Slowball: Is ‘Big Data’ Wrecking Baseball?

The Wall Street Journal: “Baseball has never been more beset by inaction. Games this season saw an average gap of 3 minutes, 48 seconds between balls in play, an all-time high … A confluence of hitting, pitching and defensive strategies spawned by the league’s ‘Moneyball’ revolution have all played a role. That makes baseball, whose early use of big-data strategies was embraced by the business world in general, a case study in its unintended consequences.”

For example: “Statistics showing precisely when starting pitchers become less effective have prompted teams to remove them from games earlier than before. That has increased one of the biggest drags on pace of play: pitching changes. Regular-season games this year saw an average of 8.4 pitchers used between both teams, an all-time high. That’s up from 5.8 pitchers a game 30 years ago.”

“Radar and camera measurements of the angle at which balls leave the bat have shown that the optimal swing angle looks more like an uppercut than many hitters preferred. Hitters, in turn, have started swinging for the fences in droves. Home runs this season reached a record level. That all-or-nothing approach means that between each home run there is a lot of standing around and waiting. Some classic displays of athleticism—a daring attempt by a runner to advance more than one base on a teammate’s hit, for instance—have become rarer.”

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Blockchain Grocery: How Walmart Delivers Food Safety

Quartz: “Thanks to technology originally designed to monitor cryptocurrency … something that could put a significant dent in the number of foodborne illnesses that occur every year. It’s part of a new program in which IBM is partnering with Walmart, Nestlé, Dole, Tyson Foods, Kroger, and others, to use blockchain technology to track food throughout the complex global supply chain.”

“Under the new system, if a consumer falls ill from E. coli traced to a batch of lettuce, a food-safety investigator could conceivably scan a barcode on the packaging to quickly learn where it came from and where other lettuce from the same batch went. Retailers will be able to quickly remove contaminated products from shelves, thus stopping the spread of illnesses.”

“Walmart has been using a pilot version of the technology, showing how blockchain can be expanded beyond the financial, health care, and natural resources sectors to be applied to the foods that consumers interact with every single day. Coupled with companies’ efforts to stop food-borne illnesses early on, this could signal a major moment in how humans keep the food system in check.”

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How Netflix Creates ‘Taste Communities’

Wired: “The Defenders provides Netflix with a unique case study. Instead of merely allowing it to find out if someone who likes, say, House of Cards also will like Daredevil (yes, BTW), it tells them which of the people who landed on Daredevil because of House of Cards will make the jump to The Defenders.”

“Wildly different programs lead people to The Defenders’ standalone shows. The top lead-in show for Luke Cage? Narcos. But for Iron Fist, it’s a Dave Chappelle special. Someone who watches Jones probably will watch Cage, but beyond that the groups of people—Netflix calls them ‘taste communities’—gravitating toward those shows enjoy very different programming.”

“Every Netflix user belongs to three or four taste communities. It’s easy to say that this influences what appears in your recommendations, but it’s not quite that simple. Membership in those communities does more than dictate the top 10 comedies appearing in a row of your queue, it determines whether comedies appear there at all … Each time you open Netflix it exposes you to 40 or 50 titles. Netflix considers it a win if you choose one of them.”

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Disney Machine-Learns for Laughs

Quartz: Disney “is using machine learning to assess the audience’s reactions to films based on their facial expressions, it wrote in a new research paper. It uses something called factorized variational auto-encoders, or FVAEs, to predict how a viewer will react to the rest of a film after tracking their facial expressions for a few minutes.”

“The FVAEs learn a set of facial expressions, such as smiles and laughter, from the audience, and then make correlations between audience members to see if a movie is getting laughs or other reactions when it should be—a much more sophisticated version of how Amazon and Netflix make suggestions for new things to buy or watch based on your shopping or viewing history.”

“By placing four infrared cameras and infrared illuminators above a theater screen, the researchers were able to identify 16 million facial landmarks, or expressions, from more than 3,100 theatergoers during 150 screenings of nine Disney movies … the data was then analyzed with a computer. (Before this gets too creepy, Disney isn’t tracking your every move at your local theater. The experiment took place during screenings at one particular 400-seat theater. And audiences likely had to choose to participate.)”

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Technology Cannot Hug a Customer

The New York Times: “Technology, some hotels are finding, has its limits. ‘Technology cannot hug a repeat guest,’ said George Aquino, the vice president and managing director of AHC+Hospitality … That is the reason his company, which manages several hotels, has been running a training program for some of its managers and other staff members to improve their hospitality skills, connect with local business leaders and learn more about local tourist offerings.”

“Similar programs are sprouting in other cities, involving not just hotels but also restaurants and even cities themselves, which see the personal touch as giving them a competitive edge. For business travelers, in particular, talking to someone knowledgeable about a city can lead to a good restaurant. And it can also help expand business leads.”

“A consulting program based in Tucson, Certified Tourism Ambassadors, trains hospitality workers. Mickey Schaefer, the chief executive and founder, said she had developed the idea in 2006 while working for the American Academy of Family Physicians to plan its conventions. Hospitality workers sometimes did not know their own cities, leading to bad experiences, she said … The program, she said, ‘is more than just helping the customer. It is helping them find the richness of whatever they are interested in.’ She added that the program also instills civic pride.”

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Deep Thinking: ‘Artificial’ Trumps ‘Intelligence’

LARB: From a review of Deep Thinking, by Garry Kasparov, the chess champion defeated by Deep Blue, a machine, in 1997: “The history of computer chess is the history of artificial intelligence. After their disappointments in trying to reverse-engineer the brain, computer scientists narrowed their sights. Abandoning their pursuit of human-like intelligence, they began to concentrate on accomplishing sophisticated, but limited, analytical tasks by capitalizing on the inhuman speed of the modern computer’s calculations.”

“This less ambitious but more pragmatic approach has paid off in areas ranging from medical diagnosis to self-driving cars. Computers are replicating the results of human thought without replicating thought itself. If in the 1950s and 1960s the emphasis in the phrase ‘artificial intelligence’ fell heavily on the word ‘intelligence,’ today it falls with even greater weight on the word ‘artificial’ … If a machine can search billions of options in a matter of milliseconds, ranking each according to how well it fulfills some specified goal, then it can outperform experts in a lot of problem-solving tasks without having to match their experience or insight.”

Also: “A bit of all-too-human deviousness was also involved in Deep Blue’s win. IBM’s coders, it was later revealed, programmed the computer to display erratic behavior — delaying certain moves, for instance, and rushing others — in an attempt to unsettle Kasparov. Computers may be innocents, but that doesn’t mean their programmers are.”

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Machine Platform Crowd: The Future Today

The Wall Street Journal: Machine Platform Crowd, a new book by Andrew McAfee and Erik Brynjolfsson, “is a book for managers whose companies sit well back from the edge and who would like a digestible introduction to technology trends that may not have reached their doorstep—yet … In the authors’ terminology, ‘Machine’ is shorthand for computers running software that, with new AI techniques called ‘deep learning,’ essentially teaches itself how to make judgments superior to those of humans. ‘Machine’ also encompasses the disappearance of employees in the services sector, leaving only the customer, robots and software—what the authors refer to as ‘virtualization.'”

“‘Platform’ refers to digital environments that bring economic actors together, exploiting free, or nearly free, online access, reproduction and distribution. Uber and Airbnb are examples of new platforms. ‘Crowd’ refers to information resources created by the uncredentialed, the nonexpert and, with rare exceptions, the unpaid. Wikipedia and the Linux operating system comprise the two most impressive achievements of the crowd.”

​”Messrs. McAfee and Brynjolfsson argue that, in the latest phase of the second machine age, incumbent businesses will be pushed aside if they fail to understand how new machines and software, platforms, and the crowd enlarge the scope of digital technologies—just as manufacturers that had appeared and thrived in the first phase of the first machine age were displaced when electricity supplanted steam power in the early 20th century.”

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Algorithmania: Nutella Prints 7 Million Unique Labels

Hyperallergic: “Nutella’s manufacturer, Ferrero, recently partnered with advertising agency Ogilvy & Mather Italia to make Nutella even more endearing and its consumption more exciting by presenting Nutella Unica, an algorithm designed to create a series of unique labels for (almost) every Nutella jar in Italy. The algorithm pulls from a database of dozens of patterns and colors to create seven million different versions of the Nutella label — pink and green, striped and polka-dotted, Pop Art-inspired and minimal.”

“Advertising for Nutella Unica compares the individuality of each jar to the people of Italy themselves (there are about 60 million people in Italy, so about 11% of them can get a jar all their own — actually quite a feat). When these exceptionally delicious artworks hit shelves in Italy in February 2017, they sold out in barely a month. Can you imagine the shopping possibilities — buying multiples, or maybe trying to find an attractive pair?”

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French Twist: How Yoplait Manufactures Authenticity

The New York Times: “Thick, sour Greek yogurts with names like Chobani, Fage and Oikos were surging in popularity. Sales of runny, sugary Yoplait were oozing off a cliff. So Yoplait executives ran to their test kitchens and developed a Greek yogurt of their own … They called it Yoplait Greek. It tanked almost immediately. And so has almost every other Greek yogurt product that Yoplait has put on shelves.”

“So now, Yoplait is opening a new front in the cultured-milk battles … They’re calling it Oui by Yoplait, in homage to the company’s French roots … if, as you are shopping, you happen to pick up a small glass pot of Oui and are momentarily transported to the French countryside, you’ll know that the company has finally figured out how to look beyond the data and embrace the narrative. Yoplait may have figured out how to fake authenticity as craftily as everyone else.”

“Yoplait began scouring its own history and ultimately found a tale that seemed to resonate: For centuries (or so the story goes), French farmers have made yogurt by putting milk, fruit and cultures into glass jars and then setting them aside. So Yoplait tweaked its recipe and began buying glass jars … It has a creamy texture and sweet flavor. And if this product is a success — if years from now someone tells the heartwarming story of how the Greek hordes were defeated by simple French pots — then we’ll know that Yoplait’s number crunchers finally figured out the formula for authenticity, and have reclaimed their crown.”

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