Tag Archives: Business

First Marijuana-Based Medicine Is Approved for Sale in U.S.

The first-ever medical treatment derived from a marijuana plant will hit the U.S. market in a few months after regulators on Monday gave the epilepsy treatment the green light.

The Food and Drug Administration approved GW Pharmaceuticals Plc’s Epidiolex to treat two rare forms of childhood epilepsy, according to a statement from the agency. The liquid is made from a compound in the marijuana plant called cannabidiol, a different chemical from tetrahydrocannabinol, or THC, which gets users high.

GW Pharmaceuticals’ Epidiolex medication.

Photographer: Kathy Young/AP

Epilepsy patients and doctors have long had interest in marijuana’s therapeutic potential. The approval marks the first time patients will have access in the U.S. to a cannabis-derived drug that has undergone a safety and efficacy review by the FDA.

“The same principles around any prescription medication can now be applied to cannabis-based medications,” GW Pharma Chief Executive Officer Justin Gover said in an interview before the FDA’s decision. “That underlies the whole value of this. We now remove ourselves from being a special case and now meet the standard criteria for prescription medications.”

FDA Commissioner Scott Gottlieb issued a separate statement stressing the importance of proper research on medical uses of marijuana and cautioning other companies that might try to push their pot treatments.

“This is an important medical advance,” Gottlieb said of Epidiolex. “But it’s also important to note that this is not an approval of marijuana or all of its components.”

GW Pharma’s American depositary receipts fell less than 1 percent to $149.85 at 1:03 p.m. in New York. They had gained 15 percent this year through Friday’s close.

GW Pharma has to wait to sell Epidiolex until the Drug Enforcement Administration decides what restrictions to place on the drug to ensure that it reaches only the patients for whom it is intended. The DEA, which classifies marijuana as an illegal drug, is required to make that determination in 90 days, Gover said. FDA staff said at an April meeting on the drug with outside advisers that cannibidiol, known as CBD, “does not appear to have abuse potential.”

Severe Forms

Epidiolex is approved to treat Lennox-Gastaut and Dravet syndromes in patients age 2 or older. Both are considered severe forms of epilepsy that begin in childhood. They’re resistant to many existing treatments, and as many as 20 percent of children with Dravet syndrome die before reaching adulthood, according to the National Institutes of Health.

GW Pharma will make Epidiolex in the U.K., where the company is based, Gover said, and export the finished product to the U.S. As of last week, the company hadn’t determined the price but was in preliminary talks with insurance companies to make them aware Epidiolex is coming, he said.

While Epidiolex is the first approved medicine that comes from a pot plant, the FDA has allowed the use a few drugs made from synthetic cannabinoids, including Insys Therapeutics Inc.’s Syndros for loss of appetite in people with AIDS and nausea caused by chemotherapy. Insys is developing a cannabidiol oral solution for a severe type of epileptic seizure known as infantile spasms, and childhood epilepsy defined by staring spells where the child isn’t aware or responsive.

(Updates with FDA commissioner comments in fifth paragraph.)

    Read more: https://www.bloomberg.com/news/articles/2018-06-25/first-marijuana-based-medicine-wins-approval-for-sale-in-u-s

    When It Comes to Tipping, Millennials Are Cheapest

    U.S. millennials are quick to whip out their wallets for pricey avocado toast and craft beer. But when it comes to rewarding the waiters and bartenders who serve them, those wallets often stay closed.

    Ten percent of millennials don’t tip at all when dining out compared with only three percent among the older generations, according to a study released Monday by CreditCards.com, an online credit card marketplace.

    And those millennials who do tip at restaurants tend to leave a median gratuity of 15 percent, less than the overall average. Gen-Xers, baby boomers and the oldest Americans, the so-called Silent Generation, are more generous, leaving between 18 and 20 percent.

    “It was interesting to see that millennials are the worst tippers—because the typical restaurant worker a millennial,” CreditCards.com senior industry analyst Matt Schulz said in an interview. “It’s self-defeating.”

    The study was conducted for CreditCards.com by market-research firm GfK, which gathered data last month from 1,000 Americans aged 18 and older. Millennials were defined as between the ages of 18 and 37.

    Beyond those poor waiters, taxi drivers and baristas fared even worse with their millennial customers. Apparently even the suggestion that a tip is expected puts some of these young people off. Eighteen percent of millennials surveyed said they typically decline to leave any amount when presented with pre-entered tipping options—say if they’re in a taxi or taking a Lyft or Uber.

    Why are these American youth, many of whom work in tip-reliant industries, so cheap? The answer may be economic. “Millennials’ financial struggles are a big reason they tip less,” Schulz said.

    But other data point to a more cynical explanation. Millennials do tend to spend more of their disposable income eating out, according to 2017 data from Merrill Lynch. After all, that tip can pay for dessert.

    But twenty and thirty-somethings aren’t the only skinflint demographic. Men, southerners, westerners, parents with young children, lower earners and the less educated said they tip less in restaurants than the overall median of 18 percent, according to the study.

    Who, then, leaves the largest tips?

    The study found people who are college educated, over the age of 65, from the Northeast and Midwest, and women all reported leaving a median of 20 percent—an above average tip.

      Read more: https://www.bloomberg.com/news/articles/2018-06-18/when-it-comes-to-tipping-millennials-are-cheapest

      Shale Country Is Out of Workers and Dangling 100% Pay Hikes

      Jerry Morales, the mayor of Midland, Texas, and a local restaurateur, is being whipsawed by the latest Permian Basin shale-oil boom.

      It’s fueling the region and starving it at the same time. Sales-tax revenue is hitting a record high, allowing the city to get around to fixing busted roads. But the crazy-low 2.1 percent unemployment rate is a bear. As the proprietor of Mulberry Cafe and Gerardo’s Casita, Morales is working hard to retain cooks. As a Republican first elected in 2014, he oversees a government payroll 200 employees short of what it needs to fully function.

      “This economy is on fire,” he said from a back table at the cafe the other day, watching as the lunchtime crowd lined up for the Asian Zing Salad and Big Mo’s Toaster hamburger.

      Fire, of course, can be dangerous. In the country’s busiest oil patch, where the rig count has climbed by nearly one third in the past year, drillers, service providers and trucking companies have been poaching in all corners, recruiting everyone from police officers to grocery clerks. So many bus drivers with the Ector County Independent School District in nearby Odessa quit for the shale fields that kids were sometimes late to class. The George W. Bush Childhood Home, a museum in Midland dedicated to the 43rd U.S. president, is smarting from a volunteer shortage.

      The oil industry has such a ferocious appetite for workers that it’ll hire just about anyone with the most basic skills. “It is crazy,” said Jazmin Jimenez, 24, who zipped through a two-week training program at New Mexico Junior College in Hobbs, about 100 miles north of Midland, and was hired by Chevron Corp. as a well-pump checker. “Honestly I never thought I’d see myself at an oilfield company. But now that I’m here — I think this is it.”

      That’s understandable, considering the $28-a-hour she makes is double what she was earning until December as a guard at the Lea County Correctional Facility in Hobbs. When the boom goes bust, as history suggests they all do, shale-extraction businesses won’t be able to out-pay most employers anymore. Jimenez said she’ll take the money as long as it lasts.

      And this one could go on for a while. Companies are more cost-conscious than ever, and the evolution of oilfield technology continues to make finding and producing oil quicker and cheaper in the pancaked layers of rock in the Permian. It now accounts for about 30 percent of all U.S. output.

      There’s no question the economic upside is big in the basin, which covers more than 75,000 square miles in west Texas and southeastern New Mexico. Midland saw year-over-year increases of at least 34 percent in sales-tax collections in each of the last four months. Morales said coffers are full enough that he may ask for raises for city workers — so they don’t bolt for the oil fields.

      Another surprise: Some of his students, with two-year associate degrees, can make more than he does, with his master’s in science, electrical and electronic engineering. At Midland College’s oil and gas program, which trains for positions like petroleum-energy technician, enrollment is down about 20 percent from last year. But schools that teach how to pass the test for a CDL — commercial drivers license — are packed.

      “A CDL is a golden ticket around here,” said Steve Sauceda, who runs the workforce training program at New Mexico Junior College. “You are employable just about anywhere.”

      And you can make a whole lot more money than waiting tables at Gerardo’s Casita. Jeremiah Fleming, 30, is on track to pull down $140,000 driving flatbed trucks for Aveda Transportation & Energy Services Inc., hauling rigs.

      “This will be my best year yet,” said Fleming, who used to work in the once-bustling shale play in North Dakota. “I wouldn’t want to go anywhere else.”

      Morales, a native Midlander and second-generation restaurateur, has seen it happen so many times before. Oil prices go up, and energy companies dangle such incredible salaries that restaurants, grocery stores, hotels and other businesses can’t compete. People complain about poor service and long lines at McDonald’s and the Walmart and their favorite Tex-Mex joints. Rents soar.

      “This is my home town. I don’t want that reputation,” he said. He’s not yet quite sure what to do about it as mayor of a city that has been on the oil-industry rollercoaster for nearly 100 years.

      He has, though, come up with strategies for his restaurants. For example, he now issues paychecks weekly, instead of twice monthly, and offers more opportunities for over-time hours. He also makes common-sense bids to employees tempted by the Permian’s siren call.

      His pitch: “If you’ll stay with me, I can give you three quarters of what the oil will give you but you don’t have to get dirty or worry about getting hurt.” And just maybe, when crude crashes, they’ll still be employed.

        Read more: https://www.bloomberg.com/news/articles/2018-06-06/shale-country-dangles-100-pay-raises-as-labor-market-runs-dry

        ‘ICE Is Everywhere’: Using Library Science to Map the Separation Crisis

        On Father’s Day, Alex Gil was IMing with his colleague Manan Ahmed when they decided they had to do something about children being separated from their parents at the US-Mexico border.

        Since May, the US government had taken more than 2,300 kids away from their families as a result of Attorney General Jeff Sessions' new "zero tolerance" immigration policy, which calls for criminally prosecuting all people entering the country illegally. Reports started surfacing of the ensuing chaos at the border; in one especially horrible case, a child was reportedly ripped from her mother's breast. As outrage grew, the question came up over and over again: Where were the children? Between the ad-hoc implementation of "zero tolerance" and the opaque bureaucracy of the immigration system in general, migrant advocates, journalists, and even politicians struggled to find clear answers.

        Gil, a father of two, knew they could be useful. As the digital scholarship librarian at Columbia University, Gil's job is to use technology to help people find information—skills he had put to use in times of crisis before.

        Torn Apart tries to map the carceral landscape for immigrants detained for being undocumented. Orange dots indicate all ICE facilities; blue dots are private juvenile detention centers.
        Torn Apart/CC BY 4.0

        Gil and Ahmed, a historian at Columbia, assembled a team of what Gil calls “digital ninjas” for a “crisis researchathon.” These volunteers were professors, graduate students, researchers, and fellows from across the country with varied academic focus, but they all had two things in common: an interest in the history of colonialism, empire, and borders; and the belief that classical research methods can be used not just to understand the past but to reveal the present.

        They set up a Telegram chat and a master Google spreadsheet, and then they began looking for any publicly available data—government immigration records, tax forms, job listings, Facebook pages—they could use to isolate and locate the detention centers that could be holding these children.

        The result of their week of frantic research is Torn Apart / Separados, an interactive web site that visualizes the vast apparatus of immigration enforcement in the US, and broadly maps the shelters where children can be housed. The name is meant to evoke not only the families who have been separated, but the way in which this sundering rips the social fabric of our country.

        “It shows that ICE is everywhere,” Gil says. “We ourselves were shocked even though we study this. A lot of America thinks this phenomenon is happening in this limited geographical space along the border. This map is telling a different story: The border is everywhere.”

        Digital Humanities and Crisis Response

        The group behind Torn Apart is a part of a growing vanguard known as the digital humanities, an interdisciplinary cohort of researchers who combine 21st-century technical skills and classical research practices to do a new kind of cultural interpretation—and sometimes activism. DH projects include historical and cultural research, archival preservation, crowdsourced mapping, social justice activism, or some combination of those things.

        "Our team is the perfect example of what Digital Humanities can be: a body of work that really cuts across units at universities, libraries, departments, and roles like faculty administration and staff to think about the ways digital tools can help us better understand culture," says Roopika Risam, a professor of English and library fellow at Salem State University and author of New Digital Worlds, about promoting equity and justice in the digital cultural record.

        Risam, Gil, Ahmed, and Torn Apart teammate Moacir de Sa Pereira, who teaches in NYU’s English department, are all members of Columbia's Group for Experimental Methods in the Humanities, or XPMethod, which is "dedicated to the rapid prototyping of speculative ideas." They were joined last week by Sylvia Fernandez and Maira Alvarez, graduate students at the University of Houston who specialize in literature of the borderlands and who co-founded Borderlands Archives Cartography, and Linda Rodriguez and Merisa Martinez.

        This is not the first time XPMethod has responded to a humanitarian crisis with a map. Last year after Hurricane Maria hit Puerto Rico, aid groups struggled to transport food and supplies across the island, a problem made worse by the inadequate and out-of-date maps that were available. The XPMethod team and 60 other volunteers from 25 institutions held an emergency mapathon to crowdsource maps and get them to people in the field. After that experience, Gil put together a toolkit so that other people could set up what Gil refers to as “nimble tent”—a popup team of digital researchers collaborating on a specific project in response to crisis.

        Their "nimble tent" work is driven by a need to be part of the solution. It's the same urge that's driven so many people in recent weeks to post on social media and raise money for advocacy groups helping immigrant families. The internet, with its vast and ephemeral nooks and archives, is a tantalizing resource in moments of social unease. For anyone with enough digital savvy and the ability to work quickly, the nimble tent model offers a way to do something, anything in response to crisis, even from halfway around the world.

        Building Torn Apart

        After a day of phone calls on Sunday, Gil and Ahmed had their team, but their mandate wasn't immediately clear. The most urgent problem, as they saw it, was that parents couldn't locate their children. While President Trump signed an executive order Wednesday to end the family separations, instead allowing indefinite detention of families together, little has been done to resolve the issue.

        Under the zero tolerance policy's initial implementation, when the government detained a family for crossing the border illegally, at first everyone was in the hands of Customs and Border Patrol, a branch of the Department of Homeland Security. But once parents were charged, they were sent into an ICE detention facility and their children were handed over to the Department of Health and Human Services and the Office of Refugee Resettlement. As WIRED and others have reported, these agencies are not set up to keep track of families as a unit, so parents trying to find their children have had very little luck. Many children and parents haven't been able to reach other by phone since being separated.

        The Torn Apart team knew there was information out there about the locations of detention centers, and which centers could hold children. That information just wasn't aggregated in one place—a problem they set out to remedy.

        First, the team looked through a trove of official ICE records released to journalists through a Freedom of Information Act request. It gave them the broad picture of where US detention centers are located, but they still needed to understand where children were being held. Risam began tracking down nonprofit facilities that contract with HHS and ORR to care for children. The ICE data referenced 113 youth shelters and their general geographical locations, but the names themselves were redacted. Then Risam found data compiled by Syracuse University in 2015 that listed the names of shelters where immigrant children had been transferred, and from there she was able to identify the nonprofits associated with them.

        With the nonprofit names in hand, Risam went looking for their corporate 990 tax documents, which gave her locations that she could cross-reference with the ICE data to map where immigrant children are held. What she found “feels very much like a patchwork of shelters," she says. Sa Pereira visualized Risam's work by demarcating 113 ORR shelters, including nonprofit, religious, and government-run facilities, as black dots on the map. Try to click on one, though, and it will move, suggesting how the government resists pinpointing these sites.

        Housing migrant children has been big business since a flood of unaccompanied minors began entering the US in 2014. Nearly 11,000 children are held in these facilities, according to HHS. Risam wasn't able to see which ones definitely held the children newly separated from their parents under Trump's policy, but mapping where children generally are allowed to be held at least gave some insight into where they could be.

        The team also used less official data. Gil found Facebook pages and Google business listings for detention centers, where parents were desperately posting asking where their children were. They combed through confirmed news reports of where children had been taken, and where detentions centers were known to be. (You can find all their data sources on the website.)

        They slept little. Gil ordered pizza for his kids instead of making dinner most nights. With the blessings of their institutions, they cleared as much time as they could to focus on the project. “It has taken a lot of emotional and mental energy,” says Alvarez, who along with Fernandez mapped the legal entry points along the border for a section of Torn Apart called "The Trap." For both women, the week was intensely personal. They grew up in the borderlands and used their experience to seek the right data—to look for pedestrian crossings versus commercial entry points, for instance.

        To the team's surprise, immigration detention facilities were not isolated at the southern border. Rather, it was a vast web that crisscrossed every state in the nation. Even the centers that hold children are farther from the border than they expected, in places like the Northeast. This, the team realized, was the story they had to tell: how immigration enforcement reaches into every part of America.

        Much of the team’s conversation during the week focused on how to display the information so families, journalists, and advocates could actually use it. They also needed to “strike a balance,” as Risam puts it, between raising awareness, protecting the privacy of the children, and discouraging harassment. Gil is aware some people might want to track down phone numbers and addresses of detention centers and harass the staff. “That can turn into a mess real quick and do more harm than good,” he says. Ultimately, they decided to show the city and state a detention center is in, but not the actual address or name of the facility, in the hopes of dissuading bad behavior.

        "The Eye" shows satellite images of ICE detention centers across America. Click on one and zoom in to the specific location. The design is meant to convey that ICE is everywhere.
        Torn Apart/CC BY 4.0

        The website, which Sa Pereira coded, is full of design choices meant to not just impart information but also to evoke a more visceral reaction. In one particularly moving visualization called the Eye, Sa Pereira positioned satellite photos of ICE detention centers over the continental US. The thumbnail grid itself is jarring, but click on one, and you zoom across America to the town or city in which the center sits. It's dizzying. These centers are often right in the middle of everyday urban and suburban life—in a nondescript New York city, for instance, or in a strip mall next to a nail salon.

        “You get that voyeuristic creepiness of looking at satellite imagery, but also a creepiness of recognition that this could be anywhere. This isn’t in the desert surrounded by barbed wire, this is down the street,” Sa Pereira says. "Children being put into cages is terrible, and it’s indicative and symptomatic of a much larger problem. This is a way to make that system visible."

        Torn Apart achieves this with maps, as well as testimonies, visualizations, and what Gil calls "textures," personal and surreal ephemera like parents asking where their children are in a Google business review or promotional material from SouthwestKey, a nonprofit immigration shelter corporation, boasting that "95 percent" of the population it serves are people of color.

        DigitalGlobe/Texas Orthoimagery

        A Living Resource

        It is more than information. It is a living resource, one the team hopes migrants will use to find their families and that researchers will build upon. Much of the project's power is in its archival potential. "No one is documenting what is happening in everyday life of migrants," according to Fernandez. "This is a digital historical record."

        As the team was finalizing Torn Apart Friday, the Washington Post published its own crowdsourced map of detention centers housing migrant children, which included some but not all the inputs gathered for Torn Apart. Gil is trying to get in touch with the paper to offer the rest of their data, and to cross-reference Torn Apart against the Post's data. The researchers' work dovetails with investigative journalism: The goal of both is to use information to make sense of confusion.

        The site went live at 12:30 pm Eastern time on Monday. The team can hopefully get some sleep, but the project is not over. Outside researchers will now peer-review it. Half of the Torn Apart crew are now in Mexico for the annual Digital Humanities Conference, where they will hold another researchathon.

        "It's like a hot potato," Gil says. "Now we want to pass on the same data source to other teams to refine and tell their own story."

        Updated to clarify that Gil and Ahmed came up with the idea for Torn Apart in an IM conversation.

        Correction at 7pm: Moacir de Sa Pereira's full last name has been corrected on second reference throughout the article.


        More Great WIRED Stories

        Read more: https://www.wired.com/story/ice-is-everywhere-using-library-science-to-map-child-separation/

        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.

        More Great WIRED Stories

        Read more: https://www.wired.com/story/the-us-again-has-worlds-most-powerful-supercomputer/

        If Trump Is Laundering Russian Money, Heres How It Works

        For Donald Trump, there was the purchase of the $12.6 million Scottish estate and the $79.7 million for golf courses in the United Kingdom, not to mention the $16.2 million for the Northern Virginia Winery. All in cash.

        For Michael Cohen, it was the lucrative day in 2014 when he sold four Manhattan buildings for $32 million—three times what he’d paid for them less than three years before.

        Recent days have been filled with a seeming tidal wave of fresh revelations from the spiraling investigation around Donald Trump’s ties to Russia, particularly around suspicious financial transactions involving Trump fixer Michael Cohen, who appears to have used the same shell company LLC to pay hush money to porn star Stormy Daniels, collect six- and seven-figure consulting deals from companies like AT&T and Novartis, and receive payments from a company with close ties to oligarch Viktor Vekselberg.

        The subtext of many of the recent tales—from Donald Trump’s massive cash-spending spree to Cohen’s $32 million flip of New York real estate—is that the atypical transactions are worthy of greater scrutiny. After all, why was the self-proclaimed “King of Debt” suddenly waist-deep in cash and on a spending spree in the midst of the global real estate crash? Where was Cohen’s money coming from—and where was it going?

        It’s the old adage from the Watergate investigation: “Follow the money.”

        The implication, particularly in the more fever-swampy portions of Twitter, is that there was money laundering afoot—probably Russian in origin. The “quid” perhaps, before the election and the “pro quo” afterward. But is that a real possibility—and if it was money laundering, by whom and how?

        The payments appear to mirror suspicious activity that led to the earliest charges and investigative avenues of special counsel Robert Mueller’s probe, the money laundering and conspiracy charges leveled against former Trump campaign chair Paul Manafort and aide Rick Gates. (Gates has since pleaded guilty; Manafort’s case continues to move forward toward trial later this year.)

        But to Treasury officials and law enforcement who have long pursued money laundering and terrorist financing probes, it’s not what Donald Trump or Michael Cohen did in any single transaction that raises red flags—it’s how they conducted business day in and day out. The layers of shell companies, the contracts involving pseudonyms, the law firm cut-outs to make deals.

        "Many of the activities, when viewed in aggregate, point to a deliberate attempt to create opacity,” says Amit Sharma, who used to work on countering terrorist financing after 9/11 at the Treasury Department. “When you take two steps back, you see a murkiness and level of complexity with which the Cohen and Trump companies have operated—what are they hiding? Why are secondary and tertiary entities signing under pseudonyms and ‘cover' names? Truly legitimate, transparent companies don’t need to do that. Does this point to corruption and/or conspiracy? It certainly looks that way! Are all activities pointing to specific money laundering transactions? Not necessarily.”

        The fundamental approach to Trump and Cohen’s empires should raise eyebrows—and evidently has with Mueller’s probe and prosecutors in the Southern District of New York—precisely because of the apparently great lengths they undertook to evade basic transparency. While not necessarily illegal—some of the tactics are, in fact, regular parts of complex businesses—the pattern of activity points to an attempt to evade one of the basic precepts of modern banking and anti-money-laundering efforts: Know your customer.

        “What we call ‘covered institutions,’ that’s any financial institution overseen by US financial regulations, they have to have a comprehensive anti-money-laundering regime. It basically come down to one central question: Do you know your customer? Who’s behind the account, who has control over an entity or can facilitate transactions on its behalf, what are its sources of funds, and what is the normal, expected nature of its business or pattern of activity for that person or entity?" Sharma says. “Anytime a bank or financial institution spots activity that doesn’t match the regular pattern, they’re required to file suspicious activity reports with the Treasury Department.” (Those exact type of reports were triggered by odd withdrawals and payments by the Russian embassy around the time of the US election, and are the subject of part of Mueller’s probe, according to Buzzfeed.)

        Yet while regulations—especially since 9/11—require in-depth documentation and identification for basic banking for individuals, it has been much easier—until literally today—for corporate entities to hide their identities behind lawyers and shell companies. “Financial institutions are mandated to collect all this data on its customers, but up until now, financial institutions have not had to do the same for companies,” Sharma says. “For companies, often it has simply been the business location and Tax ID number and we don’t know the underlying ownership. We don’t know whether it’s a Russian oligarch.” (In fact, new Treasury Department rules that require stronger due diligence on banks to understand who actually owns—or has a controlling interest in—a company only come into effect today, May 11, 2018.)

        As Sharma says, “Trump and his companies have exercised this practice for many years—it seems that every new project, every product, every new building, he’s starting a new company or legal entity to manage it. This has been the case for overseas operations and activities as well. People do this to protect themselves from liability and to create protective measures that don’t roll directly up to them personally. In any given structure, he may own a portion of a parent company that owns a controlling interest in a holding company that may own a portion or receive economic benefits of the real estate.”

        In 2016 The Wall Street Journal's Jean Eaglesham, Mark Maremont, and Lisa Schwartz outlined a specific example of just that sort of structure: “Donald Trump owns a helicopter in Scotland. To be more precise, he has a revocable trust that owns 99 percent of a Delaware limited liability company that owns 99 percent of another Delaware LLC that owns a Scottish limited company that owns another Scottish company that owns the 26-year-old Sikorsky S-76B helicopter, emblazoned with a red ‘TRUMP’ on the side of its fuselage.” All told, the Journal reported, 15 entities were used at that point to “own” Trump’s fleet of two airplanes and three helicopters.

        Layer on layer of corporate structure makes it hard for investigators, tax officials, or prying lawyers to figure out who owns what, the underlying source of money for specific transactions, whether taxes are being appropriately paid in a given jurisdiction, or who might be partners in what enterprises.

        That’s where “Section 311” comes in.

        In 2001, as part of the USA Patriot Act, the Treasury Department was given a new tool against money laundering, known as “Section 311,” after the relevant section of the law, to designate foreign financial institutions, jurisdictions, or entities as “of primary money laundering concern.”

        A Section 311 designation was meant to help authorities highlight suspicious patterns of activity without having to prove any single transaction was illegal—it’s the rough equivalent for money laundering of the criminal RICO statute, the Racketeer Influenced and Corrupt Organizations Act, that allows prosecutors to take down entire mafia families, drug cartels, and street gangs without having to prove everyone involved knew about or participated in all the various individual crimes.

        “We deliberately put these tools together to go after really bad people—organized crime, terrorists, dictators, Chinese Triads,” Sharma says. “You didn’t have to point to a single illegal transaction. The totality of the transactions should give you pause enough that we would want to be sure US institutions scaled back or ceased doing business with them.”

        The designation, which effectively forces US financial institutions to sever ties with the entity, makes it all but impossible for an entity to participate in the global financial system. In the years since, the US Treasury Department has used Section 311 to go after the banks and front companies that help North Korea evade sanctions, to go after Iran’s nuclear program and terrorism financing, to isolate Syria, to punish banks that helped Saddam Hussein launder money, and to pressure off-shore havens, like the Pacific island of Nauru, that the US believes are complicit in money laundering.

        Sharma says that if what we have seen with Michael Cohen’s business dealings existed anywhere overseas, where it intersected with an investigation or a politically exposed person or national security issue of import to the US, it would ring all sorts of alarm bells at Treasury. What seems to be continually revealed is a pattern of atypical financial transactions, and too much of it seems structured specifically to hide and evade critical information or people involved.

        That desire for opacity, though, doesn’t necessarily point to money laundering. A specific charge of “money laundering” requires that the initial funds be traced to a so-called “predicate,” a recognized serious crime. “It could be fraud, smuggling, selling high technology, proceeds of child pornography. There are hundreds of predicate crimes in the United States; the global standard is ‘all serious crimes,’” explains former Treasury agent John Cassara. The complexity of tracing money all the way through the financial system, from a legitimate asset back to a crime, or vice versa, makes these cases some of the most challenging investigators undertake. “These are very, very difficult cases—it takes a lot for the investigators, the prosecutors, the US attorney to understand,” Cassara says.

        Part of the reason the cases are so tough is that there are plenty of other, nonillegal reasons wealthy people create opacity, including to minimize taxes, to limit personal or corporate legal liability, or to shield assets in divorce proceedings.

        The truth of the matter is that the global financial system is simply too large for officials to look at very closely.

        Money laundering is a huge—literally physically huge—problem: Illicit drug sales in the United States alone are estimated at around $60 billion to $100 billion a year, which translates, Cassara says, into about 20 million physical pounds of currency, far too much to be moved easily or spent easily. “The bad guys have a logistics issue. They want to try to get into a bank or nonbank financial institution, so they can spend it,” he says.

        Globally, the International Monetary Fund estimates that between 2 and 5 percent of the world’s gross domestic product is laundered money from illicit activity. “The number I normally use is the total is in the range of $4 trillion to $5 trillion, about the amount of the entire federal government budget,” says Cassara, who spent 26 years investigating such cases and has written multiple textbooks on anti-money-laundering efforts.

        Given that scale, the hard work of thousands of investigators and tax officials the world over amounts to a drop in the proverbial bucket; authorities only seize or charge about 1 percent of suspected money laundering cases. “By any measurement, we do a terrible job of enforcing this,” Cassara says. “I have the utmost respect for my colleagues, but if you just compare the bottom line with the results, as [financial crime expert] Raymond Baker used to say, total failure is just a decimal point away.”

        That shockingly low level of enforcement helps explain how Manafort’s scheme—which Mueller’s team says involved more than $18 million, funneled through entities that included oriental rug shops just a few miles from the Treasury Department itself—ran undetected and unprosecuted for so long.

        Each year, the Treasury Department fields upward of 18 million pieces of financial intelligence, including more than 2 million suspicious activity reports from banks and financial institutions—far more than it can effectively process. Globally, there are 145 foreign financial reporting centers, like the Treasury Department’s so-called FINCEN, its intelligence and enforcement unit, which translates into tens of millions more reports and warnings. It’s relatively easy for even large-scale financial crimes to hide in that mountain of evidence. “Your inbox was always full,” Cassara says.

        Today, Cassara says, money launderers have to be incredibly stupid or incredibly unlucky to be caught. “Since 9/11, the amount of financial intelligence has grown exponentially, so bad guys are taking steps to evade those efforts,” Cassara says.

        But what’s the point of buying, say, $934,350 in oriental rugs (as Manafort is alleged to have done), or buying luxury condos in London (as Russian oligarchs are said to be fond of)? How exactly do money laundering schemes work?

        While it’s easier to grasp how to hide cash at the street level—like in Breaking Bad, when Walter White purchases a cash-intensive car wash and simply cooks the book to show he’s washing more cars than he is—money laundering at the global level follows the same three-step process:

        Step 1: Placement

        The first challenge is simply getting the money somewhere into the global financial system, which is often easier said than done. Banks are required to file reports anytime someone deposits more than $10,000 in cash, so sneaking large amounts of cash into the financial system can pose a huge challenge. Breaking large transactions into smaller ones, say multiple deposits of $9,999, to evade the transaction reports is known as “structuring” or “smurfing,” and is illegal itself. Former Speaker of the House Dennis Hastert spent time in prison for “structuring,” as part of his effort to pay hush money to one of the men he sexually abused as a high school coach, rather than for the underlying abuse. “Placement is where criminals are most vulnerable, because the money is closest to the original crime,” Cassara says. “It’s much easier to catch them at the crime than to say ‘there’s a suspicious shopping center or golf course’ and work backwards.”

        Tracing “laundered” money back to illicit proceeds is key to any investigation.

        Step 2: Layering

        The second challenge is hiding the origin of the illicit money. That’s where the layers of LLCs can be helpful. Every time money moves—from one entity’s account to another, from one bank to another, from one country to another—it helps hide the original source. “It’s confusing and makes it difficult for investigators, tax officials, or a former wife to follow that money trail. It’s using this labyrinth of LLCs and tax havens, in the US and overseas, to make it difficult and time-consuming to trace,” Cassara says. “It gets hard because of issues of [investigative] competence, venue, jurisdiction.”

        Often, this “layering” step takes place with the help of lawyers and law firms; the revelations of the Panama Papers and the follow-on Paradise Papers laid out just how large a global business it is to help elites hide their assets. “Many of these folks have go-to structures and lawyers they use to go through the layering process,” Sharma says. “A lot of laundering happens through nonfinancial businesses and professions.…The Russians use a ton of folks in Turkey, UAE, and Cyprus. That fact pattern is well-known and long-standing.”

        While the Panama and Paradise Papers revelations focused primarily on overseas entities and tax havens, the United States is actually one of the worst global offenders: The so-called “Delaware company” structure is notorious for its lax documentation and opacity, as NPR’s Planet Money found out when they set up shell companies in Belize and Delaware in 2012.

        Step 3: Integration

        Once the money is in the global financial system and its origins properly obfuscated, the final challenge is making the money accessible—that is, integrating it into the legitimate economy. At the high end of money laundering, this often means purchasing real estate.

        “Real estate is a big issue for money laundering and has been for a long time,” Cassara says. “If you’ve got a condo or a shopping center or a golf course, the money has already been placed, it’s already been layered, it’s the final stage—integrated. The authorities aren’t going to look at that. Once you see property, in whatever form it is, it’s assumed that’s good, that’s a legitimate investment.”

        Large-scale money laundering, like what corrupt regimes or oligarchs need, requires—like any good investment portfolio—a well-balanced portfolio. “If you’ve got that much money, you need diversification,” Cassara says. “You’re going to put so much into gold, so much into stocks, so much into golf courses. By that point, they’re many steps away from illicit proceeds.”

        Real estate is particularly attractive for money laundering because of the large numbers involved—a single large transaction to purchase a golf course, a luxury condo, or shopping center is an easy way to make a whole lot of money look legitimate at once without raising any eyebrows with banks or regulators. It’s also a good way to evade so-called “capital controls,” which limit, for instance, the amount of money Chinese citizens can transfer out of the country.

        The goal in such efforts isn’t necessarily to have access to immediate cash—sometimes the end goal is simply to own a physical asset. “Parking” illicit gains from corrupt regimes, including Russia or China, in luxury real estate in the West is a common pattern because it historically holds value and, if you’re the buyer, you only trigger taxes by selling, not with the initial transaction. “Real estate tends to hold its value, luxury real estate typically even goes up—it’s a great way to preserve it without losing it,” Sharma says.

        Such absentee owners—more interested in parking their assets than actually occupying a residence—has led to the phenomenon of what locals call “lights out London,” entire luxury buildings or wealthy neighborhoods where hardly anyone is ever home. More than 85,000 offshore shell companies own British real estate, and one report last year found nearly $6 billion worth of properties owned by politicians and public officials with “suspicious wealth.”

        Similar concerns have been raised about Libyan purchases in Dubai, Chinese purchases in Vancouver, and Russian purchases in Miami, among other cities. It’s such a problem in New York real estate that the Treasury Department is moving to end anonymous all-cash purchases.

        And then there’s the oriental rugs. Perhaps the oddest part of the lengthy, detailed indictment of Paul Manafort is the nearly $1 million he evidently funneled through various antique rug shops. As Adam Davidson wrote at The New Yorker last fall, “It’s hard to imagine a person who spends $12 million over six years but only shops at a handful of stores, and nearly always happens to have a bill that ends in multiple zeroes: $107,000, then $20,000, then $250,000. At an unnamed men’s-clothing store in New York, Manafort spent $32,000, $15,000, $24,000, and other multiples of a thousand. For money-laundering experts, this fact alone would be cause for suspicion. It is extremely rare for even a single purchase to end in three zeroes.”

        The rugs and clothing appear to be an example of what officials call “trade-based money laundering,” using physical goods to evade currency reporting limits. There are, after all, only three ways to move money around the globe: Through bank transfers, through cash, or through physical trade. “I argue that trade-based money laundering is actually the largest of the three money laundering ideologies but it’s the one we’ve done the least to enforce,” Cassara says. “When a buyer and a seller are working together, the price of an object can be whatever they want it to be—it could be pens, it could be gold, it could be carpets….The reason it’s so effective is that global merchant transactions is in the tens of millions of dollars [a day]. Try to find the suspect transaction in that sea.”

        It’s relatively easy for a determined money launderer to falsify invoices, either inflating or deflating price, with the willing cooperation of a commercial partner—like an oriental rug store, where you purchase a rug that’s worth $5,000 for, say, $20,000 and the store owner returns the difference to you in cash. Or buy a rug worth $20,000, and buy it from Shop A for $5,000 and sell it to Shop B for the full price—the difference becomes all clean money. “I sold those rugs to another shop, that cash from Shop B, paid to me, is effectively washed,” Sharma explains.

        That so many of the transactions and behaviors of the Trump business empire and Michael Cohen’s empire appear to hew so closely to the well-known patterns and stages of money laundering deeply troubles Sharma.

        “It falls into fact patterns that we’ve seen in other areas of Russian and Eastern European organized crime,” he says. “We’re staring at a government—that goes right to the top—that engages in very way of doing business and the exact same fact patterns that we set these tools up to combat. That’s mind-boggling to me.”

        More Trump


        Garrett M. Graff (@vermontgmg) is a contributing editor for WIRED and the author of The Threat Matrix: Inside Robert Mueller's FBI. He can be reached at garrett.graff@gmail.com.

        Read more: https://www.wired.com/story/if-trump-is-laundering-russian-money-heres-how-it-works/

        This Billionaire Has Put Half His Net Worth Into Gold

        • Egypt’s Sawiris has put half his entire net worth into gold
        • He’s been waiting 10 years for North Korea stakes to pay off

        Some big investors see warning signs ahead for markets but are holding their positions. Egyptian billionaire Naguib Sawiris is taking action: He’s put half of his $5.7 billion net worth into gold.

        He said in an interview Monday that he believes gold prices will rally further, reaching $1,800 per ounce from just above $1,300 now, while “overvalued” stock markets crash.

        “In the end you have China and they will not stop consuming. And people also tend to go to gold during crises and we are full of crises right now,” Sawiris said at his office in Cairo overlooking the Nile. “Look at the Middle East and the rest of the world and Mr. Trump doesn’t help.”

        President Donald Trump is aiding Sawiris in one way, though: If a North Korean peace deal can be reached, the Egyptian’s investments there may finally pay off. After 10 years of waiting to repatriate all his profits easily and control his mobile-phone company, Egypt’s second-richest man says an accord would let him reap some of his returns.

        “I am taking all the hits, I am being paid in a currency that doesn’t get exchanged very easily, I have put a lot of money and built a hotel and did a lot of good stuff there,” said Sawiris, who founded North Korea’s first telecom operator, Koryolink. The North Korean unit’s costs and revenues aren’t currently recognized on the financial statements of Sawiris’ Orascom Telecom Media & Technology Holding SAE.
        Sawiris over the years has been pressured by “every single Western government in the world” for his presence in the country hit by international sanctions for its nuclear threats, he said, but he considered himself a “goodwill investor.” His advice for governments and to Trump ahead of his expected meeting with North Korean Leader Kim Jong Un: Don’t bully him, and promise prosperity in exchange for concessions on nuclear.

        A successful meeting between Kim and South Korean President Moon Jae-In last week cleared the way for Trump to meet with the North Korean leader to discuss his nuclear-weapons and ballistic-missile programs. The date and the place haven’t been set. An agreement — elusive for almost seven decades — would open the door for Sawiris to restore his investments there and possibly make new ones.

        “I know these North Korean people. They are very proud, they will not yield under threat and bullying. You just smile and talk and sit down and they will come through,” he said.

        Sawiris, the son of Onsi Sawiris, who founded Orascom Construction, has built a name by investing in the telecom sector in Egypt and in less popular markets including Iraq, Pakistan, North Korea and Bangladesh. He also bought Italy’s Wind Telecomunicazioni before merging it, along with a number of his telecom assets, with Veon Ltd. in 2011.

        Since then Sawiris has diversified into the financial sector by buying out Egyptian investment bank Beltone Financial Holding and attempting to buy CI Capital Holding to create Egypt’s biggest investment bank. His offer was blocked. He also expanded in mining, becoming, with his family, the largest investor in the sector through shareholdings in Evolution Mining, Endeavour Mining Corporation and La Mancha Resources Inc.

        Gold and Cash

        The largest share of Sawiris's investments includes most of his gold stakes

        Source: Bloomberg Billionaires Index

        Figures in millions of dollars; based on his stake in La Mancha Resources *Denotes gold companies

        “I had to convince my mom in the beginning,” Sawiris said in the interview with Bloomberg Television. “It has been a very good investment for me. I recently sold a portion of my Evolution shares because I want to invest now in Latin America and Eastern Europe.”

        He’s from a family of investors. Nassef Sawiris, Naguib’s youngest brother and the richest man in Egypt, is the biggest shareholder and chief executive officer of fertilizer producer OCI NV. He’s also the biggest shareholder in contracting and engineering company Orascom Construction Ltd. He re-based his companies outside Egypt after a tax dispute with the Muslim Brotherhood government in 2013.

        Sawiris said his view of Saudi Arabia was negatively impacted by a corruption crackdown that led to the arrest of high-profile princes and billionaires in November. Authorities need to ensure there is rule of law and order and transparency, he said.

        Rather, Sawiris is giving investment priority to his homeland after an International Monetary Fund-backed reform program that began in 2016. By lifting all restrictions on the currency and cutting subsidies, it boosted investors’ confidence in the economy of the Arab world’s most populous nation.

        And he’s planning an investment debut in Egypt’s “booming” real estate market this year after hiring a consultant who said demand was strong, shrugging off concerns of a bubble in the market.

        “In my family we are investing a lot right now because we see the opportunities,” he said. “It isn’t patriotism or advertising or anything like that.”

        Read more: http://www.bloomberg.com/news/articles/2018-05-01/north-korea-is-a-bright-spot-for-billionaire-who-forecasts-crash

        Electric Buses Are Hurting the Oil Industry

        • About 279,000 barrels a day of fuel won’t be needed this year
        • China adds a London-sized electric bus fleet every five weeks

        Electric buses were seen as a joke at an industry conference in Belgium seven years ago when the Chinese manufacturer BYD Co. showed an early model.

        “Everyone was laughing at BYD for making a toy,” recalled Isbrand Ho, the Shenzhen-based company’s managing director in Europe. “And look now. Everyone has one.”

        Suddenly, buses with battery-powered motors are a serious matter with the potential to revolutionize city transport—and add to the forces reshaping the energy industry. With China leading the way, making the traditional smog-belching diesel behemoth run on electricity is starting to eat away at fossil fuel demand.

        The numbers are staggering. China had about 99 percent of the 385,000 electric buses on the roads worldwide in 2017, accounting for 17 percent of the country’s entire fleet. Every five weeks, Chinese cities add 9,500 of the zero-emissions transporters—the equivalent of London’s entire working fleet, according Bloomberg New Energy Finance.

        All this is starting to make an observable reduction in fuel demand. And because they consume 30 times more fuel than average sized cars, their impact on energy use so far has become much greater than the passenger sedans produced by companies from Tesla Inc. to Toyota Motor Corp.

        Keeping It in the Ground

        Cumulative global fuel displacement by e-buses and passenger EVs

        Source: Bloomberg New Energy Finance

        For every 1,000 battery-powered buses on the road, about 500 barrels a day of diesel fuel will be displaced from the market, according to BNEF calculations. This year, the volume of fuel not needed may rise 37 percent to 279,000 barrels a day because of electric transport including cars and light trucks, about as much oil as Greece consumes, according to BNEF. Buses account for about 233,000 barrels of that total.

        “This segment is approaching the tipping point,” said Colin McKerracher, head of advanced transport at the London-based research unit of Bloomberg LP. “City governments all over the world are being taken to task over poor urban air quality. This pressure isn’t going away, and electric bus sales are positioned to benefit.”

        China is ahead on electrifying its fleet because it has the world’s worst pollution problem. With a growing urban population and galloping energy demand, the nation’s legendary smogs were responsible for 1.6 million extra deaths in 2015, according to non-profit Berkeley Earth.

        Putting It Back

        Global fuel demand displaced by e-buses

        Source: Bloomberg New Energy Finance

        A decade ago, Shenzhen was a typical example of a booming Chinese city that had given little thought to the environment. Its smog became so notorious that the government picked it for a pilot program for energy conservation and zero emissions vehicles in 2009. Two years later, the first electric buses rolled off BYD’s production line there. And in December, all of Shenzhen’s 16,359 buses were electric.

        BYD had 13 percent of China’s electric bus market in 2016 and put 14,000 of the vehicles on the streets of Shenzhen alone. It’s built 35,000 so far and has capacity to build as many as 15,000 a year, Ho said.

        A worker charges an electric bus in Shenzhen.
        Photographer: Qilai Shen/Bloomberg

        BYD estimates its buses have logged 17 billion kilometers (10 billion miles) and saved 6.8 billion liters (1.8 billion gallons) of fuel since they started ferrying passengers around the world’s busiest cities. That, according to Ho, adds up to 18 million tons of carbon dioxide pollution avoided, which is about as much as 3.8 million cars produce in each year.

        “The first fleet of pure electric buses provided by BYD started operation in Shenzhen in 2011,” Ho said by phone. “Now, almost 10 years later, in other cities the air quality has worsened while—compared with those cities—Shenzhen’s is much better.”

        Driving the Revolution

        China electric bus sales

        Source: Bloomberg New Energy Finance

        Other cities are taking notice. Paris, London, Mexico City and Los Angeles are among 13 authorities that have committed to only buying zero emissions transport by 2025.

        London is slowly transforming its fleet. Currently four routes in the city center serviced by single-decker units are being shifted to electricity. There are plans to make significant investments to the clean its public transport networks, including retrofitting 5,000 old diesel buses in a program to ensure all buses are emission-free by 2037.

        A BYD Co. double-decker electric bus at the EV Trend Korea exhibition in Seoul on April 12.
        Photographer: SeongJoon Cho/Bloomberg

        Transport for London, responsible for the city’s transport system, declined to comment for this article because of rules around engaging with the media ahead of May local government elections.

        Those goals will have an impact on fuel consumption. London’s network draws about 1.5 million barrels a year of fuel. If the entire fleet goes electric, that may displace 430 barrels a day of diesel for each 1,000 buses going electric, reducing U.K. diesel consumption by about 0.7 percent, according to BNEF.

        Ramping Up

        Top-10 European electric bus fleets, 2017

        Across the U.K. there were 344 electric and plug-in hybrid buses in 2017, and BYD hopes to be picked to supply more. It has partnered with a Scottish bus-maker to provide the batteries for 11 new electric buses that hit the city’s roads in March.

        Falkirk-based manufacturer Alexander Dennis Ltd. began making electric buses in 2016 and has quickly become the European market leader with more than 170 vehicles operating in the U.K. alone.

        More work is on the horizon, with London’s transport authority planning a tender to electrify its iconic double-decker buses, Ho said.

        “The tech is ready,” Ho said. “We are ready, we have our plants in China, and Alexander Dennis in Scotland is geared up for TfL. Once we’re given the word, we are ready to go.”

        (Corrects fifth paragraph to clarify total attributable to buses. )

        Read more: http://www.bloomberg.com/news/articles/2018-04-23/electric-buses-are-hurting-the-oil-industry

        Emmanuel Macron Talks to WIRED About France’s AI Strategy

        On Thursday, Emmanuel Macron, the president of France, gave a speech laying out a new national strategy for artificial intelligence in his country. The French government will spend €1.5 billion ($1.85 billion) over five years to support research in the field, encourage startups, and collect data that can be used, and shared, by engineers. The goal is to start catching up to the US and China and to make sure the smartest minds in AI—hello Yann LeCun—choose Paris over Palo Alto.

        Directly after his talk, he gave an exclusive and extensive interview, entirely in English, to WIRED Editor-in-Chief Nicholas Thompson about the topic and why he has come to care so passionately about it.

        Nicholas Thompson: First off, thank you for letting me speak with you. It was refreshing to see a national leader talk about an issue like this in such depth and complexity. To get started, let me ask you an easy one. You and your team spoke to hundreds of people while preparing for this. What was the example of how AI works that struck you the most and that made you think, ‘Ok, this is going to be really, really important’?

        Emmanuel Macron: Probably in healthcare—where you have this personalized and preventive medicine and treatment. We had some innovations that I saw several times in medicine to predict, via better analysis, the diseases you may have in the future and prevent them or better treat you. A few years ago, I went to CES. I was very impressed by some of these companies. I had with me some French companies, but I discovered US, Israeli and other companies operating in the same field. Innovation that artificial intelligence brings into healthcare systems can totally change things: with new ways to treat people, to prevent various diseases, and a way—not to replace the doctors—but to reduce the potential risk.

        The second field is probably mobility: we have some great French companies and also a lot of US companies performing in this sector. Autonomous driving impresses me a lot. I think these two sectors, I would say, healthcare and mobility, really struck me as promising. It’s impossible when you are looking at these companies, not to say, Wow, something is changing drastically and what you thought was for the next decade, is in fact now. There is a huge acceleration.

        NT: It seems you’re doing this partly because it is clearly in France’s national interest to be strong in AI. But it also seemed in the speech that you feel like there are French or European values that can help shape the development of AI? Is that correct, and what are those values?

        EM: I think artificial intelligence will disrupt all the different business models and it’s the next disruption to come. So I want to be part of it. Otherwise I will just be subjected to this disruption without creating jobs in this country. So that’s where we are. And there is a huge acceleration and as always the winner takes all in this field. So that’s why my first objective in terms of education, training, research, and the creation of startups is to streamline a lot of things, to have the adaptable systems, the adapted financing, the adapted regulations, in order to build champions here and to attract the existing champions.

        Laura Stevens

        But you’re right at the same time: AI will raise a lot of issues in ethics, in politics, it will question our democracy and our collective preferences. For instance, if you take healthcare: you can totally transform medical care making it much more predictive and personalized if you get access to a lot of data. We will open our data in France. I made this decision and announced it this afternoon. But the day you start dealing with privacy issues, the day you open this data and unveil personal information, you open a Pandora’s Box, with potential use cases that will not be increasing the common good and improving the way to treat you. In particular, it’s creating a potential for all the players to select you. This can be a very profitable business model: this data can be used to better treat people, it can be used to monitor patients, but it can also be sold to an insurer that will have intelligence on you and your medical risks, and could get a lot of money out of this information. The day we start to make such business out of this data is when a huge opportunity becomes a huge risk. It could totally dismantle our national cohesion and the way we live together. This leads me to the conclusion that this huge technological revolution is in fact a political revolution.

        When you look at artificial intelligence today, the two leaders are the US and China. In the US, it is entirely driven by the private sector, large corporations, and some startups dealing with them. All the choices they will make are private choices that deal with collective values. That’s exactly the problem you have with Facebook and Cambridge Analytica or autonomous driving. On the other side, Chinese players collect a lot of data driven by a government whose principles and values are not ours. And Europe has not exactly the same collective preferences as US or China. If we want to defend our way to deal with privacy, our collective preference for individual freedom versus technological progress, integrity of human beings and human DNA, if you want to manage your own choice of society, your choice of civilization, you have to be able to be an acting part of this AI revolution . That’s the condition of having a say in designing and defining the rules of AI. That is one of the main reasons why I want to be part of this revolution and even to be one of its leaders. I want to frame the discussion at a global scale.

        AI will raise a lot of issues in ethics, in politics, it will question our democracy.

        The key driver should not only be technological progress, but human progress. This is a huge issue. I do believe that Europe is a place where we are able to assert collective preferences and articulate them with universal values. I mean, Europe is the place where the DNA of democracy was shaped, and therefore I think Europe has to get to grips with what could become a big challenge for democracies.

        NT: So the stakes here in your mind aren’t just French economic growth, it’s the whole value system that will be incorporated into this transformative technology the world over. And you want to make sure that the values you have, your country has, your continent has, are involved in that?

        EM: Sure, exactly. I want to create an advantage for my country in artificial intelligence, directly. And that’s why we have these announcements made by Facebook, Google, Samsung, IBM, DeepMind, Fujitsu who choose Paris to create AI labs and research centers: this is very important to me. Second, I want my country to be part of the revolution that AI will trigger in mobility, energy, defense, finance, healthcare and so on. Because it will create value as well. Third, I want AI to be totally federalized. Why? Because AI is about disruption and dealing with impacts of disruption. For instance, this kind of disruption can destroy a lot of jobs in some sectors and create a need to retrain people. But AI could also be one of the solutions to better train these people and help them to find new jobs, which is good for my country, and very important.

        I want my country to be the place where this new perspective on AI is built, on the basis of interdisciplinarity: this means crossing maths, social sciences, technology, and philosophy. That’s absolutely critical. Because at one point in time, if you don’t frame these innovations from the start, a worst-case scenario will force you to deal with this debate down the line. I think privacy has been a hidden debate for a long time in the US. Now, it emerged because of the Facebook issue. Security was also a hidden debate of autonomous driving. Now, because we’ve had this issue with Uber, it rises to the surface. So if you don't want to block innovation, it is better to frame it by design within ethical and philosophical boundaries. And I think we are very well equipped to do it, on top of developing the business in my country.

        But I think as well that AI could totally jeopardize democracy. For instance, we are using artificial intelligence to organize the access to universities for our students That puts a lot of responsibility on an algorithm. A lot of people see it as a black box, they don't understand how the student selection process happens. But the day they start to understand that this relies on an algorithm, this algorithm has a specific responsibility. If you want, precisely, to structure this debate, you have to create the conditions of fairness of the algorithm and of its full transparency. I have to be confident for my people that there is no bias, at least no unfair bias, in this algorithm. I have to be able to tell French citizens, “OK, I encouraged this innovation because it will allow you to get access to new services, it will improve your lives—that’s a good innovation to you.” I have to guarantee there is no bias in terms of gender, age, or other individual characteristics, except if this is the one I decided on behalf of them or in front of them. This is a huge issue that needs to be addressed. If you don’t deal with it from the very beginning, if you don’t consider it is as important as developing innovation, you will miss something and at a point in time, it will block everything. Because people will eventually reject this innovation.

        NT: So the steps you’re taking to guarantee that is that all of the algorithms developed by the French government will be open, algorithms developed by any company getting money from the French government will also be required to be open?

        EM: Yes.

        NT: And is there a third step you’re doing to help guarantee this transparency?

        I think as well that AI could totally jeopardize democracy.

        EM: We will increase the collective pressure to make these algorithms transparent. We will open data from government, publicly funded projects, and we will open access from this project and we will favor, incentivize the private players to make it totally public and transparent. Obviously some of them will say, there is a commercial value in my algorithm, I don't want to make it transparent. But I think we need a fair discussion between service providers and consumers, who are also citizens and will say: “I have to better understand your algorithm and be sure that this is trustworthy.” The power of consumption society is so strong that it gets people to accept to provide a lot of personal information in order to get access to services largely driven by artificial intelligence on their apps, laptops and so on. But at some point, as citizens, people will say, “I want to be sure that all of this personal data is not used against me, but used ethically, and that everything is monitored. I want to understand what is behind this algorithm that plays a role in my life.” And I’m sure that a lot of startups or labs or initiatives which will emerge in the future, will reach out to their customers and say “I allow you to better understand the algorithm we use and the bias or non-bias.” I’m quite sure that’s one of the next waves coming in AI. I think it will increase the pressure on private players. These new apps or sites will be able to tell people: “OK! You can go to this company or this app because we cross-check everything for you. It’s safe," or on the contrary: “If you go to this website or this app or this research model, it’s not OK, I have no guarantee, I was not able to check or access the right information about the algorithm”.

        NT: When you talk about how AI will transform democracy, do you imagine a day where you make decisions based on recommendations from AI-based algorithms, where there’s a system that tells you what a labor reform should be and you say, “OK?”

        EM: At this point, I think it could help you. But it will never replace the way you decide. When you make a decision, it’s the result of a series of cross-checks. AI can help you because sometimes when you pass a reform, you’re not totally clear about the potential effects, direct or indirect, and you can have hesitations. So it can help you to make the right decision. An algorithm is relevant for this part of the equation. For instance, on economic and social reforms, to have a clearer view about direct and indirect measurable effects. But on top of it, when you take a political decision, you need to have a part of personal judgment. That’s the quality of the decision maker, and artificial intelligence will never replace that. And there is a thing that AI could never replace; which is accountability and responsibility. Because this is his decision and will be held accountable for it, a political leader could never say, “OK I’m sorry this decision was bad because it was a decision of an algorithm.”

        NT: Let’s get back to disruption for a second. You’ve talked a lot about transportation, you talked about it in your speech. AI is going to massively disrupt transportation, and it’s going to make a lot of people lose their jobs as we go to driverless cars. It will create new jobs, but this is already an area where people in France have been protesting. There were railroad strikes this weekend, there were trucker strikes this fall. Aren’t you taking a huge risk by aligning yourself with a force that is going to disrupt an industry that has already been protesting like crazy?

        EM: Look, I think in this country—and in a lot of countries—you have a tradition of controversy. I launched a series of reforms that a lot of people thought impossible to be conducted in France. So, I'm absolutely sure it's possible, when you explain to people, when you have the energy and determination, to pass such reforms. I’m certainly not reluctant to do so and I’m certainly not, I would say, upset or threatened by dealing with artificial intelligence and convincing my people of its rightful implementation. As consumers, they are already big fans of artificial intelligence. And big fans of innovative solutions. All the tech guys can tell you that the French market is a very good market. People love technology here. I think that’s why the overall philosophy I have stuck to from the very beginning of my mandate is to say: blocking changes and being focused on protecting jobs is not the right answer. It’s the people you need to protect. You do so by giving them opportunities and by training and retraining them again to get new jobs. Don’t block the change because it’s coming and people will accept it. But try to be at the fore-front of change to better understand it and deal with it. Change can destroy jobs in the very short run, but create new ones in other sectors at the same time.

        Laura Stevens

        For me, one of the key issues of artificial intelligence is that it will probably reduce the most replicable and straining human activities. And naturally you will raise a whole range of other opportunities for people with low, middle and high qualifications. The big risk for our society is to increase opportunities only for very highly qualified people and, in a way, very low-qualified workers. It is especially necessary to monitor the qualification of the middle class, because they can be the most disrupted. If I take your examples, that would encompass taxi drivers, people working in the industry, or people working in highly repetitive tasks. So you have to train them either to change their sector of activity or to increase their qualification to work with a machine. We will need people working with machines.

        For I do not believe that autonomous vehicles will exist without any drivers at all. For me, that’s pure imagination. You already have fully automated programs to drive planes. Therefore we technically could have planes with no pilots. But you still have two pilots in every plane. Even if almost everything is automated. That’s because you need to have responsibility, precisely. So what we will reduce with autonomous cars is the number of risks. What you will reduce is how painful it is to be a driver for a long period of time ; but you will need people to make the critical choice at critical moments for autonomous vehicles. I’m almost sure about that. So AI will change the practice but it will not kill transportation jobs in many cases.

        Bottom line, my point is: I can convince my country about change precisely because I embrace it. My role is not to block this change, but to be able to train or retrain people for them to get opportunities in this new world.

        NT: Got it. I want to ask you a military question. I know that the UN has had discussions on restrictions on lethal autonomous weapons. Do you think machines—artificial intelligence machines—can ever be trusted to make decisions to kill without human intervention?

        EM: I’m dead against that. Because I think you always need responsibility and assertion of responsibility. And technically speaking, you can have in some situations, some automation which will be possible. But automation or machines put in a situation precisely to do that would create an absence of responsibility. Which, for me, is a critical issue. So that’s absolutely impossible. That’s why you always need a human check. And in certain ways, a human gateway. At a point of time, the machine can prepare everything, can reduce uncertainties, can reduce until nil the uncertainties and that’s an improvement which is impossible without it, but at a point of time, the go or no-go decision should be a human decision because you need somebody to be responsible for it.

        Blocking changes is not the right answer. You have to protect people and to think about opportunities.

        NT: Let me ask you about the national competition in artificial intelligence. Elon Musk tweeted some months ago: “Competition for AI superiority at national level most likely cause of World War3 in my opinion.” Do you think Musk is overstating it? Or do you think that this is going to get very intense, particularly between the United States and China?

        EM: I think it will become very intense. I will not be so pessimistic, because I think that the core basis of artificial intelligence is research. And research is global. And I think this artificial intelligence deals with cooperation and competition, permanently. So you need an open world and a lot of cooperation if you want to be competitive. And at a point of time, in some issues, you need competition. But I think you will have to rethink a sort of sovereignty. I addressed that in my speech today. Artificial intelligence is a global innovation scheme in which you have private big players and one government with a lot of data—China. My goal is to recreate a European sovereignty in AI, as I told you at the beginning of this discussion, especially on regulation. You will have sovereignty battles to regulate, with countries trying to defend their collective choices. You will have a trade and innovation fight precisely as you have in different sectors. But I don't believe that it will go to the extreme extents Elon Musk talks about, because I think if you want to progress, there is a huge advantage in an open innovation model.

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        NT: So this is a slightly cynical response to that, but let me ask you this: If France starts to build up an AI sector, in some ways it’s competitive to Google and Facebook. So won’t there be an incentive for Europe and for France to regulate Facebook and Google in ever-tougher ways? Doesn’t it create a strange dynamic where you might have incentives to bring more regulation and antitrust?

        EM: Look, I would say exactly the opposite. Today, Google, Facebook, and so on, on artificial intelligence, they are very much welcome. Most people like them, these companies invest in France, they also recruit a lot of our talents and they develop their jobs here. So they are part of our ecosystem. The issue for these big players is the fact that they will have to deal with several issues. First, they have a very classical issue in a monopoly situation; they are huge players. At a point of time–but I think it will be a US problem, not a European problem–at a point of time, your government, your people, may say, “Wake up. They are too big.” Not just too big to fail, but too big to be governed. Which is brand new. So at this point, you may choose to dismantle. That’s what happened at the very beginning of the oil sector when you had these big giants. That’s a competition issue.

        But second, I have a territorial issue due to the fact that they are totally digital players. They disrupt traditional economic sectors. In some ways, this might be fine because they can also provide new solutions. But we have to retrain our people. These companies will not pay for that; the government will. Today the GAFA [an acronym for Google, Apple, Facebook, and Amazon] don’t pay all the taxes they should in Europe. So they don’t contribute to dealing with negative externalities they create. And they ask the sectors they disrupt to pay, because these guys, the old sectors pay VAT, corporate taxes and so on. That’s not sustainable.

        Third, people should remain sovereign when it comes to pivacy rules. France and Europe have their preferences in this regard. I want to protect privacy in this way or in that way. You don't have the same rule in the US. And speaking about US players, how can I guarantee French people that US players will respect our regulation? So at a point of time, they will have to create actual legal bodies and incorporate it in Europe, being submitted to these rules. Which means in terms of processing information, organizing themselves, and so on, they will need, indeed, a much more European or national organization. Which in turn means that we will have to redesign themselves for a much more fragmented world. And that’s for sure because accountability and democracy happen at national or regional level but not at a global scale. If I don’t walk down this path, I cannot protect French citizens and guarantee their rights. If I don't do that, I cannot guarantee French companies they are fairly treated. Because today, when I speak about GAFA, they are very much welcome I want them to be part of my ecosystem, but they don’t play on the same level-playing field as the other players in the digital or traditional economy. And I cannot in the long run guarantee my citizens that their collective preferences or my rules can be totally implemented by these players because you don't have the same regulation on the US side. All I know is that if I don’t, at a point of time, have this discussion and regulate them, I put myself in a situation not to be sovereign anymore.

        NT: But aren’t those two goals very much in tension? You want the GAFA to come to France, you’ve touted it—Google has been invested in AI [in France] since 2012—but you also really want to crack down on them. How do you do both simultaneously?

        EM: No. Look, because I think first, you don’t just have the GAFA. You have a lot of other players, startups, and so on. And I think, even for them, I mean they are discovering they will have to deal with democratic and political issues in your country.

        NT: They’re just starting to learn that!

        EM: Yes, yes! I mean, it’s fair. That’s the end of the very first phase, that was a sort of an early phase without any regulation, where they were in a situation to set up all the rules. Now they will have to deal with governments — but I want to do it in a cooperative way. I don't want to say, “I don’t want this guy anymore.” Exactly the opposite. I want a permanent dialogue. But I want them to understand and respect my constraints. I want them to be part of my reflection and to take into consideration their own reflection. I want them to better understand the fact that it is unfeasible to have a world without any responsibility and without a clear democratic accountability.

        NT: Got it. So back to the big question, what will be success? How will you know that this has worked? And what will be failure? When you look at this a couple years from now?

        EM: Look, first of all I think it’s very hard to answer this question because by definition, I don't have a clear view on what will happen for artificial intelligence in five years time. But I would say, if I manage to develop a very strong, powerful ecosystem, number one in Europe on artificial intelligence dealing with mobility, defense, healthcare, fintech, etc. I think it will be a success. And for me, if a majority of people in France understand and endorse this change it will be a success. It will be a failure if we are stuck with fears and blocked by big scares. My concern is that there is a disconnect between the speediness of innovation and some practices, and the time for digestion for a lot of people in our democracies. I have to build a sort of reciprocal or mutual trust coming from researchers, private players, startups, and my citizens. If the first category of people trust a country as being a relevant ecosystem for them, and at the same time, if I manage to build trust with my citizens for AI, I’m done. If I fail building trust with one of them, that’s a failure.

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        Google’s New AI Head Is So Smart He Doesn’t Need AI

        Google’s heavy investment in artificial intelligence has helped the company’s software write music and beat humans at complex board games. What unlikely feats could be next? The company’s new head of AI says he’d like to see Google move deeper into areas such as healthcare. He also warns that the company will face some tricky ethical questions over appropriate uses for AI as it expands its use of the technology.

        The new AI boss at Google is Jeff Dean. The lean 50-year-old computer scientist joined the company in 1999, when it was a startup less than one year old. He earned a reputation as one of the industry’s most talented coders by helping Google become a computational powerhouse with new approaches to databases and large-scale data analysis. Google colleagues once created a joke website of “Jeff Dean facts,” including his purported role in accelerating the speed of light. Another had it that Dean doesn’t really exist—he’s an advanced AI created by Jeff Dean.

        Dean helped ignite Silicon Valley’s AI boom when he joined Google’s secretive X lab in 2011 to investigate an approach to machine learning known as deep neural networks. The project produced software that learned to recognize cats on YouTube. Google went on to use deep neural networks to greatly improve the accuracy of its speech recognition service, and has since made the technique the heart of the company’s strategy for just about everything.

        The cat-video project morphed into a research group called Google Brain, which Dean has led since 2012. He ascended to head the company’s AI efforts early this month after John Giannandrea left to lead Apple’s AI projects.

        Dean’s new job puts him at the helm of perhaps the world’s foremost AI research operation. The group churns out research papers on topics such as creating more realistic synthetic voices and teaching robots to grasp objects.

        But Dean and his group are also on the hook to invent the future of Google’s business. CEO Sundar Pichai describes the company’s strategy as “AI first,” saying that everything the company does will build on the technology.

        One way Dean hopes to help is by conjuring up new lines of business. Google’s AI research has so far mostly been used to improve or expand existing products, such as search and smartphone software. “New machine learning capabilities or research results might enable us to do new things that Google doesn't now,” says Dean. “Health is one that's pretty far along in this direction.”

        Dean wouldn’t discuss details. But two Google research projects offer clues. The company is testing software in India that can detect a complication of diabetes that causes blindness, and has also tested software that looks for signs of breast cancer on microscope slides. The FDA has begun cautiously approving AI software that helps doctors make medical decisions.

        Success in healthcare could help diversify the business of a company that, despite broad interests, relies heavily on advertising. In 2017, almost 90 percent of parent company Alphabet’s revenue came from ads.

        Dean is also excited about automating artificial intelligence—using machine-learning software to build machine-learning software. “It could be a huge enabler of the benefits of machine learning,” Dean says. “The current state is that machine learning expertise is relatively in short supply.”

        Google dubs that meta-AI project AutoML. Dean says Alphabet’s self-driving car division Waymo has been testing the technology to improve its vision systems. AutoML is also at the core of a new cloud service of the same name that helps companies create customized image-recognition systems.

        Longer term, Dean thinks automatic AI could enable robots to figure out how to cope with unfamiliar situations, such as opening a type of bottle cap they haven’t seen before. Despite successes on narrow tasks, AI researchers have struggled to make machines capable of many different thing. Dean says AutoML could bring about “really intelligent, adaptable systems that can tackle a new problem in the world when you don't have exposure of how to tackle it.”

        More powerful AI systems will also lead Google into new, uncertain moral ground. Some researchers at the company are working on how to ensure that machine-learning systems don’t breach societal expectations of fairness. In 2015, Google’s photo-organizing product tagged some images of black people as gorillas, and the service has been blind to searches for the black apes ever since.

        Google also faces other ethical decisions over allowing customers to tap its AI skills. The same week Dean took over Google’s top AI role, the New York Times reported that thousands of Google employees had signed a letter protesting a contract with the Pentagon that applies AI to interpret video from drones.

        Dean declines to comment on the project’s details, but suggests it’s more a pointer to future ethical questions than an immediate moral challenge. “The actual project is relatively mundane, sort of assembling a bunch of existing open source components together, but it does I think give us pause as a company about what we want our role to be,” Dean says. “There's a wide range of views in the company about what we should be doing there.”

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        Read more: https://www.wired.com/story/googles-new-ai-head-is-so-smart-he-doesnt-need-ai/