There won’t be a Galaxy S8 edge because Samsung is going all-in on curves

So long, edge name. We hardly knew you.

Samsung won’t be releasing a Galaxy S8 edge like it did the Galaxy S6 and Galaxy S7 variants. Instead, both the smaller and larger versions of the upcoming flagship will have curved displays, according to a leak from Evan Blass.

In case you were interested… pic.twitter.com/bpcOFZmOC3

— Evan Blass (@evleaks) February 10, 2017

A leak of what appears to be the retail box of the larger variant of the upcoming Galaxy S8 reveals its name: the Galaxy S8+. Samsung doesn’t appear to be distinguishing between flat and edge variants this year because, as we suspected from the Galaxy Note 7, all of its flagships may forgo the standard flat piece of glass for a nice, curvy distinctiveness.

What do you think of the name Galaxy S8+? We’ve seen such naming conventions before from Samsung with the confusingly-named Galaxy S6 edge+, released back in 2015, so there is precedent here.

Galaxy S8: Everything you need to know

Microsoft Build ticket registration will open up on Valentine’s Day

Microsoft has alerted the media that ticket registration for its upcoming Microsoft Build conference will kick off soon.

In an email, the company said Microsoft Build general registration will open at 9 am PST on 14 February 2017 (otherwise known as Valentine’s Day in the US and UK). The annual developers conference is being held from 10 May to 12 May at the Washington State Convention Center in Seattle. It is open to the media, developers, and anyone interested in learning about the “latest vision and direction from Microsoft”.

Microsoft

When registration opens, you can go to the Microsoft Build’s website to register. Last year, tickets to the conference sold out in record time (about a minute), and the year before that, Microsoft sold out of its allocation of tickets in around 20 minutes. Although ticket pricing increased from $2,095 to $2,195 (about £1,758) last year, it appears as though developers were not at all discouraged from attending.

Developers have to sign-up to a waiting list to try to get tickets. It’s unclear how much the tickets will be priced this year, but if you can’t swing it, or if you don’t cash out in time, Microsoft will live-stream its opening keynote so that people from around the globe can watch in real-time. Pocket-lint will cover the news as it happens, as well.

Microsoft’s Windows 10 Creator Update is expected to release in April, so we may hear more information about the rollout at Build 2017. 

Microsoft Build registration opens February 14th at 9:00am PST. #MSBuild Details: https://t.co/U0zk2qMBcj pic.twitter.com/w7MCUdjYdd

— Microsoft (@Microsoft) February 10, 2017

Pay pros for lessons on sucking less at video games

Losing to a 12-year-old in Super Smash Bros. can be a real downer, but there are a few ways to prevent that. You can challenge kids who aren’t as good, practice and improve on your own, or, if you have a few bucks to spend, get a video game tutor from Japanese company GameLesson.

The service, which doesn’t yet have a launch date, promises coaching for games like Super Smash Bros. for Wii U, Splatoon, Street Fighter V, and Shadowverse. These titles are big in the increasingly competitive eSports scene, which has been especially popular in Japan. Tutors will go directly to the homes of customers in Osaka and Tokyo, while online training is available everywhere else in the world. Instructors include Haitani, who Capcom has called “one of the Japanese gods of fighting games,” and Abadango, who is one of the highest-ranked Super Smash Bros. players in the world, according to eSports agency Panda Global.

Online sessions start at 4,500 yen (about $39) for an hour, while local training begins at 11,000 yen (about $97) per hour. However, Kotaku points out that GameLesson has hit a few roadblocks, as prospective customers have complained about pricing. Twitter users are also skeptical in light of seemingly fake customer reviews featured on the GameLesson website before the service’s launch, according to My Game News Flash. These issues have lead to an apology from Haitani and caused GameLesson’s rollout to be delayed “due to various circumstances.”

Source: Kotaku, RocketNews24, My Game News Flash

Facebook enlists help to verify ad data after inaccurate reports

Facebook reported on multiple occasions late last year that it had been misreporting both ad video views and Instant Article stats. To help smooth things over with marketers, the social network explained today how it plans to be more transparent about advertising data going forward. First, the information that Facebook sends to its partners will be subject to an audit by the Media Rating Council (MRC) to verify its accuracy.

The company says it also now works with 24 third-party verification companies to keep tabs on metrics like reach, attribution, demographics, brand lift, offline sales and mobile app stats. By working with so many measurement partners, Facebook says it allows advertisers to work with the outfit they’re most comfortable with or may already be using.

The social network will also provide those verification partners more detailed info on ad impressions for both Facebook and Instagram. That data will include the number of milliseconds an ad was on the screen, milliseconds 50 percent of the ad was visible and the milliseconds the entire ad was visible. The company also plans to offer new ad buying options, including the ability for marketers to only pay for ads that have been viewed in their entirety — up to 10 seconds. There will also be an option to buy video ads where the sound is on.

Facebook will certainly need to be more transparent when it comes to ads and it needs to do so quickly. The company was overestimating video ad views for two years and that’s a major issue when you consider how much of Facebook’s revenue is tied to its advertising platform.

Source: Facebook

Crowdfunded ‘Star Trek: DS9’ documentary imagines a new season

The creators and stars of Star Trek: Deep Space Nine have launched an Indiegogo campaign to produce a documentary about the show. What We Left Behind will tell the story of the third fourth Star Trek series as told by (most of) the people who made it. More than that, however, the film has assembled the show’s writing team (including Ronald D. Moore) to map out what a mythical eighth season of the show would look like. Sadly, the AV Club beat us two pointing out that this is basically that Portlandia sketch, but in real life.

In just over a day, the project has smashed through its original $150,000 target and is pushing toward $200,000. It’s something of a vindication for the show that — when it launched — was treated as the red-headed stepchild of the Star Trek universe. Now, after living through Voyager and Enterprise, fans have come to appreciate the show for what it is. Backers can contribute $15 to be able to watch the show when it’s available, which is expected to be around February 2018.

The team behind the documentary has plenty of experience producing Star Trek retrospectives. Director Adam Nimoy made For the Love of Spock, while producers 455 Films was behind Chaos on the Bridge, The Captains and Get a Life!. In addition to showing you what the eighth season of DS9 would have looked like, viewers will also be able to see how much the cast have changed in the last nearly two decades. Most notably, you’ll be able to see Cirroc Lofton (Jake Sisko) and Hana Hatae (Molly O’Brien) now that they’re adults.

Of course, what’s interesting here isn’t the documentary itself, but the fact that we’re inching ever closer to pop culture’s nostalgia singularity. After all, online platforms have seen plenty of classic TV shows getting revivals, many of which were produced with cash from fans. Beyond the Star Trek fan films Veronica Mars, Reading Rainbow, Mystery Science Theater 3000 and Thunderbirds 1965 were all bankrolled by members of the public. In addition, online education site Masterclass hired Aaron Sorkin to re-break the fifth season opener of The West Wing for his screenwriting course.

Let’s be honest, but for Joss Whedon’s reluctance, it’s only a matter of time before someone actually secures $50 million to make a second season of Firefly.

Via: AV Club

Source: Indiegogo

How an AI took down four world-class poker pros

That was anticlimactic,” Jason Les said with a smirk, getting up from his seat. Unlike nearly everyone else in Pittsburgh’s Rivers Casino, Les had just played his last few hands against an artificially intelligent opponent on a computer screen. After his fellow players — Daniel McAulay next to him and Jimmy Chou and Dong Kim in an office upstairs — eventually did the same, they started to commiserate. The consensus: That AI was one hell of a player.

The four of them had spent the last 20 days playing 120,000 hands of heads-up, no-limit Texas Hold’em against an artificial intelligence called Libratus created by researchers at Carnegie Mellon University. At stake: a total pot of $200,000 and, on some level, the pride of the human race. A similar scene had unfolded two years prior when Les, Kim and two other players decisively laid the smackdown on another AI called Claudico. The players hoped to put on a repeat performance, finish up the event on January 30th, and ride the rush of endorphins until they got home and resumed their usual games of online poker.

The fight wasn’t even close. All told, Libratus won by a more than 1.7 million (virtual) dollars, and just like that, the second Brains vs. AI competition came to a close. To understand what these players were up against and what makes Libratus work, let’s go back to a time before all hope of victory was lost.

Men versus machine

For the four men playing against Libratus, victory didn’t always seem impossible. The AI was in the lead from the get-go, building an impressive streak of wins for the first three days. Then came the counter-attack. Day four saw the gap narrow by $40,000, and a string of successes on day six brought the humans to within $50,000 of the lead.

Jason Les and Daniel McAulay in the final stretch of the competition.

“In the start here, we lost the first day,” Les explained. “Whatever — not a big deal. And then we were losing, but then we fought back up to nearly equal. We were feeling really confident! We know how to play, we’re going to be able to win.”

On the night after the sixth day of competition, the humans did what they did every other night: sift through the Libratus hand data provided to them by CMU in hopes of devising a winning strategy. With spirits high after a big day, they decided on a seemingly crazy strategy: three-betting on every hand that came along.

Three-betting, for the uninitiated, is poker slang for re-raising on a hand. When you decide to play a hand in a situation like this, paying the blinds is the first bet. If you’re confident in your cards, you raise — that’s the second bet. Generally, when you re-raise — the third bet — you’re pretty sure you’ve got the exchange in the bag. Based on their understanding of Libratus’ play style, the humans thought they could knock if off balance by playing this aggressively for a while. It backfired.

“We applied this crazy strategy we would never do online,” Kim explained. “Basically, we re-raised all of our hands. All of us went in, like, ‘Let’s just try this, let’s go crazy.’”

“We had a reason to believe that specific size three-bet was going to work well against the AI,” Les added. “We just fired off all day doing that.”

Les and Kim concede that they just got unlucky too, but either way: Libratus was unfazed by their plan and started demolishing them. “It just kept improving every single day, and we started going backwards and backwards,” Les said. In fairness, the humans weren’t playing with their usual setups. The four competitors are almost exclusively online poker pros, and when duking it out at virtual tables at home, they always have their HUDs handy. These heads-up displays are filled with stats and probabilities that help online players make the best moves. Their absence here in Pittsburgh was noticeable.

“Without the HUD, without the numbers, you don’t know if you’re being paranoid or not,” Daniel McAulay said, leaning back in his chair after winning a hand. “Is it folding less? We were never sure. We would always say the same thing to each other: ‘Just play it out until we get home and we’d see the sample of hands and then we’ll change the plan. But that cost us a lot of money. A lot of money.”

Those losses would only continue to mount.

Professor Tuomas Sandholm checks in on Libratus’s progress.

Chris Velazco/Engadget

Building the beast

The man responsible for the players’ anguish can usually be found in his ninth-floor office, overlooking Carnegie Mellon University’s snow-flecked quad. Professor Tuomas Sandholm might live a second life as a startup entrepreneur, but he has spent years trying to perfect the algorithms that make Libratus such a potent player. It wasn’t out of any particular love for the game — Sandholm admits he’s no poker pro — but he was fascinated by the thought of complex computer systems that make decisions better than we can. That fixation led him to develop Claudico, the earlier AI that the humans trounced, and it led him to try again with Libratus.

To think of Libratus as just a poker-playing champ is to sorely underestimate it. Instead, Sandholm says, it’s a more general set of algorithms meant to tackle any information-imperfect situation. Confused? Don’t be. Broadly speaking, the term just describes any situation where two or more parties don’t have the same information. Something unlike, say, chess:, where the entirety of the game’s world is splayed out on the board in front of players. Those players can figure out exactly what’s going on and, assuming they have decent memories, draw on their understanding of the events that led them there. This is a perfect information game.

No-limit Texas Hold’em is different. You don’t know what cards your opponent has, your opponent doesn’t know what cards you have, and those minutes playing a hand to its conclusion are spent trying to make the smartest moves possible with a shortage of intel. And unlike the limit variant, where there’s a cap on how big your bets can be, no-limit gives you the freedom to bet whatever you want. There’s so much information a person — or an AI — can infer about an opponent’s strategy based off their bets that it’s no wonder researchers have been trying to crack the game.

“Heads-up, no-limit Texas Hold’em poker has emerged as the leading benchmark for measuring the quality of these general purpose algorithms in the AI community,” Sandholm told me.

With that in mind, Sandholm and his research assistant Noam Brown built Libratus from three major components. The first is an algorithm that devises the overall strategies based on Nash equilibria. In other words, Libratus spent a total of 15 million computing hours chewing on the rules of the game before the competition, finding rational ways to act when both players are making the best possible moves with the information available. Thanks to a new logic model developed by Sandholm and Brown to minimize Libratus’s “regret,” the AI could solve larger abstractions of the game faster and with higher accuracy than before.

The second is what Sandholm calls the end-game solver. This is the part that players actually faced during their 20 days of combat. Unsurprisingly too, this is where Sandholm says most innovative breakthroughs have happened. Essentially, this allowed Libratus to cook up an approach based on the first two cards it was dealt, and modify that approach based on its opponent’s actions and the river and flop that are dealt. Sandholm says Libratus was also designed to keep tabs on how safe its options are. Let’s say a human player screws up and loses $372. That money is viewed as a gift of sorts, so the AI can freely lose up to $372 and still remain ahead.

“That gives us more flexibility for optimizing our strategies while still being safe,” Sandholm explained.

We’ll get to the last key component a little later. In any case, the sheer number of complex calculations meant Libratus couldn’t run on the desktop in Sandholm’s office. If nothing else, the human players can take solace in the fact that it took a supercomputer and millions of computing hours to beat them. If you thought Go was tough to wrap your head around, consider the complexity of no limit Texas Hold’em: When you’re dealt into a game, the hands you’re dealt and the communal cards that appear are one possibility of 10^160.

“That’s one followed by 160 zeroes,” said Sandholm. “That’s more than the number of atoms in the universe. You cannot just brute-force your way through it.” Still, it takes some degree of brute force to build as close to optimal a strategy as possible. That’s where “Bridges” comes in.

Bridges, the supercomputer that doubles as Libratus’s home.

Chris Velazco/Engadget

If Libratus is the brain of the operation, Bridges — a supercomputer made of hundreds of nodes in the basement of the Pittsburgh Supercomputing Center — is most definitely the brawn.

“Libratus is running on about 600 nodes at Bridges, out of 846 total compute nodes,” said Nick Nystrom, Senior Director of Research at the Pittsburgh Supercomputing Center. Most of those 800+ nodes have two CPUs, each with 28 computing cores and 128GB of RAM. Forty-eight of those nodes have two state-of-the-art GPUs, and still others were loaded with even more power: NVIDIA’s Tesla-series K80 and P100 GPUs.

There’s more: Forty-two of those nodes have 3TB of RAM each, and a very special four nodes have a whopping 12TB of RAM. That’s some serious firepower, but all those nodes were ingeniously woven together to maximize data bandwidth and minimize latency. It’s just as well, considering the amount of data involved: Libratus was using up to 2.6 petabytes of storage during the competition.

When not being used to best humans at card games, Bridges was being used for around 650 projects by over 2,500 people. Think of Bridges as a supercomputer for hire: Researchers from around the country are using it to gain insight into arcane subjects like genomics, genome sequence assemblies and other kinds of machine learning.

The beauty of Bridges, according to Nystrom, is that those researchers don’t need to be supercomputer buffs. “It’s a very cloud-like model letting people who are not programmers, not computer scientists, not supercomputer users make use of a supercomputer without necessarily even knowing it.” That’s what happened with Libratus, and everything seemed to be working perfectly.

Jason Les checks out the leaderboard on the final day.

Chris Velazco/Engadget

Game theory

After the humans’ gutsy attack plan failed, Libratus spent the rest of the competition inflating its virtual winnings. When the game lurched into its third week, the AI was up by a cool $750,000. Victory was assured, but the humans were feeling worn out. When I chatted with Kim and Les in their hotel bar after the penultimate day’s play, the mood was understandably somber.

“Yesterday, I think, I played really bad,” Kim said, rubbing his eyes. “I was pretty upset, and I made a lot of big mistakes. I was pretty frustrated. Today, I cut that deficit in half but it’s still probably unlike for me to win.” At this point, with so little time left and such a large gap to close, their plan was to blitz through the remaining hands and complete the task in front of them.

For these world-class players, beating Libratus had gone from being a real possibility to a pipe dream in just a matter of days. It was obvious that the AI was getting better at the game over time, sometimes by leaps and bounds that left Les, Kim, McAulay and Chou flummoxed. It wasn’t long before the pet theories began to surface. Some thought Libratus might have been playing completely differently against each of them, and others suspected the AI was adapting to their play styles while they were playing. They were wrong.

As it turned out, they weren’t the only ones looking back at the past day’s events to concoct a game plan for the days to come. Every night, after the players had retreated to their hotel rooms to strategize, the basement of the Supercomputing Center continued to thrum. Libratus was busy. Many of us watching the events unfold assumed the AI was spending its compute cycles figuring out ways to counter the players’ individual play styles and fight back, but Professor Sandholm was quick to rebut that idea. Libratus wasn’t designed to find better ways to attack its opponents; it’s designed to constantly fortify its defenses. Remember those major Libratus components I mentioned? This is the last, and perhaps most important one.

“All the time in the background, the algorithm looks at what holes the opponents have found in our strategy and how often they have played those,” Sandholm told me. “It will prioritize the holes and then compute better strategies for those parts, and we have a way of automatically gluing those fixes into the base strategy.”

If the humans leaned on a particular strategy — like their constant three-bets — Libratus could theoretically take some big losses. The reason those attacks never ended in sustained victory is because Libratus was quietly patching those holes shut by using the supercomputer in the background. The Great Wall of Libratus was only one reason the AI managed to pull so far ahead. Sandholm refers to Libratus as a “balanced” player that uses randomized actions to remain inscrutable to human competitors. More interesting, though, is how good Libratus was at finding rare edge cases where seemingly bad moves were actually excellent ones.

“It plays these weird bet sizes that are typically considered really bad moves,” Sandholm explained. These include tiny underbets like 10 percent of the pot, or huge overbets like 20 times the pot. Donk betting, limping — all sorts of strategies that are, according to the poker books and folk wisdom are bad strategies.” To the players’ shock and dismay, those “bad strategies” worked all too well.

Poker and beyond

On the afternoon of January 30th, Libratus officially won the second Brains vs AI competition. The final margin of victory: $1,766,250. Each of the players divvied up their $200,000 spoils (Dong Kim lost the least amount of money to Libratus, earning about $75,000 for his efforts), fielded questions from reporters, and eventually went to go decompress. Not much had gone their way over the past 20 days, but they just might have contributed to a more thoughtful, AI-driven future without even realizing it.

Through Libratus, Professor Sandholm had proven that algorithms could make better, more nuanced decisions than humans in one specific realm. But remember: Libratus and systems like it are general-purpose intelligences, and Sandholm sees plenty of potential applications. As an entrepreneur and negotiation buff, he’s enthusiastic about algorithms like Libratus being used for bargaining and auctions.

“When the FCC auctions spectrum licenses they sell tens of billions of dollars of spectrum per auction, yet nobody knows even one rational way of bidding,” he said. “Wouldn’t it be nice if you had some AI support?”

But there are bigger problems to tackle — ones that could affect all of us more directly. Sandholm pointed to developments in cybersecurity, military settings and finance. And, of course, there’s medicine.

“In a new project, we’re steering evolution and biological adaptation to battle viral and bacterial infections,” he said. “Think of the infection as the opponent and you’re taking sequential actions and measurements just like in a game.” Sandholm also pointed out that such algorithms could even be used to more helpfully manage diseases like cancer, both by optimizing the use of existing treatment methods and maybe even developing new ones.

Jason, Dong, Daniel and Jimmy might have lost this prolonged poker showdown, but what Sandholm and his contemporaries have learned in the process could lead to some big wins for humanity.

Shia LaBeouf’s anti-Trump livestream shut down for ‘public safety’

It was supposed to last four years, but Shia LaBeouf’s “He Will Not Divide Us” livestream has been shut down by the Museum of the Moving Image. The project was created by the actor along with two artists, Luke Turner and Nastja Säde Rönkkö, as a form of protest against Donald Trump’s presidency. In a press release, the Museum said that the installation “created a serious and ongoing public safety hazard” for itself, staff, visitors, local residents and businesses around it.

He Will Not Divide Us had one camera outside the Museum of the Moving Image, located in Queens, New York, which was streaming live to its website 24/7. While that may seem harmless, Shia LaBeouf was arrested there last month after a confrontation with a 25-year-old man who was likely a Trump supporter. If you try watching the livestream now, you’ll see the following message: “The Museum has Abandoned Us.”

“The Museum of the Moving Image abandoned the project,” reads an update on the He Will Not Divide Us site. “The artists, however, have not.”

Via: The Verge

Source: Museum of the Moving Image

Ferroelectric material could make your smartwatch run longer

Elon Musk’s big plan involves charging your electric car with shingles that are solar panels. While that’s incredibly impressive — not to mention ambitious — it uses only one form of alternative energy. Finnish scientists have a different idea: harnessing heat, kinetic energy and sunlight simultaneously to help power your gadgets. This involves using a ferroelectric material (think: the stuff inside ultrasound machines or fuel injectors for diesel engines), KBNNO, to generate electricity from heat and pressure. That’s according to a post on Phys.org.

However, this isn’t a silver bullet for infinitely renewable energy. At least not yet. The researchers conclude that other similar materials are more efficient, and that this breakthrough is more of a supplementary power source versus a primary one. “It is expected that with further compositional optimization, the properties will be improved and be more balanced, and thus will become more useful for multi-functional purposes,” the paper reads.

All that to say, the scientists from the University of Oulu know there’s still more work to be done before KBNNO is helping power smart cities or your next high-tech watch. The paper’s lead author, Yang Bai, says that within the next year he should have a “multi-energy-harvesting” device prototype ready and in a few years the tech could be ready for the market.

Via: Phys.org

Source: Applied Physics Letters

Ford bets $1 billion on an unknown self-driving AI company

Seemingly out of the blue, Ford announced today that it’s investing $1 billion in Argo AI, a Pittsburgh-based company building self-driving technology. Ford is effectively buying the previously unknown startup, which was founded by engineers from Google and Uber. Argo AI will operate as an independent subsidiary and will focus on developing a software platform for Ford’s self-driving car, which the company is targeting for 2021. Notably, Ford is also planning to license the technology out to other companies.

While the extent of the deal is surprising, it makes sense for Ford, especially after GM acquired the self-driving car startup Cruise for over $1 billion last year. As we’ve seen with the steady progress from Google’s Waymo, autonomous driving technology is evolving quickly. Car companies have to make some big moves now if they don’t want to get left behind once the technology becomes essential in a few years.

Ford will combine the team building its virtual driver system, which serves as the “brains” of the self-driving cars, together with Argo AI. Both companies will benefit from the new arrangement: Ford doesn’t have time to waste building its own AI platform from scratch, and Argo will need help getting its technology to consumers once it’s ready.

Source: Ford

Inauguration-protest arrests lead to Facebook data prosecution

If you attend a protest in Washington, D.C., nowadays, better plan on leaving your cellphone at home. That is, unless you want police to confiscate it, mine it for incriminating information and then gather even more data from their BFF — Facebook.

At least one person arrested during protests on inauguration day got an email from Facebook’s Law Enforcement Response Team alerting them that investigators wanted access to their data. Another received a Facebook data subpoena.

The email was basically a countdown to when Facebook inevitably handed that data over to DC police. That is, unless the respondent figured out how to file an objection within a ten-day window.

When over 230 people were arrested in DC during protests against Donald Trump last month, many of those rounded up were not part of the protests. Cops swept up Medics, legal observers, and six journalists from Voactiv, RT America and others.

All of their phones were confiscated and retained.

Everyone arrested now faces felony charges and up to 10 years in prison. In the Bay Area, where we love a good protest, it’s very rare that arrested protesters get prosecuted. So it’s odd to think that protesters would have their social media scrutinized after an arrest. Though, like in most cities across America, it’s extremely common for investigators to search the social media of suspects in other crimes if they believe that the suspect posted something related (like photos of a beating). SFPD even has an officer devoted to following social media — most heavily, Snapchat and Instagram, as those are apparently where you find the best crime stuff.

Oakland Police and supporting agencies like California Highway Patrol have been very transparent about monitoring Twitter to determine protest movement and plans. And we’ve been pretty vocal about pushing back. It only makes sense that we’d resist any form of surveillance, seeing that we’re ground zero around here for ethically challenged startups that invade our privacy. Fighting the surveillance state has become part of our DNA. But a wide-ranging Facebook subpoena for felony protest prosecution isn’t something we’ve seen the likes of.

The second subpoena issued to Facebook (this one by the U.S. Attorney’s Office on January 27, 2017 and signed off on by a DC Metropolitan Police Detective) obtained by press this week is chilling. It targeted another inauguration arrestee, and requests subscriber information from Facebook that includes all names, all addresses (home, business, emails), phone records, session details (IP, ports, etc), device identification info, payment information, and more.

CityLab explained, “The redacted blocks on the second page shield columns of phone numbers, which are connected to other arrestees for whom the District Attorney and police are seeking information.”

The list of phone numbers may indicate that police have gained access to someone’s phone and are building a case with what they found. A screenshot provided to CityLab indicates police began mining information from the confiscated devices right after the arrests.

On one hand, that could’ve been automated pinging by Gmail to Google’s servers. Or, it could’ve been something darker. When phones are taken as evidence, they’re are supposed to be secured in a signal-blocking Faraday bag to prevent remote wipes. Fred Jennings, a cybercrime defense attorney at the firm Tor Ekeland, P.C. in New York, told press. “If it had been secured properly and placed in the bag to safeguard it, there’d be no way for it to ping the server.”

For some of us, this sets off a different set of alarms. It’s scary enough that police are arresting journalists and mining our phones for all the terrifyingly detailed data Facebook seems all too happy to give up. But authorities with questionable intent are also collecting our contacts, and pose a very real risk for our protected sources.

Some of this could be solved by ditching our devices in favor of carrying on-the-scene burner phones. But this presents a new host of complications and problems, even for the good-intentioned protester or march participant. For one, it’s a hassle for most people. It also defeats the purpose of using your Twitter or Facebook account. More than ever, it’s vital that our voices are heard through media we share from our phones. Things like immigration ban protests and the state-level denial of chaos at the airports can’t be dismissed when the realities are documented through our established Facebook and Twitter accounts.

Keeping a record of what authorities do to us, and being able to send a signal flare for help to our networks, makes them being used against us a much bigger problem than just saying “leave your phone at home” or “don’t talk about the protest online.”

It’s not a stretch to lay blame at Facebook’s feet for taking data we don’t necessarily want to give them, and for its well-established collaboration with police against its users. It’s a bigger stretch to suggest that the agreement between Facebook and its users is any kind of informed consent.

It’s interesting that this news comes up the same week that 333,000 people signed a petition demanding Facebook improve its corporate citizenship, with 1,500 of the signees being company shareholders. That document led to a proposal to remove Mark Zuckerberg from the board.

This, it said, was necessary at a time when Facebook “faces increasing criticism regarding its perceived role in the promotion of misleading news; censorship, hate speech and alleged inconsistencies in the application of Facebook’s community standards guidelines and content policies; targeting of ad views based on race; collaboration with law enforcement and other government agencies; and calls for public accountability regarding the human rights impacts of Facebook’s practices.”

It’s that collaboration with law enforcement and human rights accountability we’ll be hearing more about as the DC arrest cases unfold. It’s not a new story, just an old one with a twist: Facebook got called out just before the US presidential election for colluding with authorities against its users’ human rights, specifically US police departments. A coalition of 70 human rights groups including the ACLU wrote a public letter to Facebook condemning the company’s zeal in doing police bidding around the world.

Facebook, of course, just wants us to live our lives so it can keep collecting data we don’t even know we’re creating. Recording and storing our location, connections, contacts, experiences, our secrets and our history.

It’s transforming our memories into a malevolent, atavistic shadow that someday may be used against us in a court of law.

Images: REUTERS/Andrew Kelly (Immigration lawyers JFK), REUTERS/Robert Galbraith (Mark Zuckerberg)