Author Archives: Rudy

Technology Speeds Up Timeline on Quarterly Close

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Companies are taking four-and-half days to collect the quarterly snapshot of their financial position, down from six days in 2009

Duke Energy reduced its quarterly closing timeline by 30% to 40% to just a handful of days by 2010.
Duke Energy reduced its quarterly closing timeline by 30% to 40% to just a handful of days by 2010. PHOTO: MONICA HERNDON/ZUMA PRESS

As accounting becomes more reliant on technology, finance chiefs across a range of sectors are reaping substantial benefits from closing their books faster.

Companies including Red Hat Inc., RHT -1.07% Duke Energy Corp. DUK -0.51% and Dun & Bradstreet Corp. DNB -0.51% have sped up their quarterly close to gain quicker access to their results.

It takes most companies four-and-half days to capture a quarterly snapshot of their financial position in 2017, down from six days in 2009, according to PricewaterhouseCoopers LLP benchmarking studies. The consulting and accounting firm examined the practices of roughly 500 companies around the world with a median revenue of $2.5 billion.

Companies that have accelerated their quarterly close say having results in hand earlier makes decision-making easier and helps the organization become more nimble. The extra time allows the finance team to perform a deeper analysis, catch errors and invest more time in planning for the next quarter.

Dash to CloseCompanies are reducing the number ofdays spent on closing their books eachquarter.THE WALL STREET JOURNALSource: PricewaterhouseCoopers LLP
.daysTop quartileMedianLower quartile200920170.02.55.07.510.0

A faster quarterly close was the priority for Eric Shander when he joined open-source software solutions company Red Hat as chief accounting officer in 2015. Mr. Shander and his team spent 14 months streamlining and accelerating the process.

Tasks such as account reconciliation were previously left to the end of the reporting period, contributing to the last-minute rush. Now, accounts are reconciled every few weeks. Mr. Shander also redistributed book-closing responsibilities across the finance team to ensure a more equitable workload.

Red Hat now closes its books comfortably in two days, down from five days previously, said Mr. Shander, who was named chief financial officer in April.

The finance team has been more productive as a result of the extra time, Mr. Shander said. They have caught and fixed errors, dug deeper into the data before announcing results and pivots to identifying priorities for the next quarter earlier, he said.

“We’re actually considering moving up some of our earnings announcements as a result of it,” he said. “It’s been a huge success.”

Advances in technologies are helping companies accelerate their book-closing process. More companies are automating their close to reduce the amount of manual activities, such as journal entries, said William Marchionni, senior business adviser at consulting firm Hackett Group HCKT -1.31% Inc.’s Finance Operations Advisory Program.

“Some top performers are getting management reporting data on revenue, shipments, cost for goods sold, and other key metrics on a daily basis from their information systems,” Mr. Marchionni said.

For Dun & Bradstreet CFO Rich Veldran, the lure of cost savings has prompted investments in robotics and automation technology that accelerate the quarterly reporting process. The data and analytics company closes its books in four days, despite operating across more than 200 countries, which adds to the complexity of its financial reporting process.

“There’s a real opportunity for us to do things in a much more automated, faster way, within finance,” Mr. Veldran said, adding that his team is already testing several potential applications for robotic process automation in the finance function.

Steven Young, CFO of Duke Energy.
Steven Young, CFO of Duke Energy. PHOTO:DUKE ENERGY

A new software system was key to helping Duke Energy streamline its quarterly close, said CFO Steven Young. The electric utility in 2007 launched a three-year revamp of its financial infrastructure, after a series of acquisitions burdened the company with a patchwork of financial systems and processes, Mr. Young said. Duke reduced its closing timeline by 30% to 40% to just a handful of days by 2010, Mr. Young said, though he declined to state the exact number of days. The company has continued to improve its quarterly close through new technologies.

“The advantage is that you get data disseminated through the organization quicker, you can then communicate trends, patterns and that can result in quicker decisions to take tactical actions in response to the data,” Mr. Young said.

Companies that operate across multiple geographies and sell different types of products and services often require more time to close their books than a single-product, single-geography business, said Beth Paul, a partner at PwC.

CFOs in a particular sector, such as airlines, autos or retail, often aim to close their books and report results around the same time to keep in line with industry norms.

“There’s a view that they need to be consistent with their peers because if you’re lagging, it could lead people to wonder why,” Ms. Paul said, adding that straggling behind the pack could raise doubts about management’s competency.

She also noted that certain sectors, such as banks and financial services, tend to close their books faster due to greater investments in technology.

Still, for many CFOs accelerating the quarterly close process remains a low priority. Instead, these companies have focused on meeting increasing regulatory demands and deployed resources to operational projects such as entering new markets or launching new product lines.

“Account-to-report has historically been the last place where companies invest. It isn’t client facing, and they have ended up doing things on a shoestring,” said Hackett Group’s Mr. Marchionni.

Next Leap for Robots: Picking Out and Boxing Your Online Order

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Developers close in on systems to move products off shelves and into boxes, as retailers aim to automate labor-intensive process

Your Next Online Order Could Be Picked Out by a Robot
Facing more pressure to speed orders more quickly to customers, a rising number of companies are using high-tech robots in their manufacturing process. But could it render humans obsolete? The WSJ takes a look inside.

Robot developers say they are close to a breakthrough—getting a machine to pick up a toy and put it in a box.

It is a simple task for a child, but for retailers it has been a big hurdle to automating one of the most labor-intensive aspects of e-commerce: grabbing items off shelves and packing them for shipping.

Several companies, including Saks Fifth Avenue owner Hudson’s Bay Co. HBC -0.27% and Chinese online-retail giant JD.com Inc., JD 1.07% have recently begun testing robotic “pickers” in their distribution centers. Some robotics companies say their machines can move gadgets, toys and consumer products 50% faster than human workers.

Retailers and logistics companies are counting on the new advances to help them keep pace with explosive growth in online sales and pressure to ship faster. U.S. e-commerce revenues hit $390 billion last year, nearly twice as much as in 2011, according to the U.S. Census Bureau. Sales are rising even faster in China, India and other developing countries.

That is propelling a global hiring spree to find people to process those orders. U.S. warehouses added 262,000 jobs over the past five years, with nearly 950,000 people working in the sector, according to the Labor Department. Labor shortages are becoming more common, particularly during the holiday rush, and wages are climbing.

Mechanical engineer Parker Heyl adjusts a robotic arm at RightHand Robotics’ test facility in Somerville, Mass.
Mechanical engineer Parker Heyl adjusts a robotic arm at RightHand Robotics’ test facility in Somerville, Mass.PHOTO: SIMON SIMARD FOR THE WALL STREET JOURNAL

Picking is the biggest labor cost in most e-commerce distribution centers, and among the least automated. Swapping in robots could cut the labor cost of fulfilling online orders by a fifth, said Marc Wulfraat, president of consulting firm MWPVL International Inc.

“When you’re talking about hundreds of millions of units, those numbers can be very significant,” he said. “It’s going to be a significant edge for whoever gets there first.”

Until recently, robots had to be trained to identify and grab each item, which is impractical in a distribution center that might stock an ever-changing array of millions of products.

Automation companies such as Kuka AG KU2 -0.45% , Dematic Corp. and Honeywell International Inc. unit Intelligrated, as well as startups like RightHand Robotics Inc. and IAM Robotics LLC are working on automating picking.

In RightHand Robotics’ Somerville, Mass., test facility, mechanical arms hunt around the clock through bins containing packages of baby wipes, jars of peanut butter and other products. Each attempt—successful or not—feeds into a database. The bigger that data set, the faster and more reliably the machines can pick, said Yaro Tenzer, the startup’s co-founder.

Hudson’s Bay is testing RightHand’s robots in a distribution center in Scarborough, Ontario.

“This thing could run 24 hours a day,” said Erik Caldwell, the retailer’s senior vice president of supply chain and digital operations, at a conference in May. “They don’t get sick; they don’t smoke.”

JD.com is developing its own picking robots, which it started testing in a Shanghai distribution center in April. The company hopes to open a fully automated warehouse there by the end of next year, said Hui Cheng, head of JD.com’s robotics-research center in Silicon Valley.

Swisslog, a subsidiary of Kuka, sells picking robots that can be integrated into the company’s other warehouse automation systems or purchased separately. The company sold its first unit in the U.S., to a large retailer, earlier this year, said A.K. Schultz, Swisslog’s vice president for retail and e-commerce. Mr. Schultz declined to name the retailer.

Previous waves of warehouse automation didn’t lead to sudden mass layoffs, partly because order volumes have been growing so fast. And automated picking is still at least a year away from commercial use, robotics experts say. The main challenge lies in creating the enormous databases of 3D-rendered objects that robots need to determine the best way to grip new objects.

RightHand Robotics co-founders Leif Jentoft, left, and Yaro Tenzer
RightHand Robotics co-founders Leif Jentoft, left, and Yaro Tenzer PHOTO: FOR THE WALL STREET JOURNAL

Some companies hope to speed development by making some research public.Amazon.com Inc. will hold its third annual automated picking competition at a robotics conference in Japan later this month. For the first time, entrants won’t know in advance all the items the robots will need to pick.

At the University of California, Berkeley, a team is simulating millions of attempts to pick 10,000 objects. Funded by Amazon, Siemens AG and others, the project is meant to build an open-source database for use in any automation system, said Ken Goldberg, the professor leading the project.

“With 10,000 objects, I’m surprised how well it did,” he said. “I would love to show it 100,000 examples and see how well it performs after that.”

Advertisers Try to Avoid the Web’s Dark Side, From Fake News to Extremist

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Marketers are reevaluating their approach to automated ad-buying and demanding more accountability

ILLUSTRATION: PETER AND MARIA HOEY

In February, Kieran Hannon, chief marketing officer of Belkin International Inc., noticed an odd tweet asking the electronics maker why it was advertising on Breitbart News Network, a right-wing website known for scorched-earth populism.

A banner ad promoting the company’s new Linksys mesh router had appeared on the site, even though Breitbart wasn’t among the roughly 200 sites Belkin had preapproved for its ads.

Mr. Hannon called his ad agency, which couldn’t explain the mix-up.

“We still don’t know how that happened,” he said.

Such headaches are becoming all too familiar for marketing executives, as they come to grips with the trade-offs inherent in automated advertising. Known as “programmatic” ad buying, it is now the way the vast majority of digital display ads are sold.

Programmatic advertising allows the buyer to target consumers across thousands of sites, based on their browsing history or shopping habits or demographics. Doing so is more cost-effective than buying more expensive ads on a handful of well-known sites.

But marketers don’t fully control whether their ads will show up in places they would rather avoid: sites featuring pornography, pirated content, fake news, videos supporting terrorists, or outlets whose traffic is artificially generated by computer programs.

The confusion stems from the convoluted infrastructure of the ad-technology world: a maze of agencies, ad networks, exchanges, publisher platforms and vendors. Instead of buying space on websites, brands can buy audiences—categories of people—and their ads are placed on sites those people visit.

The problems arise when those people are on sites where brands don’t wish to appear.

The Tangled World of Digital Ads

Online advertisers and their partners can generally target specific groups of users based on certain characteristics. But their ads can still wind up in undesirable places.

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As the issues pile up, marketers are taking action, with the help of companies that independently verify that their ads aren’t going to toxic locations. Brands are cutting down their purchase of ads through open exchanges—public pools of ad space from hundreds of thousands of sites—opting instead for methods that give them more visibility into where ads are appearing.

On open exchanges, it “just becomes harder and harder to figure out if your ad is showing up in a legitimate ad experience,” said Kristi Argyilan, senior vice president of marketing at retailer Target Corp.

Marketers have been dealing with these issues for years. But the “brand safety” risks in digital advertising have hit home with multiple high-profile episodes in recent months.

In March, a number of big brands including PepsiCo Inc., Wal-Mart Stores Inc. L’Oréal SAand AT&T Inc. pulled their ads from YouTube and the Google Display Network, a network of third-party websites, after revelations that ads ran alongside objectionable content, including videos promoting anti-Semitism and terrorism.

Google, a unit of Alphabet Inc., promised to better police its content and give marketers more information about where their ads appear on YouTube. It also said it would bolster its technology that automatically screens videos, and it set a 10,000-view threshold for a video channel to reach before it can make money from ads.​

Some advertisers, satisfied with Google’s efforts, have begun spending again, while others, including big marketers such as SC Johnson & Son Inc., Procter & Gamble Co. and J.P. Morgan Chase & Co., haven’t returned, according to people familiar with the matter.

J.P. Morgan is working with Google to get its ads back on “safe YouTube channels” and expects to return soon, one of the people said.

P&G is working closely with YouTube to test the safeguards it has put in place since the problems arose, a spokeswoman for the company said. A spokeswoman for SC Johnson declined to comment.

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“Many advertisers never left and many have decided to come back,” Google said in a statement. “While they know that no system can be perfect, they appreciate the actions we’ve taken and know we are taking this seriously and are committed to getting better and better.”

Though the number of ordinary web users who saw an ad in an offensive YouTube video was likely small, the combination of the public-relations damage from the revelations and the potential for more widespread exposure down the road led marketers to act.

Breitbart, which is popular with the “alt-right”—a loose conglomeration of groups, some of which embrace white supremacy and view multiculturalism as a threat—became a controversial landing spot for advertisers in the wake of the 2016 presidential election. Brands that have pulled out of Breitbart include Kellogg Co., eyewear company Warby Parker and insurer Allstate Corp.

A spokesman for Breitbart declined to comment.

The recurring issues have caused brands to adjust their overall approach to automated ad buying.

Colgate-Palmolive Co. is adding language to the contract it has with its ad-buying firm, which requires it to maintain blacklists of sites the company doesn’t want to have its ads appear on, according to people familiar with the matter. Colgate didn’t respond to requests for comment.

We needed to make sure our ads are showing up where our ads make contextual sense.

—Chris Drago, Hewlett Packard Enterprise’s senior director of global media

Advertisers are doubling down on using online ad verification services such as Integral Ad Science Inc. and White Ops Inc.

OpenSlate, which helps advertisers vet YouTube channels, currently works with roughly 230 advertisers, more than twice as many as last year. “The interest in finding out where your ads are running and who saw your ad has skyrocketed over the past three months,” said OpenSlate CEO Mike Henry.

More marketers are purchasing ads through “programmatic direct” deals, in which a publisher uses technology to sell directly to advertisers, and “private programmatic marketplaces,” in which a publisher or a select group of publishers can sell to a select group of advertisers, in real time. Automation is involved in both, but the risks are far lower than with open exchanges.

Display-ad spending on programmatic direct deals in the U.S. is expected to grow by 35% this year to $18.2 billion, while spending on private marketplaces will increase 39% to about $6 billion, according to eMarketer. By contrast, spending on open exchanges is forecast to grow by 8.4% this year to $8.3 billion.

Target pulled back from buying via open exchanges at the end of 2015 and now uses private marketplaces to buy ads from about 160 different publishers.

Hewlett Packard Enterprise, which spun out from Hewlett-Packard Co. in 2015, set up private marketplaces with about 15 publishers including Forbes and CNN about a year ago.

“We needed to make sure our ads are showing up where our ads make contextual sense,” said Chris Drago, the company’s senior director of global media. “I don’t want to be on Victoria’s Secret because someone is there buying bras for his wife.”

Apple’s New Big Bet: Augmented Reality

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ARKit platform puts it in a race with Facebook, Alphabet and Snap and fuels industry beliefs that it will eventually develop glasses

A demonstration of Apple’s ARKit technology on an iPad Pro on Monday. The system uses camera data to find horizontal planes in a room and estimate the amount of light available.

A demonstration of Apple’s ARKit technology on an iPad Pro on Monday. The system uses camera data to find horizontal planes in a room and estimate the amount of light available. PHOTO: DAVID PAUL MORRIS/BLOOMBERG NEWS

Apple Inc. AAPL 0.60% set its sights on a new target: becoming the world’s largest platform for augmented reality.

The ambitious announcement, which was overshadowed by the introduction Monday of the HomePod speaker, plunges Apple into a race against Alphabet Inc., Facebook Inc.,FB 0.20% Snap Inc. SNAP -3.93% and others to conquer an emerging technology that uses cameras and computers to overlay digital images on a person’s view of the real world.

It also bolsters the belief among many industry observers that Apple will build new augmented-reality features into its coming 10th-anniversary edition iPhone, and eventually develop glasses that relay information about the world so people can view maps or restaurant menus without pulling out a device.

Augmented reality shot to prominence nearly a year ago following the release of “Pokémon Go,” a game in which players scoured the map of the real world, with the help of location-tracking technology, to find digital monsters superimposed through the smartphone screen. The technology is different from virtual reality, which uses computer headsets to create fully immersive digital worlds.

Roughly 40 million people in the U.S. are expected to use augmented reality this year, up 30% from last year, according to research firm eMarketer. It estimates the total will rise to 54 million in 2019.

In a 2.5-hour keynote, Apple announced a slew of new hardware and software products. WSJ’s Joanna Stern recaps what you need to know about the most important announcements.

Craig Federighi, Apple’s head of software, demonstrated the potential of Apple’s new technology platform, ARKit, at the company’s annual Worldwide Developers Conference keynote Monday.

While viewing a table on stage through an iPhone screen, Mr. Federighi added virtual images of a steaming cup of coffee and lamp. The images appeared to rest directly on the table, recognizing the real-world surface rather than floating above it.

Apple Chief Executive Tim Cook has been a big proponent of augmented reality, saying he believes it will have broader success than virtual reality because it is less isolating.

Several companies are already working on augmented reality, including headsets in development from Microsoft Corp. and Magic Leap Inc. Alphabet’s Google Tango platform has been available on some smartphones for a about a year.

Apple’s ARKit, though, has the potential to democratize the technology by bringing it to roughly a billion devices without requiring separate hardware or software, as some competitors do. The company says the system uses camera data to find horizontal planes in a room and estimate the amount of light available.

“It’s a seminal event in the journey toward AR that Apple’s come out and shipped something,” said Matt Miesnieks, co-founder of 6D.ai, a computer-vision startup. He expects developers to use the software because of its relative simplicity and potential to reach across Apple’s large user base.

IKEA International A/S and Lego A/S are already working on augmented-reality apps using ARKit that could allow people to visualize furniture in their home or a virtual image of Lego Batman, Mr. Federighi said.

Representatives of Wingnut AR, an augmented-reality studio from “Lord Of The Rings” director Peter Jackson, showed an ARKit-based experience, seen through an iPad, in which airships battled in a virtual town square that was digitally dropped on a real table on stage, with the audience visible in the background.

Apple’s announcement came two months after Facebook opened its augmented-reality tools to developers. CEO Mark Zuckerberg said he expects the nascent technology to open the world to a new world of apps and services.

Facebook’s smaller rival, Snap, popularized simple augmented-reality tools that overlay bunny ears or dog noses on users’ faces. It also allows users to add special effects to photos and backgrounds.

By creating an augmented-reality tool kit for developers, Apple could spur its more than 680 million iPhone users to share augmented images through its iMessage service rather through Facebook or Snapchat, developers said.

ARKit indicates Apple solved a difficult technical problem—finding a way to use cameras and sensors in an iPhone to track the outside world, said Mr. Miesnieks. He said the same sensors and algorithms would run a pair of glasses, bolstering his belief that Apple plans to launch eyewear in the future.

Apple declined to comment on whether it could develop glasses.

“They’re clearly getting developers ramped up for this,” said Paul Reynolds, founder of Torch 3D, a startup focused on 3D-app development. “I’m sure for the iPhone launch they’ll have nice content around it.”

Warby’s new business model: The Vision Test

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Warby’s at-home vision test:
1. Take a quiz
Answer questions about your vision and eligibility. (Currently, some ocular conditions disqualify you.)
2. Measure
Place a credit card or driver’s license in the corner of the computer screen and point your phone’s camera at it, to determine the computer screen size and display correctly sized images.
3. Swipe away
The smartphone app tells you where to stand (and knows where you are, so you can’t cheat). The computer shows tests, such as C’s in various sizes; swipe the phone in the direction of each.
4. Get your RX
When you’re done, the results are sent to an eye doctor for review. Within 24 hours, you get a new prescription.

Why Do Gas Station Prices Constantly Change? Blame the Algorithm

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Retailers are using artificial-intelligence software to set optimal prices, testing textbook theories of competition; antitrust officials worry such systems raise prices for consumers

The Knaap Tankstation gas station in Rotterdam, Netherlands, uses a2i Systems artificial-intelligence pricing software.
The Knaap Tankstation gas station in Rotterdam, Netherlands, uses a2i Systems artificial-intelligence pricing software. PHOTO: KNAAP TANKSTATION BV

ROTTERDAM, the Netherlands—One recent afternoon at a Shell-branded station on the outskirts of this Dutch city, the price of a gallon of unleaded gas started ticking higher, rising more than 3½ cents by closing time. A little later, a competing station 3 miles down the road raised its price about the same amount.

The two stations are among thousands of companies that use artificial-intelligence software to set prices. In doing so, they are testing a fundamental precept of the market economy.

In economics textbooks, open competition between companies selling a similar product, like gasoline, tends to push prices lower. These kinds of algorithms determine the optimal price sometimes dozens of times a day. As they get better at predicting what competitors are charging and what consumers are willing to pay, there are signs they sometimes end up boosting prices together.

Advances in A.I. are allowing retail and wholesale firms to move beyond “dynamic pricing” software, which has for years helped set prices for fast-moving goods, like airline tickets or flat-screen televisions. Older pricing software often used simple rules, such as always keeping prices lower than a competitor.

On the Same Track

Two competing gas stations in the Rotterdam area both using a2i Systems pricing software roughly mirrored each other’s price moves during a selected week.

These new systems crunch mountains of historical and real-time data to predict how customers and competitors will react to any price change under different scenarios, giving them an almost superhuman insight into market dynamics. Programmed to meet a certain goal—such as boosting sales—the algorithms constantly update tactics after learning from experience.

Ulrik Blichfeldt, chief executive of Denmark-based a2i Systems A/S, whose technology powers the Rotterdam gas stations, said his software is focused primarily on modeling consumer behavior and leads to benefits for consumers as well as gas stations. The software learns when raising prices drives away customers and when it doesn’t, leading to lower prices at times when price-sensitive customers are likely to drive by, he said.

“This is not a matter of stealing more money from your customer. It’s about making margin on people who don’t care, and giving away margin to people who do care,” he said.

Driving the popularity of A.I. pricing is the pain rippling through most retail industries, long a low-margin business that’s now suffering from increased competition from online competitors.

“The problem we’re solving is that retailers are going through a bloodbath,” said Guru Hariharan, chief executive of Mountain View, Calif.-based Boomerang Commerce Inc., whose A.I.-enabled software is used by StaplesInc. and other companies.

Staples uses A.I.-enabled software to change prices on 30,000 products a day on its website.
Staples uses A.I.-enabled software to change prices on 30,000 products a day on its website. PHOTO: RICHARD B. LEVINE/ZUMA PRESS

The rise of A.I. pricing poses a challenge to antitrust law. Authorities in the EU and U.S. haven’t opened probes or accused retailers of impropriety for using A.I. to set prices. Antitrust experts say it could be difficult to prove illegal intent as is often required in collusion cases; so far, algorithmic-pricing prosecutions have involved allegations of humans explicitly designing machines to manipulate markets.

Officials say they are looking at whether they need new rules. The Organization for Economic Cooperation and Development said it plans to discuss in June at a round table how such software could make collusion easier “without any formal agreement or human interaction.”

“If professional poker players are having difficulty playing against an algorithm, imagine the difficulty a consumer might have,” said Maurice Stucke, a former antitrust attorney for the U.S. Justice Department and now a law professor at the University of Tennessee, who has written about the competition issues posed by A.I. “In all likelihood, consumers are going to end up paying a higher price.”

In one example of what can happen when prices are widely known, Germany required all gas stations to provide live fuel prices that it shared with consumer price-comparison apps. The effort appears to have boosted prices between 1.2 to 3.3 euro cents per liter, or about 5 to 13 U.S. cents per gallon, according to a discussion paper published in 2016 by the Düsseldorf Institute for Competition Economics.

Makers and users of A.I. pricing said humans remain in control and that retailers’ strategic goals vary widely, which should promote competition and lower prices.

“If you completely let the software rule, then I could see [collusion] happening,” said Faisal Masud, chief technology officer for Staples, which uses A.I.-enabled software to change prices on 30,000 products a day on its website. “But let’s be clear, whatever tools we use, the business logic remains human.”

Online retailers in the U.S., such as Amazon.com Inc. and its third-party sellers, were among the first to adopt dynamic pricing. Amazon.com declined to comment.

Since then, sectors with fast-moving goods, frequent price changes and thin margins—such as the grocery, electronics and gasoline markets—have been the quickest to adopt the latest algorithmic pricing, because they are the most keen for extra pennies of margin, analysts and executives say.

The pricing-software industry has grown in tandem with the amount of data available to—and generated by—retailers. Stores keep information on transactions, as well as information about store traffic, product location and buyer demographics. They also can buy access to databases that monitor competitors’ product assortments, availability and prices—both on the web and in stores.

A.I. is used to make sense of all that information. International Business Machines Corp. said its price-optimization business uses capabilities from its Watson cognitive-computing engine to advise retailers on pricing. Germany’s Blue Yonder GmbH, a price-optimization outfit that serves clients in the grocery, electronics and fashion industries, said it uses neural networks based on those its physicist founder built to analyze data from a particle collider.

Neural networks are a type of A.I. computer system inspired by the interconnected structure of the human brain. They are good at matching new information to old patterns in vast databases, which allows them to use real-time signals such as purchases to predict from experience how consumers and competitors will behave.

Algorithms can also figure out what products are usually purchased together, allowing them to optimize the price of a whole shopping cart. If customers tend to be sensitive to milk prices, but less so to cereal prices, the software might beat a competitor’s price on milk, and make up margin on cereal.

“They’re getting really smart,” said Nik Subramanian, chief technology officer of Brussels-based Kantify, who said its pricing software has figured out how to raise prices after it sees on a competitor’s website that it has run out of a certain product.

Algorithmic pricing works well in the retail gasoline market, because it is a high-volume commodity that is relatively uniform, leading station owners in competitive markets to squeeze every penny.

For years, price wars in cutthroat markets have followed a typical pattern. A retailer would cut prices to lure customers, then competitors would follow suit, each cutting a little more than the others, eventually pushing prices down close to the wholesale cost. Finally one seller would reach a breaking point and raise prices. Everyone would follow, and the cycle started all over.

Some economists say the price wars helped consumers with overall lower prices, but led to very thin margins for station owners.

Danish oil and energy company OK hired a2i Systems in 2011 because its network of gas stations was suffering from a decade-old price war. It changed what it charged as many as 10 times a day, enlisting a team of people to drive around the country and call in competitors’ prices, said Gert Johansen, the company’s head of concept development.

A2i Systems—the name means applied artificial intelligence—was started by Alireza Derakhshan and Frodi Hammer, both engineering graduates of the University of Southern Denmark, in Odense. Before focusing on fuel, they built other A.I. systems, including a game displayed on interactive playground floor tiles that adapted to the speed and skill level of the children running around on top.

For OK, a2i created thousands of neural networks—one for each fuel at each station—and trained them to compare live sales data to years of historical company data to predict how customers would react to price changes. Then it ran those predictions through algorithms built to pick the optimal prices and learn from their mistakes.

In a pilot study, OK split 30 stations into two sets, a control group and an a2i group. The group using the software averaged 5% higher margins, according to a paper Mr. Derakhshan presented last June at an A.I. conference in Seville, Spain.

Scandinavian supermarket chain REMA 1000 says it will roll out price-optimization software made by Texas-based Revionics Inc. in coming months.
Scandinavian supermarket chain REMA 1000 says it will roll out price-optimization software made by Texas-based Revionics Inc. in coming months. PHOTO: JOSEPH DEAN/NEWSCOM/ZUMA PRESS

The new system could make complex decisions that weren’t simply based on a competitor’s prices, Mr. Derakhshan said in an interview.

One client called to complain the software was malfunctioning. A competitor across the street had slashed prices in a promotion, but the algorithm responded by raising prices. There wasn’t a bug. Instead, the software was monitoring the real-time data and saw an influx of customers, presumably because of the long wait across the street.

“It could tell that no matter how it increased prices, people kept coming in,” said Mr. Derakhshan.

On the outskirts of Rotterdam, Koen van der Knaap began running the system on his family-owned Shell station in recent months. Down the road, a station owned by Tamoil, a gasoline retailer owned by Libya’s Oilinvest Group, uses it too.

During a late-March week for which both Tamoil and Mr. van der Knaap provided hourly data, the costs for unleaded gas at the two stations—which vary in opening hours and services—bounced around independently much of the time, and generally declined, reflecting falling oil prices that week.

During some periods, however, the stations’ price changes paralleled each other, going up or down by more than 2 U.S. cents per gallon within a few hours of each other. Often, prices dropped early in the morning and increased toward the end of the day, implying that the A.I. software may have been identifying common market-demand signals through the local noise.

The station owners say their systems frequently lower prices to gain volume when there are customers to be won.

“It can be frustrating,” said Erwin Ralan, an electronics-store manager who was filling up at the Tamoil station that week. “Prices usually go up at the end of the day. But when you’re empty and you’re in a rush, there’s not much you can do.”

The Rise of the Smart City

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Officials are tapping all kinds of data to make their cities safer, healthier and more efficient, in what may be just the start of a sweeping change in how cities are run.

As city officials across the country begin to draw on data about income, traffic, fires, illnesses and more, big changes are already under way in leading smart cities.

Cities have a way to go before they can be considered geniuses. But they’re getting smart pretty fast.

In just the past few years, mayors and other officials in cities across the country have begun to draw on the reams of data at their disposal—about income, burglaries, traffic, fires, illnesses, parking citations and more—to tackle many of the problems of urban life. Whether it’s making it easier for residents to find parking places, or guiding health inspectors to high-risk restaurants or giving smoke alarms to the households that are most likely to suffer fatal fires, big-data technologies are beginning to transform the way cities work.

Cities have just scratched the surface in using data to improve operations, but big changes are already under way in leading smart cities, says Stephen Goldsmith, a professor of government and director of the Innovations in Government Program at the Harvard Kennedy School. “In terms of city governance, we are at one of the most consequential periods in the last century,” he says.

Although cities have been using data in various forms for decades, the modern practice of civic analytics has only begun to take off in the past few years, thanks to a host of technological changes. Among them: the growth of cloud computing, which dramatically lowers the costs of storing information; new developments in machine learning, which put advanced analytical tools in the hands of city officials; the Internet of Things and the rise of inexpensive sensors that can track a vast array of information such as gunshots, traffic or air pollution; and the widespread use of smartphone apps and mobile devices that enable citizens and city workers alike to monitor problems and feed information about them back to city hall.

All this data collection raises understandable privacy concerns. Most cities have policies designed to safeguard citizen privacy and prevent the release of information that might identify any one individual. In theory, anyway. In reality, even when publicly available data is stripped of personally identifiable information, tech-savvy users can combine it with other data sets to figure out an awful lot of information about any individual. Widespread use of sensors and video can also present privacy risks unless precautions are taken. The technology “is forcing cities to confront questions of privacy that they haven’t had to confront before,” says Ben Green, a fellow at Harvard’s Berkman Klein Center for Internet and Society and lead author of a recent report on open-data privacy.

Still, cities are moving ahead, finding more ways to use the considerable amounts of data at their disposal. Here’s a look at some of the ways the information revolution is changing the way cities are run—and the lives of its residents.

Spotting potential problems… before they occur

Perhaps the most innovative way cities are employing data is to anticipate problems.

Consider the risk of death by fire. Although declining nationally, there still were 2,685 civilian deaths in building fires in 2015, the latest year for which data is available. The presence of smoke alarms is critical in preventing these deaths; the National Fire Protection Association, a nonprofit standards group, says a working fire alarm cuts the risk of dying in a home fire in half.

New Orleans, like most cities, has a program run by its Fire Department to distribute smoke detectors. But until recently, the program relied on residents to request an alarm. After a fire in which five people—three children, their mother and grandmother—perished, the department started looking for a way to make sure that they were getting alarms into homes where they could make a difference.

FIRE RISK | With census and other data, New Orleans mapped the combined risk of missing smoke alarms and fire deaths, helping officials target distribution of smoke detectors.
FIRE RISK | With census and other data, New Orleans mapped the combined risk of missing smoke alarms and fire deaths, helping officials target distribution of smoke detectors. PHOTO: CITY OF NEW ORLEANS/OPA

Oliver Wise, director of the city’s Office of Performance and Accountability, had his data team tap two Census Bureau surveys to identify city blocks most likely to contain homes without smoke detectors and at the greatest risk for fire fatalities—those with young children or the elderly. They then used other data to zero in on neighborhoods with a history of house fires. Using advanced machine-learning techniques, Mr. Wise’s office produced a map that showed those blocks where fire deaths were most likely to occur and where the Fire Department could target its smoke-detector distribution.

Since the data program began in early 2015, the department has installed about 18,000 smoke detectors, says Tim McConnell, chief of the New Orleans Fire Department. That compares with no more than 800 detectors a year under the older program. It is too early to tell how effective it has been at preventing fire deaths, Chief McConnell says, since they are so rare. But the program did have an early, notable success.

A few months after the program began, firefighters responded to a call in Central New Orleans. Arriving, the fire crew found three families—11 people in all—huddled on the lawn. The residents had been alerted by smoke detectors recently installed under the outreach program.

“That was just one of those stories where you go, ‘This works,’ ” Chief McConnell says. “For us, it’s a game changer.”

Predictive analytics have also been used to improve restaurant health inspections in Chicago. The Department of Public Health relies on about three dozen inspectors to check for possible violations at more than 15,000 food establishments across the city. It needed a better way to prioritize inspections to make sure that places with potential critical violations—those that carry the greatest risk for the spread of food-borne illness—were examined before someone actually became sick.

The data team in the city’s Department of Innovation and Technology developed an algorithm that looked at 11 variables, including whether the restaurant had previous violations, how long it has been in business (the longer, the better), the weather (violations are more likely when it’s hot), even stats about nearby burglaries (which tells something about the neighborhood, though analysts aren’t sure what).

CHECK, PLEASE | To prioritize restaurant inspections, Chicago developed an algorithm to identify eateries most likely to have violations. The darker the pink, the higher the likelihood.
CHECK, PLEASE | To prioritize restaurant inspections, Chicago developed an algorithm to identify eateries most likely to have violations. The darker the pink, the higher the likelihood. PHOTO: CITY OF CHICAGO

With the tool, the health department could identify establishments that were most likely to have problems and move them up the list for inspection. After the algorithm went into use in 2015, a follow-up analysis found that inspectors were visiting restaurants with possible critical violations seven days sooner than before. Since then, its use has resulted in a 15% rise in the number of critical violations found, though the number of illness complaints—an imperfect measure of violations—has been flat.

Sensors on everything

Just as individuals are flocking to Fitbits and other wearables to monitor their health, cities, too, are turning to sensors to track their own vital signs. Through this Internet of Things, sensor-equipped water pipes can identify leaks, electric meters can track power use, and parking meters can automatically flag violations.

As part of a smart-city initiative, Kansas City, Mo., has installed computer-equipped sensors on streetlights along a 2.2-mile light-rail line that opened in March of last year. The city uses video from the sensors to gather information about traffic and available street parking along the corridor. The data is then made available on a public website that shows the location of streetcars, areas where traffic is moving slowly, and locations with open parking spots. It also provides an hourly traffic count in the corridor for the past day.

PARK HERE | In Kansas City, Mo., sensors on streetlights along a new light-rail line gather information about traffic and available parking that the public can view online.
PARK HERE | In Kansas City, Mo., sensors on streetlights along a new light-rail line gather information about traffic and available parking that the public can view online. PHOTO: XAQT

The sensors can even count foot traffic, which could assist entrepreneurs looking to open a new coffee shop or retail outlet, and help city officials estimate the size of crowds, which is useful in responding to public disturbances or in assigning cleanup crews after events like the city’s 2015 World Series parade. Their ability to detect motion also can be used to adjust the LED streetlights so that they dim if no one is around and automatically brighten if cars or pedestrians pass by. The goal is to use data to “improve our efficiency of service and ascertain what services we ought to be providing,” says Bob Bennett, Kansas City’s chief innovation officer.

Cities are also putting sensors in the hands of citizens. In Louisville, Ky., a coalition of public, private and philanthropic organizations has provided more than 1,000 sensor-equipped inhalers to asthma sufferers to map where in the city poor air quality is triggering breathing problems. The tiny sensors, from Propeller Health, a Madison, Wis., medical-device company, have built-in GPS that collects time and location data with each puff of the inhaler.

The city is still completing its analysis of the data, but early findings were impressive, says Grace Simrall, Louisville’s chief of civic innovation. For one thing, patients in the program saw measurable improvement, in part by giving them a better understanding of their disease, and their physicians more information to devise treatment plans. And as expected, the data made it possible to show clusters of inhaler use and link it with air pollution.

In one case, sensor data spotlighted a congested road on the east side of town where inhaler use was three times as high as in other parts of the city. In response, the city planted a belt of trees separating the road from a nearby residential neighborhood; the plantings have resulted in a 60% reduction in particulate matter (which can aggravate breathing problems) behind the green belt.

Citizens as data collectors

Using the public as data collectors isn’t new—it’s the idea behind 911 and 311 systems. But smartphone apps, in the hands of residents and city workers, give cities new and more powerful ways to expand their data-collection efforts.

In Mobile, Ala., building-code inspectors armed with smartphones and Facebook Inc.’sInstagram photo-sharing app were able to inventory the city’s 1,200 blighted properties in just eight days—a task that enforcement officers had previously considered impossible with the older paper-based systems of tracking blight. With Instagram, inspectors could snap a photo of a property and have it appear on a map, showing officials where dilapidated, abandoned or other problem properties are clustered.

AIR TRIGGER | Sensor-equipped asthma inhalers in Louisville that collect data on time and place of use have improved care for individuals and helped the city address problem areas.
AIR TRIGGER | Sensor-equipped asthma inhalers in Louisville that collect data on time and place of use have improved care for individuals and helped the city address problem areas. PHOTO: PROPELLER HEALTH

The inventory was just the first step. Mobile’s two-year-old Innovation Team, funded with a grant from Bloomberg Philanthropies, cross-referenced the data with other available property information—tax records, landmark status, out-of-state ownership—to compile a “blight index,” a master profile of every problem property in the city. This made it possible to identify which property owners might need assistance in rehabbing their properties and which ones to cite for code violations. The city is wrapping up a second survey of blighted properties to measure the net change over the past year, says Jeff Carter, Innovation Team’s executive director. “Instagram was phase one, and we would never have made it to phase two without it,” Mr. Carter says.

Mobile data collection is also helping Los Angeles to clean up city streets. Teams from the city sanitation department use video and smartphones to document illegal dumping, abandoned bulky items and other trash problems. The teams can use an app to report problems needing immediate attention, but what was really noteworthy—especially for a city the size of L.A.—was that they were able to view and grade all 22,000 miles of the city’s streets and alleyways.

The result has been to give officials and the public a better picture of garbage-plagued areas that can be targeted under Mayor Eric Garcetti’s Clean Streets program. Data collected by the mobile teams is compiled in a detailed map of the city, with each street segment rated as being clean, somewhat clean and not clean. The city publishes the map online so that anyone can get a color-coded view of how streets rank for cleanliness.

STATE OF THE STREETS | This online map tracks the progress of Los Angeles’s Clean Streets program. Green means ‘clean,’ yellow ‘somewhat clean,’ and red ‘not clean.’
STATE OF THE STREETS | This online map tracks the progress of Los Angeles’s Clean Streets program. Green means ‘clean,’ yellow ‘somewhat clean,’ and red ‘not clean.’ PHOTO: CLEAN STREETS LA

The program, which recently finished its first full year, has resulted in an 80% reduction in the number of areas scored “not clean,” says Lilian Coral, Los Angeles’s chief data officer. The new data-driven approach not only has made it possible to better identify problem areas, Ms. Coral says, but it also has helped to reduce disparities in the city’s cleanup efforts, which previously depended mainly on complaints to identify locations needing attention.

In Boston, meanwhile, the city has joined with Waze, a navigation app from Google that enables drivers to share traffic and road conditions in real time.

The Boston traffic-management center uses Waze data to supplement live feeds from its network of traffic cameras and sensors, getting a more detailed picture of what’s happening on city streets. Messages from Waze users can alert the center to traffic problems—a double-parked truck or a fender-bender—as soon as they develop, allowing officials to respond more quickly.

Waze data also has helped the city to run low-cost experiments on possible traffic changes. For instance, to test how to best enforce “don’t block the box” at congested intersections, the center took more than 20 problem intersections and assigned each one either a changing message sign, a police officer or no intervention at all. Using Waze data, analysts would then see which enforcement approach was most effective at reducing congestion. As it turns out, Waze’s traffic-jam data didn’t show that either approach made much difference in reducing congestion (which may reinforce the view of those who believe little can be done to eliminate traffic headaches).

The partnership, one of 250 that Waze has signed with cities around the world, also enables the city to feed street-closure and similar information into the Waze app, making it easier for drivers to reroute trips before they get stuck in traffic.

“When residents see a problem, sometimes their reaction is to call us, but more these days their instinct is to report it through an app like Waze or Yelp , ” says Andrew Therriault, Boston’s chief data officer. “To be as responsive as possible to the public’s needs, we need to listen to their input through whichever medium they choose to share it.”

Mr. Totty, a former news editor for The Journal Report in San Francisco, can be reached atreports@wsj.com.

Appeared in the Apr. 17, 2017, print edition.

The coming revolution in insurance

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Technological change and competition disrupt a complacent industry

IN THE stormy and ever-changing world of global finance, insurance has remained a relatively placid backwater. With the notable exception of AIG, an American insurer bailed out by the taxpayer in 2008, the industry rode out the financial crisis largely unscathed. Now, however, insurers face unprecedented competitive pressure owing to technological change. This pressure is demanding not just adaptation, but transformation.

The essential product of insurance—protection, usually in the form of money, when things go wrong—has few obvious substitutes. Insurers have built huge customer bases as a result. Investment revenue has provided a reliable boost to profits. This easy life led to a complacent refusal to modernise. The industry is still astonishingly reliant on human labour. Underwriters look at data but plenty still rely on human judgment to evaluate risks and set premiums. Claims are often reviewed manually.

The march of automation and technology is an opportunity for new entrants. Although starting a new soup-to-nuts insurer from scratch is rare (see article), many companies are taking aim at parts of the insurance process. Two Sigma, a large American “quant” hedge fund, for example, is betting its number-crunching algorithms can gauge risks and set prices for insurance better and faster than any human could. Other upstarts have developed alternative sales channels. Simplesurance, a German firm, for example, has integrated product-warranty insurance into e-commerce sites.

Insurers are responding to technological disruption in a variety of ways. Two Sigma contributes its analytical prowess to a joint venture with Hamilton, a Bermudian insurer, and AIG, which actually issues the policies (currently only for small-business insurance in America). Allianz, a German insurer, simply bought into Simplesurance; many insurers have internal venture-capital arms for this purpose. A third approach is to try to foster internal innovation, as Aviva, a British insurer, has done by building a “digital garage” in Hoxton, a trendy part of London.

The biggest threat that incumbents face is to their bottom line. Life insurers, reliant on investment returns to meet guaranteed payouts, have been stung by a prolonged period of low interest rates. The tough environment has accelerated a shift in life insurance towards products that pass more of the risk to investors. Standard Life, a British firm, made the transition earlier than most, for example, and has long been primarily an asset manager (see article).

Meanwhile, providers of property-and-casualty (P&C) insurance, such as policies to protect cars or homes, have seen their pricing power come under relentless pressure, notably from price-comparison websites. In combination with the stubbornly high costs of maintaining their old systems, this has meant that profitability has steadily deteriorated. The American P&C industry, for instance, has seen its “combined ratio”, which expresses claims and costs as a percentage of premium revenue, steadily creep up from 96.2% in 2013 to 97.8% in 2015, and to an estimated 100.3% for 2016 (ie, a net underwriting loss). Henrik Naujoks of Bain & Company, a consultancy, says this has left such insurers facing a stark choice: become low-cost providers, or differentiate themselves through the services they provide.

One fairly simple way to offer distinctive services is to use existing data in new ways. Insurers have long drawn up worst-case scenarios to estimate the losses they would incur from, say, a natural catastrophe. But some have started working with clients and local authorities on preparing for such events; they are becoming, in effect, risk-prevention consultants. AXA, a French insurer, has recently started using its models on the flooding of the Seine to prepare contingency plans. Gaëlle Olivier of AXA’s P&C unit says the plans proved helpful in responding to floods in June 2016, reducing the damage.

Damage control

Tech-savvy insurers are going one step further, exploiting entirely new sources of data. Some are using sensors to track everything from boiler temperatures to health data to driving styles, and then offering policies with pricing and coverage calibrated accordingly. Data from sensors also open the door to offering new kinds of risk-prevention services. As part of Aviva’s partnership with HomeServe, a British home-services company, the insurer pays to have a sensor (“LeakBot”) installed on its customers’ incoming water pipes that can detect even minuscule leaks. HomeServe can then repair these before a pipe floods a home, causing serious damage.

The shift towards providing more services fosters competition on factors beyond price. Porto Seguros, a Brazilian insurer, offers services ranging from roadside assistance to scheduling doctor’s appointments. In France AXA provides coverage for users of BlaBlaCar, a long-distance ride-sharing app. The main aim of the policy is to guarantee that customers can still reach their destination. If, say, the car breaks down, it offers services ranging from roadside car-repair to alternative transport (eg, calling a taxi).

Insurers face many hurdles, however, to becoming service providers and risk consultants. Maurice Tulloch, head of the general-insurance arm of Aviva, admits that such services are yet to catch on with most customers. So far, his firm, like its peers, has focused on enticing them to adopt the new offerings by cutting insurance premiums, rather than on making money directly from them. It reckons it can recoup the cost of, say, the HomeServe sensors and repairs from the reduction in claims.

One example of what the future may hold comes from the car industry. Carmakers have traditionally bought product-liability insurance to cover manufacturing defects. But Volvo and Mercedes are so confident of their self-driving cars that last year they said they will not buy insurance at all. They will “self-insure”—ie, directly bear any losses from crashes.

Some think that such trends threaten the very existence of insurance. Even if they do not, Bain’s Mr Naujoks is not alone in expecting the next five years to bring more change to the insurance industry than he has seen in the past 20.

This article appeared in the Finance and economics section of the print edition under the headline “Counsel of protection”

In Its New Factory, Impossible Foods Will Make 12 Million Pounds Of Plant-Based Burgers A Year

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The exciting new plant-based burger that bleeds like real meat is scaling up to reach more customers: It wants to be in 1,000 restaurants by the end of the year.

In Its New Factory, Impossible Foods Will Make 12 Million Pounds Of Plant-Based Burgers A Year
PHOTO: COURTESY IMPOSSIBLE FOODS
 Fast Company

Inside a former baked-goods factory near the Oakland airport, a construction crew is installing giant vats that will soon be used to scale up production of the Impossible Burger–a plant-based meat designed to look and taste good enough that meat eaters will want to order it, not vegetarians.

The “meat,” developed by a team led by former Stanford biochemistry professor Patrick Brown, is currently being produced in a 10,000-square-foot pilot facility in Silicon Valley and a 1,500-square foot space in New Jersey. The new facility, at around 60,000 square feet, will dramatically scale up production capacity. When the factory is fully ramped up, it will be able to produce at least 1 million pounds of Impossible Burger meat a month, or 250 times more than today.

“It will enable us to go from something that is scarce–and we’re constantly getting complaints from customers about the fact that they can’t buy them at their local restaurant–and start to make it ubiquitous,” Brown said at an event launching the new factory.

The burger is currently available at 11 restaurants, including 3 that launched it on March 23. But by the end of the year, the company expects to supply 1,000 restaurants. It just signed a deal to have the burgers featured in the San Francisco Giant’s baseball stadium.

For the company, achieving scale is a critical part of achieving its mission. Brown started working on the project while thinking about the problem of climate change; raising cows and other animals for meat is one of the world’s largest sources of greenhouse gases. It also uses and pollutes more water than any other industry, and drives deforestation. But he realized that the majority of the world wouldn’t voluntarily go vegetarian for those reasons.

“Billions of people around the world who love meat are not going to stop demanding it, so we just have to find a better way to produce it,” he says.

PHOTO: COURTESY IMPOSSIBLE FOODS

The team studied the properties of meat–particularly heme, the molecule that makes blood red and gives meat a meaty taste–and then experimented with recreating those properties using only ingredients from plants.

“When you think about meat, there’s the muscle, there’s the connective tissue, there’s the fat, so we had to figure out how to mimic those parts of beef to figure out how to experience the texture, but also the taste,” Don DeMasi, senior vice president of engineering for Impossible Foods, tells Fast Company.

The result looks like it was made from a cow, not plants. The handful of chefs who were given first access to the product say they think of it as meat. “It kind of made this transition in my mind to be–it’s just another kind of meat,” says chef Traci Des Jardins, who has been serving Impossible burgers at her San Francisco restaurant Jardinière for about a year, and now is also serving it at Public House, her restaurant at the city’s ballpark.

PHOTO: COURTESY IMPOSSIBLE FOODS

Before it’s cooked, the product is red like raw beef; as it cooks, it browns. As the heme mixes with juices and oozes out, it can look like it’s bleeding. “You’re seeing the exact same cooking chemistry that you see in meat, literally,” says Brown.

As the company scales up its beef alternative, it will focus on restaurants. In a year, it says, U.S. restaurants serve more than 5 billion pounds of burgers, and Impossible wants its 12 million pounds to be among them. Retail will come later, along with other products that are currently in development, such as poultry and steak.

“Our long-term goal is to basically develop a new and better way to create all the foods we make from animals,” says Brown.

YouTube Tops 1 Billion Hours of Video a Day, on Pace to Eclipse TV

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Google unit posts 10-fold increase in viewership since 2012, boosted by algorithms personalizing user lineups

YouTube viewers world-wide are now watching more than 1 billion hours of videos a day, threatening to eclipse U.S. television viewership, a milestone fueled by the Google unit’s aggressive embrace of artificial intelligence to recommend videos.

YouTube surpassed the figure, which is far larger than previously reported, late last year. It represents a 10-fold increase since 2012, YouTube said, when it started building algorithms that tap user data to give each user personalized video lineups designed to keep them watching longer.

Feeding those recommendations is an unmatched collection of content: 400 hours of video are uploaded to YouTube each minute, or 65 years of video a day.

“The corpus of content continues to get richer and richer by the minute, and machine-learning algorithms do a better and better job of surfacing the content that an individual user likes,” YouTube Chief Product Officer Neal Mohan said.

MERRILL SHERMAN

YouTube’s billion-hour mark underscores the wide lead of the 12-year-old platform in online video—threatening traditional television, which lacks similarly sophisticated tools.

Facebook Inc. and Netflix Inc. said in January 2016 that users watch 100 million hours and 116 million hours, respectively, of video daily on their platforms. Nielsen data suggest Americans watch on average roughly 1.25 billion hours of live and recorded TV a day, a figure steadily dropping in recent years.

Despite its size, it is unclear if YouTube is making money. Google parent Alphabet Inc. doesn’t disclose YouTube’s performance, but people familiar with its financials said it took in about $4 billion in revenue in 2014 and roughly broke even.

YouTube makes most of its money on running ads before videos but it also spends big on technology and rights to content, including deals with TV networks for a planned web-TV service. When asked about profits last year, YouTube Chief Executive Susan Wojcicki said, “Growth is the priority.”

YouTube’s success using tailor-made video lineups illustrates how technology companies can reshape media consumption into narrow categories of interests, a trend some observers find worrying.

“If I only watch heavy-metal videos, of course it’s serving me more of those. But then I’m missing out on the diversity of culture that exists,” said Christo Wilson, a Northeastern University computer-science professor who studies the impact of algorithms. “The blessing and curse of cable and broadcast TV is it was a shared experience.…But that goes away if we each have personalized ecosystems.”

YouTube benefits from the enormous reach of Google, which handles about 93% of internet searches, according to market researcher StatCounter. Google embeds YouTube videos in search results and pre-installs the YouTube app on its Android software, which runs 88% of smartphones, according to Strategy Analytics.

That has helped drive new users to its platform. About 2 billion unique users now watch a YouTube video every 90 days, according to a former manager. In 2013, the last time YouTube disclosed its user base, it said it surpassed 1 billion monthly users. YouTube is now likely larger than the world’s biggest TV network, China Central Television, which has more than 1.2 billion viewers.

YouTube long configured video recommendations to boost total views, but that approach rewarded videos with misleading titles or preview images. To increase user engagement and retention, the company in early 2012 changed its algorithms to boost watch time instead. Immediately, clicks dropped nearly 20% partly because users stuck with videos longer. Some executives and video creators objected.

Months later, YouTube executives unveiled a goal of 1 billion hours of watch time daily by the end of 2016. At the time, optimistic forecasts projected it would reach 400 million hours by then.

YouTube retooled its algorithms using a field of artificial intelligence called machine learning to parse massive databases of user history to improve video recommendations.

If I only watch heavy-metal videos, of course it’s serving me more of those. But then I’m missing out on the diversity of culture that exists.

—Northeastern University computer-science professor Christo Wilson

Previously, the algorithms recommended content largely based on what other users clicked after watching a particular video, the former manager said. Now their “understanding of what is in a video [and] what a person or group of people would like to watch has grown dramatically,” he said.

Engineers tested each change on a control group, and only kept the change if those users spent more time on YouTube.

One strategy was to find new areas of user interest. For instance, YouTube could suggest a soccer video to users watching a lot of football, and then flood the lineup with more soccer if the first clip was a hit. “Once you realize there’s an additional preference, exploit that,” the former manager said.

But the algorithm didn’t always deliver. For instance, when Ms. Wojcicki joined as CEO in 2014, YouTube recommended videos to her about eczema because she had recently watched a clip about skin rashes after suspecting one of her children had one, said Cristos Goodrow, YouTube’s video-recommendation chief.

That made the video-recommendation team realize there were certain “single-use videos” to ignore as signals of user interest. But to mark them, they had to think of each example, such as certain health and how-to videos.

Then last year YouTube partnered with Google Brain, which develops advanced machine-learning software called deep neural networks, which have led to dramatic improvements in other fields, such as language translation. The Google Brain system was able to identify single-use video categories on its own.