Big Mountains, Big Data: How Technology Helps Push the Boundaries of Human Endeavor

“Men wanted for hazardous journey. Low wages, bitter cold, long hours of complete darkness. Safe return doubtful. Honour and recognition in event of success.”

That was the ad that Sir Ernest Shackleton is said to have placed in a London newspaper to recruit a crew for a 1914 expedition to Antarctica.

Human beings are naturally obsessed with great adventures, especially those in which the risks are formidable, the odds of success are slim, and a great story lies at the end of it all. Reach the poles, sail the Pacific on a balsa raft, climb Mount Everest — the list of such feats is long indeed. But as awe-inspiring as such exploits might have been, they often ended badly. Crews faced hypothermia, scurvy, dehydration, starvation, and more; death dogged them at every corner. For too many, the warnings in the Shackleton ad came true.

Technology Transforms Exploration

Fast forward 100 years to a time when conditions have changed dramatically, thanks largely to advances in technology. While the actual physical challenges remain about the same as before, our ability to deal with them and to survive to tell the story has increased considerably. From highly accurate tracking and measuring devices to near-total global telecommunications coverage (plus dramatically improved food and protective clothing), modern-day explorers have it much easier than their earlier counterparts.

Take Mount Everest expeditions. One of the most famous attempts on Everest was undertaken by British mountaineers George Mallory and Andrew Irvine in 1924. “Perfect weather for the job,” Mallory wrote on June 7, 1924, the day before he and Irvine left for Everest’s summit. They were never seen again. Mallory’s body was found in 1999; 90 years later, Irvine’s is still missing.

Recalling that expedition, the UK’s Guardian newspaper described the climbing attire and gear of the time in these words: “Protected from appalling weather and low temperatures by tweed and cotton, their legs bound in puttees and their feet always half-freezing in inadequate boots, climbers were experimenting on the fringes of human tolerance.”

In addition, early explorers were cut off from all communication while on the mountain. When Sir Edmund Hillary of Britain and Tenzing Norgay of Nepal became the first to summit the world’s highest peak on May 29, 1953 (or, as Hillary put it, they “knocked the bastard off”), the report of their conquest was first hand-delivered by a runner to a Nepalese village, eventually making its way to England by radio and telegraph. The news arrived in London just in time to coincide with the era’s blockbuster social event, the coronation of Queen Elizabeth II, on June 2.

Today, 86 percent of Nepal’s citizens use cell phones, up from just 15 percent in 2008.

Contrast this with the South Pole trek that British polar adventurer Ben Saunders and his teammate Tarka L’Herpiniere undertook in 2013, following explorer Robert F. Scott’s route of a century earlier. Their gear included mobile satellite hubs, freeze-proof laptops, portable solar panels, and a variety of movies and TV shows (everything from Love Actually to Breaking Bad). Saunders blogged regularly from Antarctica, and he also posted updates, pictures, and videos on YouTube, Twitter, Facebook, and other social media channels.

Today, Mount Everest ascents have become an industry, with numerous guide outfits offering deep-pocketed adventurers the trophy of a lifetime. In 2015, median expedition costs are north of $57,000 per climber, and expeditions now regularly haul routers and satellite terminals to base camp (at nearly 18,000 feet). Not to be outdone, telecom companies such as Nepalese cell provider Ncell and global giants like Huawei and China Mobile provide full 4G service on the mountain. Dubai-based Thuraya even provides a sleeve that converts a standard smartphone into a satellite phone.

Meanwhile, cell-phone penetration is rapidly increasing in Nepal. Today, 86 percent of Nepal’s citizens use cell phones, up from just 15 percent in 2008, according to a December 2014 report from the Nepal Telecommunications Authority. With the telecom infrastructure in place, it’s only a matter of time before both western climbing expeditions and local Sherpa communities start taking greater advantage of these technologies. As just one example, they might gather real-time weather data to keep expeditions better informed about changing conditions.

High-Tech Safety Improvements

Although the Himalayas are hundreds of miles inland, they are directly affected by storms that originate in the Bay of Bengal. In May 1996, one rogue storm killed eight people on Mount Everest, a tragedy described in journalist Jon Krakauer’s best-selling book Into Thin Air. Today, expedition leaders can access real-time weather and satellite data (with the assistance of technologies such as SAP HANA). That, in turn, allows them to determine more precisely how long they have before the weather turns bad, giving them enough time to move their teams to safer locations down the mountain.

Until very recently, getting past the Khumbu glacier involved playing an icy version of Russian roulette. 

For Mount Everest climbers ascending via the South Col route, one of the scariest obstacles is the Khumbu Icefall, a steep section where the Khumbu glacier drops and, in the process, breaks into massive ice chunks, some larger than a house. The Khumbu glacier moves 3 to 6 feet every day; until very recently, getting past it involved playing an icy version of Russian roulette.

That game has changed with the introduction of the Extreme Ice Survey, an innovative time-lapse photography project with cameras set up at 28 locations world wide, including one at Khumbu. The camera snaps a photo every 30 minutes during daylight hours and also uses precise geolocation indicators to determine where and how quickly the glacier is melting. Technology-savvy Everest guides now use the two-plus years of this time-lapsed imagery (about 8,000 images per year) to calculate the odds that a particular section of Khumbu will cave and determine the times that will likely be the safest to cross the icefall.

Reportedly, Mallory was once asked why he wanted to climb Mount Everest, to which he famously replied: “Because it’s there.” A century later, the climb still requires a special breed of human being who desires and is prepared to undertake a potentially hazardous journey. But in many ways, today’s technologies are helping make a safe return far less doubtful.

About SAP Startup Focus:

SAP Startup Focus works with startups in the big data, predictive analytics, and real-time analytics spaces, supporting businesses in building innovative applications using the SAP HANA database platform. More than 1,800 companies currently participate in the program. Join the conversation on Twitter by following @SAPStartups, or follow the author on Twitter: @BansalManju. 

Views from the Marketplace are paid for by our key partners. All Views from the Marketplace have been approved by our team.

More information »

View the original article here

Your iPhone Might Make You a Reality TV Star

Broadcasting everything your smartphone sees and hears could be the next trend in social media.

The first time I learned about live streaming was also the first time I realized you could play shuffleboard in Brooklyn, at the Royal Palms Shuffleboard Club.

As my friends and I learned the rules, my teammate, Kevin Porter, started talking to his iPhone. He’d just downloaded Yevvo, he explained. The premise was simple—everything his phone could see and hear, his followers could also see and hear. His girlfriend had stayed home that night, but she was (hypothetically) watching his every move, thanks to this app. He had become the star of his own reality television show, albeit with a very small audience, and we were all his cast mates.

It seems like just about everything you say and do might wind up on the Internet someday. But this was something different: with Yevvo, everything we said and did was on the Internet, right then and there. And after a few minutes, my shuffleboard teammate had a new follower. And then another. And then another.

Yevvo, which relaunched last year with new features and a new name—Air—isn’t the only app to allow people to broadcast their lives live. With the spread of high-quality smartphone cameras, not to mention camera-equipped wearable devices, the use of such apps is sure to become more common. And such personal broadcasting could start to change the social media landscape.

Back at the shuffleboard club, we weren’t really considering any of this. Air’s users notify contacts when they begin a broadcast, and then anyone following that feed can push-notify their followers to check it out too. Nothing is saved or stored for later. If you missed it in real time, you missed it. Our fans—my friend’s girlfriend, at least—rooted for us, via comments on the stream. We had a blast.

Air is not just about catering to attention-starved 30-somethings, one of the company’s Israeli co-founders, Ben Rubin, told me in an interview a while later. Rubin and two others founded Yevvo after trying to figure out which parties to go to at a tech conference in Austin, Texas. They decided it would be nice to be able to actually show each other what a given scene was like.

Since launching as Yevvo in August 2013, Air has raised more than $4 million. Rubin wouldn’t disclose how many people have downloaded Yevvo or its new incarnation, but it clearly isn’t threatening Instagram yet. The highest Yevvo was ranked in Apple App store, back in December 2013, was 223rd.

Still, there’s good reason to think live-streaming video could thrive. A service like Vine, which makes it easy to post short videos on Facebook and Twitter, is appealing because it reduces the amount of time between when something happens and when other people get to see it. Meanwhile Snapchat’s ephemeral promise makes it a medium for sharing even the most impulsive and unguarded moments, delivered to recipients instantly rather than crowded into a feed. Live-streaming apps offer a similar, even more colorful method for unfettered and unfiltered communication.

Josh Elman, a partner at the Silicon Valley investment firm Greylock, told me he’s considering getting involved with Rubin’s company because it is positioned at television media’s latest shift, from “being curated, and heavily produced, to tuning into who you want, based on who you know.”

This novel participatory medium also raises questions, though—about privacy, intimacy, and appropriate behavior on social media. Porter, the guy who first showed me Yevvo, told me several months later that he eventually deleted the app from his phone. As he continued to explore the service, he said, he increasingly found the whole concept gross.

“You can only imagine how many ‘take off your shirt’ requests an aspiring model received while streaming her photo shoot to a bunch of lonely teens,” he told me.

What ultimately convinced him to walk away was seeing older men following underage girls, something that basically creeped him out. “If only these poor parents knew that their child was upstairs being watched by (and responding to) creepy old dudes,” he wrote.

Ryan Cooley, the company’s community manager, acknowledges the “perv factor,” but he points out that Air has made many changes designed to reduce it substantially. Now you can only invite people to follow you via SMS; the app doesn’t promote particular users (and thus expose them to strangers); users have the option to approve everyone that follows them. “We won’t recommend anybody for you to follow unless we are confident you know them from the physical world,” Cooley says. “I don’t think the dirtbags are going to go away, but you’re going to see a lot more transparency.”

Despite these misgivings, I found live streaming a fascinating thing to try. It’s fair to imagine that live-streaming services could become compelling new forms of social connection. If that happens, I can’t promise I won’t ham it up on screen after a few cocktails, but at least my viewers might find it entertaining.

View the original article here

Human Face Recognition Found In Neural Network Based On Monkey Brains

A neural network that simulates the way monkeys recognise faces produces many of the idiosyncratic behaviours found in humans, says computer scientists.

When neuroscientists use functional magnetic resonance imaging to see how a monkey’s brain responds to familiar faces, something odd happens. When shown a familiar face, a monkey’s brain lights up, not in a specific area, but in nine different ones.

Neuroscientists call these areas “face patches” and think they are neural networks with the specialised functions associated with face recognition. In recent years, researchers have begun to tease apart what each of these patches do. However, how they all function together is poorly understood.

Today, we get some insight into this problem thanks to the work of Amirhossein Farzmahdi at the Institute for Research on Fundamental Sciences in Tehran, Iran, and a few pals from around the world. These guys have built a number of neural networks, each with the same functions as those found in monkey brains. They’ve then joined them together to see how they work as a whole.

The result is a neural network that can recognise faces accurately. But that’s not all. The network also displays many of the idiosyncratic properties of face recognition in humans and monkeys, for example, the inability to recognise faces easily when they are upside down.

The new neural network consists of six layers with the first four trained to extract primary features. The first two recognise edges, rather like two areas of the visual cortex known as V1 and V2. The next two layers recognise face parts, such as the pattern of eyes, nose and mouth. These layers simulate the behaviour of parts of the brain called V4 and the anterior IT neurons.

The fifth the layer is trained to recognise the same face from different angles. It is known as the view selective layer and inspired by parts of monkey brains called middle face patches.

The final layer matches the face to an identity.  This is called the identity selective layer and simulates a part of the simian brain known as the anterior face patch.

Farzmahdi and co train the layers in the system using different image databases. For example, one of the datasets contain 740 face images consisting of 37 different views of 20 people. Another dataset contains images of 90 people taken from 37 different viewing angles. They also have a number of datasets for evaluating specific properties of the neural net.

Having trained the neural network, Farzmahdi and co put it through its paces. In particular, they test whether the network demonstrates known human behaviours when recognising faces.

For example, various behavioural studies have shown that humans recognise faces most easily when seen from a three quarters point of view contains, that’s halfway between a full frontal and a profile.

Curiously, Farzmahdi and co say their network behaves in the same way—the optimal viewing angle is the same three-quarter view that humans prefer.

Another curious feature of human face recognition is that it is much harder to recognise faces when they are upside down. And Farzmahdi and co’s neural network shows exactly the same property.

What’s more, it also demonstrates the “composite face effect”. This occurs when identical images of the top of a face are aligned with different bottom halves, in which case humans perceive them as being different people. Neuroscientists say this suggests that face recognition works only on the level of whole faces rather than in parts.

Farzmahdi and co say their new neural network behaves in exactly the same way. It considers composite faces as new identities, suggesting that the network must be recognising faces as a whole, just like humans.

Finally, Farzmahdi and co say that when their neural network is trained using faces of a specific race, it finds it much harder to identify faces of a different race. Once again, that is a phenomena well known in humans. “People are better at identifying faces of their own race than other races, an effect known as other race effect,” they say.

That’s interesting work because no other face recognition system has been able to reproduce these biological characteristics. The results suggest that Farzmahdi and co have found an interesting way to reproduce these human and monkey behaviours in an artificial system for the first time. “Our proposed model…explains neural response characteristics of monkey face patches; as well several behavioral phenomena observed in humans,” they say.

The process behind this work is almost as fascinating as the result. These guys have taken certain structures found in monkey brains, built synthetic system based on the structures and then found that the artificial behaviour matches the biological behaviour.

If that works for vision, then might it also work for hearing, touch, balance, movement and so on? And beyond that there is the potential for capturing the essence of being human, which must somehow be captured by structures within the brain.

Other suggestions in the comments section please.

Clearly, the fields of synthetic neuroscience and artificial intelligence are changing. And quickly.

Ref: arxiv.org/abs/1502.01241  A Specialized Face-Processing Network Consistent With The Representational Geometry Of Monkey Face Patches

View the original article here

The Face Detection Algorithm Set To Revolutionise Image Search

The ability to spot faces from any angle, and even when partially occluded, has always been a uniquely human capability. Not any more.

Back in 2001, two computer scientists, Paul Viola and Michael Jones, triggered a revolution in the field of computer face detection. After years of stagnation, these guys’ breakthrough was an algorithm that could spot faces in an image in real time. Indeed, the so-called Viola-Jones algorithm was so fast and simple that it was soon built into standard point and shoot cameras.

Part of their trick was to ignore the much more difficult problem of face recognition and concentrate only on detection. They also focused only on faces viewed from the front, ignoring any seen from an angle. Given these bounds, they realised that the bridge of the nose usually formed a vertical line that was brighter than the eye sockets nearby. They also noticed that the eyes were often in shadow and so formed a darker horizontal band.

So Viola and Jones built an algorithm that looks first for vertical bright bands in an image that might be noses, it then looks for horizontal dark bands that might be eyes, it then looks for other general patterns associated with faces.

Detected by themselves, none of these features are strongly suggestive of a face. But when they are detected one after the other in a cascade, the result is a good indication of a face in the image. Hence the name of this process: a detector cascade. And since these tests are all simple to run, the resulting algorithm can work quickly in real-time.

But while the Viola-Jones algorithm was something of a revelation for faces seen from the front, it cannot accurately spot faces from any other angle. And that severely limits how it can be used for face search engines.

Which is why Yahoo is interested in this problem. Today, Sachin Farfade and Mohammad Saberian at Yahoo Labs in California and Li-Jia Li at Stanford University nearby, reveal a new approach to the problem that can spot faces at an angle, even when partially occluded. They say their new approach is simpler than others and yet achieves state-of-the-art performance.

Farfade and co use a fundamentally different approach to build their model.  These guys capitalise on the advances made in recent years on a type of machine learning known as a deep convolutional neural network. The idea is to train a many-layered neural network using a vast database of annotated examples, in this case pictures of faces from many angles.  

To that end, Farfade and co created a database of 200,000 images that included faces at various angles and orientations and a further 20 million images without faces. They then trained their neural net in batches of 128 images over 50,000 iterations.

The result is a single algorithm that can spot faces from a wide range of angles, even when partially occluded. And it can spot many faces in the same image with remarkable accuracy.

The team call this approach the Deep Dense Face Detector and say it compares well with other algorithms.  “We evaluated the proposed method with other deep learning based methods and showed that our method results in faster and more accurate results,” they say.

What’s more, their algorithm is significantly better at spotting faces when upside down, something other approaches haven’t perfected. And they say that it can be made even better with datasets that include more upside down faces. “In future we are planning to use better sampling strategies and more sophisticated data augmentation techniques to further improve performance of the proposed method for detecting occluded and rotated faces.”

That’s interesting work that shows how fast face detection is progressing. The deep convolutional neural network technique is only a couple of years old itself and already it has led to major advances in object and face recognition.  

The great promise of this kind of algorithm is in image search. At the moment, it is straightforward to hunt for images taken at a specific place or at a certain time. But it is hard to find images taken of specific people. This is step in that direction. It is inevitable that this capability will be with us in the not too distant future.

And when it arrives, the world will become a much smaller place. It’s not just future pictures that will become searchable but the entire history of digitised images including vast stores of video and CCTV footage. That’s going to be a powerful force, one way or another.

Ref: arxiv.org/abs/1502.02766  Multi-view Face Detection Using Deep Convolutional Neural Networks

View the original article here

A Film Studio for the Age of Virtual Reality

A Montreal-based film studio is making movies that you’ll watch with a virtual-reality headset, pointing the way to a whole new form of entertainment.

A still from Wild—The Experience, a short virtual-reality film.

Imagine sitting back in a chair, sliding a headset over your eyes and headphones over your ears. Suddenly, you’re sitting on a rock in a sun-dappled clearing, surrounded by tall trees, alone with the noises of the forest. Alone, that is, until you turn your head and spot Reese Witherspoon walking toward you, looking like a haggard camper with a giant pack on her back.

This is what it’s like to watch the opening bit of Wild—The Experience, a short virtual-reality film made as a promotion for the Witherspoon-led movie Wild, which is based on Cheryl Strayed’s book about her trek along the Pacific Crest Trail. Despite the clunky feeling of a headset on your face, for a few moments you feel transported to someone else’s reality. You sense the calming stillness of nature and see it all around you—a contrast with the weirdness of watching Witherspoon stopping to rest on your left without acknowledging your presence.

This is just one immersive experience that Félix Lajeunesse and Paul Raphaël are creating at Felix & Paul Studios, their Montreal-based film production company that focuses on live-action 3-D and virtual-reality films. Their studio and a few others are exploring ways to take virtual reality beyond video games. “We like to think of virtual reality not as a medium to actually create horror stories and heavy adrenaline-driven emotions, but rather to use it as a way to enhance the human experience,” Lajeunesse says.

The world of virtual-reality films is still small—it’s not much more than a collection of experiments, and to check any of them out you’ll need a headset of some sort. But the continued development of headsets such as Oculus Rift, the Samsung-Oculus Gear VR, and Sony’s Project Morpheus signal that immersive display technologies may finally be about to go mainstream.

Felix & Paul Studios focuses its filmmaking efforts on the Gear VR headset, which is made by Samsung and includes Oculus’s technology.

And as demand for virtual-reality devices heats up, Lajeunesse and Raphaël believe a desire for new forms of cinematic experiences will come along with it. “We believe it’s going to grow into a new, totally distinct art form,” Lajeunesse says. Hollywood seems intrigued: Wild–The Experience was commissioned by Fox Searchlight Pictures, which released Wild. Fox Searchlight did not respond to requests for comment, but Mike Dunn, president of 20th Century Fox’s home entertainment business, has said he expects to start selling virtual-reality clips related to Fox movies this year.

Making VR movies is much different from making a regular movie—for one thing, it’s much harder to control where the viewer is focusing attention. And there are still many challenges to overcome in figuring out how to enable users to really explore a live-action virtual world the way you can an animated one or a virtual-reality game. Still, the early results hint at how entrancing the results can be, suggesting how virtual reality might be used for things like education and work collaboration as well as entertainment.

Lajeunesse and Raphaël have backgrounds in traditional filmmaking, and so far they’ve focused their virtual-reality work exclusively on short, live-action pieces. Their first film, Strangers, with Montreal singer-songwriter Patrick Watson, seats you on an ottoman inside Watson’s studio so you can hang out while he plays music. It debuted at SXSW in Austin, Texas, in 2014. Since then, the duo has made several more, including Herders, which brings you into the world of Mongolia’s nomadic yak herders, and a scene from the Cirque du Soleil show Zarkana.

To make films that look and sound real, Lajeunesse and Raphaël built a 360°, stereoscopic 3-D camera system and an audio system that can record sound from all around. They had to build their own postproduction software, too, and essentially edit while wearing a Gear VR headset, Lajeunesse says. In editing, they add in a virtual spot where the viewer is meant to be sitting while taking in the virtual world.

The Wild film is the first that Felix & Paul made as a reactive experience. That is, your behavior while watching the film (such as where you look) determines whether or not a second character appears: Strayed’s mother (played by Laura Dern), who in this scene would be a figment of Strayed’s imagination.

“We like that notion of reactivity because it also enhances the sense of presence,” Lajeunesse says. Even the basics of VR filmmaking have been challenging. Early on, the most difficult task Felix & Paul faced was figuring out how to film scenes so that scale of the virtual world didn’t seem off to some viewers. And the studio’s films thus far haven’t incorporated any computer-generated effects to give the viewer a body, so you feel a bit like a ghost while watching—if you lift your real-world arm in front of your face, you won’t see anything.

There’s also the problem of standing up or even leaning over. While it’s possible to make virtual-reality games and films with computer-generated 3-D models that keep track of a user’s changing position and adjust to that shifting perspective, thus far this isn’t the case with live-action films made for VR. Right now, for instance, you can turn around while watching a Felix & Paul piece, since it’s filmed in 360 degrees, but if you get out of your chair while watching one, the whole universe will rise with you.

Despite the issues, Jeremy Bailenson, an associate professor at Stanford and the founding director of the school’s Virtual Human Interaction Lab, thinks virtual reality is an exciting medium for short live-action films. With such pieces, he says, “you get to leverage what I call the paradox of virtual reality: the brain treats it as real, yet anything is possible.”

Gain the insight you need on virtual reality at EmTech Digital.

Register today

View the original article here

Seven Must-Read Stories (Week Ending February 14, 2015)

Sorry, I could not read the content fromt this page.

View the original article here

Can Twitter Fix Its Harassment Problem without Losing Its Soul?

Harassment has become a major issue online. Twitter’s efforts to crack down on problem users might suggest a broader solution.

At least Twitter admits it has a problem. In an internal memo leaked last week, CEO Dick Costolo acknowledged what many people on Twitter already knew: 140 characters at a time, many of the service’s users are routinely harassed, abused, or threatened, and the company isn’t doing much to stop it.

Costolo’s note suggested that Twitter would take new action against harassers—a potentially important step at a time when online abuse has reached troubling proportions. Twitter’s effort might offer a template for addressing the wider problem, but it may also show the challenge of stamping out unacceptable behavior without eroding the character of an inherently unruly and combative community. Rules that reduce harassment might have the unintended consequences of slowing the flow of information and turning off some ardent users.

Online bullying—on social networks in particular—is now a mainstream issue. According to a Pew Research Center survey from late last year, 40 percent of people have been bullied on the Web, and the majority of those people (66 percent) say it most recently happened on a social network. Even if you haven’t experienced it firsthand, chances are you’ve spotted it: the same Pew report found that 73 percent of people reported seeing someone else being harassed online. Among people between the ages of 18 and 29, this figure jumps up to 92 percent.

The issue was pushed to the forefront last year via Gamergate—a spiraling campaign of online harassment that stemmed from allegations that a female video-game developer slept with a reporter in exchange for positive reviews of her game. Even more recently, the problem was highlighted by writer Lindy West’s January piece for the radio show This American Life that recounted the abuse she suffered on Twitter and how she confronted one particular troll. Costolo’s memo, in fact, was a response to an employee’s post about that piece on an internal Twitter site.

Clay Shirky, an associate professor at New York University who has written two books about social media, thinks the problem has reached a point where Twitter needs to remind users about what is and isn’t okay, and devise some consequences for misbehavior. Even though it may be labor intensive and therefore expensive to keep and enforce such rules, he says, the Twitter community would benefit from it.

“There’s a bigger threat to not taking on this problem than taking on this problem, simply because public sympathy is going to go more in the direction of the abused than the abusers,” Shirky says.

But while the time seems right for Twitter to act, it is far from clear how best to discourage such behavior. It does have methods in place to help deal with abuse, such as the ability to block and report a user who’s bothering you. Yet while that may help if you’re dealing with one or even a few bothersome tweeters, it cannot stop a deluge of nasty posts, and a determined harasser can always just make a new user profile and start the harassment anew.

How to make Twitter safer without turning off some existing users may be an even trickier question. The freewheeling exchange of views and opinions that characterizes Twitter is part of its appeal; if it were to enforce strict controls over who could talk to whom, or—like Facebook—require people to disclose their real identities, that might cut down on the flow of information.

Improvements may come if Twitter carefully combines increased communication with users and technology for automatically identifying harassers.

Twitter might reduce the burden on its own staff by involving its users more directly. Justin Patchin, a professor of criminal justice at the University of Wisconsin-Eau Claire and co-director of the school’s Cyberbullying Research Center, says Twitter could ask its community to regulate itself—perhaps by allowing users to volunteer to vote on whether or not content is abusive. 

Kate Crawford, a visiting professor at the MIT Center for Civic Media and a principal researcher at Microsoft Research, suggests that people experiencing harassment could share lists of users they have blocked with others having the same problem. “There is no quick technical fix for a social problem,” says Crawford. “What there can be is a broadening of the understanding of what the problem is.”

Twitter doesn’t preëmptively spot and wipe out offensive content, yet Jerry Zhu, an associate professor of computer science at the University of Wisconsin-Madison who has studied the use of machine learning to track abusive posts on Twitter, says artificial intelligence can help spot and censor nasty posts by looking out for key words and phrases. This is hard to do with certainty if bullying is not explicit, though; sometimes people are being nasty without using obviously mean language. “This is where the current technique is hitting the AI limit in some ways in that computers cannot be that subtle and parse that meaning out of it accurately,” he says.

While any changes will risk upsetting some users, that may be a price that Twitter is willing to pay in the short term to create a more hospitable service over the long run. Says Robin Kowalski, a psychology professor at Clemson University who studies cyberbullying: “If they lose a few users, they’re going to gain a few more.”

Gain the insight you need on emerging technologies at EmTech Digital.

Register today

View the original article here