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But somewhere along the way, the democratization of the flow of information became the democratization of the flow of disinformation. The distinction between fact and fiction was erased, creating a sprawling universe of competing claims. The internet can’t route around censorship when the people who use it remain in their own closed information loops, which is nothing more than self-imposed censorship.

The great promise of the internet was that it would bring democracies together, giving more people more access to more information, all beyond the control of any single authority. Curious citizens could develop a more nuanced understanding of what was going on; voters would be better informed; we would ferret out the truth from the bottom up and greater freedom would be the inevitable result

In industry, there is blunt-force algorithmic tension – ‘Efficiency, capitalism, commerce!’ versus ‘Robots are stealing our jobs!’ But for algorithmic art, the tension is subtler. Only 4 per cent of the work done in the United States economy requires ‘creativity at a median human level’, according to the consulting firm McKinsey and Company. So for computer art – which tries explicitly to zoom into this small piece of that vocational pie – it’s a question not of efficiency or equity, but of trust. Art requires emotional and phrenic investments, with the promised return of a shared slice of the human experience. When we view computer art, the pestering, creepy worry is: who’s on the other end of the line? Is it human? We might, then, worry that it’s not art at all.

But the honest-to-God truth, at the end of all of this, is that this whole notion is in some way a put-on: a distinction without a difference. ‘Computer art’ doesn’t really exist in an any more provocative sense than ‘paint art’ or ‘piano art’ does. The algorithmic software was written by a human, after all, using theories thought up by a human, using a computer built by a human, using specs written by a human, using materials gathered by a human, at a company staffed by humans, using tools built by a human, and so on. Computer art is human art – a subset rather than a distinction. It’s safe to release the tension.

It's not far-fetched to suggest there is a "deep state" in Washington. Former President Dwight D. Eisenhower looked at the nexus of the Pentagon and arms manufacturers and coined the phrase the "military-industrial complex." Today's observers also point to the collusion of corporate interests and D.C. power brokers as the true guiding hand in American politics.

The Trump White House already seems to be at war with what it would say is the "deep state:" thousands of federal government bureaucrats faced with the awkward reality of working for a president who campaigned loudly against Washington officialdom and promised to "drain the swamp" when in power. This week, almost 1,000 American diplomats signed a dissent memo against Trump's executive order on immigration, prompting White House press secretary Sean Spicer to icily declare that "career bureaucrats" can "either get with the program or they can go." And Trump's public spat with Acting Attorney General Sally Yates, an Obama appointee who sought to defy his immigration order, ended with him firing Yates in an angry, chilling memo that claimed she "betrayed the Department of Justice."

Right now other people are making lots of money on our data. So much money. I think the one that stands out for me is a company called Palantir, founded by Peter Thiel in 2003. It’s actually one of Silicon Valley’s biggest success stories, and is worth more than Twitter. Most people have never heard of it because it’s all operating completely behind the scenes. This company and companies like it have databases that contain every possible thing you can ever imagine, on you, and who you are, and what you’re interested in. It’s got things like your declared sexuality as well as your true sexuality, things like whether you’ve had a miscarriage, whether you’ve had an abortion. Your feelings on guns, whether you’ve used drugs, like, all of these things are being packaged up, inferred, and sold on for huge profit.

Do we need to develop a brand-new intuition about how to interact with algorithms? It’s not on us to change that as the users. It’s on the people who are designing the algorithms to make their algorithms to fit into existing human intuition.

There can be no question that violence and racism were essential traits of fascism. But for most Italians, Germans and other European fascists, the appeal was based not on racism, much less ethnic cleansing, but on the fascists’ ability to respond effectively to crises of capitalism when other political actors were not. Fascists insisted that states could and should control capitalism, that the state should and could promote social welfare, and that national communities needed to be cultivated. The fascist solution ultimately was, of course, worse than the problem. In response to the horror of fascism, in part, New Deal Democrats in the United States, and social democratic parties in Europe, also moved to re-negotiate the social contract. They promised citizens that they would control capitalism and provide social welfare policies and undertake other measures to strengthen national solidarity – but without the loss of freedom and democracy that fascism entailed.

The lesson for the present is clear: you can’t beat something with nothing. If other political actors don’t come up with more compelling solutions to the problems of capitalism, the popular appeal of the resurgent Right-wing will continue. And then the analogy with fascism and democratic collapse of the interwar years might prove even more relevant than it is now.

Gamification is the application of game elements into nongame spaces. It is the permeation of ideas and values from the sphere of play and leisure to other social spaces. It’s premised on a seductive idea: if you layer elements of games, such as rules, feedback systems, rewards and videogame-like user interfaces over reality, it will make any activity motivating, fair and (potentially) fun. ‘We are starving and games are feeding us,’ writes Jane McGonigal in Reality Is Broken (2011). ‘What if we decided to use everything we know about game design to fix what’s wrong with reality?’

But gamification’s trapping of total fun masks that we have very little control over the games we are made to play – and hides the fact that these games are not games at all. Gamified systems are tools, not toys. They can teach complex topics, engage us with otherwise difficult problems. Or they can function as subtle systems of social control.

The problem of the gamified workplace goes beyond micromanagement. The business ethicist Tae Wan Kim at Carnegie Mellon University in Pittsburgh warns that gamified systems have the potential to complicate and subvert ethical reasoning. He cites the example of a drowning child. If you save the child, motivated by empathy, sympathy or goodwill – that’s a morally good act. But say you gamify the situation. Say you earn points for saving drowning children. ‘Your gamified act is ethically unworthy,’ he explained to me in an email. Providing extrinsic gamified motivators, even if they work as intended, deprive us of the option to live worthy lives, Kim argues. ‘The workplace is a sacred space where we develop ourselves and help others,’ he notes. ‘Gamified workers have difficulty seeing what contributions they really make.’

Automation and artificial intelligence (AI) are transforming businesses and will contribute to economic growth via contributions to productivity. They will also help address “moonshot” societal challenges in areas from health to climate change.

At the same time, these technologies will transform the nature of work and the workplace itself. Machines will be able to carry out more of the tasks done by humans, complement the work that humans do, and even perform some tasks that go beyond what humans can do. As a result, some occupations will decline, others will grow, and many more will change.

While we believe there will be enough work to go around (barring extreme scenarios), society will need to grapple with significant workforce transitions and dislocation. Workers will need to acquire new skills and adapt to the increasingly capable machines alongside them in the workplace. They may have to move from declining occupations to growing and, in some cases, new occupations.

AI researchers have typically tried to get around the issues posed by by Montezuma’s Revenge and Pitfall! by instructing reinforcement-learning algorithms to explore randomly at times, while adding rewards for exploration—what’s known as “intrinsic motivation.” But the Uber researchers believe this fails to capture an important aspect of human curiosity. “We hypothesize that a major weakness of current intrinsic motivation algorithms is detachment,” they write. “Wherein the algorithms forget about promising areas they have visited, meaning they do not return to them to see if they lead to new states.”

The team’s new family of reinforcement-learning algorithms, dubbed Go-Explore, remember where they have been before, and will return to a particular area or task later on to see if it might help provide better overall results. The researchers also found that adding a little bit of domain knowledge, by having human players highlight interesting or important areas, sped up the algorithms’ learning and progress by a remarkable amount. This is significant because there may be many real-world situations where you would want an algorithm and a person to work together to solve a hard task.

But the ability to tag that data may be China’s true A.I. strength, the only one that the United States may not be able to match. In China, this new industry offers a glimpse of a future that the government has long promised: an economy built on technology rather than manufacturing.

“We’re the construction workers in the digital world. Our job is to lay one brick after another,” said Yi Yake, co-founder of a data labeling factory in Jiaxian, a city in central Henan province. “But we play an important role in A.I. Without us, they can’t build the skyscrapers.”

While A.I. engines are superfast learners and good at tackling complex calculations, they lack cognitive abilities that even the average 5-year-old possesses. Small children know that a furry brown cocker spaniel and a black Great Dane are both dogs. They can tell a Ford pickup from a Volkswagen Beetle, and yet they know both are cars.A.I. has to be taught. It must digest vast amounts of tagged photos and videos before it realizes that a black cat and a white cat are both cats. This is where the data factories and their workers come in.

What happens once Saeed and Nadia arrive at these promised lands makes up the second half of the novel, in which it seems that “the whole planet was on the move, much of the global South headed to the global North, but also Southerners moving to other Southern places and Northerners moving to other Northern places.” Here Hamid’s novel reveals itself to be a story not only of the present but of the future, where migration will be the norm. Depending on one’s point of view, this is either terrifying or hopeful. When everyone is moving, then mobility becomes normal rather than disturbing. While these movements cause unrest on the part of the “natives” — what Hamid, in a postcolonial reverse, calls the inhabitants of the host countries — the vision that he ultimately offers is peaceful.

The vast majority of young people in places like the Middle East and North Africa face a bleak socioeconomic future. Youth unemployment in the region hovers around 30 percent, which is expected to skyrocket as economies struggle to create enough jobs to keep pace with a massive demographic youth bulge. In 2000, the World Bank estimated that the region would need to create about 100 million new jobs to keep pace, and it’s nowhere near to closing the gap.

The effect of this socioeconomic crisis is about more than just employment and livelihoods for young people; it represents a fundamental breakdown of the social contract in the region. For decades, governments in the Middle East and North Africa promised socioeconomic support — free education, subsidies and public-sector employment — in exchange for limits on political participation and civic activity.