Quartz: Many global brands of the last decade have taken names of false provenance and enjoyed great success thanks to a paradigm hereby dubbed, “The Häagen-Dazs Effect.” … Häagen-Dazs ice cream, meant to sound Danish, was born in the Bronx. But the name tells a tale, from that umlaut to the silent “s” on the end, of premium, European origins. And when consumers reach for a pint, they sense the true value this faux identity adds.
“The Häagen-Dazs Effect makes an impact as well in these four notable brand names with made-up or embellished heritage and powerful identities: Sir Kensington’s makes high-end condiments and tells “the tongue-in-cheek ‘saga’ of an Oxford-educated gent who developed the original spiced ketchup recipe for Catherine the Great of Russia.” St. Germain, an elderflower spirit made in the USA, is named for the Saint-Germain-sur-Rhône region of the French Alps, where the stuff was first made.”
Warby Parker “took its name from Warby Pepper and Zagg Parker, two characters from one of Jack Kerouac’s journals … Brandy Melville was created by merchandising veteran Silvio Marsan and his son Stefan in Italy … Purportedly, the name is based on an imagined American girl (Brandy) who falls in love with an Englishman (Melville) in Rome … Ultimately, we’re talking here about the critical importance of a name that succinctly tells a compelling brand story–whether that story is imagined or not, it must be told with richness and believability. These brands are a few to do so, making the Häagen-Dazs Effect, when done right, a tactical tool for naming success.”
“Mark Riedl and Brent Harrison from the School of Interactive Computing at the Georgia Institute of Technology have just unveiled Quixote, a prototype system that is able to learn social conventions from simple stories,” reports The Guardian.
“A simple version of a story could be about going to get prescription medicine from a chemist … An AI (artificial intelligence) given the task of picking up a prescription for a human could, variously, rob the chemist and run, or be polite and wait in line. Robbing would be the fastest way to accomplish its goal, but Quixote learns that it will be rewarded if it acts like the protagonist in the story.”
“Quixote has not learned the lesson of ‘do not steal,’ Riedl says, but ‘simply prefers to not steal after reading and emulating the stories it was provided … the stories are surrogate memories for an AI that cannot ‘grow up’ immersed in a society the way people are and must quickly immerse itself in a society by reading about [it].’”
“The system was named Quixote, said Riedl, after Cervantes’ would-be knight-errant, who ‘reads stories about chivalrous knights and decides to emulate the behaviour of those knights.'”
Hyperallergic: “In plain terms, across the field, in museums, art institutions, performance forums, and even historical societies, the visitor’s experience is now being personalized. This means that not only is the visit marked by enhanced, interactive, and ‘dialogic’ engagement, but also there is an institutional recognition of the visitor as an independent maker of meaning who uses the museum in a variety of ways to fulfill particular, individual needs and desires.”
“Three key means of accomplishing this is first, recognizing visitors’ capacity to make meaning for themselves; two, partnering with them to discover what they personally want from the museum; and lastly, mobilizing the museum’s resources to meet these needs. These tasks can be met by, among other things, new curatorial strategies through which museums partner with visitors to develop activities and events: co-curation projects, and crowdsourcing exhibition content.”
“Visitors are no longer passive receptacles for the curator’s knowledge, but rather active, engaged participants.”
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.”