When Larry Page and Sergey Brin announced they were giving up their “day to day” duties at Alphabet early this month—leaving the heavy lifting to Google CEO Sundar Pichai—an era ended in more ways than one. As much as the news made history for the Mountain View search giant, it was also a fitting end to a cult of founderhood that peaked and crashed during the past 10 years. At the beginning of this decade, "the Google Guys” were still the flag-bearers of that cult.

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It was 1998 and internet mania was in full swing. Fueled by the fear of missing out on the next big e-thing, freewheeling venture capitalists and speculators poured money into companies that appeared only tangentially internet-related. Entrepreneurs responded in kind, many going so far as to add “.com” or some techy sounding prefix like “e-“ or “net-“ to their company’s name in the hopes of attracting attention from internet-obsessed investors.

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At first, you didn’t even need a pickax. The earliest prospectors of the California gold rush ventured into the Sierra foothills as solo travelers, sloshing through streams in search of nuggets dislodged by the current. That, at least, is the prevailing image: The individual renegade who headed west to strike it rich by his own initiative. But soon there were too many prospectors and too little easy gold. The task became more resource-intensive, requiring water to blast away the hills.

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Computers have gotten pretty good at making certain decisions for themselves. Automatic spam filters block most unwanted email. Some US clinics use artificial-intelligence-powered cameras to flag diabetes patients at risk of blindness. But can a machine ever be trusted to decide whether to kill a human being? It’s a question taken up by the eighth episode of the Sleepwalkers podcast, which examines the AI revolution.

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When MIT professor Regina Barzilay received her breast cancer diagnosis, she turned it into a science project. Learning that the disease could have been detected earlier if doctors had recognized the signs on previous mammograms, Barzilay, an expert in artificial intelligence, used a collection of 90,000 breast x-rays to create software for predicting a patient’s cancer risk.

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Computers have learned to see the world more clearly in recent years, thanks to some impressive leaps in artificial intelligence. But you might be surprised—and upset—to know what these AI algorithms really think of you. As a recent experiment demonstrated, the best AI vision system might see a picture of your face and spit out a racial slur, a gender stereotype, or a term that impugns your good character.

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“We’re going to be hauling some grass and some alfalfa bales today,” Cole Sonne cheerfully tells the camera as he drives a tractor over the bumps of his family’s farm in South Dakota. And for the next 12 minutes, the video will show Sonne and his dad do just that, carefully moving hundreds of the bundles, each as tall as a person, across their property. The sun shines down on the farm’s lush grass, peaceful music plays in the background—the effect is soothing.

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Nearly every day, in war zones around the world, American military forces request fire support. By radioing coordinates to a howitzer miles away, infantrymen can deliver the awful ruin of a 155 mm artillery shell on opposing forces. If defense officials in Washington have their way, artificial intelligence is about to make that process a whole lot faster. The effort to speed up fire support is one of a handful initiatives that Lt. Gen.

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Wally Kankowski owns a pool repair business in Florida and likes 12 creams in his McDonald’s coffee each morning. What he doesn’t like is the way the company is pushing him to place his order via a touchscreen kiosk instead of talking with counter staff, some of whom he has known for years. “The thing is knocking someone out of a job,” he says.

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We humans are notoriously bad at predicting the consequences of achieving our technological goals. Add seat belts to cars for safety, speeding and accidents can go up. Burn hydrocarbons for cheap energy, warm the planet. Give experts new technologies like surgical robots or predictive policing algorithms to enhance productivity, block apprentices from learning. Still, we're amazing at predicting unintended consequences compared to the intelligent technologies we're building.

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