Speaker 1: Hey, there are odd lots listeners. I'm Tracy Alloway and
Speaker 1: I'm Jill.
Speaker 2: Why isn't though?
Speaker 1: And we want to welcome you to a special presentation
Speaker 1: of the podcast search Engine. We all know that artificial
Speaker 1: intelligence might replace all sorts of jobs humans do today,
Speaker 1: but for most of us that's still mostly theoretical. There's
Speaker 1: one job, though, where robots are already taking the wheel,
Speaker 1: and that is driving.
Speaker 3: In fact, it's one of the most common jobs in
Speaker 3: America for young men without college degrees, and over the
Speaker 3: course of two episodes, Search Engine tackles both the promise
Speaker 3: and the peril this growing technology.
Speaker 1: In part one, Are You a Good Driver? The Search
Speaker 1: Engine team tells the story of how a small secret
Speaker 1: team at Google spent fifteen years teaching a computer to
Speaker 1: drive from a failed robot in the Mojave Desert to
Speaker 1: a vehicle that might actually be the safest on the road.
Speaker 1: This episode tracks the engineering breakthroughs the nearer catastrophes, and
Speaker 1: takes a skeptical look at the safety data behind Weaymo's
Speaker 1: claim that its cars are ninety percent safer than human
Speaker 1: drivers in serious crashes.
Speaker 3: All of it boils down to one big question. All
Speaker 3: the robots actually say for drivers than we are. Enjoy
Speaker 3: this presentation of Search Engine, and be sure to catch
Speaker 3: part two, titled The Trial of the Driverless Car, available
Speaker 3: wherever you get your podcasts.
Speaker 2: Before we start the story today, I want to ask
Speaker 2: you to imagine a different version of your life. You're you,
Speaker 2: but it's almost two hundred years ago, and unfortunately and
Speaker 2: our hypothetical, it's Monday morning. It's Monday morning, and it's
Speaker 2: very early pre dawn. You wake up to this really
Speaker 2: hard wrapping at your window. That's the knocker Upper here
Speaker 2: to get you up for where we're in the eighteen
Speaker 2: hundreds before the invention of the adjustable alarm clock. The
Speaker 2: knocker Upper is a job. The knocker Upper walks the
Speaker 2: neighborhood with a long stick and taps it on the
Speaker 2: windows of people's houses early in the morning to wake
Speaker 2: them up for work. Who wakes up the locker rupper
Speaker 2: for work? Nobody knows. But this is a job, a
Speaker 2: job that'll actually exist for another century. Outside the gas
Speaker 2: street lamps are still burning. The lamplighter lit them the
Speaker 2: night before. He's supposed to come at dawn to extinguish them,
Speaker 2: but it's so early that he has it yet. Your
Speaker 2: lamplighter is one of those neighbors. You have a deep
Speaker 2: fondness for a fixture. Every day you watch him make
Speaker 2: the rounds at dusk with his ladder and his light.
Speaker 2: You yourself are a driver. Professional driver two hundred years
Speaker 2: ago is also a job. You're a person who sits
Speaker 2: on a coach and holds the reins of a horse.
Speaker 2: You take passengers where they want to go. You start
Speaker 2: your workday. Okay, hypothetical. Over two of those jobs are
Speaker 2: obviously so long disappeared that most people don't know about them.
Speaker 2: The knocker upper is your iPhone alarm. The lamplighter is
Speaker 2: the electric street light. The third one driver has persisted
Speaker 2: as a job for some, as a routine human task
Speaker 2: for nearly everyone else. This is a story about whether
Speaker 2: that's about to change. It's about how the word driver,
Speaker 2: which right now makes me picture a human, could soon
Speaker 2: transform to refer to a machine, the same way the
Speaker 2: words dishwasher, printer, and computer all did. I've thought about this,
Speaker 2: maybe too much, in the year I've been working on
Speaker 2: this story. In conversations constantly, I'd asked the humans I
Speaker 2: meant the same question, Are you a good driver? Are
Speaker 2: you do you consider yourself a good driver.
Speaker 4: I do within limits. I think I'm a good driver
Speaker 4: because I understand the limitations of my driving.
Speaker 2: This is Alex Davies. He wrote an excellent book called Driven,
Speaker 2: the Race to create the Autonomous Car. Alex, like me,
Speaker 2: thinks a lot about human driving about his own personal limitations.
Speaker 2: What are the limitations?
Speaker 4: The limitations are that I can't always pay attention to
Speaker 4: everything that I get tired. I've been trying really hard
Speaker 4: to be calmer in the road. My husband and I
Speaker 4: are expecting our first baby this fall. Congratulations, thank you,
Speaker 4: and I thought that, along with reading all the baby books,
Speaker 4: a good project to work on is just be calmer
Speaker 4: in the car.
Speaker 2: A very good resolution, because, of course, for most of us,
Speaker 2: driving is the riskiest behavior we routinely engage in. In fact,
Speaker 2: even Alex, despite his good intentions, would actually get in
Speaker 2: a car accident just a few months after we first spoke.
Speaker 2: He was okay, it was the car that was totaled.
Speaker 2: Safety is the entire pitch for the driver of this car,
Speaker 2: which is really a car and by a computer driver.
Speaker 2: LESE cars don't get drunk, tired, or distracted. They never
Speaker 2: text or feel road rage, and these drivers cars, they
Speaker 2: aren't the future. They're actually already here. But it's funny.
Speaker 2: If you just don't happen to live in a place
Speaker 2: that already has them, it's easy to not see how
Speaker 2: fast things are changing. Robo taxis like Waimo are operating
Speaker 2: in ten American cities, providing millions of rides to Americans.
Speaker 2: In China, the rollout is happening even more widely. They're
Speaker 2: in twice as many cities. But here, if you live
Speaker 2: in a place like San Francisco or Austin, today, a
Speaker 2: driver's car is about as exotic as an uber. A
Speaker 2: passenger in those cities opens up their phone and decides
Speaker 2: who should drive them, a human driver or a robot driver.
Speaker 2: How that happened is a story, a story we are
Speaker 2: living through right now, whose ending promise is to totally
Speaker 2: reshape the places we live. And today we're going to
Speaker 2: tell you how we got here. In chapters, Chapter one
Speaker 2: dreams without drivers. So it turns out this dream that
Speaker 2: inventors have had to replace the human driver with some
Speaker 2: kind of machine. That dream is about as old as
Speaker 2: the Lamplighters.
Speaker 4: People have been thinking about a self driving car for
Speaker 4: so it's about as long as there's been a human
Speaker 4: driven car. Why, there's this funny thing you lose when
Speaker 4: you move from the horse to the human driven car,
Speaker 4: which is that in a horse drawn carriage, the horse
Speaker 4: is not just going to run off a cliff. If
Speaker 4: you let go of the reins, you lose sentience in
Speaker 4: your vehicle.
Speaker 2: When automobiles first arrived, these powerful and nonsensient cars, there's
Speaker 2: actually a passionate fight to keep them off the streets.
Speaker 2: It was the eighteen hundreds, and people feared these new things,
Speaker 2: the steam powered vehicles thundering down the roads that soon
Speaker 2: evolved into gas powered vehicles, also thundering down the roads.
Speaker 2: The fear was partly about jobs. These vehicles were seen
Speaker 2: as a huge threat to a whole network of working
Speaker 2: class jobs. Horse breeders and horse farriers, horse feed suppliers,
Speaker 2: horse manure haulers, horse carriage manufacturers. Not to mention the
Speaker 2: teamsters Teamsters today the word makes me think of the
Speaker 2: Teamsters union, But originally the teamsters were the workers who
Speaker 2: drove teams of horses. Teamsters were like truckers before we
Speaker 2: had trucks. Cars seemed to imperil all these horse related jobs.
Speaker 2: And even if you weren't worried about these workers, the
Speaker 2: cars were also less safe. Some anti car activists battled
Speaker 2: to stop or slow the new technology, mainly with regulations.
Speaker 2: There were red flag laws, which said if you had
Speaker 2: an automobile, you had to hire a person to walk
Speaker 2: in front of it, waving a giant red flag to
Speaker 2: warn people. In Pennsylvania, a law was prepared requiring horseless
Speaker 2: carriage drivers who encountered livestock to stop, disassemble their car,
Speaker 2: and hide the parts behind the bushes. The governor vetoed it.
Speaker 2: But to think about these crazy anti car activists is
Speaker 2: that directionally they were right. Those cars did initially wipe
Speaker 2: out a lot of jobs, even if they created more,
Speaker 2: and cars were very unsafe. The cities that threw their
Speaker 2: doors open to cars without regulation were rewarded with astonishing
Speaker 2: death rates. Detroit let drivers pretty much run wild. In
Speaker 2: the early nineteen hundreds, deaths accumulated in a Detroit without
Speaker 2: drivers licenses, stop lights, or turn signals. Many of those
Speaker 2: deaths were children. It took decades for society to mostly
Speaker 2: learn to live with cars. The rest of the story
Speaker 2: is just the world you grew up in. We invented laws, licenses,
Speaker 2: drivers ed, We learned to better design roads. We invented
Speaker 2: the highway, the seatbelt, the airbag. All those things made
Speaker 2: driving less debts, although the smartphone reverse some of that progress. Nationally, Today,
Speaker 2: deaths from cars are about as common in America as
Speaker 2: deaths from guns or opioids, about one in one hundred.
Speaker 2: It'll probably happen to someone you know in your life,
Speaker 2: maybe several someone's. Whether or not you see that as
Speaker 2: an urgent problem to solve depends on you. But as
Speaker 2: long as there have been cars, there have been people
Speaker 2: who wanted to truly solve what's left of the safety
Speaker 2: problem the best way we knew how. They wanted to
Speaker 2: make the car more like the horse it replaced, make
Speaker 2: the car more sentient.
Speaker 4: So that thought is there early and like early visions
Speaker 4: have hit include, oh well, we'll have radio controlled cars,
Speaker 4: because they had radios at the time. There's a real
Speaker 4: effort at one point to build magnets under the road,
Speaker 4: and at each stage what a self driving car can
Speaker 4: be is dictated by the technology that's available at the time.
Speaker 4: For the most part, no one's thinking that much about
Speaker 4: a vehicle that thinks for itself. They're just thinking about
Speaker 4: a vehicle that the person in it doesn't have to drive.
Speaker 2: Many different attempts, many different failures, as many wonders as
Speaker 2: we invented, we could not approach nature's most majestic creation,
Speaker 2: a horse's brain, at least not until the turn of
Speaker 2: the millennium.
Speaker 5: Are to.
Speaker 6: Deep within the Department of Defense, there's a little known
Speaker 6: military agency that has created some of the most innovative
Speaker 6: technology of the twentieth century. This is the story of Dark.
Speaker 2: Chapter two, DARPA's Million Dollar Prize.
Speaker 6: DARPA's current goal is to develop autonomous military vehicles machines
Speaker 6: that can operate on their own without drivers.
Speaker 4: Darpe has always been intrigued with him.
Speaker 2: This is from a documentary called The Million Dollar Challenge.
Speaker 2: Honestly less a doc more an ad for DARPA, the
Speaker 2: Pentagon's research arm. DARPA's mission is to try to keep
Speaker 2: American technology one generation ahead of everybody else. It doesn't
Speaker 2: always work, but DARPA has invented or funded a lot
Speaker 2: GPS and the M sixteen, the early Internet, and the
Speaker 2: Predator drone. In two thousand and two, DARPA decided to
Speaker 2: pursue the driver's car in a very unusual way.
Speaker 4: The director of DARPA at the time, a guy named
Speaker 4: Tony Tether, who had been a door to door salesman
Speaker 4: in his use definitely has that flare in that way
Speaker 4: of thinking, says, let's have a contest. Let's see who
Speaker 4: can put all of these ingredients that we've developed together
Speaker 4: into a proper self driving car. His original idea is
Speaker 4: we'll drive him down the Las Vegas Strip that's almost
Speaker 4: immediately next because it's insane.
Speaker 7: Oh right, you would have to like literally gridlock a
Speaker 7: huge American city so people could put robot cars on
Speaker 7: it exactly.
Speaker 4: So he says, Okay, do you know what, We'll do
Speaker 4: it in the desert. We'll do it in the desert
Speaker 4: outside Las Vegas, and anyone who wants to can make
Speaker 4: a team build a self driving car, bring it to
Speaker 4: the desert, and we'll race them.
Speaker 2: The driver that DARPA wanted to replace was the American soldier.
Speaker 2: DARPA wanted a vehicle that could drive itself down roads
Speaker 2: that might be filled with hidden explosive devices. So, in
Speaker 2: this moment at the tail end of the dot com boom,
Speaker 2: darpest trying to inspire tech to build something besides another
Speaker 2: website darpest. Tony Tether announces that the prize for whoever
Speaker 2: can win its Grand Challenge will be one million dollars.
Speaker 4: The rules for very open there were little rules like
Speaker 4: you couldn't have two vehicles communicating with one another, but
Speaker 4: you could build any kind of vehicle you wanted, could
Speaker 4: have six wheels. It could be a truck, it could
Speaker 4: be a motorcycle, could be a trice. It just couldn't
Speaker 4: attack other vehicles. That was rolled out early on.
Speaker 2: Oh, was that a concern that people would just like
Speaker 2: sort of battlebot the thing you're auto's vehicle would have
Speaker 2: like a little shredder that would take out somebody else's.
Speaker 4: Someone asked in the first Q and A at this
Speaker 4: like they said, can we attack other vehicles? And they
Speaker 4: said no. And it's funny you bring up BattleBots because
Speaker 4: a lot of teams who entered this had BattleBots history interesting.
Speaker 4: They were used to building robots for interesting purposes, and
Speaker 4: when they caught wind of this, they said, we can
Speaker 4: do this. We can scrap together some money and this
Speaker 4: will just be fun.
Speaker 2: I'm going to tell you what happened in this robot
Speaker 2: race in the desert, not because I care so much
Speaker 2: about these early robot vehicles, but because I care a
Speaker 2: lot about the engineers who were making them. These would
Speaker 2: be the people who would later go on to lead
Speaker 2: development for the billion dollar companies creating today's drive those cars.
Speaker 2: The people had very different views about how to get
Speaker 2: that technology ready, different values when it came to things
Speaker 2: like the acceptability of risking human life, abstract differences that
Speaker 2: would become very concrete later on, to the point where
Speaker 2: people would be charged with federal crimes. That's the future.
Speaker 2: But listening to this part of the story, what I
Speaker 2: listen for is how much of it can you detect already?
Speaker 2: How much of the differences already present. The first engineer
Speaker 2: I want you to pay attention to is a man
Speaker 2: named Chris Armson, and way back in two thousand and two,
Speaker 2: how did you end up being part of the Dark
Speaker 2: Program challenge?
Speaker 8: It sounded like fun.
Speaker 2: Chris these days, the CEO of a large tech company
Speaker 2: back then, a PhD student at Carnegie Mellon University. When
Speaker 2: he first got recruited for the race, he was out
Speaker 2: in the field observing a robot as it crept across
Speaker 2: the Autacama Desert training for its future deployment on the
Speaker 2: surface of Mars.
Speaker 8: H advisor came down and was really excited about this
Speaker 8: Darker Grind challenge thing, and the idea that you'd have
Speaker 8: a robot run across the desert at fifty miles an
Speaker 8: hour just sounded exciting having spent the last couple of
Speaker 8: weeks walking behind a robot at very low speed.
Speaker 2: So Chris would join Carnegie Mellon's Red Team and help
Speaker 2: build a car called Sandstorm, a bright red humvey with
Speaker 2: the top lopped off, a plethora of futuristic sensors mounted
Speaker 2: to it like scanners a crackpot would use to search
Speaker 2: for aliens. You can see Chris back in that documentary.
Speaker 2: He explains to the filmmaker at the time that the
Speaker 2: hard part, of course, isn't the vehicle, it's the driver.
Speaker 2: How do you even begin to teach a computer to
Speaker 2: operate a hum vy at all? How does a.
Speaker 4: Computer make the steering wheel turn?
Speaker 5: How does a computer change the.
Speaker 4: Pressure on the break and the throttle? Those are the
Speaker 4: issues that we're fighting through right now.
Speaker 2: The answer Sandstorm represented the best entry from the contest's
Speaker 2: traditional academic crowd, but there's a different crowd there too.
Speaker 2: Represented best by a man named Anthony Lewandowski. Can you
Speaker 2: tell me about Anthony Lewandowski?
Speaker 4: Anthony Lewandowski. Where to begin? So Anthony is like an entrepreneur.
Speaker 4: He's a really charming guy. He's six foot six, He's
Speaker 4: gangly as all get down. He grew up mostly in
Speaker 4: Belgium because his mom was working for the EU. For
Speaker 4: high school, he moved to Marin to live with his dad.
Speaker 4: And he's a hustler.
Speaker 9: My name is Anthony Lewandowski.
Speaker 4: I was a grad student at Berkeley.
Speaker 9: Instead of continuing on to finish my PhD, I decided
Speaker 9: it was much better to do the Grand Challenge.
Speaker 2: We asked Anthony for an interview. He didn't respond, but
Speaker 2: here he is in the footage from back then. Anthony
Speaker 2: did not have the engineering experience or resources of a
Speaker 2: team like Carnegie Mellons Red Team. So you tried something
Speaker 2: very different, a vehicle that had almost no chance of
Speaker 2: winning the race, but which was also perfectly designed to
Speaker 2: stand out to get him a lot of attention, maybe
Speaker 2: a job. The race's only self driving motorcycle, it was
Speaker 2: named ghost Rider, a stubby little thing covered in stickers
Speaker 2: with then inten on the back and cameras on the front.
Speaker 9: There's a steering actuator on the top here, which allows
Speaker 9: us to modify the steering angle. So basically, if you're driving,
Speaker 9: you start to follow the left, you steer left. That
Speaker 9: makes you turn the left, and then you get the
Speaker 9: tripleal acceleration to put you back up to the right.
Speaker 9: And you're monitoring that in real time and making small adjustments,
Speaker 9: and you stay bounced.
Speaker 10: Stroll Blight is on. The command from the tower is
Speaker 10: to move, ladies and gentlemen Sandstorm.
Speaker 2: The race happens on a Saturday in March of two
Speaker 2: thousand and.
Speaker 10: Four, autonomous vehicle traversing the desert with the goal of
Speaker 10: keeping our young military personnel out of harm's way.
Speaker 2: Oh yeah, what happens the first time they try to
Speaker 2: do this competition?
Speaker 4: The two thousand and four Grand Challenge is an utter
Speaker 4: hysterical disaster.
Speaker 2: Disaster Number one ghost Rider the motorcycle Anthony Lewandowski forgot
Speaker 2: to flip on the switch for the stabilization system. The
Speaker 2: bike immediately topples ghost Rider down.
Speaker 4: Anthony good effort, and then every vehicle after it fails miserably.
Speaker 4: Like one vehicle drives up onto a burm flips off.
Speaker 4: One vehicle, drives straight out, does an inexplicable U turn
Speaker 4: and just drives back to the starting line. And the
Speaker 4: rules are that once your vehicle starts, you can't do anything.
Speaker 2: Even Sandstorm got stuck on a burm. Chris Urmson just
Speaker 2: standing there, unable to help his robot.
Speaker 8: Poor thing was trying to get going, but its wheels
Speaker 8: were just spinning on the gravel and tried so hard
Speaker 8: that it actually melted the rubber of the tires.
Speaker 5: And so there's this plums of.
Speaker 8: Black squoke before they killed it.
Speaker 2: For the roboticists, this was obviously very disappointing. Chris Urmson
Speaker 2: compared it to an Olympic marathon where the best runner
Speaker 2: only makes it two of the twenty six miles. What
Speaker 2: this contest had done, though, was it had flushed all
Speaker 2: these inventors out. It had jumpstarted the scene that would
Speaker 2: develop this technology. One of the most important people there
Speaker 2: that day, actually just watching, was someone I haven't mentioned yet,
Speaker 2: a legendary roboticist named Sebastian Thrun.
Speaker 4: Sebastian Thrun, he was at the first Grand Challenge. He
Speaker 4: didn't bring a team, he wasn't participating. DARPA wanted to
Speaker 4: show off some other projects they'd been funding, including one
Speaker 4: of his robots. So he brings the robot and so
Speaker 4: he's there and he watches this disaster or anything's can
Speaker 4: do better for mess.
Speaker 11: I looked at the very first iteration of this quan challenge,
Speaker 11: but it didn't participate. It was a spectator.
Speaker 2: This, of course is Sebastian Thrun. He grew up in
Speaker 2: West Germany, moved to the US Toddic Carnegie Mellon before
Speaker 2: moving to Stanford. Watching that day, he saw this fundamental error.
Speaker 2: He believed all the entrance had made.
Speaker 11: I saw that all the teams treated this like a
Speaker 11: hardware problem. They looked at this and say, we have
Speaker 11: to build a bigger wheels and bigger chassis and so on.
Speaker 11: And I looked at this and said, about wait a minute.
Speaker 11: The challenge really is to build a self driving car.
Speaker 11: They can drive for the desert. I can get a
Speaker 11: rental car. They can do it just fine, provided as
Speaker 11: a person insight and the challenges we need to take
Speaker 11: the person out of the driver's seat and replace it
Speaker 11: by computer. That is not a problem with bigger tires.
Speaker 11: That's actually be a software problem.
Speaker 2: Sebastian Thrun had a dual background robotics and artificial intelligence,
Speaker 2: which probably explains his focus here on the robot driver's mind.
Speaker 2: He was thinking about something else too. The military wanted
Speaker 2: this tech to replace a relatively small number of drivers
Speaker 2: in its war zones, but Sebastian was already imagining something bigger.
Speaker 2: What would happen to traffic deaths worldwide? If one day
Speaker 2: everyone had access to a driverless car.
Speaker 11: I had experiences of losing people in my life to
Speaker 11: traffic accidents, and I felt we lost over the million
Speaker 11: people in the world to traffic accidents. Wouldn't it be
Speaker 11: amazing if Dabok invented something that would save a million
Speaker 11: lives a year.
Speaker 12: In October of two thousand and five, forty three teams
Speaker 12: have brought their vehicles to compete in a unique event,
Speaker 12: a race driven not by testosterone but computer.
Speaker 2: Coke Chapter three Machine Learning.
Speaker 12: The race course is a circular maze that zigzags for one
Speaker 12: hundred and thirty two.
Speaker 2: Eighteen months later this second Grand Challenge, DARPA doubled the
Speaker 2: bounty two million dollars. This footage is from a PBS
Speaker 2: documentary called The Great Robot Race, narrated to My Mild
Speaker 2: Joy by John Lithgow. Familiar faces have returned. Chris Earmsen
Speaker 2: back with the Carnegie Mellon team, the Sign with two vehicles,
Speaker 2: Highlander and Sandstorm. Anthony Lewandowski back with his motorcycle, which
Speaker 2: still doesn't work. He's knocked out in the qualifiers. And
Speaker 2: now there's also Stanford's entrant compared to Sandstorm, the bulked
Speaker 2: up hummer. The car looks easily a blue suv donated
Speaker 2: by Volkswagen. A baby face, Run smiles next to his
Speaker 2: soccer mom looking vehicle.
Speaker 13: The Vika's name is Stanley, so Stanley is nothing else
Speaker 13: but Stanford, but it also gives the vehicle a personality.
Speaker 13: If you think of the Vega more and more as
Speaker 13: an intelligent decision maker.
Speaker 4: Run is a computer scientist, and Thrun really broad more
Speaker 4: artificial intelligence, which at the time we're talking two thousand
Speaker 4: and five was still rather primitive, especially compared to what
Speaker 4: we have today. But he could use it to teach
Speaker 4: his vehicle how to recognize the road and how to
Speaker 4: do it much faster. They found a dirt road out
Speaker 4: near Stanford, and they drive it down a dirt road
Speaker 4: and have the car's cameras record what they were seeing.
Speaker 11: The robot Standy was able to train itself as it
Speaker 11: and the way it worked. Its eyes looked way ahead
Speaker 11: and it could see stuff way at distance. When it
Speaker 11: drives over the stuff, you could tell it wasn't a
Speaker 11: good place to drive or not, because it could measure
Speaker 11: how slippery or how bumpy the vote was. And they
Speaker 11: could then retroactively train and say, say, this green stuff
Speaker 11: over there, it's something good to drive on aka grass,
Speaker 11: and this browner stuff aka mutt is not so good
Speaker 11: to drive.
Speaker 2: And so it was able to detect patterns and generalize
Speaker 2: from what it had learned.
Speaker 4: Yeah.
Speaker 11: Absolutely, and this is like thirty times a second, I mean,
Speaker 11: just like a person.
Speaker 2: The race kicks off with Stanley Sandwich between Carnegie Mountains
Speaker 2: tow behemoths.
Speaker 12: Highlander leads the path, followed by Stanley and Sandstorm.
Speaker 2: What happens in the second race?
Speaker 4: The second race is as successful as the first race
Speaker 4: is disastrous.
Speaker 2: Nearly every entrance in the second race would go further
Speaker 2: than Sandstorm had in the first. Multiple vehicles would finish
Speaker 2: the course. The real question was who would do it fastest?
Speaker 2: And so at what point was it clear to you
Speaker 2: that you were going to win well.
Speaker 11: Once we passed the front running team, we kind of
Speaker 11: saw the vehicle descend into what was the hardest part
Speaker 11: of the race course, a very treachery mountain pass, and
Speaker 11: we saw at a distance a dust cloud. We saw
Speaker 11: a helicopter. We so a little features that must believe
Speaker 11: ow there's something happening that's magical, and this dust cloud
Speaker 11: then all of a sudden turned bluish because the cover
Speaker 11: was blue, and came closer, and then it came first
Speaker 11: to the finish line and was unbelievably magical.
Speaker 2: At the end of the dock over some criminally corny
Speaker 2: piano music. Sebastian Thron gives his post race interview. He's
Speaker 2: dressed a lot like a race car driver. Watching you
Speaker 2: could forget he wasn't in the car.
Speaker 11: It was just amazing to see this community of people,
Speaker 11: that community succeeded.
Speaker 4: Today.
Speaker 11: Behind me, there are three vobos that made it all
Speaker 11: the way through the desert, and all three of them
Speaker 11: did be unthinkable. It's such a fantastic successful this community.
Speaker 2: I think we all win a made for TV Kumbaya moment.
Speaker 2: Still years before the race to build driverless cars would
Speaker 2: enter its cutthroat phase. What would happen next is that
Speaker 2: a small band of lunatics would take driverless cars out
Speaker 2: of the desert start secretly driving them on public roads
Speaker 2: in the state of California. They would do this at
Speaker 2: the behest of a man who had been observing from
Speaker 2: the stands that day, disguised and hat and sunglasses, who
Speaker 2: watched the challenge while his mind spun this after a
Speaker 2: short break, Welcome back to the show. Chapter four, something
Speaker 2: actually useful for the world. The race in the Desert
Speaker 2: had been designed as a spectacle, something flashy to dry
Speaker 2: out America's smartest roboticists, but it had drawn another person
Speaker 2: who come for his own reasons. Google's Larry Page arrived
Speaker 2: at the Darker Grand Challenge in a baseball hat and
Speaker 2: sunglasses disguise. He found Sebastian Throne and buttonhold him, asking
Speaker 2: him a million highly specific questions about things like the
Speaker 2: wavelength his light our system used. But this meeting in
Speaker 2: the desert, this was not actually their first introduction.
Speaker 11: Well, the first time I met Larry was a bit earlier.
Speaker 11: He had built a small little robot that acted as
Speaker 11: a tailor presence for meetings, and he was trying to
Speaker 11: drive it around the Google officers instead of himself going
Speaker 11: to meeting with a robot. And he sent me a
Speaker 11: message and said, I'm going to show you the vote
Speaker 11: I've built. And I, in a spur of like craziness,
Speaker 11: I sent the message Breck saying, Larry, I'm so glad
Speaker 11: that Google it he used twenty percent of time. It
Speaker 11: was something useful for the world. I couldn't. I either
Speaker 11: expected a rapid response or never hear from him again.
Speaker 11: It turns out I was lucky. He responded immediately. I
Speaker 11: took his role, would fix it next fenty four hours.
Speaker 2: And he was very heavy. Larry Page, it turned out,
Speaker 2: had actually been interested in autonomous vehicles since at least
Speaker 2: grad school. That's what he'd wanted to do his thesis
Speaker 2: on before being guided by some wise PhD advisor towards
Speaker 2: search engines instead. Now as a spectator at DARPA's second
Speaker 2: Grand Challenge, he could see real world evidence that autonomous
Speaker 2: vehicles might actually be a thing. At first, Larry Page
Speaker 2: hires Sebastian's run along with fellow Darbik contestant Anthony Lewandowski,
Speaker 2: just to build what will become Google street View. They'll
Speaker 2: actually modify the system that Stanley the car's roof mounted
Speaker 2: cameras had used to begin photographing American streets. But before long,
Speaker 2: Larry Page returns to Sebastian with his dream of a
Speaker 2: driverless car, and so how soon after arriving at Google
Speaker 2: this project chauffeur again, like Larry Page says to you,
Speaker 2: I have a mission, like how does this happen?
Speaker 11: And this is an embarrassing moment for me. It's about
Speaker 11: two years later, two thousand and nine, where I sit
Speaker 11: in a cubicle and like Page comes by and says, Sebastian,
Speaker 11: I think you should build a self diving car that
Speaker 11: can drive anywhere in the world. And my immediate reaction was, no,
Speaker 11: taking the technology we build for this empty desert and
Speaker 11: put it in the middle of Market Street in San
Speaker 11: Francisco is going to kill somebody. And Larry would come
Speaker 11: back the next day with the same idea, and I
Speaker 11: would give them the same answer, and both of us
Speaker 11: got increasingly more frustrated. God damn it, it can't be done,
Speaker 11: and eventually came and said, look, Sebastian, OK, care, I
Speaker 11: get it. You can't do it. I want to explain
Speaker 11: to Erk Schmidt the CEO at the time and Sergey
Speaker 11: Britt my cofounder, why it can't be done? Can you
Speaker 11: give me the technical reason why it can't be done?
Speaker 11: And that's the moment of incredible pain, because I go
Speaker 11: home and I can't think of a technical reason why not.
Speaker 11: It was this kind of moment where I felt, look,
Speaker 11: I'm the world expert on self diving cars, and I'm
Speaker 11: the person who denies that it can be done. Like
Speaker 11: that taught me an incredibly important lesson about experts that
Speaker 11: for the rest of my life, I decided experts I
Speaker 11: usually explore.
Speaker 4: The past and not the future.
Speaker 11: And if you ask an expert about innovation, something crazy new,
Speaker 11: they're the least likely person to say, yes, it can
Speaker 11: be done.
Speaker 2: So this is where the Google's self driving car project
Speaker 2: begins in two thousand and nine. It's led by Sebastian,
Speaker 2: joined by others from the Darker Challenges. The methodical Chris
Speaker 2: Armsen was running most things day to day. Anthony Lewandowski,
Speaker 2: the flashy motorcycle guy, would work on hardware. Dmitriy Dolgov,
Speaker 2: another darker veteran, would be responsible for planning and optimization.
Speaker 2: It was a secret project did report directly to Larry Page,
Speaker 2: a small enough team that there'd be no bureaucracy, few emails,
Speaker 2: fewer meetings, just eleven engineers, who writer Alex Davies says,
Speaker 2: represented some of the best young talent in the country.
Speaker 4: And so Google builds this very quiet team and it
Speaker 4: says to them, buildness a self driving car. And because
Speaker 4: that goal is super nebulous, they give them two challenges.
Speaker 4: They say, safely log one hundred thousand miles on public roads.
Speaker 4: But they also give them a challenge called the Larry
Speaker 4: one K.
Speaker 11: So Larry and Serge and I said together and the
Speaker 11: two of them carved out one thousand total miles of
Speaker 11: road surface in California.
Speaker 4: They open up Google Maps and they just click around
Speaker 4: and they look for ten separate one hundred mile routes
Speaker 4: that are really tricky.
Speaker 11: Absolutely everything like the Bay Bridge and Lake Tao and
Speaker 11: Highway one to Los Angeles and Market Street and even
Speaker 11: crooked Lambas Street.
Speaker 4: And they say to the team, you have to drive
Speaker 4: each of these one hundred mile routes without one human
Speaker 4: takeover of the system, without one failure of the car to.
Speaker 2: Get off to your running start. The team licenses the
Speaker 2: code from Sanford darpa Urban Challenge vehicle. Anthony Lewandowski goes
Speaker 2: to a local Toyota dealership and buys eight priuses, takes
Speaker 2: them back to Google and retrofits them to accept a
Speaker 2: computer as a driver. He hooks that computer driver electronically
Speaker 2: into the brakes, the gas, the steering. These Priuses get
Speaker 2: a radar system behind the bumper cameras alied Our system
Speaker 2: spenning three hundred and sixty degrees on topli like radar,
Speaker 2: but it shoots lasers instead of sound waves. At first,
Speaker 2: the team gives each Prius a cool name, like night Rider.
Speaker 14: But I think we quickly realized that we're not going
Speaker 14: to be able to name all these vehicles as we
Speaker 14: scale up our fleet, and so we just started to
Speaker 14: number them, like you know, Prius twenty seven.
Speaker 2: This is Don Burnett. He'd been a researcher working on
Speaker 2: autonomous submarines. He lost a friend in a car accident,
Speaker 2: separately gotten a bad accident himself, and decided he wanted
Speaker 2: to do work on self driving cars. That's how he
Speaker 2: eventually ended up on the team.
Speaker 14: In its early days, I was on the motion planning
Speaker 14: and behavior decision making team, and my responsibility was to
Speaker 14: work on the nudging behavior.
Speaker 2: Nudging what a big truck passes a human driver on
Speaker 2: the right, The driver will nudge a little to the left.
Speaker 2: For us, it's an instinct. Don's job was to teach
Speaker 2: a computer to nudge.
Speaker 14: They're trying to encode the behavior that you would use
Speaker 14: as a driver under kind of partially good perception.
Speaker 15: And it's a really tricky problem.
Speaker 2: A team of academic roboticists, some of whom had had
Speaker 2: friends die in cars, spending Google's money to see if
Speaker 2: they could make driving safer. It was a weird era.
Speaker 2: There's this big concert venue near Google's offices called the
Speaker 2: Shoreline Amphitheater. In two thousand and nine, you could have
Speaker 2: seen Cheryl Crow there the Killers Fish. But the most
Speaker 2: interesting show that year was one almost nobody knew about.
Speaker 2: In the venue parking lot. On days when there was
Speaker 2: no concert, no tour buses around to see them, the
Speaker 2: Google team would run its first test runs of their
Speaker 2: driverless cars, essentially hiding in plane sight a prius driving
Speaker 2: itself around the Amphitheater parking lot with an attentive safety
Speaker 2: driver sitting behind the wheel just in case. The team
Speaker 2: was making sure the basics functioned that the censors could
Speaker 2: really recognize another car that the computer in the car
Speaker 2: was abiding by their orders. These were the baby steps
Speaker 2: that happened in this parking lot and at an empty
Speaker 2: airplane runway that was close to their offices. Spring two
Speaker 2: thousand and nine, the team tries actual real road driving
Speaker 2: for the first time. Chris Armson takes one of the
Speaker 2: priuses out on the Central Expressway, speed limit forty five
Speaker 2: miles per hour. There are humans driving here and immediately
Speaker 2: outside the confines of the empty parking lot and empty
Speaker 2: airplane runway. Here's what's clear. They had a real problem.
Speaker 2: The car was swerving wildly.
Speaker 8: It was weaving around like a drunken sailor. And we
Speaker 8: realized that the scale of the runway was such that
Speaker 8: you didn't notice the one or two foot kind of
Speaker 8: oscillation it had in lateral control, and you put it
Speaker 8: on Central Expressway and suddenly, you know, yep. Turns out, actually,
Speaker 8: that's a problem.
Speaker 2: One more problem to fix. Listening to the story, it's
Speaker 2: funny because I can imagine it giving me a totally
Speaker 2: different feeling than it does. A tech company with nobody's
Speaker 2: permission was testing driverless cars on public roads in California.
Speaker 2: I don't know why that strikes me as being about
Speaker 2: invention instead of just hubris and impunity. Maybe it's because
Speaker 2: I know that Google would be one of the few
Speaker 2: tech companies whose driverless cars would not cause any fatal
Speaker 2: accidents in testing, and that the team would just take
Speaker 2: more safety precautions than the other companies who'd rush in
Speaker 2: later to catch up with them once. This was an
Speaker 2: arms race. The way these cars were designed, the safety
Speaker 2: driver set behind the steering wheel, ready to take over
Speaker 2: when the other seat was their partner watching the monitor
Speaker 2: displaying a graphical interface designed by Dmitri Dolgov. The people
Speaker 2: watching the screen would call out problems ahead, some discrepancy
Speaker 2: between what the sensors were seeing and what was actually
Speaker 2: in the road. This is what teaching a car to
Speaker 2: drive actually looked like. Two person teams spanning the cars,
Speaker 2: logging errors, going back to the office to troubleshoot, and
Speaker 2: then updating the code. I asked Don Burnette about this era,
Speaker 2: and while you're doing this and then like you leave
Speaker 2: work and you get in your car that you drive
Speaker 2: as a human, did you find yourself thinking more carefully, like,
Speaker 2: how do I know what I know when I'm driving,
Speaker 2: like you're trying to teach a machine by day, did
Speaker 2: it affect how you thought about human driving? By night?
Speaker 14: Almost obnoxiously so to any passengers in the car with me.
Speaker 14: I was obsessed with one big question, which is why
Speaker 14: do humans drive the way they drive? And it turns
Speaker 14: out there were no good answers, and I still think
Speaker 14: they're not great answers. And instead of actually answering that question,
Speaker 14: we've just turned to machine learning to infer the deep
Speaker 14: truths behind why humans do what they do. Then, so
Speaker 14: there's some basic principles that you can understand, Like we
Speaker 14: try to minimize lateral acceleration, meaning you don't want to
Speaker 14: be thrown to the outside of your car when you're
Speaker 14: making a turn.
Speaker 15: So you're going to slow down, but how much do
Speaker 15: you slow down?
Speaker 4: Right?
Speaker 15: And it turns out that's contextual.
Speaker 2: Don gave me an example. So you're trying to figure
Speaker 2: out the right speed and angle for the car on
Speaker 2: one of those tight curvy on ramps onto the highway.
Speaker 2: You want it to feel comfortable for a passenger. Don says,
Speaker 2: you can work out the math. The lateral acceleration is
Speaker 2: two meters per second squared but the surprising thing is
Speaker 2: that number only applies on the on ramp.
Speaker 14: If I put you at a col de sac in
Speaker 14: a neighborhood and you were going to do a U
Speaker 14: turn at the end of the cold de sac, even
Speaker 14: though the speed is significantly slower, if you did two
Speaker 14: meters per second squared of lateral acceleration around a cul
Speaker 14: de sac, you would tell your driver they were crazy.
Speaker 14: It would be incredibly uncomfortable, like incredibly uncomfortable.
Speaker 2: You would feel like you're in Mario Kart.
Speaker 15: Yes, it would feel Mario Kart.
Speaker 14: And remember this is a force, so it's a physical
Speaker 14: feeling on your body is exactly the same. But the
Speaker 14: contextual awareness of the situation of speeding up to get
Speaker 14: on the highway versus making a U turn in a
Speaker 14: residential street tricks your brain into feeling opposite about the situation.
Speaker 14: And so it turns out the limit for a cul
Speaker 14: de sac is around point seventy five. It's almost three
Speaker 14: times less than you would be willing to tolerate as
Speaker 14: you accelerate onto a highway.
Speaker 15: And so there were.
Speaker 14: Things like that where you couldn't just say humans have
Speaker 14: specific physical restrictions right from a force's perspective, the context matters,
Speaker 14: and when the context matters, now all of a sudden,
Speaker 14: anything is game.
Speaker 15: So things like that is where.
Speaker 14: I spent my time as a researcher trying to figure out, Okay,
Speaker 14: how are we going to make this comfortable for passengers?
Speaker 2: All these little problems to solve. But there's one gift,
Speaker 2: which is that the team at this point had an
Speaker 2: overarching goal uniting them. The Darba Challenge told them drive
Speaker 2: across this patch of desert Valaria. One k Challenge told
Speaker 2: them drive these ten roots without human intervention. The specificity
Speaker 2: of the mission meant they never had to squabble about
Speaker 2: why they were there. By twenty ten, just a year in,
Speaker 2: the team was really on a roll.
Speaker 4: They start knocking out roots.
Speaker 14: Each one of the routes was unique and distinct and
Speaker 14: different and had its own challenges.
Speaker 4: Down Route one Silicon Valley to Car Mount.
Speaker 14: The Bridges run where we had to go across all
Speaker 14: of the bridges in the Bay area, starting in Mountain View,
Speaker 14: finishing crossing the Golden Gate Bridge.
Speaker 4: It's Chris Hermsen in the car. It's Anthony Lewandowski in
Speaker 4: the car.
Speaker 14: I was in the car with Dimitri, Chris and Anthony.
Speaker 14: It was the four of us in the prius.
Speaker 4: They're figuring out the technology much faster than they thought
Speaker 4: they could.
Speaker 2: The Larry one K was set up like a video game,
Speaker 2: meaning they'd get to try the route over and over
Speaker 2: until they could complete it without a single human takeover.
Speaker 2: Then they'd move on to the next one.
Speaker 8: It was really a proof of concept exercise. Can you
Speaker 8: even make this happen?
Speaker 4: Once? When they fail a route, they know what the
Speaker 4: car can't handle, so they go back and say they
Speaker 4: have to be better at doing XYZ.
Speaker 14: And then we got back to the office, we regrouped,
Speaker 14: we went back out I think at like eleven PM,
Speaker 14: and by one am we had completed the route.
Speaker 4: They buy a bottle of Corbel champagne. They all write
Speaker 4: their names on it.
Speaker 2: Corbell thirteen ninety nine, a bottle the champagne they have
Speaker 2: at Trader Joe's. They had won for every route they completed.
Speaker 4: And one by one they pick off the Larry one
Speaker 4: K routes. And they think this is going to take
Speaker 4: them about two years when they start out, and they
Speaker 4: do it in a little bit more than a year,
Speaker 4: nearly twice as fast as they had expected.
Speaker 2: By fall of twenty ten. They're done. Here's Chris Armsen.
Speaker 8: And I think we had a big party up at
Speaker 8: Sebastian's house and Los Athos Hills. So you know, it
Speaker 8: was pretty spectacular, right.
Speaker 4: They throw each other in the pool, they celebrate, and
Speaker 4: then they're not entirely sure what to do next.
Speaker 8: It was kind of a okay, And now.
Speaker 2: What the team had pulled off a kind of miracle
Speaker 2: in a year, a driverless car with human supervision, with
Speaker 2: lots of human coding, but still a driverless car successfully
Speaker 2: navigating some very tricky roads in California. They've done this safely,
Speaker 2: they've done it quickly, and now things would begin to wobble.
Speaker 2: Competition would arrive, the team itself would begin to schism,
Speaker 2: and one member, a person who believed the team was
Speaker 2: moving too slowly, would actually take matters into his own
Speaker 2: hands in a particularly extreme way after the break mutiny.
Speaker 2: Welcome back to the show. As early as twenty ten,
Speaker 2: Google's Driver's car project had developed some very impressive self
Speaker 2: driving technology, but what they were struggling to decide was this,
Speaker 2: what was the actual product they were developing. Here, here's
Speaker 2: a Bastian throne.
Speaker 11: We had a lot of debates inside Google what the
Speaker 11: right business model was. At some point, v actually had
Speaker 11: a big debate Beato just by Tesla, and Tesla was
Speaker 11: worth two billion dollars at the time. I remember this,
Speaker 11: maybe you should have been hindsight, but joking is idea.
Speaker 11: There was a debate whether this is more of an
Speaker 11: assistive technology or a disruptive replacements anology.
Speaker 2: Basically, should they follow the route that Tesla ultimately d
Speaker 2: design self driving as a feature in your car, something
Speaker 2: that could take over sometimes but still need human monitoring,
Speaker 2: or was it better to wait until the car could
Speaker 2: fully drive itself. Thron would eventually come around to this
Speaker 2: version of self driving. Specifically, he'd come around to the
Speaker 2: idea of self driving robotaxis.
Speaker 11: A taxi service type system is way more capital efficient
Speaker 11: than ownership. An owned car is being used for four
Speaker 11: percent of the time, and it's parked ninety six plent
Speaker 11: a time. Imagine a city without parked cars, where every
Speaker 11: car is being utilized called it fifty percent of the time,
Speaker 11: which means we have like only ten percent number of
Speaker 11: cars needed that we need today when we own own cars,
Speaker 11: that's going to happen. There's no absolut question.
Speaker 2: What Sebastian is describing here, as a matter of fact,
Speaker 2: is a fairly radical reimagination of American cities. The idea
Speaker 2: that robotaxis would be so cheap and widely available that
Speaker 2: most people just wouldn't own cars, that we could put
Speaker 2: something else, anything else, in the places where we put
Speaker 2: most of our parking lots and parking spaces. That is
Speaker 2: a far fetched idea, just given how much of American
Speaker 2: identity is tied into personal car ownership. A farvetched idea,
Speaker 2: and for it to begin to happen, Google would have
Speaker 2: to bring a product to market. But the years passed
Speaker 2: and they didn't, and some people who were there felt stuck.
Speaker 2: Don Burnette says he believes life at Google got dangerously cushy.
Speaker 2: The food was great, the money was too, these former
Speaker 2: academics making much more than they'd ever expected.
Speaker 14: There was a lack of urgency on the team to
Speaker 14: actually make something viable. We had a funding supply that
Speaker 14: effectively felt infinite, and maybe it was, maybe it wasn't,
Speaker 14: but it certainly felt infinite. And when you have infinite funding,
Speaker 14: you're not forced to make hard decisions, You're not forced
Speaker 14: to focus, you're not forced to look at the opportunity,
Speaker 14: the market, the customer and be the best. It was
Speaker 14: more like, hey, let's take our time, let's make sure
Speaker 14: we do it right, which is on its face a
Speaker 14: good principle, but at the end of the day, I
Speaker 14: think the lack of urgency wasn't for everyone.
Speaker 4: And within the team you got team Chris and team Anthony,
Speaker 4: and they start butting heads all the time.
Speaker 2: Chris and Anthony meaning Chris Armsen, official head of the project,
Speaker 2: versus Anthony Lewandowski, who I still think of as the
Speaker 2: motorcycle guy.
Speaker 4: The main difference in their approach is how quickly they
Speaker 4: want to move. Anthony is very okay with risk.
Speaker 2: We'll say.
Speaker 4: He gets one of these cars and he's driving it back,
Speaker 4: and he lives in Berkeley, works in Palauto. He's just
Speaker 4: using this car like the Bay Bridge every day, probably
Speaker 4: outside the bounds of what the team actually wanted, and
Speaker 4: he's not necessarily logging data. He's just enjoying his self
Speaker 4: driving car and taking it all over the place. Chris
Speaker 4: comes from an academic background. He's that Canadian, very nice,
Speaker 4: very careful, very risk averse.
Speaker 2: When I asked Chris Armson about all this, his memory
Speaker 2: was slightly different. In his memory, Team Anthony was pretty
Speaker 2: much just Anthony and Anthony, he said, was a move
Speaker 2: fast and break things kind of guy. Move fast and
Speaker 2: break things a motto famously coined by Mark Zuckerberg. It
Speaker 2: defines a way of developing technology which once might have
Speaker 2: felt cute and revolutionary, but which today, at least to me,
Speaker 2: feels pretty irresponsible. Chris didn't think that philosophy was an
Speaker 2: option for their team, even if their cars were statistically
Speaker 2: safer than human drivers. He knew that the first news
Speaker 2: story about a self driving car in a fatal accident,
Speaker 2: it was going to be a huge deal. Anecdote was
Speaker 2: going to demolish data if they weren't extremely careful. By
Speaker 2: all accounts, Anthony Lewandowski felt differently, but he actually wasn't
Speaker 2: the only one. Here's Don Burnett.
Speaker 14: There were some people on the team, very famously including myself,
Speaker 14: that started to get the itch kind of towards the
Speaker 14: three to four year mark, the itch of like, Okay,
Speaker 14: where is this going, who is it for?
Speaker 15: How are they going to use it? Where are they
Speaker 15: going to use it?
Speaker 14: And I felt like the leadership didn't have great answers
Speaker 14: to that. There was no commercial race, right. We had
Speaker 14: no competition and there was no market for the product.
Speaker 2: But competition would soon arrive in the form of Uber.
Speaker 2: This was the oh shit moment for me.
Speaker 14: Uber announced their self driving program, and I remember like
Speaker 14: it was yesterday, waking up, reading the news, going to
Speaker 14: my desk in the morning, and thinking, Oh crap, these
Speaker 14: guys are going to eat our lunch.
Speaker 2: In twenty thirteen, then CEO of Uber, Travis Kalanek, had
Speaker 2: gotten a ride in one of Google's prototype driverless cars,
Speaker 2: sitting in a taxi without a human driver. He'd understood
Speaker 2: that this could mean the end of his company, and
Speaker 2: so Uber had plunged headlong into the driverless car race.
Speaker 2: The company hired nearly half of Carnegie Mailn's top Robotics lab,
Speaker 2: and not long after we also know through court records
Speaker 2: and emails that Uber also began communicating with Anthony Lewandowski,
Speaker 2: who in twenty sixteen would leave Google, quitting just before
Speaker 2: he could be fired for a recruiting team members away,
Speaker 2: including Don Burnett. Anthony would then start his own autonomous
Speaker 2: vehicle company. Uber would soon buy that company for almost
Speaker 2: seven hundred million dollars, even though the company had no
Speaker 2: product and was only months old, which raised a mystery.
Speaker 2: Why would Uber pay so much for a company whose
Speaker 2: only assets seemed to be its people.
Speaker 4: This is where.
Speaker 16: Google goes into its computer security logs and realizes that
Speaker 16: not long before he left, Anthony Lewandowski downloaded something like
Speaker 16: fourteen thousand technical files onto his.
Speaker 4: Computer and moved them onto an external disc.
Speaker 2: Obviously you can't do that. I mean, I'm assuming obviously
Speaker 2: you can't do that.
Speaker 4: No, you definitely cannot see And this is the kind
Speaker 4: of thing that maybe if he had stayed there, this
Speaker 4: is the kind of thing Anthea would have done, and
Speaker 4: he would have been like, oh, it's just so I
Speaker 4: could have access to to it somewhere else. Then he
Speaker 4: probably would have gotten away with it. But when you
Speaker 4: then go and work for Uber and start running their
Speaker 4: direct competitor self driving car program, that's when you get
Speaker 4: in trouble. And that's when what's technically called WEIMO. At
Speaker 4: this point, Google's program sues UK and puts Anthony at
Speaker 4: the center of an enormous legal battle between these.
Speaker 15: Tech giants, secrets and subterfugia.
Speaker 4: In Silicon Valley, a former Google engineer has been charged
Speaker 4: with stealing files from Alphabet's self driving car project and
Speaker 4: taking them to Uber.
Speaker 11: Specifically, it involves a former lead engineer of Google's self
Speaker 11: driving car unit, Anthony Lewandowski.
Speaker 4: Now he's accused of using.
Speaker 11: His personal laptop and downloading more than fourteen.
Speaker 2: In twenty sixteen, Google had just spun its driverless car
Speaker 2: unit into a new entity, Weimo. Weimo sued Uber. Uber
Speaker 2: had to settled to the tune of two hundred and
Speaker 2: forty five million dollars, and in a separate criminal trial,
Speaker 2: Anthony Lewandowski pled guilty to stealing trade secrets. Afterwards, Uber
Speaker 2: continues their driverless car program without him, continuing to pursue
Speaker 2: its move fast, break things strategy, which in twenty eighteen
Speaker 2: leads to the death of a woman named Elaine Herzberg.
Speaker 8: Uber is sitting the brakes on its self driving cars
Speaker 8: after one of them hit and kill the woman in Arizona.
Speaker 1: The vehicle was in autonomous mode, but it did have
Speaker 1: a safety driver on board, but.
Speaker 17: A police report later indicating the safety driver was streaming
Speaker 17: TV shows on her phone for three hours that night,
Speaker 17: including at the time of the crash.
Speaker 2: The way this story was reported, nearly everyone blamed the
Speaker 2: safety driver. She was on her phone. She's streaming an episode.
Speaker 17: Of the Voice Tempe investigator saying, had Vasquez been paying
Speaker 17: attention to the road, she could have stopped the car
Speaker 17: forty two feet before impact the NTSB slamming.
Speaker 2: There were some important additional context, which is that Uber's
Speaker 2: robot driver was also just much worse than way Moo's,
Speaker 2: a statistic I found jaw dropping. At this point, Waymos's
Speaker 2: safety drivers were having to take over from the car
Speaker 2: once every five six hundred miles. Uber's safety drivers that
Speaker 2: year had to intervene more than once every thirteen miles.
Speaker 2: Despite that, five months before the crash, over employee objections,
Speaker 2: Uber had cut its safety crews. Instead of two humans,
Speaker 2: they just used one. One safety driver overseeing a robot
Speaker 2: driver that was arguably not ready to be on public roads.
Speaker 2: In the last moments of Alane Herzburg's life, the robot
Speaker 2: spent an indefensible five point six seconds trying and failing
Speaker 2: to guess the shape in the road there was a
Speaker 2: human body pushing a bike. Over those five point six seconds,
Speaker 2: the robot kept reclassifying our whishing an unknown object a
Speaker 2: vehicle a bicycle. During that time, spent wondering the car
Speaker 2: did not slow down. Soon after Elaine Hertzberg's death, Uber
Speaker 2: halted its testing program.
Speaker 17: Uber has temporarily suspended its driverless fleet nationwide, as the
Speaker 17: NTSB police, Uber and the National Highway Traffic Safety Administration investigate.
Speaker 2: We reached out to Uber for comment. A spokesperson said
Speaker 2: that the fatal collision was indeed a tragedy which had
Speaker 2: a significant impact on Uber and the entire industry. There'd
Speaker 2: be other competitors who would shut down after similar accidents.
Speaker 2: There would also be Tesla, which by twenty twenty was
Speaker 2: publicly marketing a product of the company called full self driving,
Speaker 2: but which absolutely was not. Meanwhile, Wimo had slowly continued
Speaker 2: develop its tech. Their robotaxis would be ready for riders
Speaker 2: by twenty twenty. The team had gotten an unexpected boost
Speaker 2: from a technology that was at the time very little understood.
Speaker 2: In twenty twenty six, when most people talk about artificial intelligence,
Speaker 2: the conversation defaults to products like chat, GPT, and Claude,
Speaker 2: But artificial intelligence has been a core part of driver
Speaker 2: lest cars going back two decades. In the twenty ten's,
Speaker 2: neural net advances meant that you can now begin to
Speaker 2: feed a computer system large amounts of data and watch
Speaker 2: as its perception, prediction, and decision making abilities improved. Here's
Speaker 2: Sebastian Thront.
Speaker 11: Their technology of massive data training was with us from
Speaker 11: the get go, but has become more and more and
Speaker 11: more and more important. The surprise for all of us
Speaker 11: has been that size matters. When you put a million
Speaker 11: documents into an AI, it's fine, one hundred million is fine,
Speaker 11: And when you put one hundred billion documents into ANI,
Speaker 11: it is umbiliately smart. And then a thing shocked everybody,
Speaker 11: myself into.
Speaker 2: The Google brand team. The deep learning people started working
Speaker 2: with the driverless car team to use training data to
Speaker 2: help the computer driver learn things like how to better
Speaker 2: predict when another car was about to suddenly switch lanes,
Speaker 2: how to more reliably spot pedestrians. Over the years, as
Speaker 2: a car drove more miles, as the team gathered more data,
Speaker 2: plugged that data into their AI systems, and tweaked those systems.
Speaker 2: The engineers say the robot driver kept improving as they
Speaker 2: tested the car in new weather conditions, they discovered problems
Speaker 2: that required hardware fixes. For instance, in Phoenix, Weimo had
Speaker 2: to design miniature wipers for their cars. Led our sensors
Speaker 2: to deal with the dust storms and heavy rains. In
Speaker 2: twenty twenty, Weaimo finally debuts to the public in Arizona.
Speaker 2: In the years after, it'll roll out to ten more
Speaker 2: American cities. A funny consequence of Weymo's long development cycle
Speaker 2: is that the public's attitude towards Silicon Valley has just
Speaker 2: really changed in that time. There's more suspicion towards Google
Speaker 2: than there was back in two thousand and nine when
Speaker 2: the project first started, And so now many people look
Speaker 2: at the Waimo driver with a raised eyebrow with a
Speaker 2: question immediately on their lips. Chapter five, Are you a
Speaker 2: good driver?
Speaker 4: All right? Autonomous vehicles can now get you around Atlanta yesterday.
Speaker 4: Driving through Austin is here, except it comes without.
Speaker 10: Drive Light hailing app is now taking passengers in Miami.
Speaker 2: A fleet of white electric Jaguars covered in forty different sensors, cameras, radar, lidar.
Speaker 2: It's an expensive car, as much as one hundred and
Speaker 2: fifty thousand dollars by some estimates. In the news stories,
Speaker 2: you see the inside where the human driver would normally sit.
Speaker 2: There's an empty seat you're not allowed in with a
Speaker 2: steering wheel in front of it. It turns itself.
Speaker 10: Cars without drivers are here.
Speaker 3: Yeah, it sounds like something out of the Jetsons.
Speaker 4: But get ready because you may look over at the
Speaker 4: car next to you and see it rolling down the street.
Speaker 2: The TV newscasters always use the same g whiz tone.
Speaker 2: They can never resist the Jetson's reference. In every city,
Speaker 2: the influencers hop into record testimonials for their daily serving
Speaker 2: of clout.
Speaker 14: So in today's video, I'm about to take my first
Speaker 14: ever driverless car.
Speaker 8: It's with an app called Weimo.
Speaker 3: Weimo is basically driverless car uber where it's like ride service.
Speaker 3: You call it going wherever you need it to go,
Speaker 3: but there's no driver.
Speaker 2: You guys, this is creepy.
Speaker 4: It's like I'm being driven around by a ghost person.
Speaker 4: It's a little terrifying.
Speaker 2: It is definitely Romo taxis pull hilariously badly. According to
Speaker 2: JD Power, a data analytics firm, among people who've not
Speaker 2: ridden in one consumer confidence is at twenty percent, but
Speaker 2: among people who have taken a ride Denver shoots up
Speaker 2: to seventy six percent. It's the thing that capture this story.
Speaker 2: But when I sad and won a couple of years ago.
Speaker 2: I just found it persuasive as an experience.
Speaker 1: You know what, I'm not as nervous as I thought
Speaker 1: I was gonna be.
Speaker 9: This is actually quite relaxing.
Speaker 2: Nice gradual turn, felt very safe.
Speaker 18: You know, it was kind of freaky at first, but
Speaker 18: now it's pretty chill smooth.
Speaker 4: Right though it wasn't driving fast, it wasn't jerking.
Speaker 18: It's driving like you always hope your Uber driver would.
Speaker 4: So I guess that's one of the big sells.
Speaker 2: Chris Arms and that methodical team leader had left Google
Speaker 2: years ago, but he told me about his experience as
Speaker 2: a civilian consumer trying away mom out in the world.
Speaker 8: My universal experience has been and you can tell me
Speaker 8: if this was your experience. The first couple of minutes
Speaker 8: in the vehicle, it's huh, that's crazy. I dished nobody
Speaker 8: behind the wheel swinging with sharks. And then a few
Speaker 8: minutes in and it's like, okay, you know, it's just
Speaker 8: just gonna drive.
Speaker 4: Is that all it does?
Speaker 8: And then you know, ten minutes and people are looking
Speaker 8: at their phone.
Speaker 2: People tend to feel safe in these but are they
Speaker 2: actually so we know that the Weimo driver has now
Speaker 2: driven over two hundred million real world miles, and they
Speaker 2: release safety data so far for the first one hundred
Speaker 2: and twenty seven million miles. Weymo's fairly transparent. They release
Speaker 2: their crash and safety data unredacted to the public. By contrast,
Speaker 2: Tesla redacts the details of its crashes. The company says
Speaker 2: they are confidential business information. In Weymo's case, I've looked
Speaker 2: at the data, I've looked at how the company interprets it,
Speaker 2: how skeptical independent researchers interpret it. I wanted to walk
Speaker 2: through it with an autonomous vehicle reporter I trust. His
Speaker 2: name is Timothy Beeley, author of the newsletter Understanding AI.
Speaker 2: I asked him how much our picture of the Weymos
Speaker 2: safety data has been evolving.
Speaker 19: So it's been pretty consistent the last couple of years.
Speaker 19: They are scaling up, and so all the numbers get bigger,
Speaker 19: like the total number of miles get bigger, the number
Speaker 19: of crashes get bigger, but the light crashes per mile
Speaker 19: have not changed a ton, Weimos says, and I think
Speaker 19: this is correct, that it's roughly eighty brass safer in
Speaker 19: terms of crashes are severe enough to turn down an airbag.
Speaker 19: Crashes severe enough to cause an injury, and also crashes
Speaker 19: involving vulnerable road users like pedestrians or bicyclists.
Speaker 2: So eighty percent fewer air bag crashes than human drivers,
Speaker 2: and actually ninety percent fewer crashes that cause a serious injury.
Speaker 2: Some independent experts have small quibbles with the methodology, but
Speaker 2: broadly they find Waymos's data credible. Timothy pointed out, there's
Speaker 2: one very important thing we don't know, the fatal crash comparison.
Speaker 2: For every one hundred million miles humans drive, we cause
Speaker 2: a little over one fatal crash. The Waimo driver has
Speaker 2: driven two hundred million miles without causing a fatal crash,
Speaker 2: but statistically speaking, that could still be a fluke. Some
Speaker 2: academics have suggested we need about three hundred million miles
Speaker 2: to have statistical confidence in the hundreds of millions of
Speaker 2: miles the Waymo driver has traveled. It was involved in
Speaker 2: two fatal crashes which it did not appear to cause.
Speaker 2: Here are the details of those crashes. In one, a
Speaker 2: speeding human driver rear ended a line of vehicles at
Speaker 2: a stoplight. There's an empty Weimo in the line of
Speaker 2: struck cars. In another crash, a Weimo is yielding for
Speaker 2: a pedestrian. It was rear ended by a motorcycle. The
Speaker 2: motorcycle driver was then struck by a second car. That's
Speaker 2: everything when Timothy bee Lee looks at the entire safety picture,
Speaker 2: the results we have so far from this big experiment
Speaker 2: Weimo is conducting on American roads, what he sees is
Speaker 2: mainly promising.
Speaker 19: So far it's been better than human drivers, and so far,
Speaker 19: I think the case for allowing them they continue.
Speaker 4: The experiment is very strong.
Speaker 2: Which doesn't mean we shouldn't scrutinize this Weimo experiment as
Speaker 2: it continues. I find myself paying a lot of attention
Speaker 2: to Weimo crashes, which isn't hard. They make headlines. The
Speaker 2: most harrowing one recently was this January.
Speaker 6: A child at near to Elementary school in Santa Monica
Speaker 6: is Weymo.
Speaker 18: A child ran across the street from behind a double
Speaker 18: part car and a Weimo hit the kid.
Speaker 13: Santa Monica police say the child, a ten year old girl,
Speaker 13: was not hurt.
Speaker 2: The company issued a statement. Weimo said its driver had
Speaker 2: breaked hard, reducing speed from seventeen to under six miles
Speaker 2: per hour, a faster reaction, they claimed than a human
Speaker 2: driver would have been capable of what happened next at
Speaker 2: the accident scene. Actually answers a question i'd had, what
Speaker 2: does a WEIMO do after a car crash. Since there's
Speaker 2: no human driver to help, WEIMO employs what they call
Speaker 2: human fleet response agents, human beings who can't remotely drive
Speaker 2: the cars, but who the car can ask questions to
Speaker 2: if it gets confused. In Santa Monica, the WEIMO called
Speaker 2: one of those humans, the human called nine one one.
Speaker 2: And this is the strangest part of Weymo's statement. Apparently
Speaker 2: the car then waited at the scene of the accident
Speaker 2: until the police dismissed it. That's what we know so far.
Speaker 2: But there's two federal agencies investigating this crash, and so
Speaker 2: we'll have a full report in the future. One problem
Speaker 2: that's not really captured in the safety data that I've
Speaker 2: seen is what i'd call troubling edge cases. You see
Speaker 2: them in videos on social media. A WAIMO gets stuck
Speaker 2: at a dead stop light or blocks an emergency vehicle,
Speaker 2: or an example, Timothy gave waymo's were driving past stopped
Speaker 2: school buses in Austin.
Speaker 19: I think it's reasonable to say this is like a
Speaker 19: clear cut rule that the vehicle should follow this role.
Speaker 19: These educads are still very rare, and so if it's
Speaker 19: a one to ten million thing, I think it's not
Speaker 19: that big a deal as long as they are making progress,
Speaker 19: which for most of these I think they are.
Speaker 2: Timothy pointed to one area where Waymo's not been as
Speaker 2: transparent as he'd like, those human response agents, some of
Speaker 2: which are based here some of the Philippines. There's questions
Speaker 2: about what specifically they do and about how this will
Speaker 2: all work as way most scales up. We asked Waymo
Speaker 2: for comment on everything you heard in this episode, especially
Speaker 2: the recent safety incidents. A spokesperson said that the data
Speaker 2: to date indicates that the Weimo driver is already making
Speaker 2: roads safer in the places where they operate, and says
Speaker 2: that Weymo can used to work with policymakers and regulators
Speaker 2: to improve its technology. That's the safety picture so far,
Speaker 2: which to me, after many months of looking at this
Speaker 2: and talking to experts, looks pretty good. As Weimo continues
Speaker 2: its rollout, other companies are quickly following behind.
Speaker 3: Amazon's new driverless taxi is launching in Las Vegas this summer,
Speaker 3: and it's expected to arrive.
Speaker 2: And now there's other robo taxi companies like Amazon, Zookes.
Speaker 2: Uber is back in the mix, not making technology, but
Speaker 2: partnering with these robo taxi companies. We Ride recently struck
Speaker 2: partnership with Uber to bring its avs to Abu Dhabi,
Speaker 2: another sign of it. And many of those early WEIMO
Speaker 2: engineers are now CEOs of autonomous companies themselves. Dmitri Dolgov
Speaker 2: is actually co CEO Weimo, but other team members run
Speaker 2: driverless trucking companies.
Speaker 18: Got Don Burnette, founder and CEO of kodiak Ai. Don,
Speaker 18: thank you so much for joining us.
Speaker 12: It's good to see you again.
Speaker 2: Don Burnett is head of kodiak Ai, which has its
Speaker 2: technology deployed in driverless trucks in the premium basin.
Speaker 18: Please welcome CEO of Aurora, Chris Ermthin.
Speaker 4: A big round of a plot.
Speaker 2: Chris Armsen now heads Aurora, which currently has semi trucks
Speaker 2: on Texas highways. And my personal favorite plot development which
Speaker 2: just emerged this week.
Speaker 18: I just broke on the information that Uber founder Travis
Speaker 18: Kalanik is starting a new self driving car company with
Speaker 18: financial backing from Uber and in partnership with Anthony Lewandowski.
Speaker 17: Now, for those who've been they.
Speaker 2: Say there's no second acts in American lives. Somehow, both
Speaker 2: of these men seem to be on their fourth. The
Speaker 2: big picture, though, is that everywhere in America today that
Speaker 2: you see a driver, taxi, truck, food delivery, there are
Speaker 2: several companies working on the robot version trying their best
Speaker 2: to make driver as a job start to go the
Speaker 2: way of the knocker Upper of the Lamplighter. Those knocker Ruppers,
Speaker 2: by the way, they disappeared quietly. The Lamplighters did not.
Speaker 2: Writer Carl Benedict Frey tells the story of the Lamplighters Union,
Speaker 2: how their strikes plunged New York City briefly into darkness
Speaker 2: to the light of lovers and thieves. In Vervier, Belgium,
Speaker 2: the Lamplighters strikes turned violent, ending in an attack on
Speaker 2: the local police headquarters. The army was brought in. The
Speaker 2: lamp Layers lost their fight, in part just because they
Speaker 2: were so outnumbered. But the drivers today fighting to save
Speaker 2: their livelihoods are a significantly bigger force.
Speaker 8: Please stand up, everybody that's ride share union members are
Speaker 8: someone who drives the vehicle.
Speaker 4: Stand up.
Speaker 2: Four point eight million Americans drive for a living. It's
Speaker 2: one of the most common jobs we have, and these
Speaker 2: workers do not plan to surrender to the California tech companies.
Speaker 2: They're doing this because they stand to make an unfathomable
Speaker 2: amount of money if they eliminate driving jobs for working
Speaker 2: class of people.
Speaker 4: I understand they this a business, they this capitalism, but
Speaker 4: not in my city at the expense of our jobs.
Speaker 2: These drivers are represented by unions backed by politicians and
Speaker 2: in cities across America blue cities. They're organizing. So far
Speaker 2: they're winning. Humans drive the city, lot machines, labor drives
Speaker 2: this city, keep the workers in the workforce.
Speaker 4: If it works in another city, great, have fun, not here,
Speaker 4: not Boston.
Speaker 9: Thank you.
Speaker 2: Next week the Fight to save a Job, to save
Speaker 2: the human Driver.
Speaker 5: Don't miss this one.
Speaker 2: Thank you for listening to our episode. I just want
Speaker 2: to say, making deeply reported stories like this one is
Speaker 2: only possible because for our listeners, particularly our premium subscribers
Speaker 2: who pay to support the show. We are releasing our
Speaker 2: full interview with Sebastian Throne, who used to lead Google
Speaker 2: X their secret Special Projects Lab. Totally fascinating conversation with
Speaker 2: the kind of person who just sort of lives in
Speaker 2: the future and has a million strange ideas about it.
Speaker 2: We are releasing that for our incognito mode members only.
Speaker 2: It'll be in your feed. If you would like to
Speaker 2: know the future, sign up at search Engine Dot Show
Speaker 2: and again. Your membership specifically enables projects like this one,
Speaker 2: so thank you. Search Engine is a presentation of Odyssey.
Speaker 2: It is created by me PJ Vote and Truthy Pinaminini.
Speaker 2: Garrett Graham is our senior producer. Emily Malterre is our
Speaker 2: associate producer. Theme, original composition and mixing by armand Bazarian.
Speaker 2: Our production intern is Piper Dumont. This episode was fact
Speaker 2: checked by Mary Mathis. Our executive producer is Lea Reese Dennis.
Speaker 2: Thanks to the rest of the team at Odyssey, Rob
Speaker 2: Mirandy Craig Cox, Eric Donnelly, Colin Gaynor, Mark Curran, just
Speaker 2: Fina Francis, Kurt Courtney, and Hillary Scheff. Thanks for listening.
Speaker 2: We'll see you next week with the second part of
Speaker 2: this story.
Speaker 1: That was part one of this two part story on
Speaker 1: one of the most transformative technologies of today, driverless car.
Speaker 1: If you want to hear how much more complicated the
Speaker 1: story gets as the technology rolls into American cities you
Speaker 1: can find search Engine wherever you get your podcasts.