“A bit more than a year after its release, Amazon’s Echo has morphed from a gimmicky experiment into a device that brims with profound possibility,” writes Farhad Manjoo in The New York Times. “Here is a small, stationary machine that you set somewhere in your house, which you address as Alexa, which performs a variety of tasks — playing music, reading the news and weather, keeping a shopping list — that you can already do on your phone.”
“But the Echo has a way of sneaking into your routines. When Alexa reorders popcorn for you, or calls an Uber car for you, when your children start asking Alexa to add Popsicles to the grocery list, you start to want pretty much everything else in life to be Alexa-enabled, too. In this way, Amazon has found a surreptitious way to bypass Apple and Google — the reigning monarchs in the smartphone world — with a gadget that has the potential to become a dominant force in the most intimate of environments: our homes.”
“Many in the industry have long looked to the smartphone as the remote control for your world. But the phone has limitations. A lot of times fiddling with a screen is just too much work. By perfecting an interface that is much better suited to home use — the determined yell! — Amazon seems on the verge of building something like Iron Man’s Jarvis, the artificial-intelligence brain at the center of all your household activities.”
“Scot Wingo, the chairman of ChannelAdvisor, an e-commerce consulting firm, said the early signs suggested that the Echo was on a path to become Amazon’s next $1 billion business.”
The New York Times: “We march through life measuring ourselves on one scale after another, from developmental markers through standardized tests and employment evaluations, cardiac risk and bone density scores. Not to mention the ready-made clothes that never fit anyone quite right. Does it have to be that way? … Absolutely not, says Todd Rose in The Age of Average, a subversive and readable introduction to what has been called the new science of the individual.”
“For educators, it’s all those brilliant underachievers (not to mention all those idiots with Harvard diplomas). For doctors, it’s all the outliers who survive dire disease predictors — or even dire diseases — decades beyond expectation … For human resource personnel, it’s the new hires who satisfy every single one of a dozen standard criteria and yet utterly fail to perform.”
The author cites “the not-unfamiliar notion that all human characteristics are multidimensional, not only in specifics but also in time and context. Reducing this mass of data to a single simple variable (as in a ‘slow’ toddler, an ‘aggressive’ teenager, a ‘prediabetic,’ a Harvard graduate) may well result in a set of flawed conclusions.”
“In other words, big data may have landed us in the Age of Average, but really enormous data, with many observations of a single person’s biology and behavior taken over time and in different contexts, may yield a far better understanding of that individual than do group norms.”
A board game from the 1960s has been updated with “digital” cards using algorithms and big data, reports The New York Times. In the past, Strat-O-Matic, a “baseball simulation game,” was “played using cards for each player based on statistics from the previous season.” In its latest iteration, called Baseball Daily, the cards are “updated daily,” allowing players “to play games in the present,” says Adam Richman, son of the game’s founder, Hal Richman.
“Every year, we try to push forward digitally,” Adam says. “We need to rethink how we are doing everything.” He adds: “This is a natural evolution that will allow more engagement for our fans and expand our purview.” The hope is that Baseball Daily will “scoop up some daily gamers who have been flocking to the fantasy sports sites FanDuel and DraftKings, although Baseball Daily does not involve cash prizes and is structured differently.”
Strat-O-Matic is also developing apps. Traditionalists will, of course, be able to continue play Strat-O-Matic the old-fashioned way, using last year’s data.
The difference between the way Netflix and Amazon use big data is the difference between a hit and an also ran, reports The Observer. Data scientist Sebastian Wernicke, in a TED Talk, “explained how two shows, which were strategically made with data analysis methods creators thought would ensure Breaking Bad caliber success, were created, and how they faired in the ratings. One, Netflix’s House of Cards, worked—the show went on to score a 9.1 on the rating curve. The other, Amazon’s Alpha House, however, fell short and landed at 7.5 on the curving, marking it as a completely average show.”
“When Amazon set out to make a data-driven show, the company held a competition. They evaluated a bunch of show ideas, selected eight of them and then created a pilot episode for each and made them available online for free. Millions watched the free episodes, and the company used data (such as how many people watched each show, how long they watched and what parts they skipped) to create a show they hoped would be destined for greatness. After crunching millions of data points, the results said they should create a sitcom about four Republican U.S. senators. Alpha House was born.”
“Around the same time, Netflix was brewing up something similar. But instead of using a competition, the company looked at the data they already had about viewing on their platform (ratings, viewing history, etc). They used that data to discover small bits and pieces about what viewers like and took a leap of faith … Amazon’s show wasn’t a booming success because it used data all the way. Netflix, however, looked at what users like and used that insight to think up a concept for what they believed would be a hit show, and it clearly worked.”
“Automated music recommendations are hardly new, but Spotify seems to have identified the ingredients of a personalized playlist that feel fresh and familiar at the same time,” reports Quartz. “That’s potentially a big advantage over competitors like Pandora, Google, and Apple, which largely have the same bottomless catalog of music but take very different approaches to picking the best songs for each user.”
“We now have more technology than ever before to ensure that if you’re the smallest, strangest musician in the world, doing something that only 20 people in the world will dig, we can now find those 20 people and connect the dots between the artist and listeners,” Matthew Ogle, who oversees the service at Spotify (said) recently. “Discovery Weekly is just a really compelling new way to do that at a scale that’s never been done before.”
Wired: “Under Armour was founded on a simple idea: Make athletes better. To do that, it’s turning human performance into a big data problem. The company is betting on the notion that the right hardware, the biggest dataset, a lot of machine learning, and powerful motivational tools can make everyone better, faster, and stronger. It’s betting that technology doesn’t exist solely to make us lazy, to bring everything to our door with the push of a button.
The centerpiece of that bet is a $400 kit, announced today, called Healthbox, that provides a scale, an activity tracker wearable, and a chest strap for measuring heart rate. The company also is updating Record, its mobile app, making it a 24/7 real-time barometer of your fitness and health. These tools, combined with three apps Under Armour has purchased in recent years, provide the most comprehensive ecosystem of fitness products yet made.”