An 8th Planet Is Found Orbiting a Distant Star, With A.I.’s Help

An 8th Planet Is Found Orbiting a Distant Star, With A.I.’s Help

Trilobites By NICHOLAS ST. FLEUR

DEC. 14, 2017

With eight planets whirling around its sun, our solar system has held the galactic title for having the most known planets of any star system in the Milky Way. But on Thursday NASA announced the discovery of a new exoplanet orbiting a distant star some 2,500 light years away from here called Kepler 90, bringing that system’s total to eight planets as well.

The new planet, known as Kepler-90i, is rocky and hot. It orbits its star about once every 14 days. The finding was made using data collected by NASA’s Kepler Space Telescope, a planet hunter that has spotted more than 2,500 confirmed exoplanets since its launch in 2009. Unlike those previous discoveries, the new exoplanet was detected with the help of an artificial intelligence researcher at Google using a machine learning technique called neural networking.

“This is the first time a neural network specifically has been used to identify a new exoplanet,” said Christopher Shallue, a software engineer at Google who helped make the finding. The technology, which is loosely inspired by the human brain, is designed to recognize patterns and classify images. It can learn to tell the difference between something simple like a cat and a dog, and also to distinguish exoplanets from cosmic noise.

For the project, the computer looked at a small chunk of data gathered by Kepler from 2009 to 2013. Of the 150,000 stars represented in Kepler’s collection, the computer combed through 670 star systems for signs of exoplanets. Astronomers spot exoplanets when the celestial bodies move, or transit, in front of their stars. The interaction causes a dip in brightness that creates a detectable signal.

So far, the data set has about 35,000 such signals. The astronomers trained the program on a set of about 15,000 signals, and it identified planets correctly 96 percent of the time. The neural network learned what was a planet and what was not a planet and was able to find the exoplanet Kepler-90i, as well as a second exoplanet named Kepler-80g around a different star system.

Next, the researchers plan to explore more star systems studied by Kepler. “We plan to search all 150,000 stars in the Kepler data system,” said Mr. Shallue.

Andrew Vanderburg, an astronomer at the University of Texas, Austin, said that Kepler-90i is about 30 percent larger than Earth and about as hot as the planet Mercury, reaching about 800 degrees Fahrenheit. Like the other seven planets in its system, it is packed close to its star. It resembles a miniature version of our solar system, he said, where the most distant known planet is about as far away from its star as the Earth is from our sun. But there could be additional, more distant planets not yet detected because planets close to their stars may be easier for astronomers to find.

Seth Shostak an astronomer with the SETI Institute in Mountain View, Calif., who was not involved in the project said the finding that Kepler 90 has eight planets shows that our solar system is “just another duck in a row.” “The bad news is we’re not quite as special as we thought we were,” he said. “But the good news is we may have a lot of cosmic company.”

It’s possible that the two systems may not be tied for long as astronomers search the outer reaches of our solar system for the elusive Planet Nine. It sets the stage for a new space race: Which team will break the intragalactic deadlock? Will artificial intelligence first detect another planet in the Kepler-90 system, or will astronomers find a distant ninth planet orbiting our sun? “It’s kind of cool to see which one will be proven next,” said Jessie Dotson, Kepler’s project scientist at NASA.

How Evil Is Tech?

How Evil Is Tech?

David Brooks NOV. 20, 2017

Not long ago, tech was the coolest industry. Everybody wanted to work at Google, Facebook and Apple. But over the past year the mood has shifted. Some now believe tech is like the tobacco industry — corporations that make billions of dollars peddling a destructive addiction. Some believe it is like the N.F.L. — something millions of people love, but which everybody knows leaves a trail of human wreckage in its wake. Surely the people in tech — who generally want to make the world a better place — don’t want to go down this road. It will be interesting to see if they can take the actions necessary to prevent their companies from becoming social pariahs.

There are three main critiques of big tech. The first is that it is destroying the young. Social media promises an end to loneliness but actually produces an increase in solitude and an intense awareness of social exclusion. Texting and other technologies give you more control over your social interactions but also lead to thinner interactions and less real engagement with the world.

As Jean Twenge has demonstrated in book and essay, since the spread of the smartphone, teens are much less likely to hang out with friends, they are less likely to date, they are less likely to work. Eighth graders who spend 10 or more hours a week on social media are 56 percent more likely to say they are unhappy than those who spend less time. Eighth graders who are heavy users of social media increase their risk of depression by 27 percent. Teens who spend three or more hours a day on electronic devices are 35 percent more likely to have a risk factor for suicide, like making a plan for how to do it. Girls, especially hard hit, have experienced a 50 percent rise in depressive symptoms.

The second critique of the tech industry is that it is causing this addiction on purpose, to make money. Tech companies understand what causes dopamine surges in the brain and they lace their products with “hijacking techniques” that lure us in and create “compulsion loops.” Snapchat has Snapstreak, which rewards friends who snap each other every single day, thus encouraging addictive behavior. News feeds are structured as “bottomless bowls” so that one page view leads down to another and another and so on forever. Most social media sites create irregularly timed rewards; you have to check your device compulsively because you never know when a burst of social affirmation from a Facebook like may come.

The third critique is that Apple, Amazon, Google and Facebook are near monopolies that use their market power to invade the private lives of their users and impose unfair conditions on content creators and smaller competitors. The political assault on this front is gaining steam. The left is attacking tech companies because they are mammoth corporations; the right is attacking them because they are culturally progressive. Tech will have few defenders on the national scene. Obviously, the smart play would be for the tech industry to get out in front and clean up its own pollution.

There are activists like Tristan Harris of Time Well Spent, who is trying to move the tech world in the right directions. There are even some good engineering responses. I use an app called Moment to track and control my phone usage. The big breakthrough will come when tech executives clearly acknowledge the central truth: Their technologies are extremely useful for the tasks and pleasures that require shallower forms of consciousness, but they often crowd out and destroy the deeper forms of consciousness people need to thrive.

Online is a place for human contact but not intimacy. Online is a place for information but not reflection. It gives you the first stereotypical thought about a person or a situation, but it’s hard to carve out time and space for the third, 15th and 43rd thought. Online is a place for exploration but discourages cohesion. It grabs control of your attention and scatters it across a vast range of diverting things. But we are happiest when we have brought our lives to a point, when we have focused attention and will on one thing, wholeheartedly with all our might.

Rabbi Abraham Joshua Heschel wrote that we take a break from the distractions of the world not as a rest to give us more strength to dive back in, but as the climax of living. “The seventh day is a palace in time which we build. It is made of soul, joy and reticence,” he said. By cutting off work and technology we enter a different state of consciousness, a different dimension of time and a different atmosphere, a “mine where the spirit’s precious metal can be found.”

Imagine if instead of claiming to offer us the best things in life, tech merely saw itself as providing efficiency devices. Its innovations can save us time on lower-level tasks so we can get offline and there experience the best things in life. Imagine if tech pitched itself that way. That would be an amazing show of realism and, especially, humility, which these days is the ultimate and most disruptive technology.

Our Love Affair With Digital Is Over

Our Love Affair With Digital Is Over

By DAVID SAX NOV. 18, 2017

A decade ago I bought my first smartphone, a clunky little BlackBerry 8830 that came in a sleek black leather sheath. I loved that phone. I loved the way it effortlessly slid in and out of its case, loved the soft purr it emitted when an email came in, loved the silent whoosh of its trackball as I played Brick Breaker on the subway and the feel of its baby keys clicking under my fat thumbs. It was the world in my hands, and when I had to turn it off, I felt anxious and alone.

Like most relationships we plunge into with hearts aflutter, our love affair with digital technology promised us the world: more friends, money and democracy! Free music, news and same-day shipping of paper towels! A laugh a minute, and a constant party at our fingertips. Many of us bought into the fantasy that digital made everything better. We surrendered to this idea, and mistook our dependence for romance, until it was too late.

Today, when my phone is on, I feel anxious and count down the hours to when I am able to turn it off and truly relax. The love affair I once enjoyed with digital technology is over — and I know I’m not alone. Ten years after the iPhone first swept us off our feet, the growing mistrust of computers in both our personal lives and the greater society we live in is inescapable.

This publishing season is flush with books raising alarms about digital technology’s pernicious effects on our lives: what smartphones are doing to our children; how Facebook and Twitter are eroding our democratic institutions; and the economic effects of tech monopolies. A recent Pew Research Center survey noted that more than 70 percent of Americans were worried about automation’s impact on jobs, while just 21 percent of respondents to a Quartz survey said they trust Facebook with their personal information. Nearly half of millennials worry about the negative effects of social media on their mental and physical health, according to the American Psychiatric Association. So what now?

As much as we might fantasize about it, we probably won’t delete our social media accounts and toss our phones in the nearest body of water. What we can do is to restore some sense of balance over our relationship with digital technology, and the best way to do that is with analog: the ying to digital’s yang. Thankfully, the analog world is still here, and not only is it surviving but, in many cases, it is thriving. Sales of old-fashioned print books are up for the third year in a row, according to the Association of American Publishers, while ebook sales have been declining. Independent bookstores have been steadily expanding for several years. Vinyl records have witnessed a decade-long boom in popularity (more than 200,000 newly pressed records are sold each week in the United States), while sales of instant-film cameras, paper notebooks, board games and Broadway tickets are all growing again.

This surprising reversal of fortune for these apparently “obsolete” analog technologies is too often written off as nostalgia for a predigital time. But younger consumers who never owned a turntable and have few memories of life before the internet drive most of the current interest in analog, and often include those who work in Silicon Valley’s most powerful companies.

Analog, although more cumbersome and costly than its digital equivalents, provides a richness of experience that is unparalleled with anything delivered through a screen. People are buying books because a book engages nearly all of their senses, from the smell of the paper and glue to the sight of the cover design and weight of the pages read, the sound of those sheets turning, and even the subtle taste of the ink on your fingertips. A book can be bought and sold, given and received, and displayed on a shelf for anyone to see. It can start conversations and cultivate romances. The limits of analog, which were once seen as a disadvantage, are increasingly one of the benefits people are turning to as a counterweight to the easy manipulation of digital.

Though a page of paper is limited by its physical size and the permanence of the ink that marks it, there is a powerful efficiency in that simplicity. The person holding the pen above that notebook page is free to write, doodle or scribble her idea however she wishes between those borders, without the restrictions or distractions imposed by software. In a world of endless email chains, group chats, pop-up messages or endlessly tweaked documents and images, the walled garden of analog saves both time and inspires creativity.

Web designers at Google have been required to use pen and paper as a first step when brainstorming new projects for the past several years, because it leads to better ideas than those begun on a screen. In contrast with the virtual “communities” we have built online, analog actually contributes to the real places where we live.

I have become friendly with Ian Cheung, the appropriately opinionated owner of June Records, up the street from my home in Toronto. I benefit not only from the tax revenues that June Records contributes as a local business (paving the roads, paying my daughter’s teachers) but also from living nearby. Like the hardware store, Italian grocer and butcher on the same block, the brick and mortar presence of June adds to my neighborhood’s sense of place (i.e., a place with a killer selection of Cannonball Adderley and local indie albums) and gives me a feeling of belonging.

I also have no doubts that, unlike Twitter, Ian would immediately kick out any Nazi or raving misogynist who started ranting inside his store. Analog excels particularly well at encouraging human interaction, which is crucial to our physical and mental well-being.

The dynamic of a teacher working in a classroom full of students has not only proven resilient, but has outperformed digital learning experiments time and again. Digital may be extremely efficient in transferring pure information, but learning happens best when we build upon the relationships between students, teachers and their peers. We do not face a simple choice of digital or analog. That is the false logic of the binary code that computers are programmed with, which ignores the complexity of life in the real world. Instead, we are faced with a decision of how to strike the right balance between the two. If we keep that in mind, we are taking the first step toward a healthy relationship with all technology, and, most important, one another.

David Sax is the author of “The Revenge of Analog: Real Things and Why They Matter.”

Capitalism Has a Problem. Is Free Money the Answer?

Capitalism Has a Problem. Is Free Money the Answer?

By PETER S. GOODMAN NOV. 15, 2017 LONDON —

One need not be a card-carrying revolutionary to deduce that global capitalism has a problem. In much of the world, angry workers denounce a shortage of jobs paying enough to support middleclass life. Economists puzzle over the fix for persistently weak wage growth, just as robots appear poised to replace millions of human workers.

At the annual gathering of the global elite in the Swiss resort of Davos, billionaire finance chieftains debate how to make capitalism kinder to the masses to defuse populism. Enter the universal basic income. The idea is gaining traction in many countries as a proposal to soften the edges of capitalism.

Though the details and philosophies vary from place to place, the general notion is that the government hands out regular checks to everyone, regardless of income or whether people are working. The money ensures food and shelter for all, while removing the stigma of public support.

Some posit basic income as a way to let market forces work their ruthless magic, delivering innovation and economic growth, while laying down a cushion for those who fail. Others present it as a means of liberating people from wretched, poverty-level jobs, allowing workers to organize for better conditions or devote time to artistic exploits. Another school sees it as the required response to an era in which work can no longer be relied upon to finance basic needs.

“We see the increasing precariousness of employment,” said Karl Widerquist, a philosopher at Georgetown University in Qatar, and a prominent advocate for a universal social safety net. “Basic income gives the worker the power to say, ‘Well, if Walmart’s not going to pay me enough, then I’m just not going to work there.’ ” The universal basic income is clearly an idea with momentum.

Early this year, Finland kicked off a two-year national experiment in basic income. In the United States, a trial was recently completed in Oakland, Calif., and another is about to launch in nearby Stockton, a community hard-hit by the Great Recession and the attendant epidemic in home foreclosures. The Canadian province of Ontario is enrolling participants for a basic income trial. Several cities in the Netherlands are exploring what happens when they hand out cash grants unconditionally to people already receiving some form of public support. A similar test is underway in Barcelona, Spain.

A nonprofit organization, GiveDirectly, is proceeding with plans to provide universal cash grants in rural Kenya. As a concept, basic income has been kicked around in various guises for centuries, gaining adherents across a strikingly broad swath of the ideological spectrum, from the English social philosopher Thomas More to the American revolutionary Thomas Paine. The populist firebrand Louisiana governor Huey Long, the civil rights icon Martin Luther King Jr., and the laissez faire economist Milton Friedman would presumably agree on little, yet all advocated some version of basic income.

In a clear sign of its modern-day currency, the International Monetary Fund — not an institution prone to utopian dreaming — recently explored basic income as a potential salve for economic inequality. Not everyone loves the idea. Conservatives fret that handing out money free of obligation will turn people into dole-dependent slackers. In the American context, any talk of a truly universal form of basic income also collides with arithmetic. Give every American $10,000 a year — a sum still below the poverty line for an individual — and the tab runs to $3 trillion a year. That is about eight times what the United States now spends on social service programs. Conversation over.

Labor-oriented economists in the United States are especially wary of basic income, given that the American social safety programs have been significantly trimmed in recent decades, with welfare, unemployment benefits and food stamps all subject to a variety of restrictions. If basic income were to replace these components as one giant program — the proposal that would appeal to libertarians — it might beckon as a fat target for additional budget trimming.

“Tens of millions of poor people would likely end up worse off,” declared Robert Greenstein, president of the Center on Budget and Policy Priorities, a Washington-based research institution, in a recent blog post. “Were we starting from scratch — and were our political culture more like Western Europe’s — U.B.I. might be a real possibility. But that’s not the world we live in.” And some advocates for working people dismiss basic income as a wrongheaded approach to the real problem of not enough quality paychecks. “People want to work,” said the Nobel laureate economist Joseph E. Stiglitz when I asked him about basic income early this year. “They don’t want handouts.” Yet some of the basic income experiments now underway are engineered precisely to encourage people to work while limiting their contact with public assistance.

Finland’s trial is giving jobless people the same amount of money they were already receiving in unemployment benefits, while relieving them of bureaucratic obligations. The bet is that people will use time now squandered submitting paperwork to train for better careers, start businesses, or take part-time jobs. Under the system the trial replaces, people living on benefits risk losing support if they secure other income. In short, basic income is being advanced not as a license for Finns to laze in the sauna, but as a means of enhancing the forces of creative destruction so central to capitalism. As the logic goes, once sustenance is eliminated as a worry, weak companies can be shuttered without concern for those thrown out of work, freeing up capital and talent for more productive ventures.

The trials in the Netherlands, conducted at the municipal level, are similarly geared to paring bureaucracy from the unemployment system. Ditto, the Barcelona experiment. Silicon Valley has embraced basic income as a crucial element in enabling the continued rollout of automation. While engineers pioneer ways to replace human laborers with robots, financiers focus on basic income as a replacement for paychecks.

The experiment in Stockton, Calif. — set to become the first city government to test basic income — is underwritten in part by an advocacy group known as the Economic Security Project, whose backers include the Facebook co-founder Chris Hughes. The trial is set to begin next year, with an undisclosed number of residents to receive $500 a month. The trial in Oakland was the work of Y Combinator, a start-up incubator. Its researchers handed out varying grants to a few dozen people as a simple feasibility test for basic income. The next phase is far more ambitious. The Y Combinator researchers plan to distribute grants to 3,000 people with below-average incomes in two as-yet undisclosed American states. They will hand out $1,000 a month to 1,000 people, no strings attached, and $50 a month to the rest, allowing for comparisons in how recipients use the money, and what impact it has on their lives.

One key element of the basic income push is the assumption that poor people are better placed than bureaucrats to determine the most beneficial use of aid money. Rather than saddle recipients with complex rules and a dizzying array of programs, better to just give people money and let them sort out how to use it. This is a central idea of GiveDirectly’s program in Kenya, where it began a pilot study last year in which it handed out small, unconditional cash grants — about $22 a month — to residents of a single village. The program is now expanding its sights, with plans to hand out grants to some 16,000 people in 120 villages. From a research standpoint, these remain early days for basic income, a time for experimentation and assessment before serious amounts of money may be devoted to a new model for public assistance.

Yet from a political standpoint, basic income appears to have found its moment, one delivered by the anxieties of the working poor combined with those of the wealthy, who see in widening inequality the potential for mobs wielding pitchforks. “The interest is exploding everywhere,” said Guy Standing, a research associate at SOAS University of London. “The debates now are extraordinarily fertile.” 

The Ivory Tower Can’t Keep Ignoring Tech

The Ivory Tower Can’t Keep Ignoring Tech

By CATHY O’NEIL NOV. 14, 2017

These days, big data, artificial intelligence and the tech platforms that put them to work have huge influence and power. Algorithms choose the information we see when we go online, the jobs we get, the colleges to which we’re admitted and the credit cards and insurance we are issued. It goes without saying that when computers are making decisions, a lot can go wrong. Our lawmakers desperately need this explained to them in an unbiased way so they can appropriately regulate, and tech companies need to be held accountable for their influence over all elements of our lives.

But academics have been asleep at the wheel, leaving the responsibility for this education to well-paid lobbyists and employees who’ve abandoned the academy. That means our main source of information on the downside of bad technology — often after something’s gone disastrously awry, such as when we learned that fake news dominated our social media feeds before last year’s presidential election, threatening our democracy — is the media. But this coverage often misses everyday issues and tends to be far too credulous when it does exists.

Much of what should concern us is more nuanced and small scale — and much less understood — than what we see in the headlines. Moreover, we shouldn’t have to depend on journalism to do the tedious, serious work of understanding the problems with algorithms any more than we depend on it to pursue the latest questions in sociology or environmental science. We need academia to step up to fill in the gaps in our collective understanding about the new role of technology in shaping our lives. We need robust research on hiring algorithms that seem to filter out people with mental health disorders, sentencing algorithms that fail twice as often for black defendants as for white defendants, statistically flawed public teacher assessments or oppressive scheduling algorithms.

And we need research to ensure that the same mistakes aren’t made again and again. It’s absolutely within the abilities of academic research to study such examples and to push against the most obvious statistical, ethical or constitutional failures and dedicate serious intellectual energy to finding solutions. And whereas professional technologists working at private companies are not in a position to critique their own work, academics theoretically enjoy much more freedom of inquiry.

To be fair, there are real obstacles. Academics largely don’t have access to the mostly private, sensitive personal data that tech companies collect; indeed even when they study data-driven subjects, they work with data and methods that typically predict much more abstract things like disease or economics than human behavior, so they’re naïve about the effects such choices can have.

The academics who do get close to the big companies in terms of technique get quickly plucked out of academia to work for them, with much higher salaries to boot. That means professors working in computer science and robotics departments — or law schools — often find themselves in situations in which positing any skeptical message about technology could present a professional conflict of interest.

The many data science institutes around the country, which have created lucrative master’s programs to train data scientists, are more focused on trying to get a piece of the big data pie — in the form of collaborations and jobs for their graduates — than they are on asking how the pie should be made. We won’t find any help there. Indeed, while West Coast schools like Stanford and the University of California, Berkeley, are renowned for creating factories that churn out the future engineers and data scientists of Silicon Valley, there are very few coveted permanent, tenure-track jobs in the country devoted to algorithmic accountability.

A final hurdle: There is essentially no distinct field of academic study that takes seriously the responsibility of understanding and critiquing the role of technology — and specifically, the algorithms that are responsible for so many decisions — in our lives. That’s not surprising. Which academic department is going to give up a valuable tenure line to devote to this, given how much academic departments fight over resources already?

There’s one solution for the short term. We urgently need an academic institute focused on algorithmic accountability. First, it should provide a comprehensive ethical training for future engineers and data scientists at the undergraduate and graduate levels, with case studies taken from real-world algorithms that are choosing the winners from the losers.

Lecturers from humanities, social sciences and philosophy departments should weigh in. Second, this academic institute should offer a series of workshops, conferences and clinics focused on the intersection of different industries with the world of A.I. and algorithms. These should include experts in the content areas, lawyers, policymakers, ethicists, journalists and data scientists, and they should be tasked with poking holes in our current regulatory framework — and imagine a more relevant one. Third, the institute should convene a committee charged with reimagining the standards and ethics of human experimentation in the age of big data, in ways that can be adopted by the tech industry.

There’s a lot at stake when it comes to the growing role of algorithms in our lives. The good news is that a lot could be explained and clarified by professional and uncompromised thinkers who are protected within the walls of academia with freedom of academic inquiry and expression. If only they would scrutinize the big tech firms rather than stand by waiting to be hired. Cathy O’Neil is a data scientist and author of the book “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.

Waymo’s Autonomous Cars Cut Out Human Drivers in Road Tests

Waymo’s Autonomous Cars Cut Out Human Drivers in Road Tests

By DAISUKE WAKABAYASHI

NOV. 7, 2017 SAN FRANCISCO —

The self-driving car is edging closer to becoming driverless. Waymo, the autonomous car company from Google’s parent company Alphabet, has started testing a fleet of self-driving vehicles without any backup drivers on public roads, its chief executive said Tuesday. The tests, which will include passengers within the next few months, mark an important milestone that brings autonomous vehicle technology closer to operating without any human intervention.

Dozens of companies are testing self-driving technology on public roads across the United States and some autonomous features are available in today’s cars. But Waymo is believed to be the first company to test vehicles on public roads without a driver ready to take over in an emergency. “Our ultimate goal is to bring our fully self-driving technology to more cities in the U.S. and around the world,”

John Krafcik, Waymo’s chief, said in prepared remarks at a technology conference in Portugal on Tuesday. “Fully self-driving cars are here.” The tests are a show of engineering prowess by Waymo at a time when traditional automakers and other tech companies like Uber race to develop similar vehicles. Waymo is limiting the trials to a region around Phoenix, where it has been conducting a ride-testing program this year, and plans to expand the testing area over time.

The company said it planned to use the driverless vehicles to launch a commercial ride-hailing service for the general public, but did not offer any detail on when, where or how. Waymo said its driverless cars hit the public roads last month. The company did not say whether it was testing the driverless cars in environments considered challenging for autonomous vehicles, like bridges or tunnels, or more difficult conditions, like driving at night or in rain and snow — usually not a big concern in the dry Phoenix climate.

While the prospect of cars without emergency drivers may raise concerns among some passengers, Waymo said it had confidence in the safety of its self-driving technology. It has included backup systems like a secondary computer to take over if the main computer fails. And though the cars are driverless, they are not entirely without humans, at least for now.

Waymo employees sit in the back seat of the cars, monitoring them, a company spokesman, Johnny Luu, said. Once passengers join the tests, they will be able to contact Waymo support staff with a button inside the car. If the cars are involved in a crash, they are programmed to respond appropriately, including pulling off the road on their own.

Driverless cars are regulated by a patchwork of state laws. Arizona, like many states, has no restrictions against operating an autonomous vehicle without a person in the driver’s seat. On the other hand, California, where Waymo is headquartered, requires any self-driving car to have a safety driver sitting in the front. In December, Waymo published a report for California’s Department of Motor Vehicles about how frequently its car “disengaged” — deactivating its autonomous mode because of a system failure or safety risk and forcing a human driver to take over. In the report, Waymo said this happened once every 5,000 miles the cars drove in 2016, compared with once every 1,250 miles in 2015.

Consumer Watchdog, a frequent critic of Alphabet, said that data demonstrated that the cars are not ready to drive without any human intervention and that Waymo was following the Silicon Valley model of “beta testing” a new technology on the public. “It’s the wrong approach when you’re dealing with self-driving cars,” said John M. Simpson, a director at Consumer Watchdog.

“When things go wrong with a robot car, you kill people.” Researchers believe self-driving cars can be safer than cars operated by human drivers because they are programmed to adhere strictly to traffic laws, they don’t get distracted, and they usually refrain from taking unnecessary risks. Timothy Tait, a spokesman for the Arizona Department of Transportation, said the state was on pace to exceed 1,000 automobile-related fatalities this year and that its top priority is the public’s safety — particularly by advancing efforts to reduce crashes and deaths on its roads. “We are closely monitoring emerging technologies like self-driving cars that may ultimately support safer travel and open up opportunities for populations who today are unable to drive for themselves,” he said in a statement.

Waymo, which started as a research and development project for Google in 2009, maintains what many in the industry consider a technological advantage over its competitors. Waymo said its autonomous vehicles had driven more than 3.4 million miles on actual roads — with safety drivers — as well as running 10 million miles every day in a virtual simulator. In his remarks, Mr. Krafcik said Waymo sees a ride-hailing taxi service as the first commercial application of the company’s driverless car technology, though there could be other uses in logistics and public transportation.

Taking the human out of the equation will fundamentally change transportation and change how people buy cars, said Mr. Krafcik, who was an executive at Hyundai Motors before joining Google. “Because you’re accessing vehicles rather than owning, in the future, you could choose from an entire fleet of vehicle options that are tailored to each trip you want to make,” he said. “They can be designed for specific purposes or tasks.”

Building A.I. That Can Build A.I.

Building A.I. That Can Build A.I.

Google and others, fighting for a small pool of researchers, are looking for automated ways to deal with a shortage of artificial intelligence experts. 

By CADE METZ

NOV. 5, 2017 SAN FRANCISCO —

They are a dream of researchers but perhaps a nightmare for highly skilled computer programmers: artificially intelligent machines that can build other artificially intelligent machines.

With recent speeches in both Silicon Valley and China, Jeff Dean, one of Google’s leading engineers, spotlighted a Google project called AutoML. ML is short for machine learning, referring to computer algorithms that can learn to perform particular tasks on their own by analyzing data. AutoML, in turn, is a machine-learning algorithm that learns to build other machine-learning algorithms. With it, Google may soon find a way to create A.I. technology that can partly take the humans out of building the A.I. systems that many believe are the future of the technology industry.

The project is part of a much larger effort to bring the latest and greatest A.I. techniques to a wider collection of companies and software developers. The tech industry is promising everything from smartphone apps that can recognize faces to cars that can drive on their own. But by some estimates, only 10,000 people worldwide have the education, experience and talent needed to build the complex and sometimes mysterious mathematical algorithms that will drive this new breed of artificial intelligence.

The world’s largest tech businesses, including Google, Facebook and Microsoft, sometimes pay millions of dollars a year to A.I. experts, effectively cornering the market for this hard-to-find talent. The shortage isn’t going away anytime soon, just because mastering these skills takes years of work.

The industry is not willing to wait. Companies are developing all sorts of tools that will make it easier for any operation to build its own A.I. software, including things like image and speech recognition services and online chatbots. “We are following the same path that computer science has followed with every new type of technology,” said Joseph Sirosh, a vice president at Microsoft, which recently unveiled a tool to help coders build deep neural networks, a type of computer algorithm that is driving much of the recent progress in the A.I. field. “We are eliminating a lot of the heavy lifting.”

This is not altruism. Researchers like Mr. Dean believe that if more people and companies are working on artificial intelligence, it will propel their own research. At the same time, companies like Google, Amazon and Microsoft see serious money in the trend that Mr. Sirosh described. All of them are selling cloud-computing services that can help other businesses and developers build A.I. “There is real demand for this,” said Matt Scott, a co-founder and the chief technical officer of Malong, a start-up in China that offers similar services. “And the tools are not yet satisfying all the demand.”

This is most likely what Google has in mind for AutoML, as the company continues to hail the project’s progress. Google’s chief executive, Sundar Pichai, boasted about AutoML last month while unveiling a new Android smartphone. Eventually, the Google project will help companies build systems with artificial intelligence even if they don’t have extensive expertise, Mr. Dean said.

Today, he estimated, no more than a few thousand companies have the right talent for building A.I., but many more have the necessary data. “We want to go from thousands of organizations solving machine learning problems to millions,” he said.

Google is investing heavily in cloud-computing services — services that help other businesses build and run software — which it expects to be one of its primary economic engines in the years to come. And after snapping up such a large portion of the world’s top A.I researchers, it has a means of jump-starting this engine.

Neural networks are rapidly accelerating the development of A.I. Rather than building an image-recognition service or a language translation app by hand, one line of code at a time, engineers can much more quickly build an algorithm that learns tasks on its own. By analyzing the sounds in a vast collection of old technical support calls, for instance, a machine-learning algorithm can learn to recognize spoken words.

But building a neural network is not like building a website or some run-of-the-mill smartphone app. It requires significant math skills, extreme trial and error, and a fair amount of intuition. Jean-François Gagné, the chief executive of an independent machine-learning lab called Element AI, refers to the process as “a new kind of computer programming.” In building a neural network, researchers run dozens or even hundreds of experiments across a vast network of machines, testing how well an algorithm can learn a task like recognizing an image or translating from one language to another. Then they adjust particular parts of the algorithm over and over again, until they settle on something that works.

Some call it a “dark art,” just because researchers find it difficult to explain why they make particular adjustments. But with AutoML, Google is trying to automate this process. It is building algorithms that analyze the development of other algorithms, learning which methods are successful and which are not. Eventually, they learn to build more effective machine learning. Google said AutoML could now build algorithms that, in some cases, identified objects in photos more accurately than services built solely by human experts.

Barret Zoph, one of the Google researchers behind the project, believes that the same method will eventually work well for other tasks, like speech recognition or machine translation. This is not always an easy thing to wrap your head around. But it is part of a significant trend in A.I. research. Experts call it “learning to learn” or “meta-learning.” Many believe such methods will significantly accelerate the progress of A.I. in both the online and physical worlds.

At the University of California, Berkeley, researchers are building techniques that could allow robots to learn new tasks based on what they have learned in the past. “Computers are going to invent the algorithms for us, essentially,” said a Berkeley professor, Pieter Abbeel. “Algorithms invented by computers can solve many, many problems very quickly — at least that is the hope.” This is also a way of expanding the number of people and businesses that can build artificial intelligence. These methods will not replace A.I. researchers entirely.

Experts, like those at Google, must still do much of the important design work. But the belief is that the work of a few experts can help many others build their own software. Renato Negrinho, a researcher at Carnegie Mellon University who is exploring technology similar to AutoML, said this was not a reality today but should be in the years to come. “It is just a matter of when,” he said.

The Upside of Being Ruled by the Five Tech Giants

NY Times TECHNOLOGY

The Upside of Being Ruled by the Five Tech Giants

Farhad Manjoo

STATE OF THE ART

NOV. 1, 2017

The tech giants are too big. But what if that’s not so bad? For a year and a half — and more urgently for much of the last month — I have warned of the growing economic, social and political power held by the five largest American tech companies: Apple, Amazon, Google, Facebook and Microsoft. Because these companies control the world’s most important tech platforms, from smartphones to app stores to the map of our social relationships, their power is growing closer to that of governments than of mere corporations. That was on stark display this week, when executives from two of the five, Facebook and Google, along with a struggling second-tier company, Twitter, testified before Congress about how their technology may have been used to influence the 2016 election.

Yet ever since I started writing about what I call the Frightful Five, some have said my very premise is off base. I have argued that the companies’ size and influence pose a danger. But another argument suggests the opposite — that it’s better to be ruled by a handful of responsive companies capable of bowing to political and legal pressure. In other words, wouldn’t you rather deal with five horse-size Zucks than 100 duck-size technoforces?

The insatiable appetite of digital technology to alter everything in its path is among the most powerful forces shaping the world today. Given all the ways that tech can go wrong — as we are seeing in the Russia influence scandal — isn’t it better that we can blame, and demand fixes from, a handful of American executives when things do go haywire?

That’s not ridiculous. Over the last few weeks, several scholars said there are good reasons to be sanguine about our new tech overlords. Below, I compiled their best arguments about the bright side of the Five.

The Five Can Be Governed

Tech is inherently messy. The greatest human inventions tend to change society in ways that are more profound than anyone ever guesses, including the people who created them. This has clearly been true for the technologies we use today, and will be even more true for the stuff we will get tomorrow. The internet, mobile phones, social networks and artificial intelligence will make a mess of the status quo — and it will be our job, as a society, to decide how to mitigate their downsides.

One benefit of having five giant companies in charge of today’s tech infrastructure is that they provide a convenient focus for addressing those problems. Consider Russian propaganda. People have worried about the internet’s capacity to foster echo chambers and conspiracy theories almost since it began; in fact, in several cases over the last two decades — from 9/11 to the Swift Boat Veterans for Truth to birtherism — the internet did play a key role in the propagation of misinformation. But because those rumors and half-truths spread in a digital media landscape that was not owned and operated by giant companies — one in which information was passed along through a Wild West of email, discussion boards and blogs — it was never conceivable to limit that era’s equivalent of fake news.

Today, it suddenly is. Because Facebook, Google and Twitter play such a central role in modern communication, they can be hauled before Congress and either regulated or shamed into addressing the problems unleashed by the technology they control. This does not mean they will succeed in fixing every problem their tech creates — and in some cases their fixes may well raise other problems, like questions about their power over freedom of expression. But at least they can try to address the wide variety of externalities posed by tech, which may have been impossible for an internet more fragmented by smaller firms.

“This is new stuff everybody is dealing with — it’s not easy,” said Rob Atkinson, president of the Information Technology and Innovation Foundation, a think tank, and co-author of “Big Is Beautiful,” a coming book that extols the social and economic virtues of big companies. (The foundation is funded, in part, by donations from tech companies.) “

So when you discover a problem, scale makes that easier. You’ve got one or two big firms, and they have a lot of public pressure to be a responsible actor.” The Five Hate One Another’s Guts Over the last few weeks, many people at large tech companies have repeatedly responded to my questions about the dangers posed by big tech with a funny argument: Yes, they would say, the other tech giants really are worrisome — so why was I including their company in that group? It was an odd line.

As an outsider to these companies, I tend to worry about the collective power of the Five, especially the way they have managed to control the fortunes of innovative start-ups. But none of the Five see themselves as part of a group — each of them worries about the threat posed by start-ups and by the other four giants, which means that none feels it has the luxury to slow down in creating the best new stuff. This dynamic — where each company competes mightily against the others — suggests some reason for optimism, said Michael Lind, who wrote “Big Is Beautiful” with Mr. Atkinson.

“As long as their innovation rents are recycled into research and development that leads to new products, then what’s to complain about?” You can see this in their product road maps. None of the Five has slowed down investing intended to further expand its area of control — for instance, Google keeps investing in search, Facebook is still spending heavily to create new social-networking features, and Amazon remains relentless in creating new ways to let people shop.

At the same time, they are all locked in intense battles for new markets and technologies. And not only do they keep creating new tech, but they are coming at it in diverse ways — with different business models, different philosophies and different sets of ethics. “So, why pre-emptively say that maybe we’ll be harmed in the future — that in 2030 they’ll jack up their prices or something?” Mr. Lind asked. “Well, deal with that as it comes.”

The Five Are American Grown

The Five achieved their dominance because they operate in areas that provide massive returns to scale. Thanks to economic dynamics like network effects — where a product, like Facebook, gets more useful as more people use it — it was perhaps inevitable that we would see the rise of a handful of large companies take control of much of the modern tech business. But it wasn’t inevitable that these companies would be based in and controlled from the United States. And it’s not obvious that will remain the case — the top tech companies of tomorrow might easily be Chinese, or Indian or Russian or European. But for now, that means we are dealing with companies that feel constrained by American laws and values. Yes, this is jingoistic; the idea of a handful of American tech giants controlling much of society has helped push regulators internationally to try to limit their power.

But we would almost certainly do the same if a bunch of foreign companies attempted to take over our economy. At least it’s our own giants that we have to fear. I don’t mean this list to get the Five off the hook. How we deal with their efforts to capture more power over the economy and our society is perhaps the next great question facing America. But this is a complex problem precisely because there are both advantages and disadvantages to their size. As I said, tech is messy.

The Real Threat of Artificial Intelligence

The Real Threat of Artificial Intelligence

By KAI­FU LEE JUNE 24, 2017 BEIJING —

NY Times Sunday Review

What worries you about the coming world of artificial intelligence? Too often the answer to this question resembles the plot of a sci­fi thriller. People worry that developments in A.I. will bring about the “singularity” — that point in history when A.I. surpasses human intelligence, leading to an unimaginable revolution in human affairs. Or they wonder whether instead of our controlling artificial intelligence, it will control us, turning us, in effect, into cyborgs.

These are interesting issues to contemplate, but they are not pressing. They concern situations that may not arise for hundreds of years, if ever. At the moment, there is no known path from our best A.I. tools (like the Google computer program that recently beat the world’s best player of the game of Go) to “general” A.I. — self­-aware computer programs that can engage in common­sense reasoning, attain knowledge in multiple domains, feel, express and understand emotions and so on.

This doesn’t mean we have nothing to worry about. On the contrary, the A.I. products that now exist are improving faster than most people realize and promise to radically transform our world, not always for the better. They are only tools, not a competing form of intelligence. But they will reshape what work means and how wealth is created, leading to unprecedented economic inequalities and even altering the global balance of power. It is imperative that we turn our attention to these imminent challenges.

What is artificial intelligence today? Roughly speaking, it’s technology that takes in huge amounts of information from a specific domain (say, loan repayment histories) and uses it to make a decision in a specific case (whether to give an individual a loan) in the service of a specified goal (maximizing profits for the lender). Think of a spreadsheet on steroids, trained on big data. These tools can outperform human beings at a given task. This kind of A.I. is spreading to thousands of domains (not just loans), and as it does, it will eliminate many jobs. Bank tellers, customer service representatives, telemarketers, stock and bond traders, even paralegals and radiologists will gradually be replaced by such software. Over time this technology will come to control semiautonomous and autonomous hardware like self­-driving cars and robots, displacing factory workers, construction workers, drivers, delivery workers and many others.

Unlike the Industrial Revolution and the computer revolution, the A.I. revolution is not taking certain jobs (artisans, personal assistants who use paper and typewriters) and replacing them with other jobs (assembly­-line workers, personal assistants conversant with computers). Instead, it is poised to bring about a wide-­scale decimation of jobs — mostly lower­-paying jobs, but some higher­-paying ones, too. This transformation will result in enormous profits for the companies that develop A.I., as well as for the companies that adopt it.

Imagine how much money a company like Uber would make if it used only robot drivers. Imagine the profits if Apple could manufacture its products without human labor. Imagine the gains to a loan company that could issue 30 million loans a year with virtually no human involvement. (As it happens, my venture capital firm has invested in just such a loan company.) We are thus facing two developments that do not sit easily together: enormous wealth concentrated in relatively few hands and enormous numbers of people out of work.

What is to be done? Part of the answer will involve educating or retraining people in tasks A.I. tools aren’t good at. Artificial intelligence is poorly suited for jobs involving creativity, planning and “cross­-domain” thinking — for example, the work of a trial lawyer. But these skills are typically required by high-­paying jobs that may be hard to retrain displaced workers to do. More promising are lower­-paying jobs involving the “people skills” that A.I. lacks: social workers, bartenders, concierges — professions requiring nuanced human interaction. But here, too, there is a problem: How many bartenders does a society really need?

The solution to the problem of mass unemployment, I suspect, will involve “service jobs of love.” These are jobs that A.I. cannot do, that society needs and that give people a sense of purpose. Examples include accompanying an older person to visit a doctor, mentoring at an orphanage and serving as a sponsor at Alcoholics Anonymous — or, potentially soon, Virtual Reality Anonymous (for those addicted to their parallel lives in computer­-generated simulations). The volunteer service jobs of today, in other words, may turn into the real jobs of the future. Other volunteer jobs may be higher-­paying and professional, such as compassionate medical service providers who serve as the “human interface” for A.I. programs that diagnose cancer.

In all cases, people will be able to choose to work fewer hours than they do now. Who will pay for these jobs? Here is where the enormous wealth concentrated in relatively few hands comes in. It strikes me as unavoidable that large chunks of the money created by A.I. will have to be transferred to those whose jobs have been displaced. This seems feasible only through Keynesian policies of increased government spending, presumably raised through taxation on wealthy companies. As for what form that social welfare would take, I would argue for a conditional universal basic income: welfare offered to those who have a financial need, on the condition they either show an effort to receive training that would make them employable or commit to a certain number of hours of “service of love” voluntarism.

To fund this, tax rates will have to be high. The government will not only have to subsidize most people’s lives and work; it will also have to compensate for the loss of individual tax revenue previously collected from employed individuals. This leads to the final and perhaps most consequential challenge of A.I. The Keynesian approach I have sketched out may be feasible in the United States and China, which will have enough successful A.I. businesses to fund welfare initiatives via taxes.

But what about other countries? They face two insurmountable problems. First, most of the money being made from artificial intelligence will go to the United States and China. A.I. is an industry in which strength begets strength: The more data you have, the better your product; the better your product, the more data you can collect; the more data you can collect, the more talent you can attract; the more talent you can attract, the better your product. It’s a virtuous circle, and the United States and China have already amassed the talent, market share and data to set it in motion.

For example, the Chinese speech­-recognition company iFlytek and several Chinese face-­recognition companies such as Megvii and SenseTime have become industry leaders, as measured by market capitalization. The United States is spearheading the development of autonomous vehicles, led by companies like Google, Tesla and Uber. As for the consumer internet market, seven American or Chinese companies — Google, Facebook, Microsoft, Amazon, Baidu, Alibaba and Tencent — are making extensive use of A.I. and expanding operations to other countries, essentially owning those A.I. markets. It seems American businesses will dominate in developed markets and some developing markets, while Chinese companies will win in most developing markets.

The other challenge for many countries that are not China or the United States is that their populations are increasing, especially in the developing world. While a large, growing population can be an economic asset (as in China and India in recent decades), in the age of A.I. it will be an economic liability because it will comprise mostly displaced workers, not productive ones. So if most countries will not be able to tax ultra­-profitable A.I. companies to subsidize their workers, what options will they have? I foresee only one: Unless they wish to plunge their people into poverty, they will be forced to negotiate with whichever country supplies most of their A.I. software — China or the United States — to essentially become that country’s economic dependent, taking in welfare subsidies in exchange for letting the “parent” nation’s A.I. companies continue to profit from the dependent country’s users.

Such economic arrangements would reshape today’s geopolitical alliances. One way or another, we are going to have to start thinking about how to minimize the looming A.I.­ fueled gap between the haves and the have-­nots, both within and between nations. Or to put the matter more optimistically: A.I. is presenting us with an opportunity to rethink economic inequality on a global scale. These challenges are too far­-ranging in their effects for any nation to isolate itself from the rest of the world.

Donald Trump, Our A.I. President

Donald Trump, Our A.I. President

Robert A. Burton THE STONE

MAY 22, 2017

It is hard to imagine a more scathing indictment of our ability to read another’s thoughts and intentions than our inability to predict Donald Trump’s next move. From the gross pre­-election misjudgments to postelection bafflement, the best pundits are at a loss to accurately anticipate his response to matters like North Korean military aggressiveness or his moment-­by­-moment political gyrations and opinion reversals.

Labeling Trump a narcissist, psychopath, megalomaniac or attention-­impaired, or all of the above, might feel explanatory, but even when armed with the best psychoanalytic insights, we have no idea what he will do when presented with a new or unforeseen circumstance.

If conventional psychology isn’t up to the task, perhaps we should step back and consider a tantalizing sci­fi alternative — that Trump doesn’t operate within conventional human cognitive constraints, but rather is a new life form, a rudimentary artificial intelligence-­based learning machine. When we strip away all moral, ethical and ideological considerations from his decisions and see them strictly in the light of machine learning, his behavior makes perfect sense.

Consider how deep learning occurs in neural networks such as Google’s Deep Mind or IBM’s Deep Blue and Watson. In the beginning, each network analyzes a number of previously recorded games, and then, through trial and error, the network tests out various strategies. Connections for winning moves are enhanced; losing connections are pruned away. The network has no idea what it is doing or why one play is better than another. It isn’t saddled with any confounding principles such as what constitutes socially acceptable or unacceptable behavior or which decisions might result in negative downstream consequences.

Metaphorically, this process is reminiscent of Richard Dawkins’s notion of the selfish gene. The goal of DNA is self-­reproduction; the sole intent of Deep Mind or Watson is to win. When Deep Mind beat the world’s best Go player, it did not consider the feelings of the loser or the potentially devastating effects of A.I. on future employment or personal identity. If any one quality could be ascribed to A.I. neural networks, it would be relentless “single­-minded” self­-interest.

Now up the stakes; instead of Go, Jeopardy, backgammon, poker and chess domination, ask a neural network to figure out the optimal strategy for the biggest game in town — the United States presidency. In this hypothetical, let’s input and analyze all available written and spoken word — from mainstream media commentary to the most obscure one-­off crank pamphlets. After running simulations of various hypotheses, the network will serve up its suggestions. It might show Trump which areas of the country are most likely to respond to personal appearances, which rallies and town hall meetings will generate the greatest photo op and TV coverage, and which publicly manifest personality traits will garner the most votes. If it determines that outrage is the only road to the presidency, it will tell Trump when and where his opinions must be scandalous and offensively polarizing.

Further imagine that the Trump A.I. machine determines that the opinions most likely to get him elected have slim chances of being carried out once he is president. To the extent that traditional politicians are embarrassed by flip-­flops or bound by ethical and moral values or both, they are likely to display a degree of restraint and hedging of controversial positions. Winning at any cost is at least partially balanced by underlying principles. Not so for a neural network. There is no cringe factor, no sense of anticipated embarrassment or humiliation with seemingly random changes of mind. There is no concern with subsequent disclosures of misrepresentations, falsifications and outright lies. As the U.C.L.A. Bruins football coach Henry Sanders, known as Red, once said, “Winning isn’t everything; it’s the only thing.”

Following the successful election, it chews on new data. When it recognizes that Obamacare won’t be easily repealed or replaced, that token intervention in Syria can’t be avoided, that NATO is a necessity and that pulling out of the Paris climate accord may create worldwide resentment, it has no qualms about changing policies and priorities. From an A.I. vantage point, the absence of a coherent agenda is entirely understandable. For example, a consistent long­-term foreign policy requires a steadfastness contrary to a learning machine’s constant upgrading in response to new data.

A caveat to media gurus, historians and policy wonks: As there are no lines of reasoning driving the network’s actions, it is not possible to reverse engineer the network to reveal the “why” of any decision. Asking why a network chose a particular action is like asking why Amazon might recommend James Ellroy and Elmore Leonard novels to someone who has just purchased “Crime and Punishment.” There is no underlying understanding of the nature of the books; the association is strictly a matter of analyzing Amazon’s click and purchase data. Without explanatory reasoning driving decision making, counterarguments become irrelevant.

The most cogent reasons that solar power is preferable to burning coal will fall on deaf circuits as long as the Trump network continues to determine that Trump is doing a great job. To know how this network assesses his performance, we need to know what basic positive and negative values Trump has inputted, and how they might compete with one another to determine whether or not he’s succeeding. It is easy to come up with a list of likely motivating goals such as power, money, brand-name recognition, public praise, vindication, retribution and idolization. As recent history has shown us, these goals seem to vary from moment to moment. Unfortunately, a constantly shifting definition of what constitutes “winning” further increases our inability to predict his behavior.

A bitter irony: Criticism may have unintended positive rather than negative effects. In a poll last month, nearly 90 percent of Trump voters felt that media criticism of Trump only reinforced their view that the president is on the right track. As neural networks have no concept of fault, a failure of a policy won’t be seen as illogical or ill conceived. Failure just means that you need to try another strategy (or get new staff members — as suggested by his advisers’ phenomenally short half­-life).

As armchair psychologists, we have the gut feeling that with enough information and psychological savvy, we can figure out what makes Trump tick. Unfortunately there is no supporting evidence for this wishful thinking. Once we accept that Donald Trump represents a black-­box, first-­generation artificial-intelligence president driven solely by self­-selected data and widely fluctuating criteria of success, we can get down to the really hard question confronting our collective future: Is there a way to affect changes in a machine devoid of the common features that bind humanity? 

Robert A. Burton THE STONE

MAY 22, 2017

It is hard to imagine a more scathing indictment of our ability to read another’s thoughts and intentions than our inability to predict Donald Trump’s next move. From the gross pre­-election misjudgments to postelection bafflement, the best pundits are at a loss to accurately anticipate his response to matters like North Korean military aggressiveness or his moment-­by­-moment political gyrations and opinion reversals.

Labeling Trump a narcissist, psychopath, megalomaniac or attention-­impaired, or all of the above, might feel explanatory, but even when armed with the best psychoanalytic insights, we have no idea what he will do when presented with a new or unforeseen circumstance.

If conventional psychology isn’t up to the task, perhaps we should step back and consider a tantalizing sci­fi alternative — that Trump doesn’t operate within conventional human cognitive constraints, but rather is a new life form, a rudimentary artificial intelligence-­based learning machine. When we strip away all moral, ethical and ideological considerations from his decisions and see them strictly in the light of machine learning, his behavior makes perfect sense.

Consider how deep learning occurs in neural networks such as Google’s Deep Mind or IBM’s Deep Blue and Watson. In the beginning, each network analyzes a number of previously recorded games, and then, through trial and error, the network tests out various strategies. Connections for winning moves are enhanced; losing connections are pruned away. The network has no idea what it is doing or why one play is better than another. It isn’t saddled with any confounding principles such as what constitutes socially acceptable or unacceptable behavior or which decisions might result in negative downstream consequences.

Metaphorically, this process is reminiscent of Richard Dawkins’s notion of the selfish gene. The goal of DNA is self-­reproduction; the sole intent of Deep Mind or Watson is to win. When Deep Mind beat the world’s best Go player, it did not consider the feelings of the loser or the potentially devastating effects of A.I. on future employment or personal identity. If any one quality could be ascribed to A.I. neural networks, it would be relentless “single­-minded” self­-interest.

Now up the stakes; instead of Go, Jeopardy, backgammon, poker and chess domination, ask a neural network to figure out the optimal strategy for the biggest game in town — the United States presidency. In this hypothetical, let’s input and analyze all available written and spoken word — from mainstream media commentary to the most obscure one-­off crank pamphlets. After running simulations of various hypotheses, the network will serve up its suggestions. It might show Trump which areas of the country are most likely to respond to personal appearances, which rallies and town hall meetings will generate the greatest photo op and TV coverage, and which publicly manifest personality traits will garner the most votes. If it determines that outrage is the only road to the presidency, it will tell Trump when and where his opinions must be scandalous and offensively polarizing.

Further imagine that the Trump A.I. machine determines that the opinions most likely to get him elected have slim chances of being carried out once he is president. To the extent that traditional politicians are embarrassed by flip-­flops or bound by ethical and moral values or both, they are likely to display a degree of restraint and hedging of controversial positions. Winning at any cost is at least partially balanced by underlying principles. Not so for a neural network. There is no cringe factor, no sense of anticipated embarrassment or humiliation with seemingly random changes of mind. There is no concern with subsequent disclosures of misrepresentations, falsifications and outright lies. As the U.C.L.A. Bruins football coach Henry Sanders, known as Red, once said, “Winning isn’t everything; it’s the only thing.”

Following the successful election, it chews on new data. When it recognizes that Obamacare won’t be easily repealed or replaced, that token intervention in Syria can’t be avoided, that NATO is a necessity and that pulling out of the Paris climate accord may create worldwide resentment, it has no qualms about changing policies and priorities. From an A.I. vantage point, the absence of a coherent agenda is entirely understandable. For example, a consistent long­-term foreign policy requires a steadfastness contrary to a learning machine’s constant upgrading in response to new data.

A caveat to media gurus, historians and policy wonks: As there are no lines of reasoning driving the network’s actions, it is not possible to reverse engineer the network to reveal the “why” of any decision. Asking why a network chose a particular action is like asking why Amazon might recommend James Ellroy and Elmore Leonard novels to someone who has just purchased “Crime and Punishment.” There is no underlying understanding of the nature of the books; the association is strictly a matter of analyzing Amazon’s click and purchase data. Without explanatory reasoning driving decision making, counterarguments become irrelevant.

The most cogent reasons that solar power is preferable to burning coal will fall on deaf circuits as long as the Trump network continues to determine that Trump is doing a great job. To know how this network assesses his performance, we need to know what basic positive and negative values Trump has inputted, and how they might compete with one another to determine whether or not he’s succeeding. It is easy to come up with a list of likely motivating goals such as power, money, brand-name recognition, public praise, vindication, retribution and idolization. As recent history has shown us, these goals seem to vary from moment to moment. Unfortunately, a constantly shifting definition of what constitutes “winning” further increases our inability to predict his behavior.

A bitter irony: Criticism may have unintended positive rather than negative effects. In a poll last month, nearly 90 percent of Trump voters felt that media criticism of Trump only reinforced their view that the president is on the right track. As neural networks have no concept of fault, a failure of a policy won’t be seen as illogical or ill conceived. Failure just means that you need to try another strategy (or get new staff members — as suggested by his advisers’ phenomenally short half­-life).

As armchair psychologists, we have the gut feeling that with enough information and psychological savvy, we can figure out what makes Trump tick. Unfortunately there is no supporting evidence for this wishful thinking. Once we accept that Donald Trump represents a black-­box, first-­generation artificial-intelligence president driven solely by self­-selected data and widely fluctuating criteria of success, we can get down to the really hard question confronting our collective future: Is there a way to affect changes in a machine devoid of the common features that bind humanity?