In the world chess championship match that ended Friday in India, Norway's Magnus Carlsen, the cool, charismatic 22-year-old challenger and the highest-rated player in chess history, defeated local hero Viswanathan Anand, the 43-year-old champion. Mr. Carlsen's winning score of three wins and seven draws will cement his place among the game's all-time greats. But his success also illustrates a paradoxical development: Chess-playing computers, far from revealing the limits of human ability, have actually pushed it to new heights. ... Before the Deep Blue match, top players were using databases of games to prepare for tournaments. Computers could display games at high speed while the players searched for the patterns and weaknesses of their opponents. The programs could spot blunders, but they didn't understand chess well enough to offer much more than that. ... Once laptops could routinely dispatch grandmasters, however, it became possible to integrate their analysis fully into other aspects of the game. Commentators at major tournaments now consult computers to check their judgment. Online, fans get excited when their own "engines" discover moves the players miss. And elite grandmasters use computers to test their opening plans and generate new ideas.

Computers seem to be replacing humans across many industries, and we're all getting very nervous. ... But if you want some reason for optimism, visit your local supermarket. ... In a recent research paper called "Dancing With Robots," the economists Frank Levy and Richard Murnane point out that computers replace human workers only when machines meet two key conditions. First, the information necessary to carry out the task must be put in a form that computers can understand, and second, the job must be routine enough that it can be expressed in a series of rules. ... Supermarket checkout machines meet the second of these conditions, but they fail on the first. They lack proper information to do the job a human would do. To put it another way: They can't tell shiitakes from Shinola. Instead of identifying your produce, the machine asks you, the customer, to type in a code for every leafy green in your cart. Many times you'll have to look up the code in an on-screen directory. If a human checker asked you to remind him what that bunch of the oblong yellow fruit in your basket was, you'd ask to see his boss. ... This deficiency extends far beyond the checkout lane.

Douglas Hofstadter, the Pulitzer Prize–winning author of Gödel, Escher, Bach, thinks we've lost sight of what artificial intelligence really means. His stubborn quest to replicate the human mind. ... “It depends on what you mean by artificial intelligence.” Douglas Hofstadter is in a grocery store in Bloomington, Indiana, picking out salad ingredients. “If somebody meant by artificial intelligence the attempt to understand the mind, or to create something human-like, they might say—maybe they wouldn’t go this far—but they might say this is some of the only good work that’s ever been done.” ... Hofstadter says this with an easy deliberateness, and he says it that way because for him, it is an uncontroversial conviction that the most-exciting projects in modern artificial intelligence, the stuff the public maybe sees as stepping stones on the way to science fiction—like Watson, IBM’s Jeopardy-playing supercomputer, or Siri, Apple’s iPhone assistant—in fact have very little to do with intelligence. For the past 30 years, most of them spent in an old house just northwest of the Indiana University campus, he and his graduate students have been picking up the slack: trying to figure out how our thinking works, by writing computer programs that think. ... Their operating premise is simple: the mind is a very unusual piece of software, and the best way to understand how a piece of software works is to write it yourself.

Doug Williams used to give polygraph exams. Now he’s going to prison for teaching people how to beat them. ... Many of the people who sought out Williams over the years had secrets: marital indiscretions or professional lapses, drug busts or sex crimes. Williams never asked for details—those weren’t his concern. He has no affection for crooked cops or sexual predators, but what he hates above all else is the polygraph machine, an “insidious Orwellian instrument of torture,” as he calls it, that sows fear and mistrust, ruining careers by tarring truthful people as liars. “It is no more accurate than the toss of a coin,” he likes to say. When he’s feeling less generous, he’ll say a coin works better. ... The quest to defeat lying is as old as humanity. In Bronze Age China and India, suspects had to chew uncooked rice and spit it out to reveal if their mouths were dry. Medieval Europe had trial by fire or water. In the 1950s and ’60s, the CIA experimented with LSD as a truth serum. Then there’s torture, formalized in ancient Greece as a method to compel honesty and recast for the 21st century as “enhanced interrogation.” ... The polygraph, invented in 1921, is today’s most widely trusted lie-detection device.

How much should you charge someone to live in your house? Or how much would you pay to live in someone else’s house? Would you pay more or less for a planned vacation or for a spur-of-the-moment getaway? ... In focus groups, we watched people go through the process of listing their properties on our site—and get stumped when they came to the price field. Many would take a look at what their neighbors were charging and pick a comparable price; this involved opening a lot of tabs in their browsers and figuring out which listings were similar to theirs. Some people had a goal in mind before they signed up, maybe to make a little extra money to help pay the mortgage or defray the costs of a vacation. So they set a price that would help them meet that goal without considering the real market value of their listing. And some people, unfortunately, just gave up. ... Clearly, Airbnb needed to offer people a better way—an automated source of pricing information to help hosts come to a decision. That’s why we started building pricing tools in 2012 and have been working to make them better ever since. ... we’ve added what we think is a unique approach to machine learning that lets our system not only learn from its own experience but also take advantage of a little human intuition when necessary.

The greatest anxiety troubling workers today is embodied in a simple question: How will we humans add value? Popular culture is obsessed by it. Humans, a new series on the AMC network, spins a story from the promise and perils of eerily humanoid robots called synths. That seems to be Hollywood’s 2015 theme of the year. Think of Ex Machina (humanoid robot outsmarts people, kills a man, enters society as a person) or Terminator Genisys (Arnold Schwarzenegger’s humanoid robot must again save the world) or Avengers: Age of Ultron (humanoid robot tries to eradicate humanity) or Chappie (bad guys try to destroy humanoid robot police officer who is reprogrammed to think and feel). The big idea is always the same: For good or ill, machines become just like people—only better. ... Fear of technological unemployment is as old as technology, and it has always been unfounded. Over time and across economies, technology has multiplied jobs and raised living standards more spectacularly than any other force in history, by far. But now growing numbers of economists and technologists wonder if just maybe that trend has run its course. ... How will we humans add value? There is an answer, but so far we’ve mostly been looking for it in the wrong way. The conventional approach has been to ask what kind of work a computer will never be able to do. ... A better strategy is to ask, What are the activities that we humans, driven by our deepest nature or by the realities of daily life, will simply insist be performed by other humans, even if computers could do them?

Matt Ginsberg’s training is in astrophysics. He got his Ph.D. from Oxford when he was 24 years old. His doctoral advisor there was the famed mathematical physicist Roger Penrose, and he recalls rubbing elbows with the academic rock stars Stephen Hawking and the late Richard Feynman. He created an artificial intelligence crossword puzzle solver called Dr. Fill and a computer bridge world champion called GIB. ... Unsurprisingly, there’s pretty heavy math involved to make this real-time sports predictor work. For one element of the system’s calculations, Ginsberg sent me a pdf with eight dense pages of physics diagrams and systems of equations and notes on derivations. It uses something called the Levenberg-Marquardt algorithm. It requires Jacobians and the taking of partial derivatives and the solving of quartics, and code efficient enough to calculate it all up to the split second. If predicting the future were easy, I suppose everybody would do it. ... One thing this project can’t predict, however, is its own future. Its uses are, so far, largely speculative, and cashing in on a minor superpower might not be easy. Even gamblers who bet during play would struggle to make much money from a half-second heads-up that a shot is going in. But Ginsberg’s system would find a natural place in the long line of sports technologies that have been used for a singular end — TV.

Central to this concern is the prospect of an “intelligence explosion,” a speculative event in which an A.I. gains the ability to improve itself, and in short order exceeds the intellectual potential of the human brain by many orders of magnitude. ... Such a system would effectively be a new kind of life, and Bostrom’s fears, in their simplest form, are evolutionary: that humanity will unexpectedly become outmatched by a smarter competitor. He sometimes notes, as a point of comparison, the trajectories of people and gorillas: both primates, but with one species dominating the planet and the other at the edge of annihilation. ... Bostrom is arguably the leading transhumanist philosopher today, a position achieved by bringing order to ideas that might otherwise never have survived outside the half-crazy Internet ecosystem where they formed. He rarely makes concrete predictions, but, by relying on probability theory, he seeks to tease out insights where insights seem impossible. ... The people who say that artificial intelligence is not a problem tend to work in artificial intelligence.

He says it’s a self-driving car that he had built in about a month. The claim seems absurd. But when I turn up that morning, in his garage there’s a white 2016 Acura ILX outfitted with a laser-based radar (lidar) system on the roof and a camera mounted near the rearview mirror. A tangle of electronics is attached to a wooden board where the glove compartment used to be, a joystick protrudes where you’d usually find a gearshift, and a 21.5-inch screen is attached to the center of the dash. “Tesla only has a 17-inch screen,” Hotz says. ... Hotz was the first person to hack Apple’s iPhone, allowing anyone—well, anyone with a soldering iron and some software smarts—to use the phone on networks other than AT&T’s. He later became the first person to run through a gantlet of hard-core defense systems in the Sony PlayStation 3 and crack that open, too. ... The technology he’s building represents an end run on much more expensive systems being designed by Google, Uber, the major automakers, and, if persistent rumors and numerous news reports are true, Apple. More short term, he thinks he can challenge Mobileye, the Israeli company that supplies Tesla Motors, BMW, Ford Motor, General Motors, and others with their current driver-assist technology. ... Hotz plans to best the Mobileye technology with off-the-shelf electronics. He’s building a kit consisting of six cameras—similar to the $13 ones found in smartphones—that would be placed around the car. ... The goal is to sell the camera and software package for $1,000 a pop either to automakers or, if need be, directly to consumers who would buy customized vehicles at a showroom run by Hotz. ... There are two breakthroughs that make Hotz’s system possible. The first comes from the rise in computing power since the days of the Grand Challenge. He uses graphics chips that normally power video game consoles to process images pulled in by the car’s camera and speedy Intel chips to run his AI calculations. ... The second advance is deep learning, an AI technology that has taken off over the past few years. It allows researchers to assign a task to computers and then sit back as the machines in essence teach themselves how to accomplish and finally master the job. ... Instead of the hundreds of thousands of lines of code found in other self-driving vehicles, Hotz’s software is based on about 2,000 lines.

Many companies already have the ability to run keyword searches of employees’ emails, looking for worrisome words and phrases like embezzle and I loathe this job. But the Stroz Friedberg software, called Scout, aspires to go a giant step further, detecting indirectly, through unconscious syntactic and grammatical clues, workers’ anger, financial or personal stress, and other tip-offs that an employee might be about to lose it. ... To measure employees’ disgruntlement, for instance, it uses an algorithm based on linguistic tells found to connote feelings of victimization, anger, and blame. ... It’s not illegal to be disgruntled. But today’s frustrated worker could engineer tomorrow’s hundred-million-­dollar data breach. Scout is being marketed as a cutting-edge weapon in the growing arsenal that helps corporations combat “insider threat,” the phenomenon of employees going bad. Workers who commit fraud or embezzlement are one example, but so are “bad leavers”—employees or contractors who, when they depart, steal intellectual property or other confidential data, sabotage the information technology system, or threaten to do so unless they’re paid off. Workplace violence is a growing concern too. ... Though companies have long been arming themselves against cyberattack by external hackers, often presumed to come from distant lands like Russia and China, they’re increasingly realizing that many assaults are launched from within—by, say, the quiet guy down the hall whose contract wasn’t renewed.

Yet the mystery of the mechanism is only partly solved. No one knows who made it, how many others like it were made, or where it was going when the ship carrying it sank. ... What if other objects like the Antikythera Mechanism have already been discovered and forgotten? There may well be documented evidence of such finds somewhere in the world, in the vast archives of human research, scholarly and otherwise, but simply no way to search for them. Until now. ... Scholars have long wrestled with “undiscovered public knowledge,” a problem that occurs when researchers arrive at conclusions independently from one another, creating fragments of understanding that are “logically related but never retrieved, brought together, [or] interpreted,” as Don Swanson wrote in an influential 1986 essay introducing the concept. ... In other words, on top of everything we don’t know, there’s everything we don’t know that we already know. ... Discovery in the online realm is powered by a mix of human curiosity and algorithmic inquiry, a dynamic that is reflected in the earliest language of the internet. The web was built to be explored not just by people, but by machines. As humans surf the web, they’re aided by algorithms doing the work beneath the surface, sequenced to monitor and rank an ever-swelling current of information for pluckable treasures. The search engine’s cultural status has evolved with the dramatic expansion of the web. ... Using machines to find meaning in vast sets of data has been one of the great promises of the computing age since long before the internet was built.

Marion Tinsley—math professor, minister, and the best checkers player in the world—sat across a game board from a computer, dying. ... Tinsley had been the world’s best for 40 years, a time during which he'd lost a handful of games to humans, but never a match. It's possible no single person had ever dominated a competitive pursuit the way Tinsley dominated checkers. But this was a different sort of competition, the Man-Machine World Championship. ... His opponent was Chinook, a checkers-playing program programmed by Jonathan Schaeffer, a round, frizzy-haired professor from the University of Alberta, who operated the machine. Through obsessive work, Chinook had become very good. It hadn't lost a game in its last 125—and since they’d come close to defeating Tinsley in 1992, Schaeffer’s team had spent thousands of hours perfecting his machine. ... The two men were slated to play 30 matches over the next two weeks. The year was 1994, before Garry Kasparov and Deep Blue or Lee Sedol and AlphaGo. ... With Tinsley gone, the only way to prove that Chinook could have beaten the man was to beat the game itself. The results would be published July 19, 2007, in Science with the headline: Checkers Is Solved. ... At the highest levels, checkers is a game of mental attrition. Most games are draws. In serious matches, players don’t begin with the standard initial starting position. Instead, a three-move opening is drawn from a stack of approved beginnings, which give some tiny advantage to one or the other player. They play that out, then switch colors. The primary way to lose is to make a mistake that your opponent can jump on.

The most intriguing part of the antenna, though, is that it gives him an ability the rest of us don’t have. He looked at the lamps on the roof deck and sensed that the infrared lights that activate them were off. He glanced at the planters and could “see” the ultraviolet markings that show where nectar is located at the centers of the flowers. He has not just matched ordinary human skills; he has exceeded them. ... He is, then, a first step toward the goal that visionary futurists have always had, an early example of what Ray Kurzweil in his well-known book The Singularity Is Near calls “the vast expansion of human potential.” ... But are we on the way to redefining how we evolve? Does evolution now mean not just the slow grind of natural selection spreading desirable genes, but also everything that we can do to amplify our powers and the powers of the things we make—a union of genes, culture, and technology? And if so, where is it taking us? ... Conventional evolution is alive and well in our species. Not long ago we knew the makeup of only a handful of the roughly 20,000 protein-encoding genes in our cells; today we know the function of about 12,000. But genes are only a tiny percentage of the DNA in our genome. More discoveries are certain to come—and quickly. From this trove of genetic information, researchers have already identified dozens of examples of relatively recent evolution. ... In our world now, the primary mover for reproductive success—and thus evolutionary change—is culture, and its weaponized cousin, technology. ... One human trait with a strong genetic component continues to increase in value, even more so as technology grows more dominant. The universal ambition of humanity remains greater intelligence.