>> NARRATOR: Tonight--
>> The race to become an A.I. superpower is on...
>> NARRATOR: The politics of artificial intelligence...
>> There will be a Chinese tech sector
and there will be a American tech sector.
>> NARRATOR: The new tech war.
>> The more data, the better the A.I. works.
So in the age of A.I., where data is the new oil,
China is the new Saudi Arabia.
>> NARRATOR: The future of work...
>> When I increase productivity through automation,
jobs go away.
>> I believe about 50% of jobs will be somewhat
or extremely threatened by A.I. in the next 15 years or so.
>> NARRATOR: A.I. and corporate surveillance...
>> We thought that we were searching Google.
We had no idea that Google was searching us.
>> NARRATOR: And the threat to democracy.
>> China is on its way to building
a total surveillance state.
>> NARRATOR: Tonight on "Frontline"...
>> It has pervaded so many elements of everyday life.
How do we make it transparent and accountable?
>> NARRATOR: ..."In the Age of A.I."
♪ ♪
>> NARRATOR: This is the world's most complex board game.
There are more possible moves in the game of Go
than there are atoms in the universe.
Legend has it that in 2300 BCE, Emperor Yao devised it
to teach his son discipline, concentration, and balance.
And, over 4,000 years later, this ancient Chinese game
would signal the start of a new industrial age.
♪ ♪
It was 2016, in Seoul, South Korea.
>> Can machines overtake human intelligence?
A breakthrough moment when the world champion
of the Asian board game Go takes on an A.I. program
developed by Google.
>> (speaking Korean):
>> In countries where it's very popular,
like China and Japan and, and South Korea, to them,
Go is not just a game, right?
It's, like, how you learn strategy.
It has an almost spiritual component.
You know, if you talk to South Koreans, right,
and Lee Sedol is the world's greatest Go player,
he's a national hero in South Korea.
They were sure that Lee Sedol would beat AlphaGo hands down.
♪ ♪
>> NARRATOR: Google's AlphaGo was a computer program that,
starting with the rules of Go
and a database of historical games,
had been designed to teach itself.
>> I was one of the commentators at the Lee Sedol games.
And yes, it was watched by tens of millions of people.
(man speaking Korean)
>> NARRATOR: Throughout Southeast Asia,
this was seen as a sports spectacle
with national pride at stake.
>> Wow, that was a player guess.
>> NARRATOR: But much more was in play.
This was the public unveiling
of a form of artificial intelligence
called deep learning,
that mimics the neural networks of the human brain.
>> So what happens with machine learning,
or artificial intelligence-- initially with AlphaGo--
is that the machine is fed all kinds of Go games,
and then it studies them, learns from them,
and figures out its own moves.
And because it's an A.I. system--
it's not just following instructions,
it's figuring out its own instructions--
it comes up with moves that humans hadn't thought of before.
So, it studies games that humans have played, it knows the rules,
and then it comes up with creative moves.
(woman speaking Korean)
(speaking Korean):
>> That's a very... that's a very surprising move.
>> I thought it was a mistake.
>> NARRATOR: Game two, move 37.
>> That move 37 was a move that humans could not fathom,
but yet it ended up being brilliant
and woke people up to say,
"Wow, after thousands of years of playing,
we never thought about making a move like that."
>> Oh, he resigned.
It looks like... Lee Sedol has just resigned, actually.
>> Yeah! >> Yes.
>> NARRATOR: In the end, the scientists watched
their algorithms win four of the games.
Lee Sedol took one.
>> What happened with Go, first and foremost,
was a huge victory for deep mind and for A.I., right?
It wasn't that the computers beat the humans,
it was that, you know, one type of intelligence beat another.
>> NARRATOR: Artificial intelligence had proven
it could marshal a vast amount of data,
beyond anything any human could handle,
and use it to teach itself how to predict an outcome.
The commercial implications were enormous.
>> While AlphaGo is a, is a toy game,
but its success and its waking everyone up, I think,
is, is going to be remembered as the pivotal moment
where A.I. became mature
and everybody jumped on the bandwagon.
♪ ♪
>> NARRATOR: This is about the consequences of that defeat.
(man speaking local language)
How the A.I. algorithms are ushering in a new age
of great potential and prosperity,
but an age that will also deepen inequality, challenge democracy,
and divide the world into two A.I. superpowers.
Tonight, five stories about how artificial intelligence
is changing our world.
♪ ♪
China has decided to chase the A.I. future.
>> The difference between the internet mindset
and the A.I. mindset...
>> NARRATOR: A future made and embraced by a new generation.
>> Well, it's hard not to feel the kind of immense energy,
and also the obvious fact of the demographics.
They're mostly very younger people,
so that this clearly is technology which is being
generated by a whole new generation.
>> NARRATOR: Orville Schell is one of
America's foremost China scholars.
>> (speaking Mandarin)
>> NARRATOR: He first came here 45 years ago.
>> When I, when I first came here, in 1975,
Chairman Mao was still alive,
the Cultural Revolution was coming on,
and there wasn't a single whiff of anything
of what you see here.
It was unimaginable.
In fact, in those years, one very much thought,
"This is the way China is, this is the way it's going to be."
And the fact that it has gone through
so many different changes since is quite extraordinary.
(man giving instructions)
>> NARRATOR: This extraordinary progress goes back
to that game of Go.
>> I think that the government recognized
that this was a sort of critical thing for the future,
and, "We need to catch up in this," that, you know,
"We cannot have a foreign company showing us up
at our own game.
And this is going to be something that is going to be
critically important in the future."
So, you know, we called it the Sputnik moment for,
for the Chinese government--
the Chinese government kind of woke up.
>> (translated): As we often say in China,
"The beginning is the most difficult part."
>> NARRATOR: In 2017, Xi Jinping announced
the government's bold new plans
to an audience of foreign diplomats.
China would catch up with the U.S. in artificial intelligence
by 2025 and lead the world by 2030.
>> (translated): ...and intensified cooperation
in frontier areas such as digital economy,
artificial intelligence, nanotechnology,
and accounting computing.
♪ ♪
>> NARRATOR: Today, China leads the world in e-commerce.
Drones deliver to rural villages.
And a society that bypassed credit cards
now shops in stores without cashiers,
where the currency is facial recognition.
>> No country has ever moved that fast.
And in a short two-and-a-half years,
China's A.I. implementation really went from minimal amount
to probably about 17 or 18 unicorns,
that is billion-dollar companies, in A.I. today.
And that, that progress is, is hard to believe.
>> NARRATOR: The progress was powered by a new generation
of ambitious young techs pouring out of Chinese universities,
competing with each other for new ideas,
and financed by a new cadre of Chinese venture capitalists.
This is Sinovation,
created by U.S.-educated A.I. scientist and businessman
Kai-Fu Lee.
>> These unicorns-- we've got one, two, three, four, five,
six, in the general A.I. area.
And unicorn means a billion-dollar company,
a company whose valuation or market capitalization
is at $1 billion or higher.
I think we put two unicorns to show $5 billion or higher.
>> NARRATOR: Kai-Fu Lee was born in Taiwan.
His parents sent him to high school in Tennessee.
His PhD thesis at Carnegie Mellon
was on computer speech recognition,
which took him to Apple.
>> Well, reality is a step closer to science fiction,
with Apple Computers' new developed program...
>> NARRATOR: And at 31, an early measure of fame.
>> Kai-Fu Lee, the inventor of Apple's
speech-recognition technology.
>> Casper, copy this to Make Write 2.
Casper, paste.
Casper, 72-point italic outline.
>> NARRATOR: He would move on to Microsoft research in Asia
and became the head of Google China.
Ten years ago, he started Sinovation in Beijing,
and began looking for promising startups and A.I. talent.
>> So, the Chinese entrepreneurial companies
started as copycats.
But over the last 15 years, China has developed its own form
of entrepreneurship, and that entrepreneurship is described
as tenacious, very fast, winner-take-all,
and incredible work ethic.
I would say these few thousand Chinese top entrepreneurs,
they could take on any entrepreneur
anywhere in the world.
>> NARRATOR: Entrepreneurs like Cao Xudong,
the 33-year-old C.E.O. of a new startup called Momenta.
This is a ring road around Beijing.
The car is driving itself.
♪ ♪
>> You see, another cutting, another cutting-in.
>> Another cut-in, yeah, yeah.
>> NARRATOR: Cao has no doubt about the inevitability
of autonomous vehicles.
>> Just like AlphaGo can beat the human player in, in Go,
I think the machine will definitely surpass
the human driver, in the end.
>> NARRATOR: Recently, there have been cautions
about how soon autonomous vehicles will be deployed,
but Cao and his team are confident
they're in for the long haul.
>> U.S. will be the first to deploy,
but China may be the first to popularize.
It is 50-50 right now.
U.S. is ahead in technology.
China has a larger market, and the Chinese government
is helping with infrastructure efforts--
for example, building a new city the size of Chicago
with autonomous driving enabled,
and also a new highway that has sensors built in
to help autonomous vehicle be safer.
>> NARRATOR: Their early investors included
Mercedes-Benz.
>> I feel very lucky and very inspiring
and very exciting that we're living in this era.
♪ ♪
>> NARRATOR: Life in China is largely conducted
on smartphones.
A billion people use WeChat, the equivalent of Facebook,
Messenger, and PayPal, and much more,
combined into just one super-app.
And there are many more.
>> China is the best place for A.I. implementation today,
because the vast amount of data that's available in China.
China has a lot more users than any other country,
three to four times more than the U.S.
There are 50 times more mobile payments than the U.S.
There are ten times more food deliveries,
which serve as data to learn more about user behavior
than the U.S.
300 times more shared bicycle rides,
and each shared bicycle ride has all kinds of sensors
submitting data up to the cloud.
We're talking about maybe ten times more data than the U.S.,
and A.I. is basically run on data and fueled by data.
The more data, the better the A.I. works,
more importantly than how brilliant the researcher is
working on the problem.
So, in the age of A.I., where data is the new oil,
China is the new Saudi Arabia.
>> NARRATOR: And access to all that data
means that the deep-learning algorithm can quickly predict
behavior, like the creditworthiness of someone
wanting a short-term loan.
>> Here is our application.
And customer can choose how many money they want to borrow
and how long they want to borrow,
and they can input their datas here.
And after, after that, you can just borrow very quickly.
>> NARRATOR: The C.E.O. shows us how quickly you can get a loan.
>> It is, it has done.
>> NARRATOR: It takes an average of eight seconds.
>> It has passed to banks. >> Wow.
>> NARRATOR: In the eight seconds,
the algorithm has assessed 5,000 personal features
from all your data.
>> 5,000 features that is related with the delinquency,
when maybe the banks only use few, maybe, maybe ten features
when they are doing their risk amendment.
>> NARRATOR: Processing millions of transactions,
it'll dig up features that would never be apparent
to a human loan officer, like how confidently you type
your loan application, or, surprisingly,
if you keep your cell phone battery charged.
>> It's very interesting, the battery of the phone
is related with their delinquency rate.
Someone who has much more lower battery,
they get much more dangerous than others.
>> It's probably unfathomable to an American
how a country can dramatically evolve itself
from a copycat laggard to, all of a sudden,
to nearly as good as the U.S. in technology.
>> NARRATOR: Like this facial-recognition startup
he invested in.
Megvii was started by three young graduates in 2011.
It's now a world leader in using A.I. to identify people.
>> It's pretty fast.
For example, on the mobile device,
we have timed the facial-recognition speed.
It's actually less than 100 milliseconds.
So, that's very, very fast.
So 0.1 second that we can, we will be able to recognize you,
even on a mobile device.
>> NARRATOR: The company claims the system is better
than any human at identifying people in its database.
And for those who aren't, it can describe them.
Like our director-- what he's wearing,
and a good guess at his age, missing it by only a few months.
>> We are the first one to really take facial recognition
to commercial quality.
>> NARRATOR: That's why in Beijing today,
you can pay for your KFC with a smile.
>> You know, it's not so surprising,
we've seen Chinese companies catching up to the U.S.
in technology for a long time.
And so, if particular effort and attention is paid
in a specific sector, it's not so surprising
that they would surpass the rest of the world.
And facial recognition is one of the, really the first places
we've seen that start to happen.
>> NARRATOR: It's a technology prized by the government,
like this program in Shenzhen to discourage jaywalking.
Offenders are shamed in public-- and with facial recognition,
can be instantly fined.
Critics warn that the government and some private companies
have been building a national database
from dozens of experimental social-credit programs.
>> The government wants to integrate
all these individual behaviors, or corporations' records,
into some kind of metrics and compute out a single number
or set of number associated with a individual,
a citizen, and using that, to implement a incentive
or punishment system.
>> NARRATOR: A high social-credit number
can be rewarded with discounts on bus fares.
A low number can lead to a travel ban.
Some say it's very popular with a Chinese public
that wants to punish bad behavior.
Others see a future that rewards party loyalty
and silences criticism.
>> Right now, there is no final system being implemented.
And from those experiments, we already see that the possibility
of what this social-credit system can do to individual.
It's very powerful-- Orwellian-like--
and it's extremely troublesome in terms of civil liberty.
>> NARRATOR: Every evening in Shanghai,
ever-present cameras record the crowds
as they surge down to the Bund,
the promenade along the banks of the Huangpu River.
Once the great trading houses of Europe came here to do business
with the Middle Kingdom.
In the last century, they were all shut down
by Mao's revolution.
But now, in the age of A.I.,
people come here to take in a spectacle
that reflects China's remarkable progress.
(spectators gasp)
And illuminates the great political paradox of capitalism
taken root in the communist state.
>> People have called it market Leninism,
authoritarian capitalism.
We are watching a kind of a Petri dish
in which an experiment of, you know, extraordinary importance
to the world is being carried out.
Whether you can combine these things
and get something that's more powerful,
that's coherent, that's durable in the world.
Whether you can bring together a one-party state
with an innovative sector, both economically
and technologically innovative,
and that's something we thought could not coexist.
>> NARRATOR: As China reinvents itself,
it has set its sights on leading the world
in artificial intelligence by 2030.
But that means taking on the world's most innovative
A.I. culture.
♪ ♪
On an interstate in the U.S. Southwest,
artificial intelligence is at work solving the problem
that's become emblematic of the new age,
replacing a human driver.
♪ ♪
This is the company's C.E.O., 24-year-old Alex Rodrigues.
>> The more things we build successfully,
the less people ask questions
about how old you are when you have working trucks.
>> NARRATOR: And this is what he's built.
Commercial goods are being driven from California
to Arizona on Interstate 10.
There is a driver in the cab, but he's not driving.
It's a path set by a C.E.O. with an unusual CV.
>> Are we ready, Henry?
The aim is to score these pucks into the scoring area.
So I, I did competitive robotics starting when I was 11,
and I took it very, very seriously.
To, to give you a sense, I won the Robotics World Championships
for the first time when I was 13.
I've been to worlds seven times
between the ages of 13 and 20-ish.
I eventually founded a team,
did a lot of work at a very high competitive level.
Things looking pretty good.
>> NARRATOR: This was a prototype of sorts,
from which he has built his multi-million-dollar company.
>> I hadn't built a robot in a while, wanted to get back to it,
and felt that this was by far the most exciting piece
of robotics technology that was up and coming.
A lot of people told us we wouldn't be able to build it.
But knew roughly the techniques that you would use.
And I was pretty confident that if you put them together,
you would get something that worked.
Took the summer off, built in my parents' garage a golf cart
that could drive itself.
>> NARRATOR: That golf cart got the attention
of Silicon Valley, and the first of several rounds
of venture capital.
He formed a team and then decided the business opportunity
was in self-driving trucks.
He says there's also a human benefit.
>> If we can build a truck that's ten times safer
than a human driver, then not much else actually matters.
When we talk to regulators, especially,
everyone agrees that the only way that we're going to get
to zero highway deaths, which is everyone's objective,
is to use self-driving.
And so, I'm sure you've heard the statistic,
more than 90% of all crashes
have a human driver as the cause.
So if you want to solve traffic fatalities,
which, in my opinion, are the single biggest tragedy
that happens year after year in the United States,
this is the only solution.
>> NARRATOR: It's an ambitious goal,
but only possible because of the recent breakthroughs
in deep learning.
>> Artificial intelligence is one of those key pieces
that has made it possible now to do driverless vehicles
where it wasn't possible ten years ago,
particularly in the ability to see and understand scenes.
A lot of people don't know this, but it's remarkably hard
for computers, until very, very recently,
to do even the most basic visual tasks,
like seeing a picture of a person
and knowing that it's a person.
And we've made gigantic strides with artificial intelligence
in being able to see and understanding tasks,
and that's obviously fundamental to being able to understand
the world around you with the sensors that,
that you have available.
>> NARRATOR: That's now possible
because of the algorithms written by Yoshua Bengio
and a small group of scientists.
>> There are many aspects of the world
which we can't explain with words.
And that part of our knowledge is actually
probably the majority of it.
So, like, the stuff we can communicate verbally
is the tip of the iceberg.
And so to get at the bottom of the iceberg, the solution was,
the computers have to acquire that knowledge by themselves
from data, from examples.
Just like children learn, most not from their teachers,
but from interacting with the world,
and playing around, and, and trying things
and seeing what works and what doesn't work.
>> NARRATOR: This is an early demonstration.
In 2013, deep-mind scientists set a machine-learning program
on the Atari video game Breakout.
The computer was only told the goal-- to win the game.
After 100 games, it learned to use the bat at the bottom
to hit the ball and break the bricks at the top.
After 300, it could do that better than a human player.
After 500 games, it came up with a creative way to win the game--
by digging a tunnel on the side
and sending the ball around the top
to break many bricks with one hit.
That was deep learning.
>> That's the A.I. program based on learning,
really, that has been so successful
in the last few years and has...
It wasn't clear ten years ago that it would work,
but it has completely changed the map
and is now used in almost every sector of society.
>> Even the best and brightest among us,
we just don't have enough compute power
inside of our heads.
>> NARRATOR: Amy Webb is a professor at N.Y.U.
and founder of the Future Today Institute.
>> As A.I. progresses, the great promise is that they...
they, these, these machines, alongside of us,
are able to think and imagine and see things
in ways that we never have before,
which means that maybe we have some kind of new,
weird, seemingly implausible solution to climate change.
Maybe we have some radically different approach
to dealing with incurable cancers.
The real practical and wonderful promise is that machines help us
be more creative, and, using that creativity,
we get to terrific solutions.
>> NARRATOR: Solutions that could come unexpectedly
to urgent problems.
>> It's going to change the face of breast cancer.
Right now, 40,000 women in the U.S. alone
die from breast cancer every single year.
>> NARRATOR: Dr. Connie Lehman is head
of the breast imaging center
at Massachusetts General Hospital in Boston.
>> We've become so complacent about it,
we almost don't think it can really be changed.
We, we somehow think we should put all of our energy
into chemotherapies to save women
with metastatic breast cancer,
and yet, you know, when we find it early, we cure it,
and we cure it without having the ravages to the body
when we diagnose it late.
This shows the progression of a small, small spot from one year
to the next, and then to the diagnosis
of the small cancer here.
>> NARRATOR: This is what happened when a woman
who had been diagnosed with breast cancer
started to ask questions
about why it couldn't have been diagnosed earlier.
>> It really brings a lot of anxiety,
and you're asking the questions, you know,
"Am I going to survive?
What's going to happen to my son?"
And I start asking other questions.
>> NARRATOR: She was used to asking questions.
At M.I.T.'s artificial-intelligence lab,
Professor Regina Barzilay uses deep learning
to teach the computer to understand language,
as well as read text and data.
>> I was really surprised that the very basic question
that I ask my physicians,
which were really excellent physicians here at MGH,
they couldn't give me answers that I was looking for.
>> NARRATOR: She was convinced that if you analyze enough data,
from mammograms to diagnostic notes,
the computer could predict early-stage conditions.
>> If we fast-forward from 2012 to '13 to 2014,
we then see when Regina was diagnosed,
because of this spot on her mammogram.
Is it possible, with more elegant computer applications,
that we might have identified this spot the year before,
or even back here?
>> So, those are standard prediction problems
in machine learning-- there is nothing special about them.
And to my big surprise, none of the technologies
that we are developing at M.I.T.,
even in the most simple form, doesn't penetrate the hospital.
>> NARRATOR: Regina and Connie began the slow process
of getting access to thousands of mammograms and records
from MGH's breast-imaging program.
>> So, our first foray was just to take all of the patients
we had at MGH during a period of time,
who had had breast surgery for a certain type
of high-risk lesion.
And we found that most of them didn't really need the surgery.
They didn't have cancer.
But about ten percent did have cancer.
With Regina's techniques in deep learning
and machine learning, we were able to predict the women
that truly needed the surgery and separate out
those that really could avoid the unnecessary surgery.
>> What machine can do, it can take hundreds of thousands
of images where the outcome is known
and learn, based on how, you know, pixels are distributed,
what are the very unique patterns that correlate highly
with future occurrence of the disease.
So, instead of using human capacity
to kind of recognize pattern, formalize pattern--
which is inherently limited by our cognitive capacity
and how much we can see and remember--
we're providing machine with a lot of data
and make it learn this prediction.
>> So, we are using technology not only to be better
at assessing the breast density,
but to get more to the point of what we're trying to predict.
"Does this woman have a cancer now,
and will she develop a cancer in five years? "
And that's, again, where the artificial intelligence,
machine and deep learning can really help us
and our patients.
>> NARRATOR: In the age of A.I.,
the algorithms are transporting us into a universe
of vast potential and transforming almost every aspect
of human endeavor and experience.
Andrew McAfee is a research scientist at M.I.T.
who co-authored "The Second Machine Age."
>> The great compliment that a songwriter gives another one is,
"Gosh, I wish I had written that one."
The great compliment a geek gives another one is,
"Wow, I wish I had drawn that graph."
So, I wish I had drawn this graph.
>> NARRATOR: The graph uses a formula
to show human development and growth since 2000 BCE.
>> The state of human civilization
is not very advanced, and it's not getting better
very quickly at all, and this is true for thousands
and thousands of years.
When we, when we formed empires and empires got overturned,
when we tried democracy, when we invented zero
and mathematics and fundamental discoveries about the universe,
big deal.
It just, the numbers don't change very much.
What's weird is that the numbers change essentially in the blink
of an eye at one point in time.
And it goes from really horizontal, unchanging,
uninteresting, to, holy Toledo, crazy vertical.
And then the question is, what on Earth happened
to cause that change?
And the answer is the Industrial Revolution.
There were other things that happened,
but really what fundamentally happened is
we overcame the limitations of our muscle power.
Something equally interesting is happening right now.
We are overcoming the limitations of our minds.
We're not getting rid of them,
we're not making them unnecessary,
but, holy cow, can we leverage them and amplify them now.
You have to be a huge pessimist
not to find that profoundly good news.
>> I really do think the world has entered a new era.
Artificial intelligence holds so much promise,
but it's going to reshape every aspect of the economy,
so many aspects of our lives.
Because A.I. is a little bit like electricity.
Everybody's going to use it.
Every company is going to be incorporating A.I.,
integrating it into what they do,
governments are going to be using it,
nonprofit organizations are going to be using it.
It's going to create all kinds of benefits
in ways large and small, and challenges for us, as well.
>> NARRATOR: The challenges, the benefits--
the autonomous truck represents both
as it maneuvers into the marketplace.
The engineers are confident that, in spite of questions
about when this will happen,
they can get it working safely sooner
than most people realize.
>> I think that you will see the first vehicles operating
with no one inside them moving freight in the next few years,
and then you're going to see that expanding to more freight,
more geographies, more weather over time as,
as that capability builds up.
We're talking, like, less than half a decade.
>> NARRATOR: He already has a Fortune 500 company
as a client, shipping appliances across the Southwest.
He says the sales pitch is straightforward.
>> They spend hundreds of millions of dollars a year
shipping parts around the country.
We can bring that cost in half.
And they're really excited to be able to start working with us,
both because of the potential,
the potential savings from deploying self-driving,
and also because of all the operational efficiencies
that they see, the biggest one being able to operate
24 hours a day.
So, right now, human drivers are limited to 11 hours
by federal law, and a driverless truck
obviously wouldn't have that limitation.
♪ ♪
>> NARRATOR: The idea of a driverless truck comes up often
in discussions about artificial intelligence.
Steve Viscelli is a sociologist who drove a truck
while researching his book "The Big Rig" about the industry.
>> This is one of the most remarkable stories
in, in U.S. labor history, I think,
is, you know, the decline of, of unionized trucking.
The industry was deregulated in 1980,
and at that time, you know, truck drivers were earning
the equivalent of over $100,000 in today's dollars.
And today the typical truck driver will earn
a little over $40,000 a year.
And I think it's an important part
of the automation story, right?
Why are they so afraid of automation?
Because we've had four decades of rising inequality in wages.
And if anybody is going to take it on the chin
from automation in the trucking industry,
the, the first in line is going to be the driver,
without a doubt.
>> NARRATOR: For his research, Viscelli tracked down truckers
and their families, like Shawn and Hope Cumbee
of Beaverton, Michigan. >> Hi.
>> Hey, Hope, I'm Steve Viscelli.
>> Hi, Steve, nice to meet you. Come on in.
>> Great to meet you, too, thanks.
>> NARRATOR: And their son Charlie.
>> This is Daddy, me, Daddy, and Mommy.
>> NARRATOR: But Daddy's not here.
Shawn Cumbee's truck has broken down in Tennessee.
Hope, who drove a truck herself, knows the business well.
>> We made $150,000, right, in a year.
That sounds great, right?
That's, like, good money.
We paid $100,000 in fuel, okay?
So, right there, now I made $50,000.
But I didn't really, because, you know,
you get an oil change every month,
so that's $300 a month.
You still have to do all the maintenance.
We had a motor blow out, right?
$13,000. Right?
I know, I mean, I choke up a little just thinking about it,
because it was...
And it was 13,000, and we were off work for two weeks.
So, by the end of the year, with that $150,000,
by the end of the year, we'd made about 20...
About $22,000.
>> NARRATOR: In a truck stop in Tennessee,
Shawn has been sidelined waiting for a new part.
The garage owner is letting him stay in the truck to save money.
>> Hi, baby.
>> (on phone): Hey, how's it going?
>> It's going. Chunky-butt!
>> Hi, Daddy! >> Hi, Chunky-butt.
What're you doing? >> (talking inaudibly)
>> Believe it or not, I do it because I love it.
I mean, you know, it's in the blood.
Third-generation driver.
And my granddaddy told me a long time ago,
when I was probably 11, 12 years old, probably,
he said, "The world meets nobody halfway.
Nobody."
He said, "If you want it, you have to earn it."
And that's what I do every day.
I live by that creed.
And I've lived by that since it was told to me.
>> So, if you're down for a week in a truck,
you still have to pay your bills.
I have enough money in my checking account at all times
to pay a month's worth of bills.
That does not include my food.
That doesn't include field trips for my son's school.
My son and I just went to our yearly doctor appointment.
I took, I took money out of my son's piggy bank to pay for it,
because it's not... it's not scheduled in.
It's, it's not something that you can, you know, afford.
I mean, like, when...
(sighs): Sorry.
>> It's okay.
♪ ♪
Have you guys ever talked about self-driving trucks?
Is he...
>> (laughing): So, kind of.
Um, I asked him once, you know.
And he laughed so hard.
He said, "No way will they ever have a truck
that can drive itself."
>> It's kind of interesting when you think about it, you know,
they're putting all this new technology into things,
but, you know, it's still man-made.
And man, you know, does make mistakes.
I really don't see it being a problem with the industry,
'cause, one, you still got to have a driver in it,
because I don't see it doing city.
I don't see it doing, you know, main things.
I don't see it backing into a dock.
I don't see the automation part, you know, doing...
maybe the box-trailer side, you know, I can see that,
but not stuff like I do.
So, I ain't really worried about the automation of trucks.
>> How near of a future is it?
>> Yeah, self-driving, um...
So, some, you know, some companies are already operating.
Embark, for instance, is one that has been doing
driverless trucks on the interstate.
And what's called exit-to-exit self-driving.
And they're currently running real freight.
>> Really? >> Yeah, on I-10.
♪ ♪
>> (on P.A.): Shower guest 100, your shower is now ready.
>> NARRATOR: Over time, it has become harder and harder
for veteran independent drivers like the Cumbees
to make a living.
They've been replaced by younger,
less experienced drivers.
>> So, the, the trucking industry's $740 billion a year,
and, again, in, in many of these operations,
labor's a third of that cost.
By my estimate, I, you know, I think we're in the range
of 300,000 or so jobs in the foreseeable future
that could be automated to some significant extent.
♪ ♪
>> (groans)
♪ ♪
>> NARRATOR: The A.I. future was built with great optimism
out here in the West.
In 2018, many of the people who invented it
gathered in San Francisco to celebrate the 25th anniversary
of the industry magazine.
>> Howdy, welcome to WIRED25.
>> NARRATOR: It is a celebration, for sure,
but there's also a growing sense of caution
and even skepticism.
>> We're having a really good weekend here.
>> NARRATOR: Nick Thompson is editor-in-chief of "Wired."
>> When it started, it was very much a magazine
about what's coming and why you should be excited about it.
Optimism was the defining feature of "Wired"
for many, many years.
Or, as our slogan used to be, "Change Is Good."
And over time, it shifted a little bit.
And now it's more, "We love technology,
but let's look at some of the big issues,
and let's look at some of them critically,
and let's look at the way algorithms are changing
the way we behave, for good and for ill."
So, the whole nature of "Wired" has gone from a champion
of technological change to more of a observer
of technological change.
>> So, um, before we start...
>> NARRATOR: There are 25 speakers,
all named as icons of the last 25 years
of technological progress.
>> So, why is Apple so secretive?
>> (chuckling)
>> NARRATOR: Jony Ive, who designed Apple's iPhone.
>> It would be bizarre not to be.
>> There's this question of, like,
what are we doing here in this life, in this reality?
>> NARRATOR: Jaron Lanier, who pioneered virtual reality.
And Jeff Bezos, the founder of Amazon.
>> Amazon was a garage startup.
Now it's a very large company.
Two kids in a dorm...
>> NARRATOR: His message is,
"All will be well in the new world."
>> I guess, first of all, I remain incredibly optimistic
about technology,
and technologies always are two-sided.
But that's not new.
That's always been the case.
And, and we will figure it out.
The last thing we would ever want to do is stop the progress
of new technologies, even when they are dual-use.
>> NARRATOR: But, says Thompson, beneath the surface,
there's a worry most of them don't like to talk about.
>> There are some people in Silicon Valley who believe that,
"You just have to trust the technology.
Throughout history, there's been a complicated relationship
between humans and machines,
we've always worried about machines,
and it's always been fine.
And we don't know how A.I. will change the labor force,
but it will be okay."
So, that argument exists.
There's another argument,
which is what I think most of them believe deep down,
which is, "This is different.
We're going to have labor-force disruption
like we've never seen before.
And if that happens, will they blame us?"
>> NARRATOR: There is, however, one of the WIRED25 icons
willing to take on the issue.
Onstage, Kai-Fu Lee dispenses with one common fear.
>> Well, I think there are so many myths out there.
I think one, one myth is that
because A.I. is so good at a single task,
that one day we'll wake up, and we'll all be enslaved
or forced to plug our brains to the A.I.
But it is nowhere close to displacing humans.
>> NARRATOR: But in interviews around the event and beyond,
he takes a decidedly contrarian position on A.I. and job loss.
>> The A.I. giants want to paint the rosier picture
because they're happily making money.
So, I think they prefer not to talk about the negative side.
I believe about 50% of jobs will be
somewhat or extremely threatened by A.I.
in the next 15 years or so.
>> NARRATOR: Kai-Fu Lee also makes a great deal
of money from A.I.
What separates him from most of his colleagues
is that he's frank about its downside.
>> Yes, yes, we, we've made about 40 investments in A.I.
I think, based on these 40 investments,
most of them are not impacting human jobs.
They're creating value, making high margins,
inventing a new model.
But I could list seven or eight
that would lead to a very clear displacement of human jobs.
>> NARRATOR: He says that A.I. is coming,
whether we like it or not.
And he wants to warn society
about what he sees as inevitable.
>> You have a view which I think is different than many others,
which is that A.I. is not going to take blue-collar jobs
so quickly, but is actually going to take white-collar jobs.
>> Yeah. Well, both will happen.
A.I. will be, at the same time, a replacement for blue-collar,
white-collar jobs, and be a great symbiotic tool
for doctors, lawyers, and you, for example.
But the white-collar jobs are easier to take,
because they're a pure quantitative analytical process.
Let's say reporters, traders, telemarketing,
telesales, customer service...
>> Analysts?
>> Analysts, yes, these can all be replaced just by a software.
To do blue-collar, some of the work requires, you know,
hand-eye coordination, things that machines are not yet
good enough to do.
>> Today, there are many people who are ringing the alarm,
"Oh, my God, what are we going to do?
Half the jobs are going away."
I believe that's true, but here's the missing fact.
I've done the research on this, and if you go back 20, 30,
or 40 years ago, you will find that 50% of the jobs
that people performed back then are gone today.
You know, where are all the telephone operators,
bowling-pin setters, elevator operators?
You used to have seas of secretaries in corporations
that have now been eliminated-- travel agents.
You can just go through field after field after field.
That same pattern has recurred many times throughout history,
with each new wave of automation.
>> But I would argue that history is only trustable
if it is multiple repetitions of similar events,
not once-in-a-blue-moon occurrence.
So, over the history of many tech inventions,
most are small things.
Only maybe three are at the magnitude of A.I. revolution--
the steam, steam engine, electricity,
and the computer revolution.
I'd say everything else is too small.
And the reason I think it might be something brand-new
is that A.I. is fundamentally replacing our cognitive process
in doing a job in its significant entirety,
and it can do it dramatically better.
>> NARRATOR: This argument about job loss
in the age of A.I. was ignited six years ago
amid the gargoyles and spires of Oxford University.
Two researchers had been poring through U.S. labor statistics,
identifying jobs that could be vulnerable to A.I. automation.
>> Well, vulnerable to automation,
in the context that we discussed five years ago now,
essentially meant that those jobs are potentially automatable
over an unspecified number of years.
And the figure we came up with was 47%.
>> NARRATOR: 47%.
That number quickly traveled the world in headlines
and news bulletins.
But authors Carl Frey and Michael Osborne
offered a caution.
They can't predict how many jobs will be lost, or how quickly.
But Frey believes that there are lessons in history.
>> And what worries me the most is that there is actually
one episode that looks quite familiar to today,
which is the British Industrial Revolution,
where wages didn't grow for nine decades,
and a lot of people actually saw living standards decline
as technology progressed.
♪ ♪
>> NARRATOR: Saginaw, Michigan, knows about decline
in living standards.
Harry Cripps, an auto worker and a local union president,
has witnessed what 40 years of automation can do to a town.
>> You know, we're one of the cities in the country that,
I think we were left behind in this recovery.
And I just... I don't know how we get on the bandwagon now.
>> NARRATOR: Once, this was the U.A.W. hall
for one local union.
Now, with falling membership, it's shared by five locals.
>> Rudy didn't get his shift.
>> NARRATOR: This day, it's the center
for a Christmas food drive.
Even in a growth economy,
unemployment here is near six percent.
Poverty in Saginaw is over 30%.
>> Our factory has about 1.9 million square feet.
Back in the '70s, that 1.9 million square feet
had about 7,500 U.A.W. automotive workers
making middle-class wage with decent benefits
and able to send their kids to college and do all the things
that the middle-class family should be able to do.
Our factory today, with automation,
would probably be about 700 United Auto Workers.
That's a dramatic change.
Lot of union brothers used to work there, buddy.
>> The TRW plant, that was unfortunate.
>> Delphi... looks like they're starting to tear it down now.
Wow.
Automations is, is definitely taking away a lot of jobs.
Robots, I don't know how they buy cars,
I don't know how they buy sandwiches,
I don't know how they go to the grocery store.
They definitely don't pay taxes, which serves the infrastructure.
So, you don't have the sheriffs and the police and the firemen,
and anybody else that supports the city is gone,
'cause there's no tax base.
Robots don't pay taxes.
>> NARRATOR: The average personal income in Saginaw
is $16,000 a year.
>> A lot of the families that I work with here in the community,
both parents are working.
They're working two jobs.
Mainly, it's the wages, you know,
people not making a decent wage to be able to support a family.
Like, back in the day, my dad even worked at the plant.
My mom stayed home, raised the children.
And that give us the opportunity to put food on the table,
and things of that nature.
And, and them times are gone.
>> If you look at this graph of what's been happening
to America since the end of World War II,
you see a line for our productivity,
and our productivity gets better over time.
It used to be the case that our pay, our income,
would increase in lockstep with those productivity increases.
The weird part about this graph is how the income has decoupled,
is not going up the same way that productivity is anymore.
>> NARRATOR: As automation has taken over,
workers are either laid off or left with less-skilled jobs
for less pay, while productivity goes up.
>> There are still plenty of factories in America.
We are a manufacturing powerhouse,
but if you go walk around an American factory,
you do not see long lines of people
doing repetitive manual labor.
You see a whole lot of automation.
If you go upstairs in that factory
and look at the payroll department,
you see one or two people looking into a screen all day.
So, the activity is still there,
but the number of jobs is very, very low,
because of automation and tech progress.
Now, dealing with that challenge,
and figuring out what the next generation
of the American middle class should be doing,
is a really important challenge,
because I am pretty confident that we are never again
going to have this large, stable, prosperous
middle class doing routine work.
♪ ♪
>> NARRATOR: Evidence of how A.I. is likely to bring
accelerated change to the U.S. workforce can be found
not far from Saginaw.
This is the U.S. headquarters
for one of the world's largest builders of industrial robots,
a Japanese-owned company called Fanuc Robotics.
>> We've been producing robots for well over 35 years.
And you can imagine, over the years,
they've changed quite a bit.
We're utilizing the artificial intelligence
to really make the robots easier to use
and be able to handle a broader spectrum of opportunities.
We see a huge growth potential in robotics.
And we see that growth potential as being, really,
there's 90% of the market left.
>> NARRATOR: The industry says optimistically
that with that growth, they can create more jobs.
>> Even if there were five people on a job,
and we reduced that down to two people,
because we automated some level of it,
we might produce two times more parts than we did before,
because we automated it.
So now, there might be the need for two more fork-truck drivers,
or two more quality-inspection personnel.
So, although we reduce some of the people,
we grow in other areas as we produce more things.
>> When I increase productivity through automation, I lose jobs.
Jobs go away.
And I don't care what the robot manufacturers say,
you aren't replacing those ten production people
that that robot is now doing that job, with ten people.
You can increase productivity to a level to stay competitive
with the global market-- that's what they're trying to do.
♪ ♪
>> NARRATOR: In the popular telling,
blame for widespread job loss has been aimed overseas,
at what's called offshoring.
>> We want to keep our factories here,
we want to keep our manufacturing here.
We don't want them moving to China, to Mexico, to Japan,
to India, to Vietnam.
>> NARRATOR: But it turns out most of the job loss
isn't because of offshoring.
>> There's been offshoring.
And I think offshoring is responsible for maybe 20%
of the jobs that have been lost.
I would say most of the jobs that have been lost,
despite what most Americans thinks, was due to automation
or productivity growth.
>> NARRATOR: Mike Hicks is an economist
at Ball State University in Muncie, Indiana.
He and sociologist Emily Wornell have been documenting
employment trends in Middle America.
Hicks says that automation has been a mostly silent job killer,
lowering the standard of living.
>> So, in the last 15 years, the standard of living has dropped
by 15, ten to 15 percent.
So, that's unusual in a developed world.
A one-year decline is a recession.
A 15-year decline gives an entirely different sense
about the prospects of a community.
And so that is common from the Canadian border
to the Gulf of Mexico
in the middle swath of the United States.
>> This is something we're gonna do for you guys.
These were left over from our suggestion drive that we did,
and we're going to give them each two.
>> That is awesome. >> I mean,
that is going to go a long ways, right?
I mean, that'll really help that family out during the holidays.
>> Yes, well, with the kids home from school,
the families have three meals a day that they got
to put on the table.
So, it's going to make a big difference.
So, thank you, guys. >> You're welcome.
>> This is wonderful. >> Let them know Merry Christmas
on behalf of us here at the local, okay?
>> Absolutely, you guys are just, just amazing, thank you.
And please, tell, tell all the workers how grateful
these families will be. >> We will.
>> I mean, this is not a small problem.
The need is so great.
And I can tell you that it's all races,
it's all income classes
that you might think someone might be from.
But I can tell you that when you see it,
and you deliver this type of gift to somebody
who is in need, just the gratitude that they show you
is incredible.
>> We actually know that people are at greater risk of mortality
for over 20 years after they lose their job due to,
due to no fault of their own, so something like automation
or offshoring.
They're at higher risk for cardiovascular disease,
they're at higher risk for depression and suicide.
But then with the intergenerational impacts,
we also see their children are more likely--
children of parents who have lost their job
due to automation-- are more likely to repeat a grade,
they're more likely to drop out of school,
they're more likely to be suspended from school,
and they have lower educational attainment
over their entire lifetimes.
>> It's the future of this, not the past, that scares me.
Because I think we're in the early decades
of what is a multi-decade adjustment period.
♪ ♪
>> NARRATOR: The world is being re-imagined.
This is a supermarket.
Robots, guided by A.I., pack everything from soap powder
to cantaloupes for online consumers.
Machines that pick groceries,
machines that can also read reports, learn routines,
and comprehend are reaching deep into factories,
stores, and offices.
At a college in Goshen, Indiana,
a group of local business and political leaders come together
to try to understand the impact of A.I. and the new machines.
Molly Kinder studies the future of work
at a Washington think tank.
>> How many people have gone into a fast-food restaurant
and done a self-ordering?
Anyone, yes?
Panera, for instance, is doing this.
Cashier was my first job, and in, in, where I live,
in Washington, DC, it's actually the number-one occupation
for the greater DC region.
There are millions of people who work in cashier positions.
This is not a futuristic challenge,