“Four years ago, we showed English language speakers random colors and asked for the color names. Four years later, with CrowdFlower contributors now in every country of the world, the experiment becomes much richer. The question is not only “Where does blue end, and red begin?”, but do people from different countries have different concepts of color boundaries?” (from “What color is this? In 9 languages” via Visual.ly).
“I think that projects like this one focus less as a traditional data visualization and more as something that reveals the hidden data that is around us in order to inspire subjective feelings in the viewer,” Posavec tells Co.Design. “I like to call this sort of work ‘data illustration’ for this reason.” Her piece titled Sentence Drawings is a sprawling, color-coded tube network that diagrams the length and topic of every sentence in the story. The narrator cuts through the image in fire engine red, while jazz appears as a soft blue and drugs are a taupe. At the end of each sentence, the line diverges from its path, making a sharp 90-degree cut. The result is a real feel for the book, even if you’ve never read it—a unique trip through the winding rant of Kerouac’s stream of consciousness. “The Writing Without Words project started as a project to visualize differences in authors’ writing styles and became something different,” Posavec explains, “where I used data to communicate what I personally found beautiful and interesting about literature.” (via 1 | On The Road, Depicted As One Big, Winding Infographic | Co.Design: business innovation design)
“The Compendious Coffee Chart lays out the entire coffee ecosystem, outlining how various methods of production, including the French press, Kyoto dripper, and Neapolitan flip, among others, are used to create coffees, cortados, cappuccinos, and more… Creating the taxonomy was not without its difficulties. ‘We had to make a judgment call on how to classify the output of the Moka Pot and the Aeropress,’ a PopChartLab team member told me. ‘It’s not quite standard brewed coffee, but we wouldn’t dare call it espresso, so we coined a term for it: fauxpresso’” (via Infographic: How To Make Every Coffee Drink You Ever Wanted | Co.Design: business innovation design).
"Unsupervised machine learning is hard. There are many examples of supervised machine learning, but these are driven by subject-matter experts that guide the machine towards specific discoveries. It will take much more than ten years to master the extraction of actual knowledge from big data sets.” —Christian Huitema, distinguished engineer, Microsoft Corporation; active leader in the IETF; based in Redmond, Washington"
— Imagining the Internet (via joshtrucks)
(via joshtrucks)
“NASA’s Kepler mission is doing what we may one day call NASA’s most important project. It’s searching for habitable planets—second Earths—that our grandchildren’s grandchildren may call home, or that could contain life as we know it. So far, NASA’s spotted about 2,300 of these exoplanets, including 48 that appear to be in a sweet spot distance away from the sun. So what do they look like? Two gurus—data artist Jer Thorp and John Underkoffler, the designer who created the interfaces in Minority Report—have produced a UI that lets you find out, using the pinch and zoom brilliance of Underkoffler’s most famous work. The project began when Thorp discovered NASA’s first paper. “It was really fascinating, but I couldn’t make much sense of the charts and graphs that were in it, so I made the first visualization to answer some questions for myself: What do 1,300 planets look like? How do these planets compare to Earth and the other planets in our solar system?” Thorp tells Co.Design. “The answers, of course, were in the paper, but I didn’t have the expertise or familiarity with the visual language to find them.” Explore the Galaxy Using the Actual Minority Report Interface - Co.Design
"A key question over the next six years is how far Google’s current techniques can take them. The strategy for the last six years has been constant: MORE DATA. But even Peter Norvig, head of Google Research, admits that there are declining returns to the more-data game. Certainly, it doesn’t appear that just adding more data is going to yield Gary Snyder’s translations of Chinese poetry. Eventually, it seems to me, Google (or any other translation software) will have to start understanding (in some way) the semantic content of the words it is arranging. And that’s a much harder AI problem to solve than the one that’s brought you the wonders of Google Translate."
"Harvard is making public the information on more than 12 million books, videos, audio recordings, images, manuscripts, maps, and more things inside its 73 libraries. Harvard can’t put the actual content of much of this material online, owing to intellectual property laws, but this so-called metadata of things like titles, publication or recording dates, book sizes or descriptions of what is in videos is also considered highly valuable. Frequently descriptors of things like audio recordings are more valuable for search engines than the material itself. Search engines frequently rely on metadata over content, particularly when it cannot easily be scanned and understood. Harvard is hoping other libraries allow access to the metadata on their volumes, which could be the start of a large and unique repository of intellectual information. “This is Big Data for books,” said David Weinberger, co-director of Harvard’s Library Lab. “There might be 100 different attributes for a single object.” At a one-day test run with 15 hackers working with information on 600,000 items, he said, people created things like visual timelines of when ideas became broadly published, maps showing locations of different items, and a “virtual stack” of related volumes garnered from various locations."
"We’re used to personalization on the consumer Web, from book recommendations on Amazon to the news feed on Facebook. But what will it mean for learning as colleges, too, increasingly mine data to shape the student experience? What does educational personalization look like? How finely should technologists try to parse it—down to individual learning styles? How will personalization conflict with existing regulations? And what are the risks?"
