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12 Ways Youre Eating in New York City Wrong

Few questions are more urgent than where to eat when you’re traveling to a place with more than two restaurants.

It’s certainly omnipresent for people who are visiting New York. (“Where should I eat?” is a question any local food critic hopes to never hear again.) The city has one of the best and deepest restaurant rosters in the world, and the amount is increasing: in 2017, there were 26,697 restaurants in the city, up from 26,110 in 2016 and roughly 1,500 more places than there were five years ago.

Of course you can make a list of must-try restaurants; that never hurts. But for a city as big and fast moving as New York, having a solid set of strategies to maximize your meals is the expert way to go. 

Here’s how you’ve been messing it up.

1. You skip breakfast.

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Unlike some breakfast-oriented cities like Atlanta and San Francisco, New York hasn’t always taken the first meal of the day seriously, beyond a bagel and schmear or slapped-together deli bacon-egg-cheese. Now the city has embraced it, and you’ll find stellar options from the maple-cinnamon crullers and smoked-gouda-and-sausage breakfast bagels at the compact Daily Provisions in Gramercy to the ranchero eggs at NoHo’s Atla. If a true New York bagel is on your bucket list, splash out with a spread at Russ & Daughters Café on the Lower East Side. For a statement breakfast in midtown, the Lobster Club highlights dishes from other Major Food Group restaurants including Sadelle’s sticky buns, as well as an over-the-top caviar breakfast egg toast. 

2. You’re taking “No” for an answer. 

If you want to eat or drink at a restaurant or bar that accepts reservations and there aren’t any, stay strong, advises Jim Meehan, co-founder of cult cocktail bar PDT. “Ask to be put on the cancellations list and tell the contact that you will be standing by for their call—and then do. Most restaurants lose 10 percent of their reservations to no-shows or last minute cancellations. If they can rely on you to fill a slot, they’ll be more inclined to offer it to you, first.”

3. You’re blinded by stars.

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Famed food writer and former editor Ruth Reichl suggests you pay attention to the second, less-expensive and less-time-intensive restaurants from great chefs.  One of the best examples is Enrique Olvera’s all-day Mexican cafe Atla instead of his high-end Cosme. Likewise the Bar Room at the Modern has a more modestly priced menu than the adjoining Modern dining room. There’s also Nomad instead of Eleven Madison Park, and JoJo instead of Jean-Georges.  

4. You’re only sitting at a table.

Among the great places to sit in New York is the bar at Gramercy Tavern.
Source: Gramercy Tavern

The time-honored tradition of eating at the bar at great restaurants like Gramercy Tavern, Le Bernardin, and Union Square Café also appeals to Reichl. It’s a more social experience than the dining room; you can often walk in and still enjoy the full menu; and it’s also a good place to get tips on where to eat from the staff and other diners if you’re visiting.

“And don’t forget the counter at the Oyster Bar,” advises Reichl, of the Grand Central Station icon. “It’s a quintessential New York experience. And you only need to have a few oysters to have the pleasure of watching those fantastic guys open the shells and concoct oyster stews.”

5. You’re only ordering cocktails.  

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We happen to be in the golden age of cocktail bars in New York, with destination versions of everything from speakeasies (Dear Irving) to tiki bars (the Polynesian) to rooftop hideaways (Broken Shaker). Many of these places also have very decent bar menus. Meehan, who put chef-made hotdogs on the map at PDT along with fully loaded tater tots, recommends eating while drinking around New York.

Among the top spots to do this are the Italian-styled Dante in the Village where the negronis (and ice) are an art form; the Office, where Michelin-starred chef Grant Achatz oversees dishes like prime ribeye tartare; the James Beard-winning, New Orleans-celebrating  Maison Premiere in Williamsburg, Brooklyn (it also has one of the city’s best $1 oyster happy hours); and Bar Goto, which serves exquisite okonomi-yaki, or Japanese omelet, to go with its meditative drinks.

6. You think Brooklyn is the only other borough. 

It’s definitely worth a trip to the original Xi’ans in Flushing.
Source: Xi'an Famous Foods

Yes, yes, Brooklyn is fantastic when it comes to food. Don’t not eat in Williamsburg; Lilia continues to be one of the top five restaurants in New York, and it’s here you’ll find the city’s best pancakes. Robert Sietsema, senior critic at Eater.com (and my sometime dining companion), spends most of his time scouting out restaurants outside of Manhattan, and encourages even first-time visitors to not be shy about exploring the borough further. He recommends Di An Di, the new Houston-style Vietnamese spot in Greenpoint; the fresh pasta specialists Faro or its offshoot General Deb’s, in Bushwick; and the “wonderful” Pheasant under the BQE.

And then there’s Queens, one of the most diverse places on earth where up to 800 languages are spoken. “They have food from everywhere, it’s such a great place to eat,” says Sietsema, “with prices that are half of what you’d pay in Manhattan—if you could find that food in Manhattan.” He shouts out Taqueria El Sinaloense, which offers regional Mexican food; Mapo B.B.Q. with its stellar Korean menu; and the O.G. Xi’an Famous Foods in a Flushing mall that started a robust pulled-noodle empire. (Fun fact: Flushing is the largest Chinatown in New York.) For the Bronx, there’s the red-sauced Italian Mario’s Restaurant on Arthur Avenue, the borough’s trapped-in-time Litty Italy.  And Lee’s Tavern on Staten Island is worth the trek for thin-crusted pizza, for those who want to name check all the outer boroughs—and wave “hi” to the Statue of Liberty on the way.

7. You’re not becoming a regular. 

Upland is a place worth repeat visits. It's the kind of place that does everything well.
Source: Upland

You can geek out on a place to go for the ultimate burger, the best fried chicken, a killer brunch. “Fine,” says magazine’s restaurant critic, Adam Platt. What’s better is to have a list hand of places that are solid all day long that work for multiple meals, palettes, and appetites.

First and foremost is Upland, from chef Justin Smilie which has a noteworthy burger, pizza, pasta, short ribs, lunch, and brunch. Similarly, Loring Place has a crowd-pleasing menu, day and night. Among other all-day destinations: Lafayette Grand Cafe in the Village, and the nearby EstelaRoberta’s in Bushwick; Frankie’s 457 Spuntino in Carroll Gardens; and Sunday in Brooklyn in good, old Williamsburg (although brunch is what you’ve seen the most social media for).  

8. You’re not pounding the pavement. 

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Even with all our restaurants, make time to eat on the street. The midtown-based Halal Guys, with their chicken and gyro combo and incendiary hot sauce, have long since replaced a “dirty-water dog” as New York’s most iconic street food. Reichl has recommendations: “I just had the most delicious Taiwanese oyster omelet in TurnStyle at the Columbus Circle subway station. The guy at N.Y. Dosas at Washington Square Park, are another stop. Now that it’s summer, what could be better than eating outside?”  

9. You’re not going big at lunch.

Le Coucou is just as pretty, and also less expensive, at lunch.
Photographer: Corry Arnold

Fancy restaurants offer great deals at lunchtime. Le Coucou has a $48 lunch with many of the same sought-after dishes that are served at its impossible-to-get-into dinner. Marea’s lunch time meal is $58. And the $38 prix fixe at Nougatine and the Terrace at Jean-Georges is still a steal. Reichl also sees opportunity, and value, at many of the city’s top Japanese restaurants. “There’s a great omakase sushi deal at Sushi Ginza Onodera—if $130 can be called a deal,” she says. “Sakagura, too. Also Sushi Yasaka, probably the best cheap sushi in the city, is remarkable at lunch. I honestly don’t know how they do it.” 

10. You’re only making reservations.

Dedicated restaurant reservationists may be practically extinct, and hotel concierges a dying breed, but for a seat at the city’s top spots, a handful of services are here to help. Resy is very good (and probably the only way to score a table at the outstanding Lucali pizza if your last name isn’t Beckham); so is Tock, which offers pre-paid, ticketed seats at Eleven Madison Park ( and their Summer House) among other high-end spots.  

But if you’re only eating at restaurants you have a confirmed seat, stop! You’ll be missing out on some of the city’s most exciting spots to eat right now. There’s the new Una Pizza Napolatana; the creative small plates wine bar Wildair (which has some reservations via Reserve); the transportive Italian caffe Via Carota; and the bold-flavored Thai restaurant Ugly Baby in Carroll Gardens, Brooklyn. 

11. You’re afraid of a little line-up.

Your reward for waiting on line at Bubby’s.
Photographer: Evan Ortiz

On the extreme end of the spectrum of the no-reservation spots are the ones with long lines—the places that don’t take your name and update you with wait time texts while you have a drink down the road. In fact, lines have become routine, notes ’s Platt. “Diners are drawn to them; it’s part of the experience and an event you can record,” he says.

Among the places where there’s an often a line that’s worth standing on: Momofuku Noodle BarDominique Ansel BakeryBubby’s for brunch; the Michelin-starred Tim Ho Wan for dim sum; and, of course, the original Shake Shack in Madison Park. Be warned, thought, that not all N.Y. lines are created equal. Exhibit A: Cookie DO NYC, which had bouncers for crowd control, and then a class action lawsuit that alleged the raw cookie dough was making people sick.

12. You’re only eating one dinner. 

Stop one on your midtown tour: The Grill.
Courtesy: The Grill

In spite of a proliferation of tasting menus, New York is still an ideal place to graze—especially when you factor in the FOMO of seeing dish after compelling dish on social media. “Forget destination restaurants,” says Platt, “every neighborhood is worth eating in now.” The best strategy is to stake one out, like this example itinerary in Manhattan's ‘Middle Village: start with the fried cauliflower buns at Nix, then get the house burger at the bar at Gotham Bar and Grill, and finish with a cheese plate and a glass of white Burgundy at Corkbuzz, all within a 5-minute walk of each other.

Alternately, stay in Midtown after your meetings, and you can have martinis at the Grill and then walk a few blocks for tacos and crab nachos at Empellon Midtown. Or saunter to the border of SoHo and the West Village for crudo and Champagne at Charlie Bird before heading around the corner to Andrew Carmellini’s the Dutch for mains, and a sceney nightcap of Indian-spiced cocktails at Bombay Bread Bar after. 

Keep it going as long as you can. Because if there’s one thing you’re definitely doing wrong when eating out in New York, it’s not doing enough of it. 

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    Charles Krauthammer, Prominent Conservative Voice, Has Died

    New York (AP) — Charles Krauthammer, the Pulitzer Prize-winning columnist and pundit who helped shape and occasionally dissented from the conservative movement as he evolved from "Great Society" Democrat to Iraq War cheerleader to denouncer of Donald Trump, died Thursday.

    He was 68.

    His death was announced by two organizations that were longtime employers, Fox News Channel and The Washington Post.

    Krauthammer had said publicly a year ago he was being treated for a cancerous tumor in his abdomen and earlier this month revealed that he likely had just weeks to live.

    "I leave this life with no regrets," Krauthammer wrote in The Washington Post, where his column had run since 1984. "It was a wonderful life — full and complete with the great loves and great endeavors that make it worth living. I am sad to leave, but I leave with the knowledge that I lived the life that I intended."

    Sometimes scornful, sometimes reflective, he was awarded a Pulitzer in 1987 for "his witty and insightful" commentary and was an influential voice among Republicans, whether through his syndicated column or his appearances on Fox News Channel. He was most associated with Brit Hume's nightly newscast and stayed with it when Bret Baier took over in 2009.

    Krauthammer is credited with coining the term "The Reagan Doctrine" for President Reagan's policy of aiding anti-Communist movements worldwide. He was a leading advocate for the Iraq War and a prominent critic of President Barack Obama, whom he praised for his "first-class intellect and first-class temperament" and denounced for having a "highly suspect" character.

    Krauthammer was a former Harvard medical student who graduated even after he was paralyzed from the neck down because of a diving board accident, continuing his studies from his hospital bed. He was a Democrat in his youth and his political engagement dated back to 1976, when he handed out leaflets for Henry Jackson's unsuccessful presidential campaign.

    But through the 1980s and beyond, Krauthammer followed a journey akin to such neo-conservative predecessors as Irving Kristol and Norman Podhoretz, turning against his old party on foreign and domestic issues. He aligned with Republicans on everything from confrontation with the Soviet Union to rejection of the "Great Society" programs enacted during the 1960s.

    "As I became convinced of the practical and theoretical defects of the social-democratic tendencies of my youth, it was but a short distance to a philosophy of restrained, free-market governance that gave more space and place to the individual and to the civil society that stands between citizen and state," he wrote in the introduction to "Things That Matter," a million-selling compilation of his writings published in 2013.

    For the Post, Time magazine, The New Republic and other publications, Krauthammer wrote on a wide range of subjects, and in "Things That Matter" listed chess, baseball, "the innocence of dogs" and "the cunning of cats" among his passions. As a psychiatrist in the 1970s, he did groundbreaking research on bipolar disorder.

    But he found nothing could live apart from government and the civic realm. "Science, medicine, art, poetry, architecture" and other fields were "fundamentally subordinate. In the end, they must bow to the sovereignty of politics."

    Ever blunt in his criticisms, Krauthammer was an "intense disliker" the liberal columnist E.J. Dionne told Politico in

    2009. And opponents had words for him. Christopher Hitchens once called him the "newest of the neocon mini-windbags," with the "arduous job, in an arduous time, of being an unpredictable conformist."

    He was attacked for his politics, and for his predictions. He was so confident of quick success in Iraq he initially labeled the 2003 invasion "The Three Week War" and defended the conflict for years. He also backed the George W. Bush administration's use of torture as an "uncontrolled experiment" carried out "sometimes clumsily, sometimes cruelly, indeed, sometimes wrongly. But successfully. It kept us safe."

    And the former president praised Krauthammer after hearing of his death.

    "For decades, Charles' words have strengthened our democracy," George W. Bush said in a statement. "His work was far-reaching and influential — and while his voice will be deeply missed, his ideas and values will always be a part of our country."

    Krauthammer was sure that Obama would lose in 2008 because of lingering fears from the Sept. 11, 2001 attacks, and foresaw Mitt Romney defeating him in 2012.

    But he prided himself on his rejection of orthodoxy and took on Republicans, too, observing during a Fox special in 2013 that "If you're going to leave the medical profession because you think you have something to say, you betray your whole life if you don't say what you think and if you don't say it honestly and bluntly."

    He criticized the death penalty and rejected intelligent design as "today's tarted-up version of creationism." In 2005, he was widely cited as a key factor in convincing Bush to rescind the Supreme Court nomination of the president's friend and legal adviser Harriet Miers, whom Krauthammer and others said lacked the necessary credentials. And he differed with such Fox commentators as Bill O'Reilly and Laura Ingraham as he found himself among the increasingly isolated "Never Trumpers," Republicans regarding the real estate baron and former "Apprentice" star as a vulgarian unfit for the presidency.

    "I used to think Trump was an 11-year-old, an undeveloped schoolyard bully," he wrote in August 2016, around the time Trump officially became the Republican nominee. "I was off by about 10 years. His needs are more primitive, an infantile hunger for approval and praise, a craving that can never be satisfied. He lives in a cocoon of solipsism where the world outside himself has value — indeed exists — only insofar as it sustains and inflates him."

    Trump, of course, tweeted about Krauthammer, who "pretends to be a smart guy, but if you look at his record, he isn't. A dummy who is on too many Fox shows. An overrated clown!"

    Krauthammer married Robyn Trethewey, an artist and former attorney, in 1974. They had a son, Daniel, who also became a columnist and commentator.

    The son of Jewish immigrants from Europe, Krauthammer was born in New York City and moved with his family to Montreal when he was 5, growing up in a French speaking home. His path to political writing was unexpected. First, at McGill University, he became editor in chief of the student newspaper after his predecessor was ousted over what Krauthammer called his "mindless, humorless Maoism."

    In the late 1970s, while a psychiatric resident at Massachusetts General Hospital, a professor with whom he had researched manic depression was appointed to a mental health agency created by President Jimmy Carter. Krauthammer went, too, began writing for The New Republic and was soon recruited to write speeches for Carter's vice president and 1980 running mate, Walter Mondale.

    Carter was defeated by Reagan and on Jan. 20, 1981, Reagan's inauguration day, Krauthammer formally joined The New Republic as a writer and editor.

    "These quite fantastic twists and turns have given me a profound respect for serendipity," he wrote in 2013. "A long forgotten, utterly trivial student council fight brought me to journalism. A moment of adolescent anger led me to the impulsive decision to quit political studies and enroll in medical school. A decade later, a random presidential appointment having nothing to do with me brought me to a place where my writing and public career could begin.

    "When a young journalist asks me today, 'How do I get to a nationally syndicated columnist?' I have my answer: 'First, go to medical school.'"

    ____

    AP Television Writer David Bauder contributed to this report.

      Read more: http://www.bloomberg.com/

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      The Mission to Build the Ultimate Burger Bot

      Weeks after he was born, Alex Vardakostas’ mother strapped him into a baby carrier and went back to work flipping burgers at A’s, the Southern California fast-food restaurant that she and her husband owned. When Vardakostas was a toddler, the town’s local newspaper, Dana Point News, ran a photograph of him peering through the restaurant’s walk-up window. As he grew older, he often played in the back of the kitchen among pallets of hamburger buns while his parents worked. At 8, he started filling drink orders, standing on top of a milk crate to reach the soda machine. Sometimes he ran food experiments, soaking burger meat in Worcestershire sauce to see if it would taste better. He learned snippets of Spanish from the line cooks, Apolinar and Ernie, and at 12 he started working beside them.

      Now 33, Vardakostas lives in San Francisco, and for the past nine years, he’s been building a robot that can cook and assemble around 100 burgers an hour—keeping pace with a typical fast-food staff—with little human intervention. “Our device isn’t meant to make employees more efficient,” Vardakostas told a reporter in 2012. “It’s meant to totally obviate them.”

      That quote turned the entrepreneur into a Silicon Valley caricature overnight, a cautionary note in think pieces foretelling the robot revolution, worker displacement be damned. (It didn’t help that Vardakostas looks the part of a dashing tech villain, with dark, wavy hair and a muscular build credited to weight lifting and a red-meat-heavy diet.) But six years on, he’s as adamant as ever. Sprawled on a couch in the robot workshop of his company, Momentum Machines, he raises his voice over the whir of an industrial saw. “I’m abso-fucking-lutely trying to obviate that role,” he says, miming the flip of a burger, over and over, eyes fixed on the imaginary patty. “As a society, if we’re pushing to keep people in a burger-flipping role, we’re doing something wrong.”

      Vardakostas insists he isn’t the heartless disruptor he’s been made out to be. His company isn’t about destroying jobs, he says; it’s about shaping the future of fast food—one in which humans will still have an important place. His skeptics will soon be able to see that vision for themselves: This summer, he’s opening the doors to a San Francisco restaurant called Creator and unveiling his gleaming burger bot—a surprisingly beautiful copper and wood machine, its spotless glass chutes stacked with vivid towers of tomato, onion, lettuce, and pickle.

      Just off Highway 1 in the surfing town of Dana Point, Vardakostas’ mom, Maheen, still works seven days a week. The slight 66-year-old stands over the A’s grill wielding a spatula, a hairnet stretched over her dark bun and a red apron around her waist, waiting for her son to put her out of a job.

      Brian Finke

      Angelo Vardakostas sailed into Los Angeles on a Greek commercial ship in 1955. Greeks were opening diners across the country at the time—mom-and-pop analogs to the McDonald’s, Carl’s Jr., and Kentucky Fried Chicken chains that were multiplying in the postwar sprawl—and Angelo hopped off at the port and started looking for a job. He worked as a dishwasher and bartender at a string of restaurants, eventually snagging a position waiting tables at a fancy Beverly Hills bistro. (Once he was sent to a table with the ingredients for Caesar salad dressing, intended to be mixed tableside; not knowing any better, he poured the raw egg directly into the salad.) By the early 1970s, Angelo had saved enough to make a down payment on a joint called Archie’s BBQ in the fast-food hub of Downey, California, a few miles away from the original Taco Bell. He rechristened Archie’s as A’s. Figuring he could save money, he later told his son, he kept the same sign and pried off the other letters.

      After a few years, Angelo decamped and opened another A’s location 50 miles south, in beachy Dana Point. In 1979, a pair of twenty­something sisters spotted a “help wanted” sign in the window. They had recently arrived from Iran, having fled the Islamic Revolution, and Angelo hired them on the spot.

      The elder sister, Maheen, had won a national math championship when she was 17 and had graduated with a master’s degree from the University of Tehran. Before leaving the country, she worked as a civil engineer for the Iranian Air Force. “I was so depressed when I got here,” she remembers. “My career was gone.” But the 27-year-old applied her methodical nature to her new tasks at A’s, taking inventory and handling large orders during the lunch rush. While Maheen was brooding and detail-­oriented, Angelo was easygoing. She found him charming. “He always brought humor,” she says. The couple married in 1982. “We didn’t have time to date,” Maheen says, with a laugh. Alex was born in 1984, and two years later the family opened another A’s outpost in San Juan Capistrano, 20 minutes from Dana Point. A year later, Alex’s brother, George, was born.

      Business picked up in the new location, and when Vardakostas was in grade school the family moved into a sprawling ranch house in San Juan Capistrano, where they added a tiled pool in the back. Vardakostas started working the grill, sneaking free food to friends from his private middle school between shifts. Some of the kids took to calling him Varda-Cheeseburger, a taunting twist on his last name. “My parents would come to the school dirty from work,” Vardakostas says. “I had a chip on my shoulder.” He got in a few fistfights, but never dared to tell his parents.

      When Vardakostas reached high school, he says, his father started taking the boys on weekly trips to the local bookstore. “We’d drink frappuccinos,” George recalls, “and everyone would pick their own book.” While Angelo flipped through The Wall Street Journal, Vardakostas paged through books on science and physics. After graduating from Capistrano Valley High School with middling grades, Vardakostas headed to nearby Saddleback College. He washed and detailed cars to make extra money, eating for free twice a day at A’s. In 2006, Vardakostas transferred to UC Santa Barbara to study physics. A classmate and friend, Steffanie Hughes, remembers him as a preppy kid, typically clad in a pink polo and Jack Purcells. She was impressed by his intelligence and intrigued by his unusual living arrangement. For his first few months in Santa Barbara, Vardakostas was staying at a Motel 6. He would spend hours studying in the driver’s seat of his used Mercedes—a gift from his dad when he transferred to UCSB—which he liked to park at the beach. Though he loved his classes on quantum mechanics and electromagnetics, he says, his thoughts would often return to his parents and their longtime employees passing years in the A’s kitchen, cooking burger after burger. An idea came to him his junior year, as he lay awake at 4 am in a bout of insomnia: “What if I could create a robotic kitchen?” The idea excited him. “Once you have a vision about how things could be better, it grows like a weed,” he says. A couple of weeks before graduation, he told Hughes about his burger bot scheme. Her reaction was one he’d hear repeatedly in the ensuing decade. “You’re going to displace workers,” she told him.

      After graduating in 2007, Vardakostas got a job automating data at a semiconductor company. Still, he says, he was fixated on the idea of a burger bot. “I was thinking, why the hell isn’t anybody doing this?” He installed design software on his laptop and started studying robotics after work. Within two years, he quit his job and began building crude burger-making robot prototypes in his parents’ garage. First up: the tomato slicer, pieced together for $25 using an Allen key set, PVC piping, and some balsa wood he bought at Home Depot.

      Maheen urged him to get out of the burger business. His brother was baffled by his garage tinkering. “I mean, why don’t you want a sexier job? Make the next iPhone,” George told him. One night, a guy overheard Vardakostas talking about his burger bot at an Orange County bar and blurted out, “If my kid did that, I would shoot him.” Vardakostas stopped telling people about his plan.

      Alex and his parents at Burger Stop in 1985 in San Clemente.
      Courtesy of the Vardakostas family

      By 2010, Vardakostas’ robot was starting to show promise, but he knew he’d need heavy machinery to build a working prototype. He joined TechShop, a DIY makerspace in Menlo Park, and couch-crashed with Hughes, who had landed a job at Apple and was living in San Jose. Intimidated by the CNC tools, he introduced himself to a twentysomething guy in work boots he’d noticed expertly working the milling machine. The guy, Steven Frehn, was a mechanical engineer and recent Stanford grad—“one of these genius kids,” Vardakostas thought. Frehn grew up in a dusty stretch of Southern California making sketches of electric cars and cities crowned with solar panels. In high school, he landed an internship working for NASA, automating sensors at an Air Force base. Now he was building his own solar panels and sweeping TechShop’s floor in exchange for free use of the equipment. When Frehn asked what he was working on, Vardakostas was cagey. “A machine to cut vegetables,” he replied.

      The two struck up an unlikely friendship. Vardakostas is charismatic and creative, Hughes says, while Frehn is grounded and practical. Eventually, Vardakostas revealed his concept for the burger bot. “I immediately thought it was amazing,” Frehn says, “but it sounded like a lot of work.”

      Vardakostas returned to his machine—and his parents’ garage—in Orange County. When he didn’t want to make the six-hour drive to San Jose, he would occasionally send Frehn robotic components via same-day delivery for quick alterations; Frehn would use TechShop’s tools and rush-mail the part back. After about seven months, Vardakostas’ makeshift vegetable slicer was functional.

      Momentum Machines CEO Alex Vardakostas samples robot-made burgers at Creator, his San Francisco restaurant.

      Brian Finke

      Air pressure pushes buns through a blade that slices them in half.

      Brian Finke

      Encouraged, Vardakostas moved on to building the conveyor belt that would move the burger down an automated assembly line, the bun slicer and toaster, and the electric grill. In the fall of 2011, after two years, a burger emerged from his machine. The robot was viable.

      Now Vardakostas needed money. Hughes arranged a meeting with Lemnos Labs, one of Silicon Valley’s first hardware incubators, and in November of 2011, two Lemnos partners flew to the Vardakostas home in San Juan Capistrano to visit the entrepreneur-in-waiting. Vardakostas delivered his pitch in his childhood bedroom; Lemnos partner Helen Boniske remembers that physics books were strewn on the floor.

      Then he led the partners to his parents’ three-car garage, now dominated by a 6-foot-tall burger beast. Vardakostas clicked Place Order on his laptop, and the machine sprang into action. A presliced bun ran through a toaster on a squeaky conveyor belt. The bottom half slid down a chute beneath the vegetable slicers, where robotic blades cut pickles, tomatoes, and onions. The patty traveled through a charbroiler on a separate conveyor belt, then glided down a chute onto the bottom bun. The top bun dropped onto the sandwich and a mechanical arm pushed the entire burger into a white paper bag. “For one dude to build this thing in a garage,” Boniske says, “it was an incredible feat of engineering.” Lemnos offered Vardakostas about $50,000 in seed money and invited him to join their ranks.

      Two months later, Vardakostas moved to San Francisco and set up his workshop in Lemnos’ SoMa district headquarters. He posted an ad on Craigslist seeking machining engineers and hired two recent college grads: Jack McDonald, a mechanical engineer from UC Berkeley, and Lucas Lincoln, a roboticist from the University of Utah. Frehn soon joined the group full-time.

      The foursome set to work building a new, improved burger bot prototype, sometimes pulling days so long, Vardakostas says, that he slept in a sleeping bag under his desk. But because he wasn’t looking to sell his machine to fast-food chains, venture capital firms were wary of investing. By now, Vardakostas had become convinced that his company could transform not only the repetitive act of burger making but also the entire fast-food business model, from the ingredients used to the wage structure. His dream, he says, is to open a chain of Creator restaurants across the country, delivering high-quality, inexpensive food to the masses. “It was right on the edge, man,” McDonald recalls. “We believed in the idea, but it’s a lot harder to convince other people that it’s the future.” To stretch their seed money, they often ate their machine’s own imperfect trial-run burgers for lunch.

      One day that fall, Avidan Ross, a roboticist turned venture capitalist, visited Lemnos Labs and spotted the burger bot across the room. “I said ‘What is that?!’ ” he remembers. “I have to meet these people.” Whereas other investors at the time were “caught up in iPhone apps, trying to find the next Snapchat,” he says, his newly launched VC firm, now called Root Ventures, was focused on hardware. In a stroke of luck for Vardakostas, Ross was a kindred tinkerer: He had built his own pizza oven and several barbecue contraptions in his backyard, one of which tweeted its temperature every five minutes. Ross had also given a lot of thought to how robotics might be used to automate costly cooking techniques. Early in 2013, he wrote Momentum Machines a check for about $300,000. Google Ventures and Khosla Ventures soon followed.

      BRIAN FINKE
      BRIAN FINKE

      Momentum Machines isn’t the first to attempt to automate restaurant kitchens. In the 1960s, the American Machine & Foundry Company unveiled a fast-food device that churned out burgers, hot dogs, fries, and milkshakes at a Long Island drive-in. An attendant punched in the orders on a push-button dashboard that controlled the machinery. Though the contraption saved roughly $1,900 in cook’s wages each month, it also cost $1,500 to lease. It never caught on. More recently, fast-food chains have been taking small steps toward automation, especially in ordering, but also in the more complicated process of making food. McDonald’s has been installing self-service kiosks as part of its “Experience of the Future” campaign. Chains from Taco Bell to Burger King have adopted ordering apps. This spring, Little Caesars received a patent for a pizza-making robot. Over the past two years, Miso Robotics in Pasadena, California, has been developing Flippy, a burger-flipping robotic arm that works with most restaurants’ preexisting grills. Flippy was slated to be deployed at CaliBurger restaurants around the country this year, but its March debut was inauspicious: After a couple of hours at the chain’s Pasadena location, it fell behind on orders and was decommissioned for improvements.

      The technical complexities, coupled with the cost of building a kitchen bot, mean that it will take time before robotics transforms the fast-food industry. Still, chains continue to pursue automation because they think it will boost their profits; labor costs typically make up around 30 percent of restaurant expenses. “The fact of the matter is businesses will automate when it’s cost-effective,” says Teofilo Reyes, a policy expert at Restaurant Opportunities United, a nonprofit that advocates better conditions for fast-food workers. Replacing multiple salaries with the one-time cost of a robot is an enticing business strategy, especially in an industry with a high turnover rate. Martin Ford, author of Rise of the Robots: Technology and the Threat of a Jobless Future, predicts that within the next five to 10 years, major fast-food chains will be able to reduce staff by 30 to 40 percent due to automation.

      The impact of such cuts on overall employment rates is unknown, says Sylvia Allegretto, a labor economist at UC Berkeley. “The big mistake everyone makes is they can’t foresee the new jobs that will come online because of the technology,” she argues. The car may have put blacksmiths out of business, but it also created assembly-line jobs. Of course, automation in manufacturing has now put assembly-line workers at risk. They’re being replaced by robots, overseen by a small group of humans with the expertise to manage them.

      How to Work a Burger Bot

      1. Ordering
      Diners customize their meals through Creator’s app, which sends the information to the bot.

      2. Toasted bun
      Air pressure pushes the brioche bun through a blade that slices it in half. It travels down a vertical toaster before dropping into a compostable container.

      3. Produce
      The bun moves on a conveyor belt below chutes of tomatoes, onions, pickles, and shredded lettuce. The robot cleaves a fresh portion from each of the vegetables.

      4. Beef
      Hunks of brisket and chuck are tumbled with seasonings in a vacuum chamber. The bot grinds and shapes 5 ounces of meat into a puck, then a mechanized arm deposits the patty between two griddles.

      5. Grill
      The patty is cooked at 350 degrees until medium rare. When it’s done, a mechanized spatula places the patty onto the open bun.

      6. Condiments
      Convection heat melts shredded cheese. Requested sauces and seasonings—including coffee-flavored salt, chipotle powder, and curry ketchup—are deposited from various dispensers.

      7. Quality control
      The burger emerges from the robot, where it’s checked by a human worker. —L.S.

      Vardakostas won’t share his financial projections, but his business model makes some ambitious assumptions in its path to success. He says that the robot will eventually make burgers more efficiently than a typical fast-food restaurant, though at its current rate—about 100 burgers per machine, per hour—a McDonald’s-­style restaurant could keep up. App-based ordering means that Creator will be able to serve more customers, faster. The restaurant may also shore up its bottom line by serving beer, wine, and fries, items with a high profit margin. Vardakostas says he plans to spend around 45 percent of his revenue on burger ingredients, which include pasture-raised beef and organic vegetables. Most restaurants spend roughly half that on total food costs.

      To Erik Brynjolfsson, coauthor of The Second Machine Age, it makes sense that Momentum Machines is opening its own restaurant rather than shopping its bot around to existing chains. “You can’t just pop the robot into a restaurant and leave the whole rest of the business the same,” he says. “You have to reinvent the roles of the people, the types of ingredients, your price points. Replacing a human burger-flipper with a machine isn’t the big payoff—the payoff is inventing a totally new kind of restaurant.”

      While robots will serve as Creator’s chefs and cashless cashiers, they won’t be without human support. This spring, Momentum Machines hired its first restaurant employees, including a general manager, a host to explain how the smartphone ordering process works, and “burger buffs” trained to maintain the machine and deliver meals to tables. Up to nine employees will work during Creator’s peak hours—on par with a standard fast-food restaurant—and Vardakostas says he’ll pay them $16 an hour, $1 above San Francisco’s minimum wage.

      All this raises the question: Can Creator actually make money, or will it become another over­hyped gimmick propped up by VC funding? “It’s to be determined,” says Aaron Noveshen, founder of the restaurant consultancy the Culinary Edge and an early Momentum Machines adviser. “If it doesn’t take five people to stand next to the robot to make it work, then they can reach profitability.” Helen Boniske believes Alex could charge more than his proposed price of $6 to $7 per burger, with an eye to Creator’s eventual expansion.

      While Creator is a contained testing ground, for now, the idea of robotic kitchens catching on throughout the restaurant industry is unsettling to many. “For some reason, with our burger bot, people have a visceral reaction: This machine is doing exactly what you see a human doing,” acknowledges McDonald, one of Momentum’s original engineers. There is something especially troubling about fast-food workers being tossed aside—perhaps because those jobs are viewed as a place for people who have limited options. The median income for a fast-food worker is around $21,000, and more than half receive some public assistance. “The reality is that many people who work in fast food may be well suited for routine jobs,” Ford says.

      Alex balks at such sentiments. He sees burger flippers as trapped by their jobs, not clinging to them. “You don’t grow up next to fast-food workers without realizing these people are capable of so much more—it becomes this sort of haunting thing,” he says. “People say, oh, flipping burgers is the only thing they can do. That’s fucking bigoted. Dude, no, we can do a lot more than flip burgers. We just haven’t had a chance.”

      For a line cook who just lost his job, though, Vardakostas’ vision may offer little consolation.

      Momentum Machines engineers receive real-time obstruction alerts from the burger bot during testing.

      BRIAN FINKE

      Vardakostas loads stacks of pickles, tomatoes, and onions into his machine. Each topping is sliced to order.

      BRIAN FINKE

      At Creator in San Francisco, Vardakostas walks over to inspect his machine’s latest burger. For the past year, the restaurant’s unfinished dining area has been his second office, his 50 employees gliding between the two buildings on scooters and skateboards. At the moment, the restaurant windows are frosted over to thwart oglers, and the rare visitor is required to sign a nondisclosure agreement and cover their phone’s camera lens with a sticker. It’s mid-April, and the team is customizing burger orders from Creator’s smartphone app for the first time, requesting extra cheese or chipotle powder instead of jalapeño salt. Half a dozen developers and software engineers are seated at the dining tables with their laptops, obsessively tracking the real-time progress of the two identical robots across the room.

      Amid the bustle of machinery, finishing touches are being put in place to make the space feel more like a homey café than, say, a dystopian factory. One wall is painted with yellow Fibonacci spirals. Burger ingredients chill in glass-front refrigerators alongside meticulously written explanations of their provenance. Customers will be invited to browse books while they wait for their orders, from design tomes to Eric Schlosser’s Fast Food Nation.

      After nearly a decade of R&D, Vardakostas says, “we had our pick” of VC firms during last year’s fund-raising round. He recently received investments from Root Ventures, Zynga cofounder Justin Waldron, Great Oaks Venture Capital in New York, and K5 Ventures in Orange County. According to its 2017 SEC filings, Momentum Machines raised $18.4 million in funds.

      Despite his insistence that he’s not selling his robot, Vardakostas claims his company has heard from fast-food chains and sports stadiums that are interested in purchasing it. “We were able to get them an introduction to Burger King really early on,” Boniske says. “It was just too early to have a substantial discussion. Burger King’s reps said ‘I don’t believe it’s possible.’ ” It’s hard to know if Vardakostas will sell in the end, but it’s easy to imagine. Maybe Creator’s opening will be an inflection point, like the day in 1948 when two McDonald brothers decided to make their customers walk up to the counter to collect their burgers, rather than hiring servers to deliver them to cars. Maybe nothing much will change at all.

      In Dana Point, Maheen says she awaits the day she can install one of her son’s burger robots at A’s. She says she sees his machines as the next chapter in their family’s American success story, payoff for all those years she and her husband spent in the kitchen. “You know who wants to lose their jobs?” Maheen asks wryly, slouched in a booth at A’s during a weekend lull. “It’s the managers.” Once her son’s long-­promised burger bot arrives, she says, she may even consider retiring.


      Lauren Smiley (@lauren smiley) wrote about virtual elder care in issue 26.01.

      This article appears in the July issue. Subscribe now.

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      The Crazy Hacks One Woman Used to Make Money on Mechanical Turk

      When her husband lost his factory job in 2010, Kristy Milland ran through her options.

      Until that point, she’d been working at home, earning extra money through odd jobs like selling collectables on eBay. She hadn’t waited on tables, had no experience in fast food, and had not learned any skills that might be particularly useful in a factory. She’d once applied for a job at McDonald’s, but nobody had called her for an interview.

      Jobs were more difficult to find in her hometown of Toronto since the beginning of the Great Recession. But there was one place where Milland knew she could get work immediately.

      Adapted from Gigged: The End of the Job and the Future of Work by Sarah Kessler.

      St. Martin's Press

      Launched in 2005, Mechanical Turk is an online “crowdsourcing” marketplace run by Amazon. Its clients post work tasks on a dashboard that a “crowd” of independent workers can choose to complete, often for cents apiece.

      Amazon had not launched the Mechanical Turk platform with a promise to create work, in the way that Uber would later brag about adding “20,000 new driver jobs” to the economy every month. Rather, it had built the website as a way to integrate human intelligence with code—as a service for programmers. As TechCrunch’s founder put it shortly after the product launched: “[It] is brilliant because it will help application developers overcome certain types of problems (resulting in the possibility for new kinds of applications) and somewhat scary because I can’t get the Matrix-we-are-all-plugged-into-a-machine vision out of my head.” He called the workers who would be plugged into this Matrix “Volunteers.”

      Some common tasks on Mechanical Turk included labeling photos that are used to “train” artificial intelligence, filling in spreadsheets with contract information, or writing product descriptions for websites. An entrepreneur once pitched me an app that—through his proprietary system—would provide accurate calorie estimates for meals based only on a photo. Sure enough, shortly later, I found a posting on Mechanical Turk for the company that asked workers to label the food. The technology was humans. But it looked like magic.

      About the Author

      Sarah Kessler is an editor at Quartz. Previously she was a senior writer at Fast Company, and her reporting has been cited by New York magazine, The Washington Post, and NPR.

      Some of Milland’s more remunerative work came from employers who posted hundreds or thousands of tasks at a time that could be completed in rapid succession. Milland would install small software programs that allowed her to complete, say, a simple categorization task by hitting a key on her keyboard (“y” for yellow or “b” for bird) rather than clicking a mouse. Categorizing an item every five seconds for an hour, at $0.03 per image would pay $21.60 per hour. She also took on more complicated tasks. Writing descriptions for product sites, for instance, could pay $1.50 per paragraph. So if she did one every five minutes, she would make $18 an hour. It was a matter of doing the work quickly and sticking with it for a long time.

      Turker Nation, a forum where Milland was a moderator, had a place where Turkers alerted each other about these “good work” opportunities, which paid well and could be completed in large batches. To make sure that she didn’t miss any of them, Milland set up an automated system that, when a new “good work” task was posted, would check to see how much it paid and whether she met its qualifications. If she was eligible for a task that paid $0.05, her computer would alert her with a “ping” noise. If she were eligible for a task that paid between $0.05 and $0.25, her computer would sound an alarm that sounded like a laundry machine finishing. If she were eligible for a task that paid more than $0.25, a siren would sound.

      No matter where Milland was in her house, if she heard the alarm go off, she would run to her computer. There were thousands of other Mechanical Turk workers competing with each other to grab the high-paying work, which was assigned to whoever could claim it first. Milland would sleep in her office so that she could listen for the alarm to go off at night without waking her husband. When she spotted good tasks, often through her alarm system, she used an automated tool to keep her queue full with the maximum 25 tasks that could be assigned to her at one time, and then worked furiously to finish them and grab more before they were snatched by other people.

      She created alerts with different sounds: Tasks paying 5 cents prompted a "ping." From 5 to 25 cents, a laundry-machine alarm. If a task paid more than 25 cents, a siren would sound.

      One of tasks she didn’t like to miss was answering questions from a Q&A service in which people, in the days before mobile internet browsers, could text a number with a burning inquiry such as “Where is the nearest Italian restaurant?” These were posted every 15 minutes, and there were a few different aspects that made them good tasks. The first was that people often asked the same questions, and Milland had compiled a spreadsheet of answers that made these common questions quick to answer. She could get through a batch of several hundred in about five minutes. The second was that to incentivize good work, each month Amazon paid a bonus of a few hundred dollars to the worker whose answers received the highest number of “thumbs up” votes from users. Each question might only pay pennies, but this bonus was significant. It meant that Milland never wanted to miss a batch. Her routine was to listen for the alarm, complete the batch in five minutes, take 10 minutes off, and then get back to work when the next batch of questions dropped.

      Another task that she prioritized was one that asked customers to take photos of products in order to find the same product on Amazon. It was how the company encouraged customers to comparison shop, but not everyone used this feature of Amazon’s app as intended. When they sent pictures of genitals, Milland sent back a link to a book called I’m Calling the Police. It was worth dealing with rude inquiries like these because she had found a way to earn extra money when Amazon’s users sent photos of actual products: She sent them her affiliate marketing links and earned a percentage of purchases they made after clicking on them.

      Milland also started proactively asking requesters if they needed help designing their requests, contacting them through the Mechanical Turk site. Sometimes she collected consulting fees for teaching them how to improve their results.

      The paradigm on both the employer and worker side of Mechanical Turk was less of a relationship between two colleagues than it was two people trying to beat a system. In one common example, companies posted the same work three different times on Mechanical Turk in order to check the accuracy of responses. If one worker submitted a different answer than the other two who completed the task, the company assumed that worker had given a bad answer and rejected her work (which meant she wouldn’t be paid). To beat this system on the worker side, all a Mechanical Turk worker needed were two accounts to agree with each other. Some Turkers built automated bots to submit arbitrary (but matching) work results. The bots collected payment because they agreed, while the person who had earnestly done the work didn’t get paid. All Turkers could do when someone rigged a task this way was to tell each other to avoid it.

      She opened a task to find a slide show of still shots taken from ISIS videos. People kneeling next to an explosive wire, preparing to die.

      Milland didn’t feel like she could leave her apartment, or even her computer, lest she miss out on an opportunity to work on good tasks. Unlike an employee at a fast-food restaurant or a cleaning company, she didn’t get paid for downtime, and she could earn more money by working smarter and faster. The psychology was that of a game that required her to be constantly on alert. In a way, that psychology kept her going: She’d set a goal for $100 per day, and, cent by cent, she often met it.

      What It Costs

      Making $100 a day on Mechanical Turk was possible, but not always pleasant. There were times when Milland stumbled into emotionally taxing work that in a regular workplace would have come with preparation and consent. On one such occasion, she opened a task to find a slide show of still shots taken from ISIS videos. People kneeling next to an explosive wire, preparing to die. A wicker basket full of human heads. It came with instructions similar to any other photo tagging job. Another slideshow contained photos of animal abuse so graphic that years later Milland had trouble taking her dogs to the vet without crying.

      The only indication that something exceptionally graphic could be found inside a task was often an “adult only” qualification. Employers used this designation on any jobs that involved user-generated content that they couldn’t control. Such jobs could pay well, and they most often didn’t contain anything disturbing. Milland considered them worth taking. And so she accepted that psychological stress would be part of the job.

      So, too, would physical stress. She’d ignored the small, hard bump that had developed on her wrist until it started to grow. It got a little bigger every day, until eventually it was the size of a marble and interfering with the way she held her mouse. When she finally went to the doctor, she learned that it was a ganglion cyst. He recommended surgery, but that would mean post-surgery prescriptions not covered by Canada’s state health insurance. Another traditional treatment for “Bible bumps,” as they are sometimes called, is to hit them with a heavy book. And so one day, when she couldn’t stand it anymore, Milland gave the bump a good bashing.

      Eventually the pain went away. Then a new pain appeared, up her wrist, toward her elbow. A neurologist told her that it was carpal tunnel and a repetitive strain injury. The ideal response would have been rest. But there is no workers’ compensation in the gig economy. There is no paid sick leave in the gig economy. And among US workers who rely on sites like Mechanical Turk for their entire income, almost 40 percent don’t have health insurance. Milland lived in Canada, with universal healthcare, but she couldn’t afford the break. She wore a wrist brace and an elbow brace and kept on clicking.

      Mechanical Turk was the only option when Milland’s family needed money quickly. It allowed her husband to go back to high school to get his diploma. And it allowed her to earn an income from home without a college degree or a thick resume. She made it work.

      But when, two years after she’d been working on Mechanical Turk, Milland’s husband finally landed a job, as a forklift driver at a printer company, she told him she never wanted to depend on Mechanical Turk again.

      Shortly later, she applied for university.

      Adapted from Gigged: The End of the Job and the Future of Work by Sarah Kessler. Copyright © 2018 by the author and reprinted with permission of St. Martin's Press, LLC.

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      The US Again Has Worlds Most Powerful Supercomputer

      Plenty of people around the world got new gadgets Friday, but one in Eastern Tennessee stands out. Summit, a new supercomputer unveiled at Oak Ridge National Lab is, unofficially for now, the most powerful calculating machine on the planet. It was designed in part to scale up the artificial intelligence techniques that power some of the recent tricks in your smartphone.

      America hasn’t possessed the world’s most powerful supercomputer since June 2013, when a Chinese machine first claimed the title. Summit is expected to end that run when the official ranking of supercomputers, from an organization called Top500, is updated later this month.

      Supercomputers have lost some of their allure in the era of cloud computing and humongous data centers. But many thorny computational problems require the giant machines. A US government report last year said the nation should invest more in supercomputing, to keep pace with China on defense projects such as nuclear weapons and hypersonic aircraft, and commercial innovations in aerospace, oil discovery, and pharmaceuticals.

      Summit, built by IBM, occupies floor space equivalent to two tennis courts, and slurps 4,000 gallons of water a minute around a circulatory system to cool its 37,000 processors. Oak Ridge says its new baby can deliver a peak performance of 200 quadrillion calculations per second (that’s 200 followed by 15 zeros) using a standard measure used to rate supercomputers, or 200 petaflops. That’s about a million times faster than a typical laptop, and nearly twice the peak performance of China’s top-ranking Sunway TaihuLight.

      The view inside one of the Summit supercomputer’s 4,608 servers.

      Oak Ridge National Laboratory

      During early testing, researchers at Oak Ridge used Summit to perform more than a quintillion calculations per second in a project analyzing variation between human genome sequences. They claim that's the first time a scientific calculation has reached that computational scale.

      America’s new best computer is significant for more than just the geopolitics of computational brawn. It’s designed to be more suited than previous supercomputers to running the machine learning techniques popular with tech companies such as Google and Apple.

      One reason computers have lately got much better at recognizing our voices and beating us at board games is that researchers discovered that graphics chips could put more power behind an old machine learning technique known as deep neural networks. Facebook recently disclosed that a single AI experiment using billions of Instagram photos occupied hundreds of graphics chips for almost a month.

      Summit has nearly 28,000 graphics processors made by Nvidia, alongside more than 9,000 conventional processors from IBM. Such heavy use of graphic chips is unusual for a supercomputer, and it should enable breakthroughs in deploying machine learning on tough scientific problems, says Thomas Zacharia, director of Oak Ridge National Lab. “We set out to build the world’s most powerful supercomputer,” he says, “but it's also the world’s smartest supercomputer.”

      Summit’s thousands of servers could fill two tennis courts.

      Carlos Jones/Oak Ridge National Laboratory

      Eliu Huerta, a researcher at the National Center for Supercomputing Applications, at the University of Illinois at Urbana-Champaign, describes Summit’s giant GPU pool as “like a dreamland.” Huerta previously used machine learning on a supercomputer called Blue Waters to detect signs of gravitational waves in data from the LIGO observatory that won its founders the 2017 Nobel Prize in physics. He hopes Summit’s might will help analyze the roughly 15 terabytes of imagery expected to arrive each night from the Large Synoptic Survey Telescope, due to switch on in 2019.

      Summit will also be used to apply deep learning to problems in chemistry and biology. Zacharia says it could contribute to an Energy Department project using medical records from 22 million veterans, about a quarter-million of which include full genome sequences.

      Some people worried about US competitiveness in oversized calculating machines hope that the hoopla around Summit will inspire more interest in building its successors.

      The US, China, Japan, and the European Union have all declared the first “exascale” computer—with more than 1,000 petaflops of computing power—as the next big milestone in large-scale computing. China claims it will achieve that milestone by 2020, says Stephen Ezell, vice president for global innovation policy at the Information Technology and Innovation Foundation. The US may get there in 2021 if Summit’s successor, known as Aurora, is completed on schedule, but the program has previously had delays.

      The Trump administration’s budget this spring asked for $376 million in extra funding to help meet the 2021 target. It’s now up to the nation’s legislators to approve it. “High-performance computing is absolutely essential for a country’s national security, economic competitiveness, and ability to take on scientific challenges,” Ezell says.

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      Your Instagram #Dogs and #Cats Are Training Facebook’s AI

      Using a social network like Facebook is a two-way street, part-shrouded in shadow. The benefits of sharing banter and photos with friends and family—for free—are obvious and immediate. So are the financial rewards for Facebook; but you don’t get to see all of the company’s uses for your data.

      An artificial intelligence experiment of unprecedented scale disclosed by Facebook Wednesday offers a glimpse of one such use case. It shows how our social lives provide troves of valuable data for training machine-learning algorithms. It’s a resource that could help Facebook compete with Google, Amazon, and other tech giants with their own AI ambitions.

      Facebook researchers describe using 3.5 billion public Instagram photos—carrying 17,000 hashtags appended by users—to train algorithms to categorize images for themselves. It provided a way to sidestep having to pay humans to label photos for such projects. The cache of Instagram photos is more than 10 times the size of a giant training set for image algorithms disclosed by Google last July.

      Having so many images for training helped Facebook’s team set a new record on a test that challenges software to assign photos to 1,000 categories including cat, car wheel, and Christmas stocking. Facebook says that algorithms trained on 1 billion Instagram images correctly identified 85.4 percent of photos on the test, known as ImageNet; the previous best was 83.1 percent, set by Google earlier this year.

      Image-recognition algorithms used on real-world problems are generally trained for narrower tasks, allowing greater accuracy; ImageNet is used by researchers as a measure of a machine learning system’s potential. Using a common trick called transfer learning, Facebook could fine-tune its Instagram-derived algorithms for specific tasks. The method involves using a large dataset to imbue a computer vision system with some basic visual sense, then training versions for different tasks using smaller and more specific datasets.

      As you would guess, Instagram hashtags skew towards certain subjects, such as #dogs, #cats, and #sunsets. Thanks to transfer learning they could still help the company with grittier problems. CEO Mark Zuckerberg told Congress this month that AI would help his company improve its ability to remove violent or extremist content. The company already uses image algorithms that look for nudity and violence in images and video.

      LEARN MORE

      The WIRED Guide to Artificial Intelligence

      Manohar Paluri, who leads Facebook’s applied computer vision group, says machine-vision models pre-trained on Instagram data could become useful on all kinds of problems. “We have a universal visual model that can be used and re-tuned for various efforts within the company,” says Paluri. Possible applications include enhancing Facebook’s systems that prompt people to reminisce over old photos, describe images to the visually impaired, and identify objectionable or illegal content, he says. (If you don’t want your Instagram snaps to be part of that, Facebook says you can withdraw your photos from its research projects by setting your Instagram account to private.)

      Facebook’s project also illustrates how companies need to spend heavily on computers and power bills to compete in AI. Computer-vision systems trained from Instagram data could tag images in seconds, says Paluri. But training algorithms on the full 3.5 billion Instagram photos occupied 336 high-powered graphics processors, spread across 42 servers, for more than three weeks solid.

      That might sound like a long time. Reza Zadeh, CEO of computer vision startup Matroid and an adjunct professor at Stanford, says it in fact demonstrates how nimble a well-resourced company with top-tier researchers can be, and how the scale of AI experiments has grown. Just last summer, it took Google two months to train software on a set of 300 million photos, in experiments using many fewer graphics processors.

      High-powered chips designed for machine learning are becoming more widely available, but few companies have access to so much data or so much processing power. With top machine-learning researchers expensive to hire, the more quickly they can run their experiments, the more productive they can be. “When companies are competing, that’s a big edge,” Zadeh says.

      Desire to keep that edge, and the ambition revealed by the scale of its Instagram experiments, help explain why Facebook recently said it is planning to design its own chips for machine learning—following in the footsteps of Google and others.

      Still, progress in AI requires more than just data and computers. Zadeh says he was surprised to see that the Instagram-trained algorithm didn’t lead to better performance on a test that challenges software to locate objects within images. That suggests existing machine learning software needs to be redesigned to take full advantage of giant photo collections, he says. Being able to locate objects in images is important for applications such as autonomous vehicles and augmented reality, where software needs to locate objects in the world.

      Paluri is under no illusions about the limitations of Facebook’s big experiment. Image algorithms can excel at narrowly focused tasks, and training with billions of images can help. But machines don’t yet display a general ability to understand the visual world like humans do. Making progress on that will require some fundamentally new ideas. “We are not going to solve any of these problems just by pushing brute force scale,” Paluri says. “We need new techniques.”

      Artificial Intelligence, Real Smarts

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      Why we should bulldoze the business school

      The long read: There are 13,000 business schools on Earth. Thats 13,000 too many. And I should know Ive taught in them for 20 years

      Visit the average university campus and it is likely that the newest and most ostentatious building will be occupied by the business school. The business school has the best building because it makes the biggest profits (or, euphemistically, contribution or surplus) as you might expect, from a form of knowledge that teaches people how to make profits.

      Business schools have huge influence, yet they are also widely regarded to be intellectually fraudulent places, fostering a culture of short-termism and greed. (There is a whole genre of jokes about what MBA Master of Business Administration really stands for: Mediocre But Arrogant, Management by Accident, More Bad Advice, Master Bullshit Artist and so on.) Critics of business schools come in many shapes and sizes: employers complain that graduates lack practical skills, conservative voices scorn the arriviste MBA, Europeans moan about Americanisation, radicals wail about the concentration of power in the hands of the running dogs of capital. Since 2008, many commentators have also suggested that business schools were complicit in producing the crash.

      Having taught in business schools for 20 years, I have come to believe that the best solution to these problems is to shut down business schools altogether. This is not a typical view among my colleagues. Even so, it is remarkable just how much criticism of business schools over the past decade has come from inside the schools themselves. Many business school professors, particularly in north America, have argued that their institutions have gone horribly astray. B-schools have been corrupted, they say, by deans following the money, teachers giving the punters what they want, researchers pumping out paint-by-numbers papers for journals that no one reads and students expecting a qualification in return for their cash (or, more likely, their parents cash). At the end of it all, most business-school graduates wont become high-level managers anyway, just precarious cubicle drones in anonymous office blocks.

      These are not complaints from professors of sociology, state policymakers or even outraged anti-capitalist activists. These are views in books written by insiders, by employees of business schools who themselves feel some sense of disquiet or even disgust at what they are getting up to. Of course, these dissenting views are still those of a minority. Most work within business schools is blithely unconcerned with any expression of doubt, participants being too busy oiling the wheels to worry about where the engine is going. Still, this internal criticism is loud and significant.

      The problem is that these insiders dissent has become so thoroughly institutionalised within the well-carpeted corridors that it now passes unremarked, just an everyday counterpoint to business as usual. Careers are made by wailing loudly in books and papers about the problems with business schools. The business school has been described by two insiders as a cancerous machine spewing out sick and irrelevant detritus. Even titles such as Against Management, Fucking Management and The Greedy Bastards Guide to Business appear not to cause any particular difficulties for their authors. I know this, because I wrote the first two. Frankly, the idea that I was permitted to get away with this speaks volumes about the extent to which this sort of criticism means anything very much at all. In fact, it is rewarded, because the fact that I publish is more important than what I publish.

      Most solutions to the problem of the B-school shy away from radical restructuring, and instead tend to suggest a return to supposedly more traditional business practices, or a form of moral rearmament decorated with terms such as responsibility and ethics. All of these suggestions leave the basic problem untouched, that the business school only teaches one form of organising market managerialism.

      Thats why I think that we should call in the bulldozers and demand an entirely new way of thinking about management, business and markets. If we want those in power to become more responsible, then we must stop teaching students that heroic transformational leaders are the answer to every problem, or that the purpose of learning about taxation laws is to evade taxation, or that creating new desires is the purpose of marketing. In every case, the business school acts as an apologist, selling ideology as if it were science.


      Universities have been around for a millenium, but the vast majority of business schools only came into existence in the last century. Despite loud and continual claims that they were a US invention, the first was probably the cole Suprieure de Commerce de Paris, founded in 1819 as a privately funded attempt to produce a grande cole for business. A century later, hundreds of business schools had popped up across Europe and the US, and from the 1950s onwards, they began to grow rapidly in other parts of the world.

      In 2011, the Association to Advance Collegiate Schools of Business estimated that there were then nearly 13,000 business schools in the world. India alone is estimated to have 3,000 private schools of business. Pause for a moment, and consider that figure. Think about the huge numbers of people employed by those institutions, about the armies of graduates marching out with business degrees, about the gigantic sums of money circulating in the name of business education. (In 2013, the top 20 US MBA programmes already charged at least $100,000 (72,000). At the time of writing, London Business School is advertising a tuition fee of 84,500 for its MBA.) No wonder that the bandwagon keeps rolling.

      For the most part, business schools all assume a similar form. The architecture is generic modern glass, panel, brick. Outside, theres some expensive signage offering an inoffensive logo, probably in blue, probably with a square on it. The door opens, automatically. Inside, theres a female receptionist dressed office-smart. Some abstract art hangs on the walls, and perhaps a banner or two with some hopeful assertions: We mean business. Teaching and Research for Impact. A big screen will hang somewhere over the lobby, running a Bloomberg news ticker and advertising visiting speakers and talks about preparing your CV. Shiny marketing leaflets sit in dispensing racks, with images of a diverse tableau of open-faced students on the cover. On the leaflets, you can find an alphabet of mastery: MBA, MSc Management, MSc Accounting, MSc Management and Accounting, MSc Marketing, MSc International Business, MSc Operations Management.

      There will be plush lecture theatres with thick carpet, perhaps named after companies or personal donors. The lectern bears the logo of the business school. In fact, pretty much everything bears the weight of the logo, like someone who worries their possessions might get stolen and so marks them with their name. Unlike some of the shabby buildings in other parts of the university, the business school tries hard to project efficiency and confidence. The business school knows what it is doing and has its well-scrubbed face aimed firmly at the busy future. It cares about what people think of it.

      Even if the reality isnt always as shiny if the roof leaks a little and the toilet is blocked that is what the business-school dean would like to think that their school was like, or what they would want their school to be. A clean machine for turning income from students into profits.


      What do business schools actually teach? This is a more complicated question than it first appears. Much writing on education has explored the ways in which a hidden curriculum supplies lessons to students without doing so explicitly. From the 1970s onwards, researchers explored how social class, gender, ethnicity, sexuality and so on were being implicitly taught in the classroom. This might involve segregating students into separate classes the girls doing domestic science and the boys doing metalwork, say which, in turn, implies what is natural or appropriate for different groups of people. The hidden curriculum can be taught in other ways too, by the ways in which teaching and assessment are practised, or through what is or isnt included in the curriculum. The hidden curriculum tells us what matters and who matters, which places are most important and what topics can be ignored.

      Business
      Illustration: Michael Kirkham

      In many countries, a lot of work has been done on trying to deal with these issues. Materials on black history, women in science or pop songs as poetry are now fairly routine. That doesnt mean that the hidden curriculum is no longer a problem, but at least in many of the more enlightened educational systems, it is not now routinely assumed that there is one history, one set of actors, one way of telling the story.

      But in the business school, both the explicit and hidden curriculums sing the same song. The things taught and the way that they are taught generally meanthat the virtues of capitalist market managerialism are told and sold as if there were no other ways of seeingthe world.

      If we educate our graduates in the inevitability of tooth-and-claw capitalism, it is hardly surprising that we end up with justifications for massive salary payments to people who take huge risks with other peoples money. If we teach that there is nothing else below the bottom line, then ideas about sustainability, diversity, responsibility and so on become mere decoration. The message that management research and teaching often provides is that capitalism is inevitable, and that the financial and legal techniques for running capitalism are a form of science. This combination of ideology and technocracy is what has made the business school into such an effective, and dangerous, institution.

      We can see how this works if we look a bit more closely at the business-school curriculum and how it is taught. Take finance, for instance. This is a field concerned with understanding how people with money invest it. It assumes that there are people with money or capital that can be used as security for money, and hence it also assumes substantial inequalities of income and wealth. The greater the inequalities within any given society, the greater the interest in finance, as well as the market in luxury yachts. Finance academics almost always assume that earning rent on capital (however it was acquired) is a legitimate and perhaps even praiseworthy activity, with skilful investors being lionised for their technical skills and success. The purpose of this form of knowledge is to maximise the rent from wealth, often by developing mathematical or legal mechanisms that can multiply it. Successful financial strategies are those that produce the maximum return in the shortest period, and hence that further exacerbate the social inequalities that made them possible in the first place.

      Or consider human resource management. This field applies theories of rational egoism roughly the idea that people act according to rational calculations about what will maximise their own interest to the management of human beings in organisations. The name of the field is telling, since it implies that human beings are akin to technological or financial resources insofar as they are an element to be used by management in order to produce a successful organisation. Despite its use of the word, human resource management is not particularly interested in what it is like to be a human being. Its object of interest are categories women, ethnic minorities, the underperforming employee and their relationship to the functioning of the organisation. It is also the part of the business school most likely to be dealing with the problem of organised resistance to management strategies, usually in the form of trade unions. And in case it needs saying, human resource management is not on the side of the trade union. That would be partisan. It is a function which, in its most ambitious manifestation, seeks to become strategic, to assist senior management in the formulation of their plans to open a factory here, or close a branch office there.

      A similar kind of lens could be applied to other modules found in most business schools accounting, marketing, international business, innovation, logistics but Ill conclude with business ethics and corporate social responsibility pretty much the only areas within the business school that have developed a sustained critique of the consequences of management education and practice. These are domains that pride themselves on being gadflies to the business school, insisting that its dominant forms of education, teaching and research require reform. The complaints that propel writing and teaching in these areas are predictable but important sustainability, inequality, the production of graduates who are taught that greed is good.

      The problem is that business ethics and corporate social responsibility are subjects used as window dressing in the marketing of the business school, and as a fig leaf to cover the conscience of B-school deans as if talking about ethics and responsibility were the same as doing something about it. They almost never systematically address the simple idea that since current social and economic relations produce the problems that ethics and corporate social responsibility courses treat as subjects to be studied, it is those social and economic relations that need to be changed.


      You might well think that each of these areas of research and teaching are innocuous enough in themselves, and collectively they just appear to cover all the different dimensions of business activity money, people, technology, transport, selling and so on. But it is worth spelling out the shared assumptions of every subject studied at business school.

      The first thing that all these areas share is a powerful sense that market managerial forms of social order are desirable. The acceleration of global trade, the use of market mechanisms and managerial techniques, the extension of technologies such as accounting, finance and operations are not routinely questioned. This is a progressive account of the modern world, one that relies on the promise of technology, choice, plenty and wealth. Within the business school, capitalism is assumed to be the end of history, an economic model that has trumped all the others, and is now taught as science, rather than ideology.

      The second is the assumption that human behaviour of employees, customers, managers and so on is best understood as if we are all rational egoists. This provides a set of background assumptions that allow for the development of models of how human beings might be managed in the interests of the business organisation. Motivating employees, correcting market failures, designing lean management systems or persuading consumers to spend money are all instances of the same sort of problem. The foregrounded interest here is that of the person who wants control, and the people who are the objects of that interest can then be treated as people who can be manipulated.

      The final similarity I want to point to concerns the nature of the knowledge being produced and disseminated by the business school itself. Because it borrows the gown and mortarboard of the university, and cloaks its knowledge in the apparatus of science journals, professors, big words it is relatively easy to imagine that the knowledge the business school sells and the way that it sells it somehow less vulgar and stupid than it really is.


      The easiest summary of all of the above, and one that would inform most peoples understandings of what goes on in the B-school, is that they are places that teach people how to get money out of the pockets of ordinary people and keep it for themselves. In some senses, thats a description of capitalism, but there is also a sense here that business schools actually teach that greed is good. As Joel M Podolny, the former dean of Yale School of Management, once opined: The way business schools today compete leads students to ask, What can I do to make the most money? and the manner in which faculty members teach allows students to regard the moral consequences of their actions as mere afterthoughts.

      Business
      Illustration: Michael Kirkham

      This picture is, to some extent, backed up by research, although some of this is of dubious quality. There are various surveys of business-school students that suggest that they have an instrumental approach to education; that is to say, they want what marketing and branding tells them that they want. In terms of the classroom, they expect the teaching of uncomplicated and practical concepts and tools that they deem will be helpful to them in their future careers. Philosophy is for the birds.

      As someone who has taught in business schools for decades, this sort of finding doesnt surprise me, though others suggest rather more incendiary findings. One US survey compared MBA students to people who were imprisoned in low-security prisons and found that the latter were more ethical. Another suggested that the likelihood of committing some form of corporate crime increased if the individual concerned had experience of graduate business education, or military service. (Both careers presumably involve absolving responsibility to an organisation.) Other surveys suggest that students come in believing in employee wellbeing and customersatisfaction and leave thinking that shareholder value is the most important issue, and that business-school students are more likely to cheat than students in other subjects.

      Whether the causes and effects (or indeed the findings) are as neat as surveys like this might suggest is something that I doubt, but it would be equally daft to suggest that the business school has no effect on its graduates. Having an MBA might not make a student greedy, impatient or unethical, but both the B-schools explicit and hidden curriculums do teach lessons. Not that these lessons are acknowledged when something goes wrong, because then the business school usually denies all responsibility. Thats a tricky position, though, because, as a 2009 Economist editorial put it, You cannot claim that your mission is to educate the leaders who make a difference to the world and then wash your hands of your alumni when the difference they make is malign.

      After the 2007 crash, there was a game of pass-the-blame-parcel going on, so its not surprising that most business-school deans were also trying to blame consumers for borrowing too much, the bankers for behaving so riskily, rotten apples for being so bad and the system for being, well, the system. Who, after all, would want to claim that they merely taught greed?


      The sorts of doors to knowledge we find in universities are based on exclusions. A subject is made up by teaching this and not that, about space (geography) and not time (history), about collectives of people (sociology) and not about individuals (psychology), and so on. Of course, there are leakages and these are often where the most interesting thinking happens, but this partitioning of the world is constitutive of any university discipline. We cannot study everything, all the time, which is why there are names of departments over the doors to buildings and corridors.

      However, the B-school is an even more extreme case. It is constituted through separating commercial life from the rest of life, but then undergoes a further specialisation. The business school assumes capitalism, corporations and managers as the default form of organisation, and everything else as history, anomaly, exception, alternative. In terms of curriculum and research, everything else is peripheral.

      Most business schools exist as parts of universities, and universities are generally understood as institutions with responsibilities to the societies they serve. Why then do we assume that degree courses in business should only teach one form of organisation capitalism as if that were the only way in which human life could be arranged?

      The sort of world that is being produced by the market managerialism that the business school sells is not a pleasant one. Its a sort of utopia for the wealthy and powerful, a group that the students are encouraged to imagine themselves joining, but such privilege is bought at a very high cost, resulting in environmental catastrophe, resource wars and forced migration, inequality within and between countries, the encouragement of hyper-consumption as well as persistently anti-democratic practices at work.

      Selling the business school works by ignoring these problems, or by mentioning them as challenges and then ignoring them in the practices of teaching and research. If we want to be able to respond to the challenges that face human life on this planet, then we need to research and teach about as many different forms of organising as we are able to collectively imagine. For us to assume that global capitalism can continue as it is means to assume a path to destruction.So if we are going to move away from business as usual, then we also need to radically reimagine the business school as usual. And this meansmore than pious murmurings about corporate social responsibility. It means doing away with what we have, and starting again.

      Shut Down the Business School: Whats Wrong with Management Education will be published by Pluto Press in May

      Follow the long read on Twitter at @gdnlongread, or sign up to the long read weekly email here.

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      United Bans 25 Pet Breeds From Hold, Will Resume Flying Others

      United Continental Holdings Inc. will ban 25 different pet breeds when it resumes flying pets this summer, four months after a dog’s death prompted the airline to review its policies for transporting animals.

      The carrier will again accept dogs and cats in the cargo hold starting July 9 if the animal’s guardian is booked on the same flight, spokesman Charles Hobart said Tuesday. United is also teaming with American Humane to “improve the well-being of all pets that travel on” the Chicago-based airline, according to a company statement.

      United announced the changes less than two months after a bruising week of public-relations fiascoes involving dogs. A French bulldog died March 12 after a flight attendant had the pet and its animal crate placed in an overhead bin. In a separate incident, the airline sent a Kansas-bound German shepherd to Japan. United also took criticism over its record of animal deaths in 2017, when it accounted for 18 of the 24 animals that died on a major airline.

      The airline will no longer allow 21 dog and four cat breeds that are prone to physical problems from heat or other travel stress, including bulldogs, boxers and Boston terriers. A complete list is available on the company’s website.

      Under its previous rates for the PetSafe program, United charged more than $2,400 for some large animals on some European and Pacific routes. Domestically, pets had cost $201-$963 depending on the animal’s size.

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      How Artificial Intelligence Canand Can’tFix Facebook

      Facebook has problems. Fake news. Terrorism. Russian propaganda. And maybe soon regulation. The company’s solution: Turn them into artificial-intelligence problems. The strategy will require Facebook to make progress on some of the biggest challenges in computing.

      During two congressional sessions last month, CEO Mark Zuckerberg referenced AI more than 30 times in explaining how the company would better police activity on its platform. The man tasked with delivering on those promises, CTO Mike Schroepfer, picked up that theme in a keynote and interview at Facebook’s annual developer conference Wednesday.

      Schroepfer told thousands of developers and journalists that “AI is the best tool we have to keep our community safe at scale.” After the congressional hearings, critics accused Zuckerberg of invoking AI to mislead people into thinking the company’s challenges are simply technological. Schroepfer told WIRED Wednesday that the company had made mistakes. But he said that for Facebook—with more than 2 billion people on its service each month—AI is the only way to address them.

      Even if the company could afford to have humans check every post, it wouldn’t want to. “If I told you that there was a human reading every single one of your posts before it went up it would change what you would post,” Schroepfer says.

      Facebook already uses automation to police its platform, with some success. Since 2011, the company has used a tool called PhotoDNA, originally developed by Microsoft, to detect child pornography, for example. Schroepfer says the company’s algorithms have steadily improved enough to flag other images it wants to keep off its platform.

      First came nudity and pornography, which Schroepfer describes as “on the easier side of the spectrum to identify.” Next came photos and videos that depict “gore and graphic violence”—think Isis beheading videos—which at a pixel-by-pixel level are difficult to distinguish from more benign imagery. “We're now fairly effective at that,” Schroepfer says.

      But tough problems remain. Schroepfer says Facebook in recent months has been investing a “a whole heck of a lot more” into the teams working on problems like election integrity, bad ads, and fake news. “It's fair to say we've pivoted a whole lot of the energy of the company over the last number of months towards all of these issues,” he says. Zuckerberg said earlier this week that he expected to spend three years building up better systems to catch unwanted content.

      Facebook’s plan for an AI safety net faces larger challenges on problems that require machines to read, not see. For software to help fight fake news, online harassment, and propaganda campaigns like that mounted by Russia during the 2016 election, it needs to understand what people are saying.

      LEARN MORE

      The WIRED Guide to Artificial Intelligence

      Despite the success of web search and automated translation, software is still not very good at understanding the nuance and context of language. Facebook’s director of AI and machine learning, Srinivas Narayanan, illustrated the challenge in Wednesday’s keynote using the phrase “Look at that pig!” It might be welcome to someone sharing a snap of their porcine pet, less so as a comment on a wedding photo.

      Facebook shows some progress with algorithms that read. On Wednesday, the company said that a system that looks for signs a person may harm himself had prompted more than 1,000 calls to first responders since it was deployed late last year. Language algorithms helped Facebook remove almost 2 million pieces of terrorist-related content in the first quarter of this year.

      Schroepfer says Facebook has improved its systems for detecting bullying by training them on fake data from software taught to generate insults. In a process called adversarial training, both the abuse hurler and blocker become more effective over time. That places Facebook among a growing number of companies using synthetic, or fake, data to train machine learning systems.

      Another hurdle: other languages. Facebook’s language technology works best in English, not just because the company is American, but because the technology is typically trained using text taken from the internet, where English dominates. Facebook’s figures indicate that more than half of its users don’t speak English. “That's a huge problem,” Schroepfer says.

      Facebook is so dominant in some parts of the world that its language skills could even be a matter of life and death. UN investigators examining claims of genocide in Myanmar after the deaths of Rohingya Muslims said the company’s services had played a role in spreading hate speech against the group. Facebook has admitted that the crisis caught it without enough Burmese language content reviewers.

      Facebook is working on a project called MUSE that could one day make technology developed for one language work in a different language, without needing piles of new training data. Until it is practical, Facebook’s progress on expanding its AI systems to new languages depends on gathering new data to bring its systems up to speed.

      In some cases—and places—that data could be slow to arrive. As the Myanmar problems showed, Facebook hasn’t chosen to build up the same language resources everywhere. In a conference session Tuesday on Facebook’s efforts to slow the spread of fake news, executive Tessa Lyons-Laing said machine learning software was learning to flag misinformation from the work of fact checkers at organizations like AP, who manually mark fake stories for Facebook. But she said the technology would only work where Facebook establishes relationships with local fact-checking groups and has built up a good collection of their data.

      Schroepfer says that finding ways to move forward without having to depend on fresh human input is one of his main strategies for advancing AI. On Wednesday Facebook researchers showed how billions of Instagram hashtags provided a free data source to set a new record in image recognition. On many of Facebook’s trickiest problems, there’s no way to cut human judgment out of the loop. “AI is not a substitute for people when it comes to deciding what's okay and what's not okay up-front,” says Schroepfer. “AI is a great implementation tool to implement the rules once people have decided them.”

      Social Problems

      Read more: http://www.wired.com/

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      How did we let modern slavery become part of our everyday lives? | Felicity Lawrence

      Society abhors exploitation but we are complicit. The cheap goods and services consumers expect makes exploitation inevitable, says Guardian special correspondent Felicity Lawrence

      Since the Modern Slavery Act of 2015, British companies over a certain size have been required to report on slavery in their supply chains. Their statements are both shocking and admirable. Shocking because they make clear that the incidence of slavery has become normalised once again and not just in criminal operations such as the illegal drugs trade or trafficking for prostitution, but in the mainstream economy. The declarations are prefaced with management expressions of abhorrence, of course, but there they are, another note alongside the annual accounts. They are admirable, however, in that transparency must be the first step to tackling this phenomenon.

      Last month the National Crime Agency reported a 35% annual rise in the number of suspected slavery victims found in the UK, with more than 5,000 people referred to the government mechanism that supports them in 2017. Labour exploitation, rather than sexual exploitation, was the most common type of modern slavery cited.

      The list of high-risk sectors for slavery declared in company statements is long: temporary workers in distribution and office cleaning; agency labour in logistics operations; subcontracted car-washes cleaning company vehicles; construction workers building and renovating company premises; outsourced security staff. A catalogue of the casualised workforce, in other words. It is hardly surprising that the most egregious forms of exploitation should appear where economic, legal and moral responsibility has been deliberately diffused. Modern slavery is the flipside of the coin that has seen corporates offshore their profits and dodge tax. Both represent a sloughing-off of what were seen in the past as important obligations to society.

      Then there are the more specific areas of production, where big high-street retailers statements acknowledge that forced or trafficked labour, often of refugees, is a well-known and recurring issue: the British and Irish fishing fleets, the UK meat and poultry processing industry, Leicester garment manufacturing, the Thai prawn supply chain, the Italian tomato industry, the Spanish horticulture sector, the Assam tea chain, and the Turkish garment sector.

      Separate from corporate reporting, the Gangmaster and Labour Abuse Authority was given powers in April 2017 to investigate exploitation beyond its original narrow remit of food and agriculture. The more it looks, the more it finds, and some of this new activity accounts for the increasing numbers of suspected victims of modern slavery. (Another factor is the statutory defence introduced in the Modern Slavery Act for those forced into criminal activity such as drug dealing: increasing use of that defence in drug cases probably accounts for British victims unusually making up the largest number by nationality this year. Albania, Vietnam, China, Nigeria, Romania, Sudan, Eritrea, India and Poland make up the rest of the top 10 source countries.)

      The main concentrations the GLAA sees are among migrant workers in hand carwashes, nail bars, domestic building projects such as basement excavations, the hospitality trade, hotel cleaning, takeaway restaurants and domestic cleaning.

      How did slavery, which we thought was abolished, reach into our everyday consumption? While it is quite right that companies should have their reputational feet held to the fire for abuse that arises out of their economic model, there are also uncomfortable truths here for affluent consumers of personal services.

      Things that were until recently luxuries manicures, clothes that change fashion every few weeks, regular holiday breaks to hotels, eating out frequently, having your car hand-valeted, using manual labour to dig out a basement under your house are now presented to us as affordable, everyday even. Where they have become so, it is in large part thanks to other people being badly paid at best, or victims of modern slavery at worst. The squeezed middle has been bought off by the illusion that it can share the consuming habits of those with runaway incomes at the top; but it cant not without squeezing those further down the chain.

      In a world where the state has often absented itself from the enforcement of employment law, and where so many human interactions are reduced to financial exchanges at whatever rate the market will take, people have become commodities to use or sell. When competition and austerity are king, it is every man and woman for themselves and their family. Too often, we close our eyes and try to protect our own.

      People-traffickers target the vulnerable including those with learning disabilities or raised in care, homeless people, those with alcohol and drug problems or previous convictions. They are the people easiest to control and least likely to attract sympathy. Anti-immigration sentiment has encouraged people to see these victims as foreign, as other. How else to explain why neighbours, work colleagues and customers so often fail to notice modern slavery?

      Take the group of trafficked Lithuanians working brutal hours on egg farms around the country who were kept under control in their Kent ganghouses by threats and fighting dogs. What did farm managers and local residents on the same quiet streets see and hear? Alarming antisocial behaviour, and fights in a foreign language that made them want to turn away and keep their heads down, or fellow human beings suffering intolerable abuse and anaesthetising themselves from the trauma with drink?

      Both the National Audit Office and the parliamentary select committee for work and pensions have highlighted serious shortcomings in the support for victims of modern slavery once they have been identified. The anti-slavery commissioner, Kevin Hyland, also pointed out to the committee that every time a suspected victim of slavery is referred to the national referral mechanism, a crime is being alleged. Yet there is only a one-in-four chance of these cases being recorded as a potential crime, let alone investigated. If there were 4,000 rapes in the UK and only one in four was recorded by the police, there would be an outcry, he said. These failings need state remedies.

      Meanwhile, we all need to recognise the signs. Where workers are putting in excessive hours, where they have no language to communicate with customers or where employers seem quick to speak for them, where they live in houses of multiple occupancy, we should be alert to the possibility of modern slavery.

      If you are being offered a service for much less than you would expect to pay for it, someone is almost certainly being exploited. A car wash that takes six men 15 minutes and costs 10 does not pay the legal minimum wage. If something seems too cheap to be true, it probably is.

      Felicity Lawrence is a special correspondent for the Guardian

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