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This was the public unveiling of a form of artificial intelligence called 'deep learning' that mimics the neural networks of the human brain.

NICHOLAS THOMPSON, Editor-in-chief, Wired: So what happens with machine learning, artificial intelligence, initially with AlphaGo, is that the machine is fed all kinds of Go games and then it studies them;

KAI-FU LEE: 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.'

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.

KAI-FU LEE: While AlphaGo is a toy game, but its success and its waking everyone up I think is going to be remembered as the pivotal moment where AI became mature and everybody jumped on the bandwagon.

XI JINPING: [Speaking Chinese] —and intensified cooperation in frontier areas such as digital economy, artificial intelligence, nanotechnology and quantum computing.

And in a short 2 1/2 years, China's AI implementation really went from a minimal amount to probably about 17 or 18 unicorns—that is, billion-dollar companies—in AI today.

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.

And a unicorn means a billion-dollar company—a company whose valuation or market capitalization is at $1 billion or higher.

thesis at Carnegie Mellon was on computer speech recognition, which took him to Apple— JOAN LUNDEN: Well, reality is a step closer to science fiction with Apple Computer's new developed program that allows— NARRATOR: —and at 31, an early measure of fame.

KAI-FU LEE: 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.

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.

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 10 times more food deliveries, which serve as data to learn more about user behavior, than the U.S. Three hundred times more shared bicycle rides, and each shared bicycle ride has all kinds of sensors submitting data up to the cloud.

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.

The customer can choose how many money they want to borrow and how long they want to borrow and they can input their datas here.

JIAO KE: Five thousand features that is related with the delinquency, when maybe the banks only use few—maybe 10 features when they are doing their risk management.

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.

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.

Critics warn that the government and some private companies have been building a national database from dozens of experimental “social credit” programs.

XIAO QIANG, Research scientist, UC Berkeley: The government wants to integrate all these individual behaviors, or corporations' records, into some kind of matrix and compute out a single number or set of number associated with a individual citizen, and using that to implement a incentive or punishment system.

But now, in the age of AI, people come here to take in a spectacle that reflects China’s remarkable progress and illuminates the great political paradox of capitalism taken root in the communist state.

We are watching a kind of a petri dish in which an experiment of 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;

NARRATOR: 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.

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.

ALEX RODRIGUES: If we can build a truck that’s 10 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 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.

ALEX RODRIGUES: Artificial intelligence is one of those key pieces that has made it possible now to do driverless vehicles where it wasn't possible 10 years ago, particularly in the ability to see and understand scenes.

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 do scene-understanding tasks, and that's obviously fundamental to being able to understand the world around you with the sensors that you have available.

After 100 games NARRATOR: After 100 games, it learned to use the bat at the bottom to hit the ball and break the bricks at the top.

After 500 games NARRATOR: 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.

YOSHUA BENGIO: That's the AI program based on learning, really, that has been so successful in the last few years, and has—it wasn't clear 10 years ago that it would work, but it has completely changed the map and is now used in almost every sector of society.

AMY WEBB: As AI progresses, the great promise is that they—these machines alongside of us are able to think and imagine and see things in ways that we never have before.

We somehow think we should put all of our energy into chemotherapies to save women with metastatic breast cancer, and yet, when we find it early, we cure it, and we cure it without having the ravages to the body when we diagnose it late.

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.

NARRATOR: Regina and Connie began the slow process of getting access to thousands of mammograms and records from MGH’s breast imaging program.

CONSTANCE LEHMAN: 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.

With Regina's techniques and 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.

REGINA BARZILAY: What machine can do, it can take hundreds of thousands of images where the outcome is known and learn, based on how 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 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.

CONSTANCE LEHMAN: 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?

NARRATOR: In the age of AI, the algorithms are transporting us into a universe of vast potential and transforming almost every aspect of human endeavor and experience.

ANDREW McAFEE: 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.

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.

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 gonna see that expanding to more freight, more geographies, more weather over time as that capability builds up.

And they're really excited to be able to start working with us, both because of 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.

The industry was deregulated in 1980, and at that time truck drivers were earning the equivalent of over $100,000 in today's dollars.

Because we've had four decades of rising inequality in wages, and if anybody is going to take it on the chin from automation, the trucking industry—the first in line is going to be the driver, without a doubt.

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.

The last thing we would ever want to do is stop the progress of new technologies, even when there are dual-use— NARRATOR: But, says Thompson, beneath the surface there’s a worry most of them don’t like to talk about.

I think one myth is that because AI 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 AI.

KAI-FU LEE: The AI giants want to paint a rosier picture because they're happily making money, so I think they prefer not to talk about the negative side.

MALE CNBC NEWSREADER: You have a view that is different than many others, which is that AI is not going to take blue-collar jobs so quickly but is actually going to take white-collar jobs.

AI 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.

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.

And the reason I think it might be something brand new is that AI 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 AI was ignited six years ago amid the gargoyles and spires of Oxford University.

CARL FREY: 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%.

That number quickly travelled 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.

CARL FREY: 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.

HARRY CRIPPS, President, UAW Local 668: 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.

Back in the '70s, that 1.9 million square feet had about 7,500 UAW 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 families should be able to do.

They definitely don't pay taxes, which hurts 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.

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.

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'm pretty confident that we are never again going to have this large, stable, prosperous middle class doing routine work.

We're utilizing the artificial intelligence to really make the robots easier to use and be able to handle a broader spectrum of opportunities.

MIKE CICCO: Even if there were five people on a job and we reduce 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.

And I don't care what the robot manufacturers say, you aren't replacing those 10 production people that that robot is now doing that job with 10 people.

NARRATOR: In the popular telling, blame for widespread job loss has been aimed overseas, at what’s called 'offshoring.'

[Crying] EMILY WORNELL, Ball State University: We actually know that people are at greater risk of mortality for over 20 years after they lose their job due to no fault of their own, so something like automation or offshoring.

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;

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 AI and the new machines.

In the popular discussions about robots and automation and work, almost every image is of a man on a factory floor or a truck driver.

And yet, in our data, when we looked, women disproportionately hold the jobs that today are at highest risk of automation, and that's not really being talked about.

And that's in part because women are overrepresented in some of these marginalized occupations like a cashier or a fast-food worker, and also in large numbers in clerical jobs in offices, HR departments, payroll, finance;

And whatever it is, every company's using everything that's developed, everything that's disruptive, and thinking about 'How do I apply that to my business to make myself more efficient?'

And I do think that when we look at some of the studies about opportunity in this country and the inequality of opportunity, the likelihood that you won't be able to advance from where your parents were, I think that is very serious and gets to the heart of the way we like to think of America as the land of opportunity.

JERRY KAPLAN: There's many factors that are driving inequality today, and unfortunately, artificial intelligence, without being thoughtful about it, is a driver for increased inequality because it's a form of automation, and automation is the substitution of capital for labor.

So Karl Marx was right: It's a struggle between capital and labor, and with artificial intelligence we're putting our finger on the scale on the side of capital.

And how we wish to distribute the benefits, the economic benefits that that will create is going to be a major moral consideration for society over the next several decades.

It may not be specifically related to AI, but the AI will exacerbate that, and that, I think, will tear the society apart because the rich will have just too much, and those who are have-nots will have perhaps very little way of digging themselves out of the hole.

I mean, for whatever reason, whatever the hot button was that really hit home with these Americans that voted for him were—it was a protest vote.

Think about the massive data that Facebook has on user preferences and how it can very smartly target an ad that you might buy something and get a much bigger cut that a smaller company couldn't do.

So, it's—AI is a set of tools that helps you maximize an objective function, and that objective function initially will simply be 'make more money.'

NARRATOR: And it is how these companies make that money, and how their algorithms reach deeper and deeper into our work, our daily lives and our democracy, that makes many people increasingly uncomfortable.

There's also a danger, because the entities in the companies that are in control of those algorithms don't necessarily have the same goals as you, and this is where I think people need to be aware that—of what's going on, so that they can have more control over it.

For the last seven years she has worked on a new book, making the case that we have now entered a new phase of the economy, which she calls “surveillance capitalism.” SHOSHANA ZUBOFF: So, famously, industrial capitalism claimed nature—innocent rivers and meadows and forests, and so forth—for the market dynamic to be reborn as real estate—as land that could be sold and purchased.

Private, human experience is claimed as a free source of raw material, fabricated into predictions of human behavior.

And I—there had been something in the press that day about privacy, in the paper, and I remember asking him—he worked for Google—'What's the big deal about all—why are people so worked up about it?'

NARRATOR: While Google had rapidly become the default search engine for tens of millions of users, their investors were pressuring them to make more money.

And so parallel to this were another set of discoveries where it turns out that whenever we search or whenever we browse, we're leaving behind traces, digital traces of our behavior.

SHOSHANA ZUBOFF: What happened was they decided to turn to those data logs in a systematic way and to begin to use these surplus data as a way to come up with fine-grained predictions of what a user would click on—what kind of ad a user would click on.

that essentially, because technology always made things better in the '50s, '60s, '70s, '80s and '90s, we developed a sense of inevitability that we'll always make things better.

NARRATOR: In 2010, Facebook experimented with AI’s predictive powers in what they called a “social contagion experiment.” They wanted to see if, through online messaging, they could influence real-world behavior.

They would conduct other “massive contagion” experiments—among them, one showing that by adjusting their feeds, they could make users happy or sad.

ROGER McNAMEE: Private corporations have built a corporate surveillance state without our awareness or permission, and the systems necessary to make it work are getting a lot better, specifically with what are known as 'internet of things'—smart appliances, powered by the Alexa voice recognition system or the Google Home system.

AMY WEBB, Founder, Future Today Institute: The more and more that you use spoken interfaces—so, smart speakers—they're being trained not just to recognize who you are, but they're starting to take baselines and comparing changes over time.

So that is an extraordinary amount of information that can be gleaned by you simply waking up and asking your smart speaker, 'What's the weather today?'

SHOSHANA ZUBOFF: The point is that this is the same microbehavioral targeting that is directed toward individuals based on intimate, detailed understanding of personalities.

NARRATOR: The Cambridge Analytica scandal of 2018 engulfed Facebook, forcing Mark Zuckerberg to appear before Congress to explain how the data of up to 87 million Facebook users had been harvested by a political consulting company based in the U.K.

SHOSHANA ZUBOFF: And now we know that any billionaire with enough money, who can buy the data, buy the machine intelligence capabilities, buy the skilled data scientists, they, too, can commandeer the public and infect and infiltrate and upend our democracy with the same methodologies that surveillance capitalism uses every single day.

He has said Facebook will now make data protection a priority, and the company has suspended tens of thousands of third-party apps from its platform as a result of an internal investigation.

What we're looking at now, with current tools and machine learning, is the ability for manipulation, both in terms of elections and opinions, but more broadly, just how information travels.

It's a little bit like the physicists around the Second World War who rose up to tell the governments, 'Wait, nuclear power can be dangerous, and nuclear war can be really, really destructive.'

And today the equivalent of a physicist of the '40s and '50s and '60s are the computer scientists who are doing machine learning and AI.

If less than 100,000 votes separated the last two candidates in the last presidential election in three states, this is not— NARRATOR: He began a solitary campaign.

You’re talking about convincing a relatively tiny fraction of the voters in a handful of states to either come out and vote or stay home.

NARRATOR: Mactaggart started a signature drive for a California ballot initiative for a law to give consumers control of their digital data.

Pharmaceuticals, even food products, all of these industries are better because the public has confidence in the products, in part because of a mixture of responsible companies and responsible regulation.

But while hearings are held and antitrust legislation threatened, the problem is that AI has already spread so far into our lives and work.

While we can see a phone and look at it and we know that there's some AI technology behind it, many of us don't know that when we go for a job interview and we sit down and we have a conversation, that we're being filmed, and that our microexpressions are being analyzed by hiring companies.

Or that if you're in the criminal justice system, that there are risk-assessment algorithms that are deciding your 'risk number,' which could determine whether or not you receive bail or not.

In an authoritarian state, social stability is the watchword of the government, and artificial intelligence has increased its ability to scan the country for signs of unrest.

matching with the most advanced artificial intelligence algorithm which they can actually use this data, real-time data, to pick up a face or pick up an action.

PAUL MOZUR: The place is just filled with these screens where you can see the computers are actually reading people's faces and trying to digest that data, and basically track and identify who each person is.

So a big part of it is government spending, and so the technology's really taken off, and a lot of companies have started to sort of glom onto this idea that this is the future.

That data feeds an AI system that the government claims can predict individuals prone to “terrorism” and detect those in need of “reeducation” in scores of recently built camps.

But even outside of the facilities in which these people are being held, most of the population there is being subjected to extraordinary levels of high-tech surveillance such that almost no aspect of life anymore takes place outside the state's line of sight.

And so the kinds of behavior that's now being monitored—you know, which language do you speak at home, whether you're talking to your relatives in other countries, how often you pray—that information is now being Hoovered up and used to decide whether people should be subjected to political reeducation in these camps.

And for Uighurs on the outside, Xinjiang has already been described as an “open-air prison.” Surveillance company video NURY TURKEL, Uyghur Human Rights Project: Trying to have a normal life as a Uighur is impossible both inside and outside of China.

Imagine police take your phone and run data scan and force you to install compulsory software allowing your phone calls and messages to be monitored.

Not only that, the Chinese government has been promoting its methods, its technology, it is—to other countries—namely Pakistan, Venezuela, Sudan and others—to utilize to squelch political resentment or prevent a political upheaval in their various societies.

NARRATOR: Like Xi Jinping’s 2018 visit to Senegal, where Chinese contractors had just built a new stadium, arranged loans for new infrastructure development and, said the foreign ministry, there would be help 'maintaining social stability.'

And it'll cost you $300 million and we'll build a ton of cameras and we'll build you a main center where you have police who can watch these cameras.'

she is the CFO of the Chinese telecom Huawei— NARRATOR: News of the dramatic arrest of an important Huawei executive was ostensibly about the company doing business with Iran, but it seemed to be more about American distrust of the company’s technology.

From its headquarters in southern China—designed to look like fanciful European capitals—Huawei is the second-biggest seller of smartphones and the world leader in building 5G networks, the high-speed backbone for the age of AI.

NARRATOR: The U.S. Commerce Department has recently blacklisted eight companies for doing business with government agencies in Xinjiang, claiming they are aiding in the repression of the Muslim minority.

They have strongly objected to the blacklist, saying that it’s “a misunderstanding of our company and our technology.” President Xi has increased his authoritarian grip on the country.

While companies like Baidu, Alibaba and Tencent are growing more powerful and competitive, they’re also beginning to have difficulty accessing American technology and are racing to develop their own.

With a continuing trade war and growing distrust, the longtime argument for engagement between the two countries has been losing ground.

ORVILLE SCHELL: I've seen more and more of my colleagues move from a position when they thought, 'Well, if we just keep engaging China, the lines between the two countries will slowly converge,' whether it's in economics, technology, politics.

And that's cast an entirely different light on technology, because if you're diverging, and you're heading into a world of antagonism—conflict, possibly—then suddenly technology is something that you don’t want to share;

And I think the tipping-point moment we are at now, which is what is casting the whole question of things like artificial intelligence and technological innovation into a completely different framework, is that if in fact China and the U.S. are in some way fundamentally antagonistic to each other, then we are in a completely different world.

NARRATOR: In the age of AI, a new reality is emerging: that with so much accumulated investment and intellectual power, the world is already dominated by just two AI superpowers.

NARRATOR: “Never,” he writes, “has the potential for human flourishing been higher or the stakes of failure greater.” KAI-FU LEE: So if one has to say, 'Who’s ahead?', I would say today China is quickly catching up.

China actually began its big push in AI only 2 1/2 years ago, when the AlphaGo-Lee Sedol match became the Sputnik moment.

So how do governments limit themselves in on the one hand using this AI technology and the database to maintain a safe environment for its citizens, but not encroach on individuals' rights and privacies.

do think that democracy is threatened by the progress of these tools unless we improve our social norms and we increase the collective wisdom at the planet level to deal with that increased power.

Jim Goodnight, the 'Godfather of A.I.,' predicts the future fate of the US workforce

The spark that ignited the artificial intelligence movement was a statistical data analysis system developed by Jim Goodnight when he was a statistics professor at North Carolina State University 45 years ago.

He never imagined that the technology he created to improve crop yields would evolve into sophisticated data analytics software, a precursor to modern day AI.

Goodnight — considered the Godfather of AI — now sits at the helm of the world's largest privately held software companies by revenue: SAS Institute.

Despite its low profile, last year the Cary, North Carolina-based company had revenues of $3.27 billion, thanks to analytic and AI platforms used by more than 83,000 businesses, governments and universities.

Neural networks, which mimic the way the human brain operates, and other machine learning tools are being used to do all sorts of predictions in a host of industries.

Areas where I see a surge in demand are 5G technology, connected devices, cloud services, autonomous driving, machine learning and fintech.

believe we will see things like computer vision — which involves machines capturing, processing and analyzing real-world images and video to extract information from the physical world — being used.

Over the past few decades, sensors and image processors have been created to match or even exceed the human eye's capabilities.

With larger, more optically perfect lenses and nanometer-scaled image sensors and processors, the precision and sensitivity of modern cameras are incredible, especially compared to common human eyes.

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