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Original author: 
Kara Swisher

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Earlier today, Yahoo said it had acquired the trendy and decidedly stylish news reading app Summly, along with its telegenic and very young entrepreneur Nick D’Aloisio.

Yahoo said it plans to close down the actual app and use the algorithmic summation technology that the 17-year-old D’Aloisio built with a small team of five, along with a major assist from Silicon Valley research institute SRI International, throughout its products.

While Yahoo did not disclose the price, several sources told me that the company paid $30 million — 90 percent in cash and 10 percent in stock — to buy the London-based Apple smartphone app.

And despite its elegant delivery, that’s a very high price, especially since Summly has been downloaded slightly less than one million times since launch — after a quick start amid much publicity over its founder — with about 90 million “summaries” read. Of course, like many such apps, it also had no monetization plan as yet.

What Yahoo is getting, though, is perhaps more valuable — the ability to put the fresh-faced D’Aloisio front and center of its noisy efforts to make consumers see Yahoo as a mobile-first company. That has been the goal of CEO Marissa Mayer, who has bought up a range of small mobile startups since she took over nine months ago and who has talked about the need for Yahoo to focus on the mobile arena above all.

Mayer met with D’Aloisio, said sources, although the deal was struck by voluble M&A head Jackie Reses.

Said one person close to the deal, about the founder: “Nick will be a great person to put in front of the media and consumers with Mayer to make Yahoo seem like it is a place that loves both entrepreneurs and mobile experiences, which in turn will presumably attract others like him.”

Having met the young man in question, who was in San Francisco in the fall on a fundraising trip, I can see the appeal. He’s both well-spoken and adorkable, as well as very adept at charming cranky media types like me by radiating with the kinetic energy of someone born in the mobile world (you can see that in full force in the video below with actor and Summly investor Stephen Fry).

Still, D’Aloisio is very young and presumably has a lot of other entrepreneurial goals and that’s why he agreed as part of the deal to only officially stay 18 months at Yahoo, multiple sources told me. In many cases, startup founders strike such short-term employment deals with big companies, agreeing to stay for a certain determined time period.

He will also remain in England, where he lives with his parents, said sources. In addition, only two of Summly’s employees will go to Yahoo with D’Aloisio.

That’s $10 million each, along with a nifty app Yahoo will not be using as is (too bad, as it would up the hip and fun factor of Yahoo’s apps by a factor of a gazillion if it were maintained).

“It works out on a lot of levels,” said another person close to the situation. “Nick is a founder that will make Mayer and Yahoo look cutting edge.”

Cue the parade of PR profiles of the young genius made millionaire, helping Yahoo become relevant again.

I have an email for comment into the always friendly D’Aloisio. But I don’t expect a reply, since he has apparently been specifically instructed by the martinets of Yahoo PR not to talk to me any longer — well, for 18 months at least! (Don’t worry, Nick, I don’t blame you and will still listen to whatever you are pitching next, since you are so dang compelling and I enjoyed using Summly!)

Until then, here’s the faboo Summly video, with the best chairs ever:

Summly Launch from Summly on Vimeo.

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Many mobile phone owners use their devices for non-urgent purposes like gaming (an addiction to Draw Something doesn’t qualify as urgent). But a huge chunk of U.S. consumers are using their cellphones and smartphones for more pressing needs — something Pew Internet Research is calling the “just-in-time” phenomenon.

A new Pew survey of more than 2,200 U.S. adults shows that 70 percent of all cellphone owners and 86 percent of smartphone owners say they’ve used their phones in the past 30 days to access immediate information, solve a problem or get help in an emergency.

The fact that cellphones and smartphones are being used as need-it-now devices really isn’t that surprising, since they put the world’s trove of information in our pockets. What’s more interesting is how those situations are categorized — something the mobile ad industry might want to pay heed to.

The majority of those surveyed — 41 percent — say they’ve used their phones for the basic task of coordinating meetings or get-togethers.

That outweighs the number of people who say they’ve used their phones to look up a restaurant (30 percent), check sports scores (23 percent) and get transit information (20 percent).

Less than one-fifth of those surveyed said they’ve used their phone in an emergency situation in the past 30 days, which is probably a good thing.

Another interesting tidbit: Despite the fact that slightly more women than men now own smartphones, as my AllThingsD colleague Ina Fried reports, men who own mobile phones are more likely than women to look up information during an argument. Some 31 percent of men admit to doing this, compared with 22 percent of women.

Could this be because women are less likely to experience memory loss? Just saying …

(Image courtesy of Flickr/Brenderous)

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This is a topological similarity network of 452 NBA players during the 2010-2011 season. Players (in circles) are connected to other players by edges (lines) based on how similar they are with regard to points, rebounds, assists, steals, rebounds, blocks, turnovers and fouls, all normalized to per-minute values in the 2010-2011 season. Further, the network is colored by a player's points-per-minute average, with blue being low and red being high.

For as long as basketball has been played, it’s been played with five positions. Today they are point guard, shooting guard, small forward, power forward and center. A California data geek sees 13 more hidden among them, with the power to help even the Charlotte Bobcats improve their lineup and win more games.

Muthu Alagappan is a Stanford University senior, a basketball fan and an intern at Ayasdi, a data visualization company. Ayasdi takes huge amounts of info like tumor samples and displays it in interactive shapes that highlight patterns like genetic markers that indicate a likelihood of ovarian cancer. It’s called topological data analysis, and it can be applied to sports, too.

That is exactly what Alagappan did.

Dallas Mavericks' Dirk Nowitzki (41) is not a forward and Jason Terry (31) is not a guard, but rather a scoring rebounder and an offensive ball handler under an analytics model that reveals 13 new positions. Photo: David J. Phillip/Associated Press

He used the company’s software to crunch a data set of last season’s stats for 452 NBA players. He discovered new ways to group players (.pdf) based on performance after noting, for example, that Rajon Rondo of the Boston Celtics had more in common with Miami Heat forward Shane Battier than with fellow point guard Tony Parker of the San Antonio Spurs.

After reading his map, Alagappan came up with 13 new positions based on the three typical roles of guard, forward and center:

  1. Offensive Ball-Handler. This guy handles the ball and specializes in points, free throws and shots attempted, but is below average in steals and blocks. Examples include Jason Terry and Tony Parker.
  2. Defensive Ball-Handler. This is a defense-minded player who handles the ball and specializes in assists and steals, but is only so-so when it comes to points, free throws and shots. See also: Mike Conley and Kyle Lowry.
  3. Combo Ball-Handler. These players are adept at both offense and defense but don’t stand out in either category. Examples include Jameer Nelson and John Wall.
  4. Shooting Ball-Handler. Someone with a knack for scoring, characterized by above-average field goal attempts and points. Stephen Curry and Manu Ginobili are examples.
  5. Role-Playing Ball-Handler. These guys play fewer minutes and don’t have as big a statistical impact on the game. Hello, Arron Afflalo and Rudy Fernandez.
  6. 3-Point Rebounder. Such a player is a ball-handler and big man above average in rebounds and three-pointers, both attempted and made, compared to ball-handlers. Luol Deng and Chase Budinger fit the bill.
  7. Scoring Rebounder. He grabs the ball frequently and demands attention when on offense. Dirk Nowitzki and LaMarcus Aldridge play this position.
  8. Paint Protector. A big man like Marcus Camby and Tyson Chandler known for blocking shots and getting rebounds, but also for racking up more fouls than points.
  9. Scoring Paint Protector. These players stand out on offense and defense, scoring, rebounding and blocking shots at a very high rate. Examples include Kevin Love and Blake Griffin.
  10. NBA 1st-Team. This is a select group of players so far above average in every statistical category that the software simply groups them together regardless of their height or weight. Kevin Durant and LeBron James fall in this category.
  11. NBA 2nd-Team. Not quite as good, but still really, really good. Rudy Gay and Caron Butler are examples.
  12. Role Player. Slightly less skilled than the 2nd-team guys, and they don’t play many minutes. Guys like Shane Battier and Ronnie Brewer fall under this position.
  13. One-of-a-Kind. These guys are so good they are off the charts — literally. The software could not connect them to any other player. Derrick Rose and Dwight Howard are examples, but you already knew that.

The 13 positions are based on how players compare to the league average in seven statistical categories: Points, rebounds, assists, steals, blocked shots, turnovers and fouls. The stats were normalized on a per-minute basis to adjust for playing time, so starters got the same consideration as backups.

That said, the names of some of these new positions could use a bit of work. For example, Rondo, the Celtics’ floor leader, is classified as a “role player,” which is commonly used in basketball to describe a so-so player with a specific, if unremarkable, set of skills.

This is the same topological network of players, with red regions indicating the Dallas Mavericks. This representation shows the diversity of playing styles of Mavericks’ players.

Even if no one is going to refer to Dirk Nowitzki of the Dallas Mavericks as one the league’s best “scoring rebounders” any time soon, Alagappan’s prize-winning analysis could change how coaches and general managers think about the roles their players fill. Alagappan proved the title-winning Mavs had a solid diversity of “ball handlers” and “paint protectors,” giving them the ability to put a balanced lineup on the floor with few weak spots. The Western Conference cellar dwellers the Minnesota Timberwolves, on the other hand, had too many players with similar styles and a dearth of “scoring rebounders” and “paint protectors,” leaving them vulnerable along the front line.

This is the same topological network of players, with red regions indicating the Minnesota Timberwolves.

Alagappan’s findings won the award for best Evolution of Sport this spring at the annual MIT Sloan Sports Analytics Conference.

Whenever sports and numbers meet, the Moneyball question inevitably arises: Is it possible to use big data sets to find undervalued players? Alagappan believes it is.

He isolated the 40 players in the “scoring rebounder” section who best epitomized that group. At the top were the stars you might expect: Carmelo Anthony and Amare Stoudemire of the New York Knicks, along with Nowitzki and the Los Angeles Lakers’ Paul Gasol. But lesser-known players like Marreese Speights of the Memphis Grizzlies and the Lakers’ Devin Ebanks produced statistically similar per-minute results. Even better, where Anthony’s salary averages around $18.5 million per year, the Lakers are paying Ebanks about $740,000.

Another inevitable question: Could Ayasdi’s software have predicted the success of Knicks rookie Jeremy Lin? Alagappan concedes Lin’s college stats wouldn’t have suggested or predicted Linsanity, but he did create a similarity network to identify those players most similar to Lin in college. Three names emerged from the 3,400 analyzed: DeMarcus Cousins, who the Sacramento Kings picked fifth overall in the 2010 NBA draft; Alec Burks, picked 12th in 2011 by the Utah Jazz; and Nik Raivio, a University of Portland guard currently playing ball in Kaposvar, Hungary.

The lesson? For teams who buy into this new classification of players, the next Jeremy Lin might be in Hungary, awaiting your call.

Photo: Dallas Mavericks’ Dirk Nowitzki (41) and Jason Terry (31) defend Miami Heat’s Dwyane Wade during the second half of Game 2 of the 2011 NBA Finals. Photo: David J. Phillip/Associated Press

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How do you visualize the invisible? How do expose a process with multiple parameters in a way that’s straightforward and musically intuitive? Can messing about with granular sound feel like touching that sound – something untouchable?

Music’s ephemeral, unseeable quality, and the ways we approach sound in computer music in similarly abstract ways, are part of the pleasure of making noise. But working out how to then design around that can be equally satisfying. That’s why it’s wonderful to see work like the upcoming Borderlands for iPad and desktop. It solves a problem familiar to computer users – designing an interface for a granular playback instrument – but does so in a way that’s uncommonly clear. And with free code and research sharing, it could help inspire other projects, too.

Its creator also reminds, us, though, that the impetus for all of this can be the quest for beautiful sound.

Creator Chris Carlson is publishing source code and a presentation for the NIME [New Interfaces for Musical Expression] conference. But this isn’t just an academic problem or a fun design exercise: he also uses this tool in performance, so the design is informed by those needs. (I’m especially attuned to this particular problem, as I was recently mucking about with a Pd patch of mine that did similar things, working out how to perform with it and what the interface should look like. I know I’m not alone, either.)

The basic function of the app: load up a selection of audio clips, and the software distributes them graphically in the interface. Next:

A “grain cloud” may be added to the screen under the current mouse position with the press of a key. This cloud has an internal timing system that triggers individual grain voices in sequence. The user has control over the number of grain voices in a cloud, the overlap of these grains, the duration, the pitch, the window/envelope, and the extent of random motion in the XY plane. By selecting a cloud and moving it over a rectangle, the sound contained in the rectangle will be sampled at the relative position of each grain voice as it is triggered. By moving the cloud in along the dimension of the rectangle that is orthogonal to the time dimension, the amplitude of the resulting grain bursts changes.

You can see how Chris is imagining this conceptually in a sketch he shares on his site:

An extended demo shows in greater detail how this all works:

Chris is a second-year Master’s student at Stanford University’s Center for Computer Research in Music and Acoustics [CCRMA] in California. The iPad version is coming soon, but you can get started with the Linux and Mac versions right away, and even join a SoundCloud group to share what you’re making. You’ll find all the details, and links to source code, on the CCRMA site. (And if someone feels like building this on Windows, you can save Chris the trouble.)

https://ccrma.stanford.edu/~carlsonc/256a/Borderlands/index.html

I also love this Max Mathews quote Chris shares as inspiration:

Max Mathews, in a lecture delivered at Stanford in the fall of 2010
“Any sound that the human ear can hear can be made by a sequence of digits. And that’s a true theorem. Most of the sounds that you make, shall we say randomly are either uninteresting, or horrible, or downright dangerous to your hearing. There’s an awful lot to be learned on how to make sounds that are beautiful.”

Beyond the technology, beyond this design I admire, anything that sends you on the path to making beautiful sound seems to be a worthy exercise. It’s a challenge you can face every day and never grow tired.

http://modulationindex.com/ [Chris' site, with more information]

Thanks to Ingmar Koch (Dr. Walker) for the tip!

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Researchers at MIT have refined a software-based chip simulator that tests chip designs with large numbers of cores for flaws, adding the ability to measure designs' potential power consumption, as well as processing times for tasks, memory access, and core-to-core communications patterns. The team from MIT's Department of Electrical Engineering and Computer Science is using the simulator to test possible designs for a new processor targeted for fabrication later this year—one that they hope will have over 100 cores.

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The Afghan box camera—a homemade wooden device known as the kamra-e-faoree, meaning “instant camera”—has been used to preserve memories in Afghanistan for generations. It is part of the local landscape, with street photographers dotting city thoroughfares. It is itself a part of Afghan history, having been briefly banned by the Taliban, but these days, the box camera is in danger of disappearing. Fewer and fewer people know how to make and use the traditional tool, which uses no film but can both capture and develop an image.

Lukas Birk and Sean Foley, an Austrian artist and an Irish ethnographer, respectively, had discovered the box cameras while visiting Afghanistan on research trips. They learned that the devices, which came to the region in the early 20th century, were being replaced by their digital descendants among photographers who could afford it—or lying unused by photographers who couldn’t afford to refill on photographic supplies. The art of the karma-e faoree had been passed down through families, but Birk and Foley thought that this generation was going to be the last.

They were both struck by the importance of the cameras in local history and the poignancy of the medium’s persistence, and were also interested in the potential stories to be told when Afghans were photographed by other Afghans.

But the photographs produced by the cameras were the real draw. “We’re both visual people,” said Birk in an email, “and box camera photography is a feast for the eyes.”

So, in 2011, funded by a Kickstarter project, the two traveled to Afghanistan to begin research on a project about the Afghan box camera. The website they produced from that trip features box-camera tutorials, profiles of itinerant photographers and examples of box-camera photography and traditional hand-tinting from Afghanistan and the surrounding region. But the 2011 trip was not the end of their exploration of the box camera. Birk and Foley have started a Kickstarter page to raise money for another trip to Afghanistan, slated for this spring, with plans to produce a book with the additional material.

“Right now we can still talk about it as a living form of photography, maybe for another couple of years, before it will completely disappear,” Birk said.

Those interested in the box camera technology, which allows the photographers to snap and develop their pictures all at once, can watch a movie about it below. (Birk notes that he is doing his best Werner Herzog impression as the narrator, hoping to evoke the style of vintage ethnographic films.)

Find out more about the Afghan Box Camera project here or donate to the project’s Kickstarter fund here.

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Electronic music making has had several major epochs. There was the rise of the hardware synth, first with modular patch cords and later streamlined into encapsulated controls, in the form of knobs and switches. There was the digital synth, in code and graphical patches. And there was the two-dimensional user interface.

We may be on the cusp of a new age: the three-dimensional paradigm for music making.

AudioGL, a spectacularly-ambitious project by Toronto-based engineer and musician Jonathan Heppner, is one step closer to reality. Three years in the making, the tool is already surprisingly mature. And a crowd-sourced funding campaign promises to bring beta releases as soon as this summer. In the demo video above, you can see an overview of some of its broad capabilities:

  • Synthesis, via modular connections
  • Sample loading
  • The ability to zoom into more conventional 2D sequences, piano roll views, and envelopes/automation
  • Grouping of related nodes
  • Patch sharing
  • Graphical feedback for envelopes and automation, tracked across z-axis wireframes, like circuitry

All of this is presented in a mind-boggling visual display, resembling nothing more than constellations of stars.

Is it just me, or does this make anyone else want to somehow combine modular synthesis with a space strategy sim like Galactic Civilizations? Then again, that might cause some sort of nerd singularity that would tear apart the fabric of the space-time continuum – or at least ensure we never have any normal human relationships again.

Anyway, the vitals:

  • It runs on a lowly Lenovo tablet right now, with integrated graphics.
  • The goal is to make it run on your PC by the end of the year. (Mac users hardly need a better reason to dual boot. Why are you booting into Windows? Because I run a single application that makes it the future.)
  • MIDI and ReWire are onboard, with OSC and VST coming.
  • With crowd funding, you’ll get a Win32/64 release planned by the end of the year, and betas by summer (Windows) or fall/winter (Mac).

I like this quote:

Some things which have influenced the design of AudioGL:
Catia – Dassault Systèmes
AutoCAD – Autodesk
Cubase – Steinberg
Nord Modular – Clavia
The Demoscene

Indeed. And with computer software now reaching a high degree of maturity, such mash-ups could open new worlds.

Learn about the project, and contribute by the 23rd of March via the (excellent) IndieGogo:

http://audiogl.com

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Imperfect makes for perfect. My head is spinning.
The battle of Uncanny Valley is where CGI finally triumphed over reality: pixels stood proudly over humans showing off their parametrization maps and tone mapping that accurately depicted the imperfection of human skin and declared victory over reality. The first shot in the war fired when researcher Jorge Jimenez released this work on real-time realistic skin rendering, showing off the difference SSSS (Sexy Separable Subsurface Scattering – okay, I added that first ‘S’) makes.
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