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the explanation they usually provide for those choices is that they just want what’s best for their children. This rationale is generally considered understandable, even honorable, but can you talk about its dark side?

we have this collectively agreed-upon idea in our society that being a “good parent” means exactly that—providing the best opportunities you can for your own child.

some of these parents are also people who believe strongly in the importance of diversity and multiculturalism and who want to resist racial inequality. And these two things are sort of at odds with one another.

These affluent white parents are in a position where they can set up their kids’ lives so that they’re better than other kids’ lives.

how frequently some of these children used the phrase That’s racist or You’re racist. They were using this word in contexts that had nothing to do with race: They were playing chess, and they would talk about what color chess pieces they wanted to have, and then one of them would say, “Oh, that’s racist”

part of it is how we choose to define “the best.” Some of the parents in my book, they rejected the idea that their child needed to be in all the AP classes. They valued other elements of their children’s personalities, such as their concerns about ethics or fairness or social justice

There were also affluent parents who were very much opposed to having police officers in schools, and they were using their position of influence in the community to try to get the police officers out of there. Maybe others would be aware of their own presence at PTA meetings, making sure they’re not dominating them and making sure they’re not putting their own agenda ahead of their peers’ agendas.

When we think about parents calling up the school and demanding that their child have the best math teacher, what does that mean for the kids who don’t get the best math teacher?

when things basically got too challenging, they just picked their kid up and took him to a different school, a private school. And the ability to do that was not only a reflection of their economic privilege—they had the resources to suddenly, mid–school year, send their kid to an expensive private school—but also a reflection of racial privilege in that you can somehow escape racism when you want to as a white person

kids themselves actively contribute to the formation of racist beliefs. How does that work?

Can you talk about how, for a lot of affluent white parents, diversity is something that can be toggled on and off as they please?

These kids have taken this phrase, That’s racist, and inverted it in a way such that it’s become meaningless.

I really think—and this might sound kind of crazy—that white parents, and parents in general, need to understand that all children are worthy of their consideration. This idea that your own child is the most important thing—that’s something we could try to rethink.

My overall point is that in this moment when being a good citizen conflicts with being a good parent, I think that most white parents choose to be good parents, when, sometimes at the very least, they should choose to be good citizens

pragmatically speaking, wouldn’t that ignore a biological impulse to look after one’s own?

the way we think about what it means to be a parent is to some extent socially constructed. We have other societies that do things differently. I think when we look across time and history and geography, we can see that the way that we’re doing it—prioritizing your own child over everyone else—is one way, but I don’t think that has to be the only way

I don’t have any grand answer, but I think people could think in bigger ways about what it means to care about one another and what it means to actually have a society that cares about kids.

Tencent's app, WeChat, is currently the largest and most commonly used messaging system in the world, with almost 1 billion users.

"He's regarded as being a sort of low-profile, not particularly interlinked or interconnected premier. He's got probably the worst job in China.

it's not a sort of political power. It's administrative power," he said.

"Power is about initiating, setting frameworks, setting the agenda. Well you can see people doing that, but Li Keqiang is more of an administrator."

The company's founder, Ma Huateng, is China's third richest man, according to Forbes, just behind Jack Ma and Dalian Wanda founder Wang Jianlin.

Ma, who is also known by his nickname "Pony," founded Tencent, the company which owns WeChat, in 1998 with his university classmates.

, Premier Li is number two in China's power structure, but his influence is far from assured.

It is those restrictions which make working in the world of China's internet so complex and potentially dangerous.

China's Great Firewall is rising higher than ever

"One challenge for tech titans like Ma in the coming years is whether they can keep on the good side of the authorities

Since then, Wang has grown to be a powerful, feared figure among Chinese officials.

Jack Ma is without a doubt one of China's most powerful people and possibly the country's most public face internationally next to President Xi.

As a result of Xi and Wang's crackdown, conspicuous spending and flaunting of wealth by officials has shrunk dramatically and as a result, Wang's political capital has continued to rise.

Wang's rise could be complicated by his age. He'll be 69 at the upcoming 19th Party Congress, meaning by custom he should retire.

he's the flamboyant and personable former English teacher who likes to dance to Michael Jackson tunes.

"He's been an essential lieutenant for Xi ... the president would be a weakened force without him at the top table," he said, placing Wang second

Ma, whose Chinese name is Ma Yun, is the executive chairman and founder of Alibaba,

Ma's peer on the China rich list, has had to abandon a series of major international deals after coming under scrutiny from Beijing.

Most people can't see the day after tomorrow."

China's president and, more importantly, the general secretary of the Chinese Communist Party, analysts say Xi is already the country's most powerful leader since Paramount Leader Deng Xiaoping

Xi's power is only set to grow.

far more than his predecessors,

most recently Chen Min'er who was promoted as party secretary of Chongqing.

Guo Wengui, the US-based businessman and perennial thorn in the side of the Chinese Communist Party.

and the chess game itself goes on inside a highly impenetrable black box," he said.

Now, based on the U.S. response since February, Lipsitch believes that we’re still likely to see the virus spread to the point of becoming endemic.

That would mean it is with us indefinitely, and the current pandemic would end when we reach levels of “herd immunity,” traditionally defined as the threshold at which enough people in a group have immune protection so the virus can no longer cause huge spikes in disease.

Anthony Fauci, the head of the National Institute of Allergy and Infectious Diseases, said that, because of a “general anti-science, anti-authority, anti-vaccine feeling,” the U.S. is “unlikely” to achieve herd immunity even after a vaccine is available.

The case-fatality rate for COVID-19 is now very roughly 1 percent overall. In the absolute simplest, linear model, if 70 percent of the world were to get infected, that would mean more than 54 million deaths.

Without a better plan, this threshold—the percentage of people who have been infected that would constitute herd immunity—seems to have become central to our fates.

Some mathematicians believe that it’s much lower than initially imagined. At least, it could be, if we choose the right future.

Gomes explains, “There doesn’t need to be a lot of variation in a population for epidemics to slow down quite drastically.”

in dynamic systems, the outcomes are more like those in chess: The next play is influenced by the previous one. Differences in outcome can grow exponentially, reinforcing one another until the situation becomes, through a series of individually predictable moves, radically different from other possible scenarios. You have some chance of being able to predict the first move in a game of chess, but good luck predicting the last.

“selective depletion” of people who are more susceptible—can quickly decelerate a virus’s spread. When Gomes uses this sort of pattern to model the coronavirus’s spread, the compounding effects of heterogeneity seem to show that the onslaught of cases and deaths seen in initial spikes around the world are unlikely to happen a second time.

Based on data from several countries in Europe, she said, her results show a herd-immunity threshold much lower than that of other models.“We just keep running the models, and it keeps coming back at less than 20 percent,” Gomes said. “It’s very striking.”

If that proves correct, it would be life-altering news. It wouldn’t mean that the virus is gone. But by Gomes’s estimates, if roughly one out of every five people in a given population is immune to the virus, that seems to be enough to slow its spread to a level where each infectious person is infecting an average of less than one other person

That’s the classic definition of herd immunity. It would mean, for instance, that at 25 percent antibody prevalence, New York City could continue its careful reopening without fear of another major surge in cases.

he believes that the best we can do is continually update models based on what is happening in the real world. She can’t say why the threshold in her models is consistently at or below 20 percent, but it is. “If heterogeneity isn’t the cause,” she said, “then I’d like for someone to explain what is.”

Lipsitch also believes that heterogeneity is important to factor into any model. It was one reason he updated his prediction, not long after we spoke in February, of what the herd-immunity threshold would be. Instead of 40 to 70 percent, he lowered it to 20 to 60 percent. When we spoke last week, he said he still stands by that, but he is skeptical that the number lands close to the 20 percent end of the range. “I think it’s unlikely,” he said, but added, “This virus is proving there can be orders-of-magnitude differences in attack rates, depending on political and societal decisions, which I don’t know how to forecast.”

Biological variations in susceptibility could come down to factors as simple as who has more nose hair, or who talks the loudest and most explosively, and Langwig shares the belief that these factors can create heterogeneity in susceptibility and transmission. Those effects can compound to dramatically change the math behind predictions for the future.

What’s important to her, rather, is that people are not misled by the idea of herd immunity. In the context of vaccination, herd-immunity thresholds are relatively fixed and predictable. In the context of an ongoing pandemic, thinking of this threshold as some static concept can be dangerously misleading.

She worries that many people conflate academic projections about reaching herd immunity with a “let it run wild” fatalism. “My view is that trying to take that route would lead to mass death and devastation,” she says.

Left totally unchecked, Bansal says, the percentage of infected people could go even higher than 70 percent.

“Within certain populations that lack heterogeneity, like within a nursing home or school, you may even see the herd-immunity threshold be above 70 percent,” Bansal says. If a population average led people in those settings to get complacent, there could be needless death.

Bansal believes that heterogeneity of behavior is the key determinant of our futures. “That magic number that we’re describing as a herd-immunity threshold very much depends on how individuals behave,” Bansal says, since R0 clearly changes with behaviors. On average, the R0 of the coronavirus currently seems to be between 2 and 3, according to Lipsitch.

Social distancing and other reactive measures changed the R0 value, and they will continue to do so. The virus has certain immutable properties, but there is nothing immutable about how many infections it causes in the real world.

The threshold can change based on how a virus spreads. The spread keeps on changing based on how we react to it at every stage, and the effects compound. Small preventive measures have big downstream effects

In other words, the herd in question determines its immunity. There is no mystery in how to drop the R0 to below 1 and reach an effective herd immunity: masks, social distancing, hand-washing, and everything everyone is tired of hearing about. It is already being done.

“I think it no longer seems impossible that Switzerland or Germany could remain near where they are in terms of cases, meaning not very much larger outbreaks, until there’s a vaccine,” he said. They seem to have the will and systems in place to keep their economies closed enough to maintain their current equilibrium.

Other wealthy countries could hypothetically create societies that are effectively immune to further surges, where the effective herd-immunity threshold is low.

We have the wealth in this country to care for people, and to set the herd-immunity threshold where we choose. Parts of the world are illuminating a third way forward, something in between total lockdown and simply resuming the old ways of life. It happens through individual choices and collective actions, reimagining new ways of living, and having the state support and leadership to make those ways possible

as much attention as we give to the virus, and to drugs and our immune systems, the variable in the system is us. There will only be as much chaos as we allow.

. The group also organized an open letter, signed by 52 dissidents from 28 countries, warning that “to win the global fight against authoritarianism, America must once again believe in and live up to its own values.”

Western leaders should push to end the conflict as soon as possible, Kasparov argues, by giving Ukraine the heavy weapons its leaders say they need.

Here in the United States, Kasparov said, “It also could be a great moment for us to revise our commitment to democracy,” adding, “Because, let’s be honest, there was complacency.”

Kasparov was feuding online with Elon Musk, the Tesla founder. On Monday, Musk floated a 280-character proposal to end the war in Ukraine that, to Kasparov, seemed too friendly to the Kremlin. He called Musk’s proposal “moral idiocy.”

Kasparov’s response to Americans of all stripes is that although their democracy may be teetering, it’s still a beacon of hope to millions around the world. And as the war in Ukraine shows, maintaining it requires constant vigilance.

“You have to be an active member of society,” he said. “You have to be engaged. That is the message.”

During his first week in office, Mr. Trump reiterated the unfounded charge that millions of people had voted illegally. When challenged on the evident falsehood, Sean Spicer, the White House press secretary, seemed to argue that Mr. Trump’s belief that something was true qualified as evidence. The press secretary also declined to answer a straightforward question about the unemployment rate, suggesting that the number will henceforth be whatever the Trump administration wants it to be.

Unfortunately, that also means that the more the fact-based media tries to debunk the president’s falsehoods, the further it will entrench the battle lines.

The news media’s spectacular failure to get the election right has made it only easier for many conservatives to ignore anything that happens outside the right’s bubble and for the Trump White House to fabricate facts with little fear of alienating its base.

He can do this because members of the Trump administration feel confident that the alternative-reality media will provide air cover, even if they are caught fabricating facts or twisting words (like claiming that the “ban” on Muslim immigrants wasn’t really a “ban”). Indeed, they believe they have shifted the paradigm of media coverage, replacing the traditional media with their own.

In a stunning demonstration of the power and resiliency of our new post-factual political culture, Mr. Trump and his allies in the right media have already turned the term “fake news” against its critics, essentially draining it of any meaning

now any news deemed to be biased, annoying or negative can be labeled “fake news.”

Even as he continues to attack the “dishonest media,” Mr. Trump and his allies are empowering this alt-reality media, providing White House access to Breitbart and other post-factual outlets that are already morphing into fierce defenders of the administration.

The relationship appears to be symbiotic, as Mr. Trump often seems to pick up on talking points from Fox News and has tweeted out links from websites notorious for their casual relationship to the truth, including sites like Gateway Pundit, a hoax-peddling site that announced, shortly after the inauguration, that it would have a White House correspondent.

All administrations lie, but what we are seeing here is an attack on credibility itself.

The Russian dissident and chess grandmaster Garry Kasparov drew upon long familiarity with that process when he tweeted: “The point of modern propaganda isn’t only to misinform or push an agenda. It is to exhaust your critical thinking, to annihilate truth.”

the real threat is not merely that a large number of Americans have become accustomed to rejecting factual information, or even that they have become habituated to believing hoaxes. The real danger is that, inundated with “alternative facts,” many voters will simply shrug, asking, “What is truth?” — and not wait for an answer.

In that world, the leader becomes the only reliable source of truth; a familiar phenomenon in an authoritarian state, but a radical departure from the norms of a democratic society. The battle over truth is now central to our politics.

as George Orwell said: “The very concept of objective truth is fading out of the world. Lies will pass into history.”

Trump and company seem to be betting that much of the electorate will not care if the president tells demonstrable lies, and will pick and choose whatever “alternative facts” confirm their views.

If we want to restore respect for facts and break through the intellectual ghettos on both the right and left, the mainstream media will have to be aggressive without being hysterical and adversarial without being unduly oppositional.

it will be incumbent on conservative media outlets to push back as well. Conservatism should be a reality-based philosophy, and the movement will be better off if it recognizes that facts really do matter.

The conservative media ecosystem — like the rest of us — has to recognize how critical, but also how fragile, credibility is in the Orwellian age of Donald Trump.

Despite his manic temperament, impulsiveness and emotional infantility, this acumen gives him real and in some ways profound communication skills. The two don't cancel each other out. They are both always present. They grow from the same root.

Despite all their differences, Trump meets his voters in a common perception (real or not) of being shunned, ignored and disrespected by 'elites'. In short, his politics and his connection with his core voters is based on grievance. This is a profound and enduring connection. This part of his constituency likely amounts to only 25% or 30% of the electorate at most. But it is a powerful anchor on the right.

the greatest single explanation of Trump is that his politics profoundly galvanized a minority of the electorate and only a minority of the electorate. Almost everyone who wasn't galvanized was repulsed. But once he had secured the GOP nomination with that minority, the power of partisan polarization kicked in to lock into place perhaps the next 15% to 20% of the electorate which otherwise would never have supported him.

As long as Trump remains "us" to Republican voters I see little reason to think anything we can imagine will shake that very high level of support he gets from self-identified Republicans. That likely means that, among other things, no matter how unpopular Trump gets, Republican lawmakers will continue to support him because the chances of ending their careers is greater in a GOP primary than in a general election

if Trump's ideology is fluid, he has drawn around him advisors who can only be termed extremists. I believe the chief reason is that Trump's authoritarian personality resonates with extremist politics and vice versa. We should expect them to keep catalyzing each other in dangerous and frightening ways.

What does all this mean? We should not think in terms of counter-intuition or 12 dimensional chess. Trump wants to be President and he wants to win and be the best. But he is generally unpopular, has a policy agenda which has great difficulty achieving majority support and a temperament which makes effective governance profoundly difficult.

That mix makes the praise and affirmation he craves as President extremely challenging to achieve. Like many with similar temperaments and personalities he has a chronic need to generate drama and confrontation to stabilize himself. It's that simple. It won't change. It won't get better.

Unfortunately, that also means that the more the fact-based media tries to debunk the president’s falsehoods, the further it will entrench the battle lines.

The press secretary also declined to answer a straightforward question about the unemployment rate, suggesting that the number will henceforth be whatever the Trump administration wants it to be.

In a stunning demonstration of the power and resiliency of our new post-factual political culture, Mr. Trump and his allies in the right media have already turned the term “fake news” against its critics, essentially draining it of any meaning. During the campaign, actual “fake news” — deliberate hoaxes — polluted political discourse and clogged social media timelines.

For years, the widely read Drudge Report has linked to the bizarre conspiracy theorist Alex Jones, who believes that both the attacks of Sept. 11 and the Sandy Hook shootings were government-inspired “false flag” operations.

But now any news deemed to be biased, annoying or negative can be labeled “fake news.” Erroneous reports that the bust of the Rev. Dr. Martin Luther King Jr. had been removed from the Oval Office or misleading reports that sanctions against Russia had been lifted will be seized on by Mr. Trump’s White House to reinforce his indictment.

Mr. Trump and his allies are empowering this alt-reality media, providing White House access to Breitbart and other post-factual outlets that are already morphing into fierce defenders of the administration.

By now, it ought to be evident that enemies are important to this administration, whether they are foreigners, refugees, international bankers or the press.

But discrediting independent sources of information also has two major advantages for Mr. Trump: It helps insulate him from criticism and it allows him to create his own narratives, metrics and “alternative facts.”

The Russian dissident and chess grandmaster Garry Kasparov drew upon long familiarity with that process when he tweeted: “The point of modern propaganda isn’t only to misinform or push an agenda. It is to exhaust your critical thinking, to annihilate truth.”

In that world, the leader becomes the only reliable source of truth; a familiar phenomenon in an authoritarian state, but a radical departure from the norms of a democratic society. The battle over truth is now central to our politics.

Scottie Nell Hughes, was asked to defend the clearly false statement by Mr. Trump that millions of votes had been cast illegally. She answered by explaining that everybody now had their own way of interpreting whether a fact was true or not.

Or as George Orwell said: “The very concept of objective truth is fading out of the world. Lies will pass into history.”

In the meantime, we must recognize the magnitude of the challenge. If we want to restore respect for facts and break through the intellectual ghettos on both the right and left, the mainstream media will have to be aggressive without being hysterical and adversarial without being unduly oppositional.

There may be short-term advantages to running headlines about millions of illegal immigrants voting or secret United Nations plots to steal your guns, but the longer the right enables such fabrications, the weaker it will be in the long run. As uncomfortable as it may be, it will fall to the conservative media to police its worst actors.

The impacts of technological change take their time appearing. They also vary hugely from industry to industry. Although in many simple economic models technology pairs neatly with capital and labour to produce output, in practice technological changes do not affect all workers the same way. Some find that their skills are complementary to new technologies. Others find themselves out of work.

The case for a highly disruptive period of economic growth is made by Erik Brynjolfsson and Andrew McAfee, professors at MIT, in “The Second Machine Age”, a book to be published later this month. Like the first great era of industrialisation, they argue, it should deliver enormous benefits—but not without a period of disorienting and uncomfortable change

Their argument rests on an underappreciated aspect of the exponential growth in chip processing speed, memory capacity and other computer metrics: that the amount of progress computers will make in the next few years is always equal to the progress they have made since the very beginning. Mr Brynjolfsson and Mr McAfee reckon that the main bottleneck on innovation is the time it takes society to sort through the many combinations and permutations of new technologies and business models.

A startling progression of inventions seems to bear their thesis out. Ten years ago technologically minded economists pointed to driving cars in traffic as the sort of human accomplishment that computers were highly unlikely to master. Now Google cars are rolling round California driver-free

Even after computers beat grandmasters at chess (once thought highly unlikely), nobody thought they could take on people at free-form games played in natural language. Then Watson, a pattern-recognising supercomputer developed by IBM, bested the best human competitors in America’s popular and syntactically tricksy general-knowledge quiz show “Jeopardy!” Versions of Watson are being marketed to firms

Text-mining programs will displace professional jobs in legal services. Biopsies will be analysed more efficiently by image-processing software than lab technicians. Accountants may follow travel agents and tellers into the unemployment line as tax software improves. Machines are already turning basic sports results and financial data into good-enough news stories.

A taxi driver will be a rarity in many places by the 2030s or 2040s. That sounds like bad news for journalists who rely on that most reliable source of local knowledge and prejudice—but will there be many journalists left to care? Will there be airline pilots? Or traffic cops? Or soldiers?

Tyler Cowen, an economist at George Mason University and a much-read blogger, writes in his most recent book, “Average is Over”, that rich economies seem to be bifurcating into a small group of workers with skills highly complementary with machine intelligence, for whom he has high hopes, and the rest, for whom not so much.

the second machine age will make such trial and error easier. It will be shockingly easy to launch a startup, bring a new product to market and sell to billions of global consumers (see article). Those who create or invest in blockbuster ideas may earn unprecedented returns as a result.

Thomas Piketty, an economist at the Paris School of Economics, argues along similar lines that America may be pioneering a hyper-unequal economic model in which a top 1% of capital-owners and “supermanagers” grab a growing share of national income and accumulate an increasing concentration of national wealth

The rise of the middle-class—a 20th-century innovation—was a hugely important political and social development across the world. The squeezing out of that class could generate a more antagonistic, unstable and potentially dangerous politics.

The current doldrum in wages may, like that of the early industrial era, be a temporary matter, with the good times about to roll (see chart 3). These jobs may look distinctly different from those they replace. Just as past mechanisation freed, or forced, workers into jobs requiring more cognitive dexterity, leaps in machine intelligence could create space for people to specialise in more emotive occupations, as yet unsuited to machines: a world of artists and therapists, love counsellors and yoga instructors.

though growth in areas of the economy that are not easily automated provides jobs, it does not necessarily help real wages. Mr Summers points out that prices of things-made-of-widgets have fallen remarkably in past decades; America’s Bureau of Labour Statistics reckons that today you could get the equivalent of an early 1980s television for a twentieth of its then price,

owever, prices of things not made of widgets, most notably college education and health care, have shot up

As innovation continues, automation may bring down costs in some of those stubborn areas as well, though those dominated by scarcity—such as houses in desirable places—are likely to resist the trend, as may those where the state keeps market forces at bay. But if innovation does make health care or higher education cheaper, it will probably be at the cost of more jobs, and give rise to yet more concentration of income.

Adaptation to past waves of progress rested on political and policy responses. The most obvious are the massive improvements in educational attainment brought on first by the institution of universal secondary education and then by the rise of university attendance. Policies aimed at similar gains would now seem to be in order. But as Mr Cowen has pointed out, the gains of the 19th and 20th centuries will be hard to duplicate.

Boosting the skills and earning power of the children of 19th-century farmers and labourers took little more than offering schools where they could learn to read, write and do algebra. Pushing a large proportion of college graduates to complete graduate work successfully will be harder and more expensive. Perhaps cheap and innovative online education will indeed make new attainment possible. But as Mr Cowen notes, such programmes may tend to deliver big gains only for the most conscientious students.

Everyone should be able to benefit from productivity gains—in that, Keynes was united with his successors. His worry about technological unemployment was mainly a worry about a “temporary phase of maladjustment” as society and the economy adjusted to ever greater levels of productivity

However, society may find itself sorely tested if, as seems possible, growth and innovation deliver handsome gains to the skilled, while the rest cling to dwindling employment opportunities at stagnant wages.

in the past 30 years, software and robots have thrived at replacing a particular kind of occupation: the average-wage, middle-skill, routine-heavy worker, especially in manufacturing and office admin. 

the next wave of computer progress will continue to shred human work where it already has: manufacturing, administrative support, retail, and transportation. Most remaining factory jobs are "likely to diminish over the next decades," they write. Cashiers, counter clerks, and telemarketers are similarly endangered

here's a chart of the ten jobs with a 99-percent likelihood of being replaced by machines and software. They are mostly routine-based jobs (telemarketing, sewing) and work that can be solved by smart algorithms (tax preparation, data entry keyers, and insurance underwriters)

I've also listed the dozen jobs they consider least likely to be automated. Health care workers, people entrusted with our safety, and management positions dominate the list.

Although the past 30 years have hollowed out the middle, high- and low-skill jobs have actually increased, as if protected from the invading armies of robots by their own moats

If you wanted to use this graph as a guide to the future of automation, your upshot would be: Machines are better at rules and routines; people are better at directing and diagnosing. But it doesn't have to stay that way.

Higher-skill workers have been protected by a kind of social-intelligence moat. Computers are historically good at executing routines, but they're bad at finding patterns, communicating with people, and making decisions, which is what managers are paid to do

lower-skill workers have been protected by the Moravec moat. Hans Moravec was a futurist who pointed out that machine technology mimicked a savant infant: Machines could do long math equations instantly and beat anybody in chess, but they can't answer a simple question or walk up a flight of stairs. As a result, menial work done by people without much education (like home health care workers, or fast-food attendants) have been spared, too.

robots are finally crossing these moats by moving and thinking like people. Amazon has bought robots to work its warehouses. Narrative Science can write earnings summaries that are indistinguishable from wire reports. We can say to our phones I'm lost, help and our phones can tell us how to get home. 

In a decade, the idea of computers driving cars went from impossible to boring.

The first wave showed that machines are better at assembling things. The second showed that machines are better at organization things. Now data analytics and self-driving cars suggest they might be better at pattern-recognition and driving. So what are we better at?

One conclusion to draw from this is that humans are, and will always be, superior at working with, and caring for, other humans. In this light, automation doesn't make the world worse. Far from it: It creates new opportunities for human ingenuity.  

But robots are already creeping into diagnostics and surgeries. Schools are already experimenting with software that replaces teaching hours. The fact that some industries have been safe from automation for the last three decades doesn't guarantee that they'll be safe for the next one.

It would be anxious enough if we knew exactly which jobs are next in line for automation. The truth is scarier. We don't really have a clue.

I spent a lot of time in the classrooms of gifted individuals who sometimes used very little actual technology but really thought about interaction in profound and inspiring ways.

This artificial intelligence breakthrough, he argues, is being driven by cheap parallel computation technologies, big data collection and better algorithms. The upshot is clear, “The business plans of the next 10,000 start-ups are easy to forecast: Take X and add A.I.”

Advances in artificial intelligence will accelerate this centralizing trend. That’s because A.I. companies will be able to reap the rewards of network effects. The bigger their network and the more data they collect, the more effective and attractive they become.

The Internet has created a long tail, but almost all the revenue and power is among the small elite at the head.

in 2001, the top 10 websites accounted for 31 percent of all U.S. page views, but, by 2010, they accounted for 75 percent of them.

As a result, our A.I. future is likely to be ruled by an oligarchy of two or three large, general-purpose cloud-based commercial intelligences.”

In the age of smart machines, we’re not human because we have big brains. We’re human because we have social skills, emotional capacities and moral intuitions.

The second implication is philosophical. A.I. will redefine what it means to be human. Our identity as humans is shaped by what machines and other animals can’t do

For the last few centuries, reason was seen as the ultimate human faculty. But now machines are better at many of the tasks we associate with thinking — like playing chess, winning at Jeopardy, and doing math.

On the other hand, machines cannot beat us at the things we do without conscious thinking: developing tastes and affections, mimicking each other and building emotional attachments, experiencing imaginative breakthroughs, forming moral sentiments.

engineers at a few gigantic companies will have vast-though-hidden power to shape how data are collected and framed, to harvest huge amounts of information, to build the frameworks through which the rest of us make decisions and to steer our choices. If you think this power will be used for entirely benign ends, then you have not read enough history.

I could paint two divergent A.I. futures, one deeply humanistic, and one soullessly utilitarian.

In the humanistic one, machines liberate us from mental drudgery so we can focus on higher and happier things. In this future, differences in innate I.Q. are less important. Everybody has Google on their phones so having a great memory or the ability to calculate with big numbers doesn’t help as much.

In this future, there is increasing emphasis on personal and moral faculties: being likable, industrious, trustworthy and affectionate. People are evaluated more on these traits, which supplement machine thinking, and not the rote ones that duplicate it

In the cold, utilitarian future, on the other hand, people become less idiosyncratic. If the choice architecture behind many decisions is based on big data from vast crowds, everybody follows the prompts and chooses to be like each other. The machine prompts us to consume what is popular, the things that are easy and mentally undemanding.

The kidnappings, which were carried out by different groups of fighters jousting for influence and territory in Syria, became more frequent. In June 2013, four French journalists were abducted. In September, the militants grabbed three Spanish journalists.

At first, the abuse did not appear to have a larger purpose. Nor did the jihadists seem to have a plan for their growing number of hostages.Mr. Bontinck said Mr. Foley and Mr. Cantlie had first been held by the Nusra Front, a Qaeda affiliate. Their guards, an English-speaking trio whom they nicknamed “the Beatles,” seemed to take pleasure in brutalizing them.Later, they were handed over to a group called the Mujahedeen Shura Council, led by French speakers.Mr. Foley and Mr. Cantlie were moved at least three times before being transferred to a prison underneath the Children’s Hospital of Aleppo.

but as conditions grew more desperate, they turned on one another. Some, including Mr. Foley, sought comfort in the faith of their captors, embracing Islam and taking Muslim names.

When Mr. Bontinck was released, he jotted down the phone number of Mr. Foley’s parents and promised to call them. They made plans to meet again.He left thinking that the journalists, like him, would soon be freed.

After months of holding them without making any demands, the jihadists suddenly devised a plan to ransom them. Starting last November, each prisoner was told to hand over the email address of a relative. Mr. Foley gave the address of his younger brother.The group sent a blitz of messages to the families of the hostages.Those who were able to lay the emails side by side could see they had been cut and pasted from the same template.

Within this subset, the person who suffered the cruelest treatment, the former hostages said, was Mr. Foley. In addition to receiving prolonged beatings, he underwent mock executions and was repeatedly waterboarded.

Mr. Foley shared his meager rations. In the cold of the Syrian winter, he offered another prisoner his only blanket.He kept the others entertained, proposing games and activities like Risk, a board game that involves moving imaginary armies across a map: another favorite pastime in the Foley family. The hostages made a chess set out of discarded paper. They re-enacted movies, retelling them scene by scene. And they arranged for members of the group to give lectures on topics they knew well.

By June, the cellblock that had once held at least 23 people had been reduced to just seven. Four of them were Americans, and three were British — all citizens of countries whose governments had refused to pay ransoms.

One has to live in the Muslim world to understand the immense transformative influence of religious television channels on societ

There are thousands of Islamist newspapers and clergies that impose a unitary vision of the world, tradition and clothing on the public space, on the wording of the government’s laws and on the rituals of a society they deem to be contaminated.

Since ISIS is first and foremost a culture, not a militia, how do you prevent future generations from turning to jihadism when the influence of Fatwa Valley and its clerics and its culture and its immense editorial industry remains intact?

It is worth reading certain Islamist newspapers to see their reactions to the attacks in Paris. The West is cast as a land of “infidels.” The attacks were the result of the onslaught against Islam. Muslims and Arabs have become the enemies of the secular and the Jews

Is curing the disease therefore a simple matter? Hardly. Saudi Arabia remains an ally of the West in the many chess games playing out in the Middle East. It is preferred to Iran, that gray Daesh. And there’s the trap. Denial creates the illusion of equilibrium

Jihadism is denounced as the scourge of the century but no consideration is given to what created it or supports it. This may allow saving face, but not saving lives.

Daesh has a mother: the invasion of Iraq. But it also has a father: Saudi Arabia and its religious-industrial complex.

Until that point is understood, battles may be won, but the war will be lost. Jihadists will be killed, only to be reborn again in future generations and raised on the same books.

Condoleezza Rice, a former U.S. secretary of state who serves on the C3.ai board and was recently named the next director of the Hoover Institution

Known as the C3.ai Digital Transformation Institute, the new research consortium includes commitments from Princeton, Carnegie Mellon, the Massachusetts Institute of Technology, the University of California, the University of Illinois and the University of Chicago, as well as C3.ai and Microsoft.

Thomas M. Siebel, founder and chief executive of C3.ai, an artificial intelligence company in Redwood City, Calif., said the public-private consortium would spend $367 million in its initial five years, aiming its first awards at finding ways to slow the new coronavirus that is sweeping the globe.

The new institute plans to award up to 26 grants annually, each featuring up to $500,000 in research funds in addition to computing resources.

The institute’s co-directors are S. Shankar Sastry of the University of California, Berkeley, and Rayadurgam Srikant of the University of Illinois, Urbana-Champaign.

Successful A.I. can be extremely hard to deliver, especially in thorny real-world problems such as self-driving cars.

In recent decades, many rich Americans have sought to reinvent themselves as patrons of social progress through science research

Forbes puts Mr. Siebel’s current net worth at $3.6 billion. His First Virtual Group is a diversified holding company that includes philanthropic ventures.

The first part of the company’s name, Mr. Siebel said in an email, stands for the convergence of three digital trends: big data, cloud computing and the internet of things, with A.I. amplifying their power. Last year, he laid out his thesis in a book

“In no way am I suggesting that A.I. is all sweetness and light,” Mr. Siebel said. But the new institute, he added, is “a place where it can be a force for good.”

We came back to our office and started working, fueled by adrenaline. Our first few attempts didn’t succeed, but eventually we made four copies of the prototype on a small 3-D printing machine that we have in our office.

The day after, we returned to the hospital and gave our valves to a doctor who tested them. They worked and he asked for 100 more. So we went back to the office, and returned to the hospital with 100 more. We hoped that this would last them for a few days. Still, the coronavirus rages on. A few hospitals in northern Italy asked us to make copies of the same piece. We are printing them now.

This sparked a second idea: to modify a snorkeling mask already on the market to create a ventilation-assisted mask for hospitals in need of additional equipment, which was successful when the hospital tested it on a patient in need.

We don’t say this to brag, but to show what is possible. In a moment of crisis, and in a moment when commerce globally is shutting down, there are still many do-it-yourself ways of helping the people around you.

Code is too hard to think about. Before trying to understand the attempts themselves, then, it’s worth understanding why this might be: what it is about code that makes it so foreign to the mind, and so unlike anything that came before it.

Technological progress used to change the way the world looked—you could watch the roads getting paved; you could see the skylines rise. Today you can hardly tell when something is remade, because so often it is remade by code.

Software has enabled us to make the most intricate machines that have ever existed. And yet we have hardly noticed, because all of that complexity is packed into tiny silicon chips as millions and millions of lines of cod

The programmer, the renowned Dutch computer scientist Edsger Dijkstra wrote in 1988, “has to be able to think in terms of conceptual hierarchies that are much deeper than a single mind ever needed to face before.” Dijkstra meant this as a warning.

“The serious problems that have happened with software have to do with requirements, not coding errors.” When you’re writing code that controls a car’s throttle, for instance, what’s important is the rules about when and how and by how much to open it. But these systems have become so complicated that hardly anyone can keep them straight in their head. “There’s 100 million lines of code in cars now,” Leveson says. “You just cannot anticipate all these things.”

What made programming so difficult was that it required you to think like a computer.

The introduction of programming languages like Fortran and C, which resemble English, and tools, known as “integrated development environments,” or IDEs, that help correct simple mistakes (like Microsoft Word’s grammar checker but for code), obscured, though did little to actually change, this basic alienation—the fact that the programmer didn’t work on a problem directly, but rather spent their days writing out instructions for a machine.

“The problem is that software engineers don’t understand the problem they’re trying to solve, and don’t care to,” says Leveson, the MIT software-safety expert. The reason is that they’re too wrapped up in getting their code to work.

As programmers eagerly poured software into critical systems, they became, more and more, the linchpins of the built world—and Dijkstra thought they had perhaps overestimated themselves.

a nearly decade-long investigation into claims of so-called unintended acceleration in Toyota cars. Toyota blamed the incidents on poorly designed floor mats, “sticky” pedals, and driver error, but outsiders suspected that faulty software might be responsible

software experts spend 18 months with the Toyota code, picking up where NASA left off. Barr described what they found as “spaghetti code,” programmer lingo for software that has become a tangled mess. Code turns to spaghetti when it accretes over many years, with feature after feature piling on top of, and being woven around

Using the same model as the Camry involved in the accident, Barr’s team demonstrated that there were actually more than 10 million ways for the onboard computer to cause unintended acceleration. They showed that as little as a single bit flip—a one in the computer’s memory becoming a zero or vice versa—could make a car run out of control. The fail-safe code that Toyota had put in place wasn’t enough to stop it

. In all, Toyota recalled more than 9 million cars, and paid nearly $3 billion in settlements and fines related to unintended acceleration.

The problem is that programmers are having a hard time keeping up with their own creations. Since the 1980s, the way programmers work and the tools they use have changed remarkably little.

“Visual Studio is one of the single largest pieces of software in the world,” he said. “It’s over 55 million lines of code. And one of the things that I found out in this study is more than 98 percent of it is completely irrelevant. All this work had been put into this thing, but it missed the fundamental problems that people faced. And the biggest one that I took away from it was that basically people are playing computer inside their head.” Programmers were like chess players trying to play with a blindfold on—so much of their mental energy is spent just trying to picture where the pieces are that there’s hardly any left over to think about the game itself.

The fact that the two of them were thinking about the same problem in the same terms, at the same time, was not a coincidence. They had both just seen the same remarkable talk, given to a group of software-engineering students in a Montreal hotel by a computer researcher named Bret Victor. The talk, which went viral when it was posted online in February 2012, seemed to be making two bold claims. The first was that the way we make software is fundamentally broken. The second was that Victor knew how to fix it.

Though he runs a lab that studies the future of computing, he seems less interested in technology per se than in the minds of the people who use it. Like any good toolmaker, he has a way of looking at the world that is equal parts technical and humane. He graduated top of his class at the California Institute of Technology for electrical engineering,

WYSIWYG (pronounced “wizzywig”) came along. It stood for “What You See Is What You Get.”

“Our current conception of what a computer program is,” he said, is “derived straight from Fortran and ALGOL in the late ’50s. Those languages were designed for punch cards.”

in early 2012, Victor had finally landed upon the principle that seemed to thread through all of his work. (He actually called the talk “Inventing on Principle.”) The principle was this: “Creators need an immediate connection to what they’re creating.” The problem with programming was that it violated the principle. That’s why software systems were so hard to think about, and so rife with bugs: The programmer, staring at a page of text, was abstracted from whatever it was they were actually making.

Victor’s point was that programming itself should be like that. For him, the idea that people were doing important work, like designing adaptive cruise-control systems or trying to understand cancer, by staring at a text editor, was appalling.

With the right interface, it was almost as if you weren’t working with code at all; you were manipulating the game’s behavior directly.

When the audience first saw this in action, they literally gasped. They knew they weren’t looking at a kid’s game, but rather the future of their industry. Most software involved behavior that unfolded, in complex ways, over time, and Victor had shown that if you were imaginative enough, you could develop ways to see that behavior and change it, as if playing with it in your hands. One programmer who saw the talk wrote later: “Suddenly all of my tools feel obsolete.”

hen John Resig saw the “Inventing on Principle” talk, he scrapped his plans for the Khan Academy programming curriculum. He wanted the site’s programming exercises to work just like Victor’s demos. On the left-hand side you’d have the code, and on the right, the running program: a picture or game or simulation. If you changed the code, it’d instantly change the picture. “In an environment that is truly responsive,” Resig wrote about the approach, “you can completely change the model of how a student learns ... [They] can now immediately see the result and intuit how underlying systems inherently work without ever following an explicit explanation.” Khan Academy has become perhaps the largest computer-programming class in the world, with a million students, on average, actively using the program each month.

. In traditional programming, your task is to take complex rules and translate them into code; most of your energy is spent doing the translating, rather than thinking about the rules themselves. In the model-based approach, all you have is the rules. So that’s what you spend your time thinking about. It’s a way of focusing less on the machine and more on the problem you’re trying to get it to solve.

“Everyone thought I was interested in programming environments,” he said. Really he was interested in how people see and understand systems—as he puts it, in the “visual representation of dynamic behavior.” Although code had increasingly become the tool of choice for creating dynamic behavior, it remained one of the worst tools for understanding it. The point of “Inventing on Principle” was to show that you could mitigate that problem by making the connection between a system’s behavior and its code immediate.

In a pair of later talks, “Stop Drawing Dead Fish” and “Drawing Dynamic Visualizations,” Victor went one further. He demoed two programs he’d built—the first for animators, the second for scientists trying to visualize their data—each of which took a process that used to involve writing lots of custom code and reduced it to playing around in a WYSIWYG interface.

Victor suggested that the same trick could be pulled for nearly every problem where code was being written today. “I’m not sure that programming has to exist at all,” he told me. “Or at least software developers.” In his mind, a software developer’s proper role was to create tools that removed the need for software developers. Only then would people with the most urgent computational problems be able to grasp those problems directly, without the intermediate muck of code.

Victor implored professional software developers to stop pouring their talent into tools for building apps like Snapchat and Uber. “The inconveniences of daily life are not the significant problems,” he wrote. Instead, they should focus on scientists and engineers—as he put it to me, “these people that are doing work that actually matters, and critically matters, and using really, really bad tools.”

“people are not so easily transitioning to model-based software development: They perceive it as another opportunity to lose control, even more than they have already.”

In a model-based design tool, you’d represent this rule with a small diagram, as though drawing the logic out on a whiteboard, made of boxes that represent different states—like “door open,” “moving,” and “door closed”—and lines that define how you can get from one state to the other. The diagrams make the system’s rules obvious: Just by looking, you can see that the only way to get the elevator moving is to close the door, or that the only way to get the door open is to stop.

Bantegnie’s company is one of the pioneers in the industrial use of model-based design, in which you no longer write code directly. Instead, you create a kind of flowchart that describes the rules your program should follow (the “model”), and the computer generates code for you based on those rules

“Typically the main problem with software coding—and I’m a coder myself,” Bantegnie says, “is not the skills of the coders. The people know how to code. The problem is what to code. Because most of the requirements are kind of natural language, ambiguous, and a requirement is never extremely precise, it’s often understood differently by the guy who’s supposed to code.”

On this view, software becomes unruly because the media for describing what software should do—conversations, prose descriptions, drawings on a sheet of paper—are too different from the media describing what software does do, namely, code itself.

for this approach to succeed, much of the work has to be done well before the project even begins. Someone first has to build a tool for developing models that are natural for people—that feel just like the notes and drawings they’d make on their own—while still being unambiguous enough for a computer to understand. They have to make a program that turns these models into real code. And finally they have to prove that the generated code will always do what it’s supposed to.

tice brings order and accountability to large codebases. But, Shivappa says, “it’s a very labor-intensive process.” He estimates that before they used model-based design, on a two-year-long project only two to three months was spent writing code—the rest was spent working on the documentation.

uch of the benefit of the model-based approach comes from being able to add requirements on the fly while still ensuring that existing ones are met; with every change, the computer can verify that your program still works. You’re free to tweak your blueprint without fear of introducing new bugs. Your code is, in FAA parlance, “correct by construction.”

The ideas spread. The notion of liveness, of being able to see data flowing through your program instantly, made its way into flagship programming tools offered by Google and Apple. The default language for making new iPhone and Mac apps, called Swift, was developed by Apple from the ground up to support an environment, called Playgrounds, that was directly inspired by Light Table.

The bias against model-based design, sometimes known as model-driven engineering, or MDE, is in fact so ingrained that according to a recent paper, “Some even argue that there is a stronger need to investigate people’s perception of MDE than to research new MDE technologies.”

“Human intuition is poor at estimating the true probability of supposedly ‘extremely rare’ combinations of events in systems operating at a scale of millions of requests per second,” he wrote in a paper. “That human fallibility means that some of the more subtle, dangerous bugs turn out to be errors in design; the code faithfully implements the intended design, but the design fails to correctly handle a particular ‘rare’ scenario.”

Newcombe was convinced that the algorithms behind truly critical systems—systems storing a significant portion of the web’s data, for instance—ought to be not just good, but perfect. A single subtle bug could be catastrophic. But he knew how hard bugs were to find, especially as an algorithm grew more complex. You could do all the testing you wanted and you’d never find them all.

An algorithm written in TLA+ could in principle be proven correct. In practice, it allowed you to create a realistic model of your problem and test it not just thoroughly, but exhaustively. This was exactly what he’d been looking for: a language for writing perfect algorithms.

TLA+, which stands for “Temporal Logic of Actions,” is similar in spirit to model-based design: It’s a language for writing down the requirements—TLA+ calls them “specifications”—of computer programs. These specifications can then be completely verified by a computer. That is, before you write any code, you write a concise outline of your program’s logic, along with the constraints you need it to satisfy

Programmers are drawn to the nitty-gritty of coding because code is what makes programs go; spending time on anything else can seem like a distraction. And there is a patient joy, a meditative kind of satisfaction, to be had from puzzling out the micro-mechanics of code. But code, Lamport argues, was never meant to be a medium for thought. “It really does constrain your ability to think when you’re thinking in terms of a programming language,”

Code makes you miss the forest for the trees: It draws your attention to the working of individual pieces, rather than to the bigger picture of how your program fits together, or what it’s supposed to do—and whether it actually does what you think. This is why Lamport created TLA+. As with model-based design, TLA+ draws your focus to the high-level structure of a system, its essential logic, rather than to the code that implements it.

But TLA+ occupies just a small, far corner of the mainstream, if it can be said to take up any space there at all. Even to a seasoned engineer like Newcombe, the language read at first as bizarre and esoteric—a zoo of symbols.

this is a failure of education. Though programming was born in mathematics, it has since largely been divorced from it. Most programmers aren’t very fluent in the kind of math—logic and set theory, mostly—that you need to work with TLA+. “Very few programmers—and including very few teachers of programming—understand the very basic concepts and how they’re applied in practice. And they seem to think that all they need is code,” Lamport says. “The idea that there’s some higher level than the code in which you need to be able to think precisely, and that mathematics actually allows you to think precisely about it, is just completely foreign. Because they never learned it.”

“In the 15th century,” he said, “people used to build cathedrals without knowing calculus, and nowadays I don’t think you’d allow anyone to build a cathedral without knowing calculus. And I would hope that after some suitably long period of time, people won’t be allowed to write programs if they don’t understand these simple things.”

Programmers, as a species, are relentlessly pragmatic. Tools like TLA+ reek of the ivory tower. When programmers encounter “formal methods” (so called because they involve mathematical, “formally” precise descriptions of programs), their deep-seated instinct is to recoil.

Formal methods had an image problem. And the way to fix it wasn’t to implore programmers to change—it was to change yourself. Newcombe realized that to bring tools like TLA+ to the programming mainstream, you had to start speaking their language.

he presented TLA+ as a new kind of “pseudocode,” a stepping-stone to real code that allowed you to exhaustively test your algorithms—and that got you thinking precisely early on in the design process. “Engineers think in terms of debugging rather than ‘verification,’” he wrote, so he titled his internal talk on the subject to fellow Amazon engineers “Debugging Designs.” Rather than bemoan the fact that programmers see the world in code, Newcombe embraced it. He knew he’d lose them otherwise. “I’ve had a bunch of people say, ‘Now I get it,’” Newcombe says.

In the world of the self-driving car, software can’t be an afterthought. It can’t be built like today’s airline-reservation systems or 911 systems or stock-trading systems. Code will be put in charge of hundreds of millions of lives on the road and it has to work. That is no small task.

That danger is especially pronounced in the case of North Korea today, because it does not have any strong historical antecedents but its own. Despite all the attempts to draw assorted comparisons to explain the crisis, the best guide for it is to be found not through the close study of Khrushchev or the Kaiser, but of the Kim family.

By contrast, all evidence suggests North Korea’s nuclear and ballistic missile programs are an existential question for Kim Jong Un. Rather than an impulsive move on Pyongyang’s part, the capabilities the regime is now showcasing are the culmination of a patient, decades-long drive to acquire them. For the North, the nuclear and missile combination represents not a clever chess move in a far-off corner of the world by a global power maneuvering for advantage, but the vital guarantee of survival at home by a pygmy, pariah regime surrounded by nuclear-armed great powers.

The First World War ranks as an even less helpful guide to the Korean crisis.

Unlike in 1914, no one involved in policymaking in the relevant capitals—in this case Washington, Beijing, Tokyo, or Seoul—has evinced any illusions about the devastating consequences likely to attend a conflict. On the contrary, everyone anticipates that, should a war come to pass, it will be a bloodbath

Nor is the Kim regime akin to the Nazis. While the government ruling from Pyongyang is uniquely loathsome and evil—perpetrating crimes against humanity of a scope and scale arguably unparalleled anywhere else on earth—it has displayed none of the fanatical urgency of Hitler in his genocidal drive to conquer Europe.

The broader U.S. experience during the Cold War—particularly with respect to theories of nuclear deterrence and alliance management—offers a richer and more promising field for exploration.

In the North Korean case, by contrast, robust missile defenses are a smart and stabilizing investment for the U.S. to make—necessary to disabuse Pyongyang of any hope that it might use its nuclear arsenal as a shield behind which to conduct acts of conventional aggression against its neighbors.

The past several decades suggest that, contrary to conventional characterization of Kim Jong Un as a madman, North Korean leaders are more predictable than not.

Far more than these or other imperfect analogues, the history that ultimately provides the most useful template for thinking about the current crisis with North Korea is that of North Korea itself.

They show a strong survival instinct, and have been savvy and ruthless in remaining in power as communist regimes have crumbled nearly everywhere else. And out of this desire to survive lies the potential foundation for a successful American strategy.

When it comes to drawing analogies, the historian Lawrence Freedman once offered a useful admonishment. “History,” he said, “should alert you to factors of which to be aware, dangers that might be lurking unseen, possibilities that might be worth exploring, or questions to ask. It can provide suggestions but not rules to be followed.”

The truth is that America, and the world, have never faced a situation quite like the one that presently confronts us on the Korean peninsula. Rather than searching for an episode from the past that neatly tells us what to do now, the better approach is to acknowledge the uniqueness of today’s challenge and attempt to understand it on its own terms. In this respect, North Korea illustrates the distinction between analogy and analysis—and that the beginning of wisdom is the ability to tell them apart.