Jumat, 30 Agustus 2013

Saturn Moon Titan Sports Thick Icy Shell & Bizarre Interior




" The tough icy shell of Saturn's largest moon Titan is apparently far stronger than previously thought, researchers say.

These surprising new findings add to hints Titan possesses an extraordinarily bizarre interior, scientists added.

Past research suggested Titan has an ocean hidden under its outer icy shell 30 to 120 miles (50 to 200 kilometers) thick. Investigators aim to explore this underground ocean in the hopes of finding alien life on Titan, since virtually wherever there is water on Earth, there is life. [See more photos of Titan, Saturn's largest moon]

To learn more about Titan's icy shell, planetary scientist Doug Hemingway at the University of California, Santa Cruz, analyzed the Cassini probe's scans of Titan's gravity field. The strength of the gravitational pull any point on a surface exerts depends on the amount of mass underneath it. The stronger the pull, the more the mass.

The researchers then compared these gravity results with the structure of Titan's surface. They expected that regions of high elevation would have the strongest gravitational pull, since one might suppose they had extra matter underneath them. Conversely, they expected regions of low elevation would have the weakest gravitational pull.

What the investigators discovered shocked them. The regions of high elevation on Titan had the weakest gravitational pull.

"It was very surprising to see that," Hemingway told SPACE.com. "We assumed at first that we got things wrong, that we were seeing the data backwards, but after we ran out of options to make that finding go away, we came up with a model that explains these observations."

To explain these gravity anomalies, Hemingway said to imagine mountains on Titan having roots. "It's like how most of an iceberg actually lies submerged underwater," he said. "If that root is really big, bigger than normal, it would displace water underneath it."

Ice has a lower density than water — a chunk of ice weighs less than a similar volume of water. These high-elevation areas on Titan apparently have roots large enough to displace a lot of water under them, meaning they exert a weaker gravitational pull.

Ice is buoyant in water. "In order to essentially hold these big icebergs down and keep them from bobbing up, that means Titan's shell has to be extremely rigid," Hemingway said.

It remains uncertain what makes Titan's shell this rigid. The ice might possess cage-like molecules known as clathrates that could make it stiffer. Also, "if the ocean underneath the shell is colder than before thought, that could make the ice shell thicker and thus more rigid," Hemingway said.

This rigidity could mean Titan's shell is less geologically active than once thought. "If at least the top 40 kilometers (25 miles) is very stiff and cold and dead, if you want something like cryovolcanoes that erupt water instead of lava on Titan's surface, you have to be more creative about how that might happen," Hemingway said.

Their model also suggests Titan's shell has seen an extensive amount of erosion, with features carved more than 650 feet (200 meters) deep on it surface. "We now need different groups of people to figure out how so much material could get broken up and transported long distances across Titan's surface," Hemingway said.

One implication of these new findings relates to whether or not Titan's interior is separated into distinct layers. If researchers have underestimated Titan's gravity field, one might suspect its core is a giant blob of matter that is not made up of distinct layers as one would expect from such a large body. For instance, Earth is separated into a crust, mantle and core, and even large asteroids such as Vesta seem to have interiors divided into several layers.

"Maybe Titan is a mixture of ice and rock from the core nearly all the way out, and it's only in the last part near its surface that it's differentiated into ice and water," Hemingway said. "But we could be wrong there."

To help solve this mystery, "what we need is a Titan orbiter," Hemingway said. "That way we can have much better readings of Titan and learn more about its ice shell and its interior."

The scientists detailed their findings in the Aug. 29 issue of the journal Nature.

Follow us @Spacedotcom, Facebook and Google+. Original article on SPACE.com."





Saturn Moon Titan Sports Thick Icy Shell & Bizarre Interior




" The tough icy shell of Saturn's largest moon Titan is apparently far stronger than previously thought, researchers say.

These surprising new findings add to hints Titan possesses an extraordinarily bizarre interior, scientists added.

Past research suggested Titan has an ocean hidden under its outer icy shell 30 to 120 miles (50 to 200 kilometers) thick. Investigators aim to explore this underground ocean in the hopes of finding alien life on Titan, since virtually wherever there is water on Earth, there is life. [See more photos of Titan, Saturn's largest moon]

To learn more about Titan's icy shell, planetary scientist Doug Hemingway at the University of California, Santa Cruz, analyzed the Cassini probe's scans of Titan's gravity field. The strength of the gravitational pull any point on a surface exerts depends on the amount of mass underneath it. The stronger the pull, the more the mass.

The researchers then compared these gravity results with the structure of Titan's surface. They expected that regions of high elevation would have the strongest gravitational pull, since one might suppose they had extra matter underneath them. Conversely, they expected regions of low elevation would have the weakest gravitational pull.

What the investigators discovered shocked them. The regions of high elevation on Titan had the weakest gravitational pull.

"It was very surprising to see that," Hemingway told SPACE.com. "We assumed at first that we got things wrong, that we were seeing the data backwards, but after we ran out of options to make that finding go away, we came up with a model that explains these observations."

To explain these gravity anomalies, Hemingway said to imagine mountains on Titan having roots. "It's like how most of an iceberg actually lies submerged underwater," he said. "If that root is really big, bigger than normal, it would displace water underneath it."

Ice has a lower density than water — a chunk of ice weighs less than a similar volume of water. These high-elevation areas on Titan apparently have roots large enough to displace a lot of water under them, meaning they exert a weaker gravitational pull.

Ice is buoyant in water. "In order to essentially hold these big icebergs down and keep them from bobbing up, that means Titan's shell has to be extremely rigid," Hemingway said.

It remains uncertain what makes Titan's shell this rigid. The ice might possess cage-like molecules known as clathrates that could make it stiffer. Also, "if the ocean underneath the shell is colder than before thought, that could make the ice shell thicker and thus more rigid," Hemingway said.

This rigidity could mean Titan's shell is less geologically active than once thought. "If at least the top 40 kilometers (25 miles) is very stiff and cold and dead, if you want something like cryovolcanoes that erupt water instead of lava on Titan's surface, you have to be more creative about how that might happen," Hemingway said.

Their model also suggests Titan's shell has seen an extensive amount of erosion, with features carved more than 650 feet (200 meters) deep on it surface. "We now need different groups of people to figure out how so much material could get broken up and transported long distances across Titan's surface," Hemingway said.

One implication of these new findings relates to whether or not Titan's interior is separated into distinct layers. If researchers have underestimated Titan's gravity field, one might suspect its core is a giant blob of matter that is not made up of distinct layers as one would expect from such a large body. For instance, Earth is separated into a crust, mantle and core, and even large asteroids such as Vesta seem to have interiors divided into several layers.

"Maybe Titan is a mixture of ice and rock from the core nearly all the way out, and it's only in the last part near its surface that it's differentiated into ice and water," Hemingway said. "But we could be wrong there."

To help solve this mystery, "what we need is a Titan orbiter," Hemingway said. "That way we can have much better readings of Titan and learn more about its ice shell and its interior."

The scientists detailed their findings in the Aug. 29 issue of the journal Nature.

Follow us @Spacedotcom, Facebook and Google+. Original article on SPACE.com."





Lady Gaga Wears Sheer Pink Bra, Giant Mermaid-Themed Hairdo in London: Picture




"Lady Gaga will take it off, but she isn't taking it all off just yet! The button-pushing pop singer stepped out of her hotel in London on Tuesday, Aug. 27, in a buzz-worthy ensemble that unabashedly showed off her assets.

Gaga turned heads as she walked gingerly down the steps of her hotel clad in a see-through pink bra and shiny black fisherman-style jumpsuit, complete with suspenders and mile-high black lace-up boots.

To top off her sea-faring look, the 27-year-old singer had her hair teased into a high bow adorned with starfish and shells, and donned her trademark dark shades.

"Thanku so much monsters for sending me so much love this wk!" she tweeted. "You mean the world to me. Cant wait to play new music from ARTPOP this weekend!"

To emphasize her point, the "Applause" singer had the words "art" and "pop" written on the palms of her hands.

On Sunday, Aug. 25, Gaga opened the MTV Video Music Awards with a series of costumes that included, at different points, a white nun-like habit, a sequined jacket, and even a seashell-bikini paired with a barely there thong.

This article originally appeared on Usmagazine.com: Lady Gaga Wears Sheer Pink Bra, Giant Mermaid-Themed Hairdo in London: Picture"





Does Stacy Keibler Have a New Billionaire Boyfriend?




"Has Stacy Keibler already moved on from ex George Clooney? Well, … yes and no.

A new report from Life & Style on Wednesday claims the 33-year-old beauty has a new man in her life.

"She's dating millionaire Jared Pobre, and it's pretty serious," a pal tells the tabloid. "She was in Europe with him and all over Italy a couple of weeks ago."

However, sources close to the former WWE wrestler tell omg! that's not entirely true.

"They aren't dating," an insider tells omg!. "They have been friends for years."

Don't take that to mean Stacy is still pining over Clooney, 52.

"They ended on good terms," another source tells omg!. "They remain friends, she's doing just fine."

Clearly!

The 5'11" stunner was photographed yacht hopping with recently single Naomi Campbell earlier this month in Ibiza, Spain.

"She's having a great time hanging with friends," the second Keibler insider insists.

Sounds like this "Supermarket Superstar" is still very much on the market."





Kamis, 29 Agustus 2013

Jessica Simpson Shows Off Son Ace Knute for the First Time




"Nearly two months after giving birth to son Ace Knute, Jessica Simpson is ready to share her baby boy with the world!

The 33-year-old, who welcomed the newest addition on June 30, is debuting her little one on the cover of Us Weekly, posing along with 15-month-old daughter Maxwell.

Jessica Simpson, Maxwell Johnson, and Ace Knute Johnson (UsWeekly)

The mommy-of-two, who has been engaged to fiancĂ© Eric Johnson since November 2010, seems to be happier than ever, telling the mag, "With two kids, we have our hands full, but every day is a new adventure. … It's fun! I feel very at peace with being a mom."

But will the cute couple, who had back-to-back babies, add more to their beautiful brood anytime soon?

"Pregnancy is alot. It was hard to do two so close together," admits the fashion mogul mama. "I have this huge sense of accomplishment, and I feel in my heart that I'm done. But obviously, accidents do happen!"

Check out the video for details on Jessica's life as a mom, and be sure to tune in to "omg! Insider" on TV tonight for more on this story."





Jessica Simpson Shows Off Son Ace Knute for the First Time




"Nearly two months after giving birth to son Ace Knute, Jessica Simpson is ready to share her baby boy with the world!

The 33-year-old, who welcomed the newest addition on June 30, is debuting her little one on the cover of Us Weekly, posing along with 15-month-old daughter Maxwell.

Jessica Simpson, Maxwell Johnson, and Ace Knute Johnson (UsWeekly)

The mommy-of-two, who has been engaged to fiancĂ© Eric Johnson since November 2010, seems to be happier than ever, telling the mag, "With two kids, we have our hands full, but every day is a new adventure. … It's fun! I feel very at peace with being a mom."

But will the cute couple, who had back-to-back babies, add more to their beautiful brood anytime soon?

"Pregnancy is alot. It was hard to do two so close together," admits the fashion mogul mama. "I have this huge sense of accomplishment, and I feel in my heart that I'm done. But obviously, accidents do happen!"

Check out the video for details on Jessica's life as a mom, and be sure to tune in to "omg! Insider" on TV tonight for more on this story."





Rabu, 28 Agustus 2013

What is 'Big Data,' anyway? Authors of a new book try to explain




""Big data" has become a really big buzz-phrase — tossed around in conversations about everything from business to surveillance; cited as a tool to improve driving, hiring, understanding dogs, and everything else; and, inevitably, dismissed as a bunch of hype.

But what exactly is big data, anyway? Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schönberger and Kenneth Cukier, offers an answer. Their book is a wide-ranging assessment of "the ability of society to harness information in novel ways to produce useful insights or goods and services of significant value." And while they acknowledge that the term itself has become amorphous, they frame their subject pretty clearly: "Big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more."

That (not to mention the book's subtitle) might sound a little hype-y, but Big Data is fairly even-handed: Early chapters explore the hope and potential around the way massive information sets are being created and mined, but later ones are clear about risks, pitfalls, and dangers. Mayer-Schönberger is Professor of Internet Governance and Regulation at the Oxford Internet Institute / Oxford University; Cukier is "data editor" for The Economist. Their book raised a few questions for me — so I asked the authors. Here's what they said.

I'd like to start toward the end: One of your later chapters examines "the dark side of big data," and among other things you note concerns about privacy and the possibility of using "big-data predictions" to in effect penalize people for behavior they seem likely to engage in, but haven't. You even mention the NSA at one point. So I wonder what you've made of the debate about more recent surveillance revelations related to the agency: There's a lot of focus on the collection of the data, for instance, but should we be talking about how it's analyzed?

Kenneth Cukier: The question draws an excellent distinction — one that's sadly missing from the debate. The disclosures have been mostly about the collection and not the use of the data. And when intelligence agencies explain how they work with the data, the method seems oddly old-school: targeted surveillance, not too different from the days of alligator-clips atop copper wires. Of course we're probably not told the whole story and they're actually running massive statistical regressions across all the data to hunt for patterns that they didn't know to look for in advance. That's what Facebook and LinkedIn data-scientists would do with it. But we haven't yet seen evidence that this is what the NSA is doing.

That said, the collection alone is troubling because it is happening with insufficient oversight. And the goal of intelligence is to prevent bad things from happening — it's about prediction. As we lay out in the book, this may be troubling when people are penalized for what they only have propensity to do, not for what they've done. So we have to be very careful using this ability, as it improves to the degree that it becomes more established.

You make a compelling case about the limitations of sampling (as opposed to more comprehensive big data approaches) and how we've come to accept it perhaps more than we should. But among the examples you mention is voter intent. It's not like there's a comprehensive database of who everyone intends to vote for, is there? How does big data actually provide an alternative here? Isn't there a distinction between what we want to measure and what we can measure?

Cukier: Actually, there is a database of every voter and their intentions. Both major parties contract with different data providers that are loosely affiliated with the parties, to tap databases of all Americans. The first variable is if the person is registered to vote and if he or she actually cast a ballot in the most recent election. The Democrats in 2012 had an internal database of every voter in America and asked three questions of it: Do you support Obama; are you likely to vote; and if you are undecided, are you persuadable? By ranking people based on that last measure, the Dems could know where to best spend their advertising budget for maximum impact.

Big data was critical: sampling works well for basic questions like what candidate a person supports. But it's less useful when you want to drill down into the granular — like what candidate Asian-American women with college degrees support. To do that, you may need to give up your sample and go for it all.

Yet the broader point is correct: there is a difference between what we want to measure and what we can measure. And we need to be on guard that we don't confuse the two. For example, in the Vietnam War, the Pentagon used the metric of the body count as a way to measure progress, when that data wasn't really meaningful to what they wanted to depict. Sadly, I fret this fallibility is something that we'll just have to learn to live with, as we have in so many other domains.

Many of your examples involve scrutinizing data that already exists (including instances where it's mined for reasons that have nothing to do with why it was gathered), but I was very interested to learn about "datafication" that involves setting out to collect new information in new ways: For instance, UPS "datifying" its vehicle fleet by gathering mechanical information that predicts and minimizes breakdowns. This almost seem like a distinct category to me. Do you think of it as a fundamentally different form of big data?

Viktor Mayer-Schönberger: It is tempting to be dazzled by the many new types of data that are being collected — from engine sensors in UPS vehicles, to heart rates in

premature babies, to human posture. But that is how datafication works in practice: at first we think it is impossible to render something in data form, then somebody comes up with a nifty and cost-efficient idea to do so, and we are amazed by the applications that this will enable, and then we come to accept it as the new normal. A few years ago, this happened with geo-location data, and before it was with web browsing data (gleaned through cookies). It is a sign of the continuing progress of datafication.

You're right that dataficiation is fundamentally different than big data. For example, the 19th century American navigator Commodore Maury, who invented tidal maps, datafied the logbooks of past sea voyages by extracting information about the wind and waves at a given location. But we can get the most of big data today because so many new elements of our lives are being rendered into a data form, which was extremely hard to do in the past.

You emphasize that making the most of big data means we have to "shed some of [our] obsession for causality in exchange for simple correlations: not knowing why but only what." This means breaking from the tradition of coming up with a hypothesis and testing it: It doesn't matter whether we can explain a correlation that big data reveals, we should just act on it. That's a big shift! I'm curious if when you're out talking about the book whether you get a lot of resistance to that idea, because it seems crucial to what you call the "big data mindset."

Mayer-Schönberger: Yes, we do encounter resistance on this point, but intriguingly, it's rarely from the real experts in their field. They often know how tentative their causal conclusions are, or how much they are actually based on correlations rather than truly comprehending the exact causality of things. Also, we often get mischaracterized as either suggesting that theories don't matter or causality is not important. We don't argue either. In fact, theories will continue to matter very much, but the concrete hypothesis derived from a theory less so.

Take Google Flu Trends. The theory that what people search for could correlate with human health in a given location was crucial for Google Flu Trends to happen. But none of Google's engineers could ever have guessed the exact hypothesis to test — that is, the exact search terms that best predict the spread of the flu. After all, the company handles around 3 billion searches every day. So big data analysis did that for them.

Causal connections are really valuable where and if one can find them. But looking for them at great cost and coming up empty is less useful, we suggest, than looking for correlations — not least because such correlations can help identify what potential connections between two phenomena should be investigated for a possible causal link. In that very sense, big data analysis actually helps causal investigations as well.

Finally, I was struck by how many examples in the book involved businesses that have amassed incredible data sets and learned to use them to boost sales or improve marketing. You have the story of how Wal Mart mined its past data and figured out that people preparing for a hurricane by purchasing flashlights and the like also tended to buy Pop-Tarts — so it put Pop-Tarts at the front of the store during hurricane season, and sales increased. Is there any concern about how much big data is in effect owned by business, and deployed largely in the service of the profit motive? I think one thing that makes people nervous about the big data idea is that it's so often opaque. But do the benefits outweigh those concerns? Should we stop worrying and just be thankful for the conveniently placed Pop-Tarts?

Mayer-Schönberger: There is a value in having conveniently placed Pop-Tarts, and it isn't just that Wal Mart is making more money. It is also that shoppers find faster what they are likely looking for. Sometimes big data gets badly mischaracterized as just a tool to create more targeted advertising online. But UPS uses big data to save millions of gallons of fuel — and thus improve both its bottom line and the environment. Google aiding public health agencies in predicting the spread of the flu, or Decide.com helping consumers save a bundle has nothing to do with targeted advertising, and create positive effects beyond a single company's quarterly profit. We need to cast our gaze wider when we want to understand big data's upside (and incidentally, also its "dark sides").

My thanks to Mayer-Schönberger and Cukier for taking the time to answer these questions. Their book is: Big Data: A Revolution That Will Transform How We Live, Work, and Think. "





What is 'Big Data,' anyway? Authors of a new book try to explain




""Big data" has become a really big buzz-phrase — tossed around in conversations about everything from business to surveillance; cited as a tool to improve driving, hiring, understanding dogs, and everything else; and, inevitably, dismissed as a bunch of hype.

But what exactly is big data, anyway? Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schönberger and Kenneth Cukier, offers an answer. Their book is a wide-ranging assessment of "the ability of society to harness information in novel ways to produce useful insights or goods and services of significant value." And while they acknowledge that the term itself has become amorphous, they frame their subject pretty clearly: "Big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more."

That (not to mention the book's subtitle) might sound a little hype-y, but Big Data is fairly even-handed: Early chapters explore the hope and potential around the way massive information sets are being created and mined, but later ones are clear about risks, pitfalls, and dangers. Mayer-Schönberger is Professor of Internet Governance and Regulation at the Oxford Internet Institute / Oxford University; Cukier is "data editor" for The Economist. Their book raised a few questions for me — so I asked the authors. Here's what they said.

I'd like to start toward the end: One of your later chapters examines "the dark side of big data," and among other things you note concerns about privacy and the possibility of using "big-data predictions" to in effect penalize people for behavior they seem likely to engage in, but haven't. You even mention the NSA at one point. So I wonder what you've made of the debate about more recent surveillance revelations related to the agency: There's a lot of focus on the collection of the data, for instance, but should we be talking about how it's analyzed?

Kenneth Cukier: The question draws an excellent distinction — one that's sadly missing from the debate. The disclosures have been mostly about the collection and not the use of the data. And when intelligence agencies explain how they work with the data, the method seems oddly old-school: targeted surveillance, not too different from the days of alligator-clips atop copper wires. Of course we're probably not told the whole story and they're actually running massive statistical regressions across all the data to hunt for patterns that they didn't know to look for in advance. That's what Facebook and LinkedIn data-scientists would do with it. But we haven't yet seen evidence that this is what the NSA is doing.

That said, the collection alone is troubling because it is happening with insufficient oversight. And the goal of intelligence is to prevent bad things from happening — it's about prediction. As we lay out in the book, this may be troubling when people are penalized for what they only have propensity to do, not for what they've done. So we have to be very careful using this ability, as it improves to the degree that it becomes more established.

You make a compelling case about the limitations of sampling (as opposed to more comprehensive big data approaches) and how we've come to accept it perhaps more than we should. But among the examples you mention is voter intent. It's not like there's a comprehensive database of who everyone intends to vote for, is there? How does big data actually provide an alternative here? Isn't there a distinction between what we want to measure and what we can measure?

Cukier: Actually, there is a database of every voter and their intentions. Both major parties contract with different data providers that are loosely affiliated with the parties, to tap databases of all Americans. The first variable is if the person is registered to vote and if he or she actually cast a ballot in the most recent election. The Democrats in 2012 had an internal database of every voter in America and asked three questions of it: Do you support Obama; are you likely to vote; and if you are undecided, are you persuadable? By ranking people based on that last measure, the Dems could know where to best spend their advertising budget for maximum impact.

Big data was critical: sampling works well for basic questions like what candidate a person supports. But it's less useful when you want to drill down into the granular — like what candidate Asian-American women with college degrees support. To do that, you may need to give up your sample and go for it all.

Yet the broader point is correct: there is a difference between what we want to measure and what we can measure. And we need to be on guard that we don't confuse the two. For example, in the Vietnam War, the Pentagon used the metric of the body count as a way to measure progress, when that data wasn't really meaningful to what they wanted to depict. Sadly, I fret this fallibility is something that we'll just have to learn to live with, as we have in so many other domains.

Many of your examples involve scrutinizing data that already exists (including instances where it's mined for reasons that have nothing to do with why it was gathered), but I was very interested to learn about "datafication" that involves setting out to collect new information in new ways: For instance, UPS "datifying" its vehicle fleet by gathering mechanical information that predicts and minimizes breakdowns. This almost seem like a distinct category to me. Do you think of it as a fundamentally different form of big data?

Viktor Mayer-Schönberger: It is tempting to be dazzled by the many new types of data that are being collected — from engine sensors in UPS vehicles, to heart rates in

premature babies, to human posture. But that is how datafication works in practice: at first we think it is impossible to render something in data form, then somebody comes up with a nifty and cost-efficient idea to do so, and we are amazed by the applications that this will enable, and then we come to accept it as the new normal. A few years ago, this happened with geo-location data, and before it was with web browsing data (gleaned through cookies). It is a sign of the continuing progress of datafication.

You're right that dataficiation is fundamentally different than big data. For example, the 19th century American navigator Commodore Maury, who invented tidal maps, datafied the logbooks of past sea voyages by extracting information about the wind and waves at a given location. But we can get the most of big data today because so many new elements of our lives are being rendered into a data form, which was extremely hard to do in the past.

You emphasize that making the most of big data means we have to "shed some of [our] obsession for causality in exchange for simple correlations: not knowing why but only what." This means breaking from the tradition of coming up with a hypothesis and testing it: It doesn't matter whether we can explain a correlation that big data reveals, we should just act on it. That's a big shift! I'm curious if when you're out talking about the book whether you get a lot of resistance to that idea, because it seems crucial to what you call the "big data mindset."

Mayer-Schönberger: Yes, we do encounter resistance on this point, but intriguingly, it's rarely from the real experts in their field. They often know how tentative their causal conclusions are, or how much they are actually based on correlations rather than truly comprehending the exact causality of things. Also, we often get mischaracterized as either suggesting that theories don't matter or causality is not important. We don't argue either. In fact, theories will continue to matter very much, but the concrete hypothesis derived from a theory less so.

Take Google Flu Trends. The theory that what people search for could correlate with human health in a given location was crucial for Google Flu Trends to happen. But none of Google's engineers could ever have guessed the exact hypothesis to test — that is, the exact search terms that best predict the spread of the flu. After all, the company handles around 3 billion searches every day. So big data analysis did that for them.

Causal connections are really valuable where and if one can find them. But looking for them at great cost and coming up empty is less useful, we suggest, than looking for correlations — not least because such correlations can help identify what potential connections between two phenomena should be investigated for a possible causal link. In that very sense, big data analysis actually helps causal investigations as well.

Finally, I was struck by how many examples in the book involved businesses that have amassed incredible data sets and learned to use them to boost sales or improve marketing. You have the story of how Wal Mart mined its past data and figured out that people preparing for a hurricane by purchasing flashlights and the like also tended to buy Pop-Tarts — so it put Pop-Tarts at the front of the store during hurricane season, and sales increased. Is there any concern about how much big data is in effect owned by business, and deployed largely in the service of the profit motive? I think one thing that makes people nervous about the big data idea is that it's so often opaque. But do the benefits outweigh those concerns? Should we stop worrying and just be thankful for the conveniently placed Pop-Tarts?

Mayer-Schönberger: There is a value in having conveniently placed Pop-Tarts, and it isn't just that Wal Mart is making more money. It is also that shoppers find faster what they are likely looking for. Sometimes big data gets badly mischaracterized as just a tool to create more targeted advertising online. But UPS uses big data to save millions of gallons of fuel — and thus improve both its bottom line and the environment. Google aiding public health agencies in predicting the spread of the flu, or Decide.com helping consumers save a bundle has nothing to do with targeted advertising, and create positive effects beyond a single company's quarterly profit. We need to cast our gaze wider when we want to understand big data's upside (and incidentally, also its "dark sides").

My thanks to Mayer-Schönberger and Cukier for taking the time to answer these questions. Their book is: Big Data: A Revolution That Will Transform How We Live, Work, and Think. "





What is 'Big Data,' anyway? Authors of a new book try to explain




""Big data" has become a really big buzz-phrase — tossed around in conversations about everything from business to surveillance; cited as a tool to improve driving, hiring, understanding dogs, and everything else; and, inevitably, dismissed as a bunch of hype.

But what exactly is big data, anyway? Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schönberger and Kenneth Cukier, offers an answer. Their book is a wide-ranging assessment of "the ability of society to harness information in novel ways to produce useful insights or goods and services of significant value." And while they acknowledge that the term itself has become amorphous, they frame their subject pretty clearly: "Big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more."

That (not to mention the book's subtitle) might sound a little hype-y, but Big Data is fairly even-handed: Early chapters explore the hope and potential around the way massive information sets are being created and mined, but later ones are clear about risks, pitfalls, and dangers. Mayer-Schönberger is Professor of Internet Governance and Regulation at the Oxford Internet Institute / Oxford University; Cukier is "data editor" for The Economist. Their book raised a few questions for me — so I asked the authors. Here's what they said.

I'd like to start toward the end: One of your later chapters examines "the dark side of big data," and among other things you note concerns about privacy and the possibility of using "big-data predictions" to in effect penalize people for behavior they seem likely to engage in, but haven't. You even mention the NSA at one point. So I wonder what you've made of the debate about more recent surveillance revelations related to the agency: There's a lot of focus on the collection of the data, for instance, but should we be talking about how it's analyzed?

Kenneth Cukier: The question draws an excellent distinction — one that's sadly missing from the debate. The disclosures have been mostly about the collection and not the use of the data. And when intelligence agencies explain how they work with the data, the method seems oddly old-school: targeted surveillance, not too different from the days of alligator-clips atop copper wires. Of course we're probably not told the whole story and they're actually running massive statistical regressions across all the data to hunt for patterns that they didn't know to look for in advance. That's what Facebook and LinkedIn data-scientists would do with it. But we haven't yet seen evidence that this is what the NSA is doing.

That said, the collection alone is troubling because it is happening with insufficient oversight. And the goal of intelligence is to prevent bad things from happening — it's about prediction. As we lay out in the book, this may be troubling when people are penalized for what they only have propensity to do, not for what they've done. So we have to be very careful using this ability, as it improves to the degree that it becomes more established.

You make a compelling case about the limitations of sampling (as opposed to more comprehensive big data approaches) and how we've come to accept it perhaps more than we should. But among the examples you mention is voter intent. It's not like there's a comprehensive database of who everyone intends to vote for, is there? How does big data actually provide an alternative here? Isn't there a distinction between what we want to measure and what we can measure?

Cukier: Actually, there is a database of every voter and their intentions. Both major parties contract with different data providers that are loosely affiliated with the parties, to tap databases of all Americans. The first variable is if the person is registered to vote and if he or she actually cast a ballot in the most recent election. The Democrats in 2012 had an internal database of every voter in America and asked three questions of it: Do you support Obama; are you likely to vote; and if you are undecided, are you persuadable? By ranking people based on that last measure, the Dems could know where to best spend their advertising budget for maximum impact.

Big data was critical: sampling works well for basic questions like what candidate a person supports. But it's less useful when you want to drill down into the granular — like what candidate Asian-American women with college degrees support. To do that, you may need to give up your sample and go for it all.

Yet the broader point is correct: there is a difference between what we want to measure and what we can measure. And we need to be on guard that we don't confuse the two. For example, in the Vietnam War, the Pentagon used the metric of the body count as a way to measure progress, when that data wasn't really meaningful to what they wanted to depict. Sadly, I fret this fallibility is something that we'll just have to learn to live with, as we have in so many other domains.

Many of your examples involve scrutinizing data that already exists (including instances where it's mined for reasons that have nothing to do with why it was gathered), but I was very interested to learn about "datafication" that involves setting out to collect new information in new ways: For instance, UPS "datifying" its vehicle fleet by gathering mechanical information that predicts and minimizes breakdowns. This almost seem like a distinct category to me. Do you think of it as a fundamentally different form of big data?

Viktor Mayer-Schönberger: It is tempting to be dazzled by the many new types of data that are being collected — from engine sensors in UPS vehicles, to heart rates in

premature babies, to human posture. But that is how datafication works in practice: at first we think it is impossible to render something in data form, then somebody comes up with a nifty and cost-efficient idea to do so, and we are amazed by the applications that this will enable, and then we come to accept it as the new normal. A few years ago, this happened with geo-location data, and before it was with web browsing data (gleaned through cookies). It is a sign of the continuing progress of datafication.

You're right that dataficiation is fundamentally different than big data. For example, the 19th century American navigator Commodore Maury, who invented tidal maps, datafied the logbooks of past sea voyages by extracting information about the wind and waves at a given location. But we can get the most of big data today because so many new elements of our lives are being rendered into a data form, which was extremely hard to do in the past.

You emphasize that making the most of big data means we have to "shed some of [our] obsession for causality in exchange for simple correlations: not knowing why but only what." This means breaking from the tradition of coming up with a hypothesis and testing it: It doesn't matter whether we can explain a correlation that big data reveals, we should just act on it. That's a big shift! I'm curious if when you're out talking about the book whether you get a lot of resistance to that idea, because it seems crucial to what you call the "big data mindset."

Mayer-Schönberger: Yes, we do encounter resistance on this point, but intriguingly, it's rarely from the real experts in their field. They often know how tentative their causal conclusions are, or how much they are actually based on correlations rather than truly comprehending the exact causality of things. Also, we often get mischaracterized as either suggesting that theories don't matter or causality is not important. We don't argue either. In fact, theories will continue to matter very much, but the concrete hypothesis derived from a theory less so.

Take Google Flu Trends. The theory that what people search for could correlate with human health in a given location was crucial for Google Flu Trends to happen. But none of Google's engineers could ever have guessed the exact hypothesis to test — that is, the exact search terms that best predict the spread of the flu. After all, the company handles around 3 billion searches every day. So big data analysis did that for them.

Causal connections are really valuable where and if one can find them. But looking for them at great cost and coming up empty is less useful, we suggest, than looking for correlations — not least because such correlations can help identify what potential connections between two phenomena should be investigated for a possible causal link. In that very sense, big data analysis actually helps causal investigations as well.

Finally, I was struck by how many examples in the book involved businesses that have amassed incredible data sets and learned to use them to boost sales or improve marketing. You have the story of how Wal Mart mined its past data and figured out that people preparing for a hurricane by purchasing flashlights and the like also tended to buy Pop-Tarts — so it put Pop-Tarts at the front of the store during hurricane season, and sales increased. Is there any concern about how much big data is in effect owned by business, and deployed largely in the service of the profit motive? I think one thing that makes people nervous about the big data idea is that it's so often opaque. But do the benefits outweigh those concerns? Should we stop worrying and just be thankful for the conveniently placed Pop-Tarts?

Mayer-Schönberger: There is a value in having conveniently placed Pop-Tarts, and it isn't just that Wal Mart is making more money. It is also that shoppers find faster what they are likely looking for. Sometimes big data gets badly mischaracterized as just a tool to create more targeted advertising online. But UPS uses big data to save millions of gallons of fuel — and thus improve both its bottom line and the environment. Google aiding public health agencies in predicting the spread of the flu, or Decide.com helping consumers save a bundle has nothing to do with targeted advertising, and create positive effects beyond a single company's quarterly profit. We need to cast our gaze wider when we want to understand big data's upside (and incidentally, also its "dark sides").

My thanks to Mayer-Schönberger and Cukier for taking the time to answer these questions. Their book is: Big Data: A Revolution That Will Transform How We Live, Work, and Think. "





Justin Timberlake Defends Miley Cyrus' VMAs Performance: "It's Not Like She Did It at the Grammys"




"Much ado about nothing? Justin Timberlake thinks so. Fans and fellow celebs can't stop talking about Miley Cyrus' raunchy VMAs performance with "Blurred Lines" singer Robin Thicke, but Timberlake, for one, says the uproar is uncalled for.

"Listen, man, you know, it's the VMAs. What did you guys expect?" the "Suit & Tie" singer, 32, told Fresh 102.7 radio host Jim Douglas on Tuesday, Aug. 27, two days after the show. "I like Miley. I like her a lot. I think, you know, she's young. She's letting everybody know that she's growing up." (Timberlake can relate. He's a former Disney star himself.)

The singer also pointed out that the VMAs have historically been the one awards show where pretty much anything goes. "I just think it's the VMAs. It's not like she did it at the Grammys," he said. "Let her do her thing, you know?"

He then went on to note several other "scandalous" moments in the show's past, including one involving ex-girlfriend Britney Spears, another Disney alum. "Madonna: wedding suit, humping the stage. Britney: strip tease. This is not an uncommon thing," he said. "I actually thought all the bears were really cool." (Cyrus' now-infamous performance featured her twerking with giant teddy bears, a nod to her music video for "We Can't Stop.")

The 'N Sync alum -- who reunited with his former bandmates at the VMAs before accepting the Michael Jackson Video Vanguard Award -- said the best part of the whole spectacle, though, was the audience. "My favorite part of the Miley Cyrus performance is the Smith family reaction," he joked, referring to a viral picture of Will Smith and kids Willow and Jaden looking aghast while watching the action onstage. (In fact, the funny snapshot was part of a widespread mixup; the trio were actually reacting to Lady Gaga's performance, not Cyrus'.)

"I was late to the game on that," the star said of the photo. "I was just shown that this morning, so it's fresh in my mind."

Incidentally, Timberlake isn't the only one coming to Cyrus' defense. The 20-year-old "We Can't Stop" singer's dad, Billy Ray Cyrus, also spoke out on Aug. 27, telling Entertainment Tonight that he'll "always be here" for her. "She's still my little girl, and I'm still her dad, regardless of how this circus we call show business plays out," he said. "I love her unconditionally and that will never change."

This article originally appeared on Usmagazine.com: Justin Timberlake Defends Miley Cyrus' VMAs Performance: "It's Not Like She Did It at the Grammys""





Exclusive…’Crazy, Absurd!’ Go Behind the Scenes of Avril Lavigne’s ‘Rock N Roll’ Video!




"If you haven't already seen Avril Lavigne's video for "Rock 'N' Roll," just be forewarned that the clip basically defies a cohesive description. Between the pop singer's warrior-military garb, the car driven by a dog, the famous guest stars (including a smooch with "Wonder Years" actress Danica McKellar!), the blade-adorned guitar...oh yeah, and the bearshark...this is one heck of a sensational visual journey.

In fact, as the director of the clip himself puts it, it's crazy, absurd, and badass, which is just the way rock 'n' roll should be, right? Yahoo! Music is excited to present this exclusive behind-the-scenes video detailing the making of this opus, with more director's commentary, as well as Lavigne herself weighing in on the action.

Meet the dog! See Sid from Slipknot chuck a baby doll! Get the inside scoop and a closeup on that kiss with "Winnie!" Watch the bearshark twerk! (Well, okay, maybe that's taking it a bit far, but he does do a little dance. ) It's all here."





Burning Question: Could Miley’s VMA Antics Really Have Bad Impact on Kids?




"Q: Did Miley Cyrus's performance at the MTV Music Video Awards go too far, considering that it was on basic cable and many kids were likely watching?

A: At first I wanted to wave this question away. After all, Cyrus is just the latest celebrity to try her hand at Madonna-style vamping, complete with on-stage pseudo-masturbation and borrowing a faux-taboo cultural phenomenon wholesale from a minority group. Sunday's VMAs performance was more desperation than daring; at times Cyrus seemed to have to remind herself to stick out her tongue as she clomped down a set of stairs.

But child-development experts see things very differently. They argue that Cyrus betrayed and possibly even damaged younger kids who saw the singer's medley.

After all, they point out, children of any age could access Cyrus's performance much more easily than a show by Madonna in her erotic heyday.

When Madge simulated masturbation as part of her landmark Blond Ambition tour, people had to pay for tickets to see that, or pay for tickets to see a documentary that showed that, or subscribe to HBO, a premium channel. Contrast that with MTV's VMAs; all a kid needed was basic cable and a less-than-vigilant parent to witness Cyrus rubbing her 20-year-old ass against Robin Thicke, a guy old enough to be her very affectionate uncle.

Then there was the content of Cyrus's performance itself: twerking, leering, grinding, and much pretend stroking of the genitalia through a nude bikini, the same look showcased on the SFW version of Robin Thicke's vaguely rapey "Blurred Lines" video.

"The message is, 'This is what you want to do if you want to be cute, if you want the boys to like you, if you want girls to think you're cool,'" child behavior and development specialist Betsy Brown Braun tells me. "It makes something that I believe is socially unacceptable, socially desirable."

MTV's telecast, seen by 10.1 million viewers, was rated TV-14, meaning the network deemed it acceptable for most adolescents.

That was something the conservative watchdog Parents Television Council pounced on this morning: "MTV continues to sexually exploit young women by promoting acts that incorporate 'twerking' in a nude-colored bikini. How is this image of former child star Miley Cyrus appropriate for 14-year-olds?," asks Dan Isett's, the PTC's director of public policy.

Neither MTV nor Team Miley immediately commented on the criticism.

Of course, no kid will suffer permanent PTSD because of a single Cyrus performance — at least, not because of her ham-handed attempts at grownup-ness. But child development specialists do count the medley among a barrage of videos, still images and performances that connect maturity and adulthood with overt sexuality.

"You get this repeated parade of performances," Dr. Robyn Silverman tells me. "Once it was Britney, then Christina, then Amanda Bynes, then Lindsay Lohan, and now it's Miley Cyrus — you start to get the feeling that this is the natural progression, that this is the way that teens should behave when they want to be seen as adults."

And Cyrus's current audience is way too young to even be thinking of such things, experts say.

"When girls were 10, they were watching Miley Cyrus, and she was a cool teenager," Brown Braun tells me. "Now they're 14, and the message is that this is an acceptable way to be for 14-year-olds. They have a relationship with Miley, so it's like, 'Oh, look what you're good friend is doing up there.'"

Got a Burning Question? Tweet it to us @YahooBurningQs"





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Exclusive…’Crazy, Absurd!’ Go Behind the Scenes of Avril Lavigne’s ‘Rock N Roll’ Video!

Posted: 27 Aug 2013 12:28 PM PDT




"If you haven't already seen Avril Lavigne's video for "Rock 'N' Roll," just be forewarned that the clip basically defies a cohesive description. Between the pop singer's warrior-military garb, the car driven by a dog, the famous guest stars (including a smooch with "Wonder Years" actress Danica McKellar!), the blade-adorned guitar...oh yeah, and the bearshark...this is one heck of a sensational visual journey.

In fact, as the director of the clip himself puts it, it's crazy, absurd, and badass, which is just the way rock 'n' roll should be, right? Yahoo! Music is excited to present this exclusive behind-the-scenes video detailing the making of this opus, with more director's commentary, as well as Lavigne herself weighing in on the action.

Meet the dog! See Sid from Slipknot chuck a baby doll! Get the inside scoop and a closeup on that kiss with "Winnie!" Watch the bearshark twerk! (Well, okay, maybe that's taking it a bit far, but he does do a little dance. ) It's all here."





Minggu, 25 Agustus 2013

Ashley Benson (2 Hot 2 Handle)









Flashback: Outside the 1993 MTV Video Music Awards









Even Cord Cutters Will Have to Pay the Cable Bill




"Internet television finally seems nigh. ESPN (DIS) on Wednesday said it has held introductory discussions to offer its channels through Web-based TV services. That comes on the heels of news that Sony (SNE) had reached a preliminary Internet distribution deal with Viacom (VIAB) and that Google (GOOG) was talking to the NFL about buying the rights to its Sunday Ticket package. And, of course, the reports of Apple TV (AAPL) persist.

These deals seem like a cord cutter's dream. But don't think you're going to get out of those monthly cable bills. The big cable companies also provide Internet service, and the industry is getting increasingly aggressive about billing customers based on how much data they use, as opposed to a monthly flat rate. Comcast (CMCSA), Time Warner Cable (TWC), and Mediacom recently introduced this kind of plan. Given that streaming video gobbles up bandwidth, this could be big business for the cable companies.

A Time Warner Cable spokesman described the move as a way to allow light Internet users to pay less, and this week the company began offering $5 to $8 monthly discounts to customers who use less data. At the same time, people who use more data will pay more—maybe a lot more. According to Comcast figures, replacing HD video from cable with Internet programming would likely use about 648 gigabytes of data per month. Under Comcast's new pricing plans, that would cost customers an additional $60 each month.

This strategy isn't new (phone companies bill this way), not even to cable companies, which for years have been saying usage-based pricing is inevitable. But the industry may have made things significantly harder by taking so long to get to it. With Internet television appearing to be a serious competitor to traditional cable service, regulators could see price caps as a way to squeeze out competition from Internet companies. In a report released this week, an advisory committee to the Federal Communications Commission said there are many open questions surrounding the subject (PDF).

Cable industry analyst Craig Moffett says the chances of regulators approving usage-based pricing systems are relatively low if there are already viable Internet television services on the market. "In effect, we are now in a race," he says. "So the question becomes this: What will appear first, a credible online video alternative, or widespread adoption of usage-based pricing?"

The problem, says Michael Weinberg, vice president of an open internet advocacy organization, is that data caps would force companies to pay Internet service providers for special treatment. Such deals are reportedly already in the works for wireless data plans, and there are signs of similar moves in broadband. Last year, Comcast announced a plan where its own online video services would not count against data plans for its Xfinity customers who were streaming video via Xbox or TiVo (TIVO). The company has declined to enforce its data caps up to this point, and the FCC has yet to respond to advocates demanding action. Comcast has not responded to a request for comment.

In a letter submitted to the FCC on Thursday, Weinberg asked the agency to weigh in. He says the potential to price competitors out of the market should show how the cable companies are gearing up to squeeze people who have no other options. "If the market sustains that kind of thing, if it doesn't punish you, it means there isn't much of a market," he says."





Affleck as Batman? Fans outraged









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Celeb Workout Buddies

Posted: 24 Aug 2013 06:51 AM PDT