00:59:14 It’s, we literally went in school and in K through 12,
00:59:18 we teach kids like get the highest math score.
00:59:20 And if you are a rational human being,
00:59:22 you might evolve from this education say,
00:59:25 having the best math score and the best English scores
00:59:28 make me the best leader.
00:59:29 And it turns out not to be that case.
00:59:31 It’s actually really wrong because making the,
00:59:34 first of all, in terms of math scores,
00:59:35 I think it’s perfectly fine to hire somebody
00:59:37 with great math skills.
00:59:38 You don’t have to do it yourself.
00:59:40 You can hire someone with good empathy for you.
00:59:42 That’s much harder,
00:59:43 but you can always hire someone with great math skills.
00:59:46 But we live in an affluent world
00:59:48 where we constantly deal with other people.
00:59:51 And that’s a beauty.
00:59:51 It’s not a nuisance.
00:59:52 It’s a beauty.
00:59:53 So if we somehow develop that muscle
00:59:55 that we can do that well and empower others
00:59:59 in the workplace, I think we’re gonna be super successful.
01:00:02 And I know many fellow robot assistant computer scientists
01:00:07 that I will insist to take this course.
01:00:09 Not to be named here.
01:00:12 Not to be named.
01:00:13 Many, many years ago, 1903,
01:00:17 the Wright brothers flew in Kitty Hawk for the first time.
01:00:22 And you’ve launched a company of the same name, Kitty Hawk,
01:00:26 with the dream of building flying cars, eVTOLs.
01:00:32 So at the big picture,
01:00:34 what are the big challenges of making this thing
01:00:36 that actually have inspired generations of people
01:00:39 about what the future looks like?
01:00:41 What does it take?
01:00:42 What are the biggest challenges?
01:00:43 So flying cars has always been a dream.
01:00:47 Every boy, every girl wants to fly.
01:00:49 Let’s be honest.
01:00:50 Yes.
01:00:51 And let’s go back in our history
01:00:52 of your dreaming of flying.
01:00:53 I think honestly, my single most remembered childhood dream
01:00:57 has been a dream where I was sitting on a pillow
01:00:59 and I could fly.
01:01:00 I was like five years old.
01:01:02 I remember like maybe three dreams of my childhood,
01:01:04 but that’s the one I remember most vividly.
01:01:07 And then Peter Thiel famously said,
01:01:09 they promised us flying cars
01:01:10 and they gave us 140 characters pointing as Twitter
01:01:14 at the time, limited message size to 140 characters.
01:01:18 So if you’re coming back now to really go
01:01:20 for these super impactful stuff like flying cars
01:01:23 and to be precise, they’re not really cars.
01:01:25 They don’t have wheels.
01:01:27 They’re actually much closer to a helicopter
01:01:28 than anything else.
01:01:29 They take off vertically and they fly horizontally,
01:01:32 but they have important differences.
01:01:34 One difference is that they are much quieter.
01:01:37 We just released a vehicle called Project Heaviside
01:01:41 that can fly over you as low as a helicopter
01:01:43 and you basically can’t hear.
01:01:45 It’s like 38 decibels.
01:01:46 It’s like, if you were inside the library,
01:01:49 you might be able to hear it,
01:01:50 but anywhere outdoors, your ambient noise is higher.
01:01:53 Secondly, they’re much more affordable.
01:01:57 They’re much more affordable than helicopters.
01:01:58 And the reason is helicopters are expensive
01:02:01 for many reasons.
01:02:04 There’s lots of single point of figures in a helicopter.
01:02:06 There’s a bolt between the blades
01:02:09 that’s caused Jesus bolt.
01:02:10 And the reason why it’s called Jesus bolt
01:02:12 is that if this bolt breaks, you will die.
01:02:16 There is no second solution in helicopter flight.
01:02:19 Whereas we have these distributed mechanism.
01:02:21 When you go from gasoline to electric,
01:02:23 you can now have many, many, many small motors
01:02:25 as opposed to one big motor.
01:02:27 And that means if you lose one of those motors,
01:02:28 not a big deal.
01:02:29 Heaviside, if it loses a motor, has eight of those.
01:02:32 If it loses one of those eight motors,
01:02:34 so it’s seven left, it can take off just like before
01:02:37 and land just like before.
01:02:40 We are now also moving into a technology
01:02:42 that doesn’t require a commercial pilot
01:02:44 because in some level,
01:02:45 flight is actually easier than ground transportation
01:02:48 like in self driving cars.
01:02:51 The world is full of like children and bicycles
01:02:54 and other cars and mailboxes and curbs and shrubs
01:02:57 and what have you.
01:02:58 All these things you have to avoid.
01:03:00 When you go above the buildings and tree lines,
01:03:03 there’s nothing there.
01:03:04 I mean, you can do the test right now,
01:03:06 look outside and count the number of things you see flying.
01:03:09 I’d be shocked if you could see more than two things.
01:03:11 It’s probably just zero.
01:03:13 In the Bay Area, the most I’ve ever seen was six.
01:03:16 And maybe it’s 15 or 20,
01:03:18 but not 10,000.
01:03:20 So the sky is very ample and very empty and very free.
01:03:24 So the vision is, can we build a socially acceptable
01:03:27 mass transit solution for daily transportation
01:03:32 that is affordable?
01:03:34 And we have an existence proof.
01:03:36 Heaviside can fly 100 miles in range
01:03:39 with still 30% electric reserves.
01:03:43 It can fly up to like 180 miles an hour.
01:03:46 We know that that solution at scale
01:03:48 would make your ground transportation
01:03:51 10 times as fast as a car
01:03:53 based on use census or statistics data,
01:03:57 which means you would take your 300 hours of daily,
01:04:00 of yearly commute down to 30 hours
01:04:03 and give you 270 hours back.
01:04:05 Who wouldn’t want, I mean, who doesn’t hate traffic?
01:04:07 Like I hate, give me the person that doesn’t hate traffic.
01:04:10 I hate traffic.
01:04:11 Every time I’m in traffic, I hate it.
01:04:13 And if we could free the world from traffic,
01:04:17 we have technology.
01:04:18 We can free the world from traffic.
01:04:20 We have the technology.
01:04:21 It’s there.
01:04:22 We have an existence proof.
01:04:23 It’s not a technological problem anymore.
01:04:25 Do you think there is a future where tens of thousands,
01:04:29 maybe hundreds of thousands of both delivery drones
01:04:34 and flying cars of this kind, EV talls fill the sky?
01:04:39 I absolutely believe this.
01:04:40 And there’s obviously the societal acceptance
01:04:43 is a major question.
01:04:45 And of course, safety is.
01:04:46 I believe in safety,
01:04:48 we’re gonna exceed ground transportation safety
01:04:50 as has happened for aviation already, commercial aviation.
01:04:54 And in terms of acceptance,
01:04:56 I think one of the key things is noise.
01:04:58 That’s why we are focusing relentlessly on noise
01:05:00 and we build perhaps the quietest electric vehicle
01:05:05 ever built.
01:05:07 The nice thing about the sky is it’s three dimensional.
01:05:09 So any mathematician will immediately recognize
01:05:12 the difference between 1D of like a regular highway
01:05:14 to 3D of a sky.
01:05:17 But to make it clear for the layman,
01:05:20 say you wanna make 100 vertical lanes
01:05:22 of highway 101 in San Francisco,
01:05:25 because you believe building 100 vertical lanes
01:05:27 is the right solution.
01:05:28 Imagine how much it would cost to stack 100 vertical lanes
01:05:31 physically onto 101.
01:05:33 That would be prohibitive.
01:05:34 That would be consuming the world’s GDP for an entire year
01:05:37 just for one highway.
01:05:39 It’s amazingly expensive.
01:05:41 In the sky, it would just be a recompilation
01:05:43 of a piece of software because all these lanes are virtual.
01:05:46 That means any vehicle that is in conflict
01:05:49 with another vehicle would just go to different altitudes
01:05:51 and then the conflict is gone.
01:05:53 And if you don’t believe this,
01:05:55 that’s exactly how commercial aviation works.
01:05:58 When you fly from New York to San Francisco,
01:06:01 another plane flies from San Francisco to New York,
01:06:04 they are different altitudes.
01:06:05 So they don’t hit each other.
01:06:06 It’s a solved problem for the jet space
01:06:10 and it will be a solved problem for the urban space.
01:06:12 There’s companies like Google Wing and Amazon
01:06:15 working on very innovative solutions.
01:06:17 How do we have space management?
01:06:18 They use exactly the same principles as we use today
01:06:21 to route today’s jets.
01:06:23 There’s nothing hard about this.
01:06:25 Do you envision autonomy being a key part of it
01:06:29 so that the flying vehicles are either semi autonomous
01:06:34 semi autonomous or fully autonomous?
01:06:36 100% autonomous.
01:06:37 You don’t want idiots like me flying in the sky,
01:06:40 I promise you.
01:06:41 And if you have 10,000,
01:06:44 watch the movie, The Fifth Element
01:06:46 to get a feel for what will happen if it’s not autonomous.
01:06:49 And a centralized, that’s a really interesting idea
01:06:51 of a centralized sort of management system
01:06:55 for lanes and so on.
01:06:56 So actually just being able to have
01:07:00 similar as we have in the current commercial aviation,
01:07:03 but scale it up to much, much more vehicles.
01:07:05 That’s a really interesting optimization problem.
01:07:07 It is very mathematically, very, very straightforward.
01:07:11 Like the gap we leave between jets is gargantuous.
01:07:13 And part of the reason is there isn’t that many jets.
01:07:16 So it just feels like a good solution.
01:07:18 Today, when you get vectored by air traffic control,
01:07:22 someone talks to you, right?
01:07:23 So any ATC controller might have up to maybe 20 planes
01:07:26 on the same frequency.
01:07:28 And then they talk to you, you have to talk back.
01:07:30 And it feels right because there isn’t more than 20 planes
01:07:32 around anyhow, so you can talk to everybody.
01:07:34 But if there’s 20,000 things around,
01:07:36 you can’t talk to everybody anymore.
01:07:37 So we have to do something that’s called digital,
01:07:40 like text messaging.
01:07:41 Like we do have solutions.
01:07:43 Like we have what, four or five billion smartphones
01:07:45 in the world now, right?
01:07:46 And they’re all connected.
01:07:47 And somehow we solve the scale problem for smartphones.
01:07:50 We know where they all are.
01:07:51 They can talk to somebody and they’re very reliable.
01:07:54 They’re amazingly reliable.
01:07:56 We could use the same system,
01:07:58 the same scale for air traffic control.
01:08:01 So instead of me as a pilot talking to a human being
01:08:04 and in the middle of the conversation
01:08:06 receiving a new frequency, like how ancient is that?
01:08:09 We could digitize this stuff
01:08:11 and digitally transmit the right flight coordinates.
01:08:15 And that solution will automatically scale
01:08:18 to 10,000 vehicles.
01:08:20 We talked about empathy a little bit.
01:08:22 Do you think we will one day build an AI system
01:08:25 that a human being can love
01:08:27 and that loves that human back, like in the movie, Her?
01:08:31 Look, I’m a pragmatist.
01:08:33 For me, AI is a tool.
01:08:35 It’s like a shovel.
01:08:36 And the ethics of using the shovel are always
01:08:40 with us, the people.
01:08:41 And it has to be this way.
01:08:44 In terms of emotions,
01:08:47 I would hate to come into my kitchen
01:08:49 and see that my refrigerator spoiled all my food,
01:08:54 then have it explained to me
01:08:55 that it fell in love with the dishwasher
01:08:57 and it wasn’t as nice as the dishwasher.
01:08:59 So as a result, it neglected me.
01:09:02 That would just be a bad experience
01:09:05 and it would be a bad product.
01:09:07 I would probably not recommend this refrigerator
01:09:09 to my friends.
01:09:11 And that’s where I draw the line.
01:09:12 I think to me, technology has to be reliable
01:09:16 and has to be predictable.
01:09:17 I want my car to work.
01:09:19 I don’t want to fall in love with my car.
01:09:22 I just want it to work.
01:09:24 I want it to compliment me, not to replace me.
01:09:27 I have very unique human properties
01:09:30 and I want the machines to make me,
01:09:33 turn me into a superhuman.
01:09:35 Like I’m already a superhuman today,
01:09:37 thanks to the machines that surround me.
01:09:39 And I give you examples.
01:09:40 I can run across the Atlantic
01:09:44 at near the speed of sound at 36,000 feet today.
01:09:48 That’s kind of amazing.
01:09:49 I can, my voice now carries me all the way to Australia
01:09:54 using a smartphone today.
01:09:56 And it’s not the speed of sound, which would take hours.
01:10:00 It’s the speed of light.
01:10:01 My voice travels at the speed of light.
01:10:03 How cool is that?
01:10:04 That makes me superhuman.
01:10:06 I would even argue my flushing toilet makes me superhuman.
01:10:10 Just think of the time before flushing toilets.
01:10:13 And maybe you have a very old person in your family
01:10:16 that you can ask about this
01:10:18 or take a trip to rural India to experience it.
01:10:23 It makes me superhuman.
01:10:25 So to me, what technology does, it compliments me.
01:10:28 It makes me stronger.
01:10:30 Therefore, words like love and compassion
01:10:33 have very little interest in this for machines.
01:10:38 I have interest in people.
01:10:40 You don’t think, first of all, beautifully put,
01:10:44 beautifully argued,
01:10:45 but do you think love has use in our tools?
01:10:49 Compassion.
01:10:50 I think love is a beautiful human concept.
01:10:53 And if you think of what love really is,
01:10:55 love is a means to convey safety, to convey trust.
01:11:03 I think trust has a huge need in technology as well,
01:11:07 not just people.
01:11:09 We want to trust our technology the same way,
01:11:12 in a similar way we trust people.
01:11:15 In human interaction, standards have emerged
01:11:19 and feelings, emotions have emerged,
01:11:21 maybe genetically, maybe biologically,
01:11:23 that are able to convey sense of trust, sense of safety,
01:11:26 sense of passion, of love, of dedication
01:11:28 that makes the human fabric.
01:11:30 And I’m a big slacker for love.
01:11:33 I want to be loved.
01:11:34 I want to be trusted.
01:11:35 I want to be admired.
01:11:36 All these wonderful things.
01:11:38 And because all of us, we have this beautiful system,
01:11:42 I wouldn’t just blindly copy this to the machines.
01:11:44 Here’s why.
01:11:46 When you look at, say, transportation,
01:11:49 you could have observed that up to the end
01:11:53 of the 19th century, almost all transportation used
01:11:57 any number of legs, from one leg to two legs
01:11:59 to a thousand legs.
01:12:01 And you could have concluded that is the right way
01:12:03 to move about the environment.
01:12:06 We’ve been made the exception of birds
01:12:08 who use flapping wings.
01:12:08 In fact, there are many people in aviation
01:12:10 that flap wings to their arms and jump from cliffs.
01:12:13 Most of them didn’t survive.
01:12:16 Then the interesting thing is that the technology solutions
01:12:19 are very different.
01:12:21 Like in technology, it’s really easy to build a wheel.
01:12:23 In biology, it’s super hard to build a wheel.
01:12:25 There’s very few perpetually rotating things in biology
01:12:30 and they usually run cells and things.
01:12:34 In engineering, we can build wheels.
01:12:37 And those wheels gave rise to cars.
01:12:41 Similar wheels gave rise to aviation.
01:12:44 Like there’s no thing that flies
01:12:46 that wouldn’t have something that rotates,
01:12:48 like a jet engine or helicopter blades.
01:12:52 So the solutions have used very different physical laws
01:12:55 than nature, and that’s great.
01:12:58 So for me to be too much focused on,
01:13:00 oh, this is how nature does it, let’s just replicate it.
01:13:03 If you really believed that the solution
01:13:05 to the agricultural evolution was a humanoid robot,
01:13:08 you would still be waiting today.
01:13:10 Again, beautifully put.
01:13:12 You said that you don’t take yourself too seriously.
01:13:15 Did I say that?
01:13:18 You want me to say that?
01:13:19 Maybe.
01:13:20 You’re not taking me seriously.
01:13:20 I’m not, that’s right.
01:13:22 Good, you’re right, I don’t wanna.
01:13:24 I just made that up.
01:13:25 But you have a humor and a lightness about life
01:13:29 that I think is beautiful and inspiring to a lot of people.
01:13:33 Where does that come from?
01:13:35 The smile, the humor, the lightness
01:13:38 amidst all the chaos of the hard work that you’re in,
01:13:42 where does that come from?
01:13:43 I just love my life.
01:13:44 I love the people around me.
01:13:47 I’m just so glad to be alive.
01:13:49 Like I’m, what, 52, hard to believe.
01:13:53 People say 52 is a new 51, so now I feel better.
01:13:56 But in looking around the world,
01:14:01 looking around the world, just go back 200, 300 years.
01:14:06 Humanity is, what, 300,000 years old?
01:14:09 But for the first 300,000 years minus the last 100,
01:14:13 our life expectancy would have been
01:14:17 plus or minus 30 years roughly, give or take.
01:14:20 So I would be long dead now.
01:14:24 That makes me just enjoy every single day of my life
01:14:26 because I don’t deserve this.
01:14:28 Why am I born today when so many of my ancestors
01:14:32 died of horrible deaths, like famines, massive wars
01:14:38 that ravaged Europe for the last 1,000 years
01:14:41 mystically disappeared after World War II
01:14:44 when the Americans and the Allies
01:14:46 did something amazing to my country
01:14:48 that didn’t deserve it, the country of Germany.
01:14:51 This is so amazing.
01:14:52 And then when you’re alive and feel this every day,
01:14:56 then it’s just so amazing what we can accomplish,
01:15:02 what we can do.
01:15:03 We live in a world that is so incredibly,
01:15:06 vastly changing every day.
01:15:08 Almost everything that we cherish from your smartphone
01:15:12 to your flushing toilet, to all these basic inventions,
01:15:16 your new clothes you’re wearing, your watch, your plane,
01:15:19 penicillin, I don’t know, anesthesia for surgery,
01:15:24 penicillin have been invented in the last 150 years.
01:15:29 So in the last 150 years, something magical happened.
01:15:31 And I would trace it back to Gutenberg
01:15:33 and the printing press that has been able
01:15:34 to disseminate information more efficiently than before
01:15:37 that all of a sudden we were able to invent agriculture
01:15:41 and nitrogen fertilization that made agriculture
01:15:44 so much more potent that we didn’t have to work
01:15:47 in the farms anymore and we could start reading and writing
01:15:49 and we could become all these wonderful things
01:15:51 we are today, from airline pilot to massage therapist
01:15:53 to software engineer.
01:15:56 It’s just amazing.
01:15:57 Like living in that time is such a blessing.
01:16:00 We should sometimes really think about this, right?
01:16:03 Steven Pinker, who is a very famous author and philosopher
01:16:06 whom I really adore, wrote a great book called
01:16:08 Enlightenment Now.
01:16:09 And that’s maybe the one book I would recommend.
01:16:11 And he asks the question,
01:16:13 if there was only a single article written
01:16:15 in the 20th century, it’s only one article, what would it be?
01:16:18 What’s the most important innovation,
01:16:20 the most important thing that happened?
01:16:22 And he would say this article would credit
01:16:24 a guy named Karl Bosch.
01:16:27 And I challenge anybody, have you ever heard
01:16:29 of the name Karl Foch?
01:16:31 I hadn’t, okay.
01:16:32 There’s a Bosch Corporation in Germany,
01:16:35 but it’s not associated with Karl Bosch.
01:16:38 So I looked it up.
01:16:39 Karl Bosch invented nitrogen fertilization.
01:16:42 And in doing so, together with an older invention
01:16:45 of irrigation, was able to increase the yields
01:16:49 per agricultural land by a factor of 26.
01:16:52 So a 2,500% increase in fertility of land.
01:16:57 And that, so Steve Pinker argues,
01:17:00 saved over 2 billion lives today.
01:17:03 2 billion people who would be dead
01:17:05 if this man hadn’t done what he had done, okay?
01:17:08 Think about that impact and what that means to society.
01:17:12 That’s the way I look at the world.
01:17:14 I mean, it’s so amazing to be alive and to be part of this.
01:17:16 And I’m so glad I lived after Karl Bosch and not before.
01:17:21 I don’t think there’s a better way to end this, Sebastian.
01:17:23 It’s an honor to talk to you,
01:17:25 to have had the chance to learn from you.
01:17:27 Thank you so much for talking to me.
01:17:28 Thanks for coming out.
01:17:29 It’s been a real pleasure.
01:17:30 Thank you for listening to this conversation
01:17:32 with Sebastian Thrun.
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01:17:58 And now, let me leave you with some words of wisdom
01:18:01 from Sebastian Thrun.
01:18:03 It’s important to celebrate your failures
01:18:05 as much as your successes.
01:18:07 If you celebrate your failures really well,
01:18:09 if you say, wow, I failed, I tried, I was wrong,
01:18:13 but I learned something, then you realize you have no fear.
01:18:18 And when your fear goes away, you can move the world.
01:18:22 Thank you for listening and hope to see you next time.