Archiv für den Monat Juli 2015

SayMore – not a new Tariff from T-Mobile but Call Strangers On The Phone by YouTube’s Brent Hurley


Somewhere between texting and video chat, the mobile phone stopped being a phone. But there’s a special intimacy unique to simply talking to someone, and early YouTuber Brent Hurley wants to bring it back with SayMore. His new app lets you browse pre-made discussion topics, preview the profile of a conversation partner, and start a free VOIP call with them. You know, so you can just talk about your feelings.

Backed by Brent’s brother, YouTube co-founder Chad Hurley, plus its early CFO Gideon Yu and biz dev wizard Chris Maxcy, SayMore is now available on iOS.

While it might be novel to chat up a stranger, attempts in the meatspace like Highlight and over video like ChatRoulette and Airtime have petered out. SayMore will need a way to keep people coming back to blather or home in on some truly lonely users who need the company of strangers. Otherwise, it might see its line go dead.

IMG_1666Once you fill out a brief profile blurb, SayMore gives you a curated list of things to talk about. Some examples include:

  • Eurozone: What’s next for Greece and the EU?
  • Fashion: Share tips on how to spruce up your summer wardrobe!
  • Technology: Is Uber good for the world?
  • Parenting: What can I do to give my kids a leg up in this world?
  • Trending: Is social media bringing us closer together or driving us apart?

Pick one you like, vet the person you’ll connect with, and you start the call with one tap. For privacy, you never enter your phone number or see anyone else’s, or their last name. The calls run with HD voice over cellular or Wi-Fi for free, and sound better than a normal phone call.

If you’re the only one interested in a topic right now, SayMore lets you select it and a couple other topics you’re into, and choose how long to be available for someone to call you. Then you can go about your day and wait for a ping.

Conveniently, when I first tried using SayMore, I got connected to Brent. I’d picked through some of the tech topic prompts and chose one about “Startup Founders: What’s keeping you up at night?”

Brent came up with SayMore after an inspired conversation with the guy next to him on a plane. We gabbed about whether people really needed another way to communicate. Brent explained that it can be tough to cut through the manicured success theater people purport on a place like Facebook, but the power of voice can trigger real emotions.

SayMore COnvo“We do think that humans are social creatures by nature,” Brent says. SayMore is there “Anytime they want to have a deeper connection rather than posting a status update where people might just Like it or retweet it, but there isn’t a dialogue that takes place.” He concludes, “the current social networks are more broadcast platforms.”

Eventually, SayMore wants to let you friend the people you chat with “to create a conversation graph — a new friend list centered around conversations.” Then when you have something you want to talk about, you could send the topic out to all your SayMore friends, and whoever’s free could jump on the line with you. That could be the answer to how SayMore avoids people getting bored of strangers and never coming back.

Still, with all the vivid communication mediums and other digital distractions, it’s hard to imagine SayMore occupying people’s time. It’s nice to imagine people looking up from their phones and taking a stroll through their neighborhood, observing life outside the screen while they chat about something they care about. But the reason people love texting is that phone calls take energy to keep alive. Most people would rather just bury their face in something mindless like Candy Crush, or browse their News Feed where there’s no pressure to create, just consume.

Loneliness and alienation are real problems worth solving. But SayMore will have to fight the current as everyone says less.


World’s New Blood Test

155149909grey-sClick to Open Overlay Gallery





Future of Retail

Silicon Valley’s Best Hope for Beating Amazon Is Live


Apple’s Shyness around Apple Watch

Apple Doesn’t Want You to Know How Many Watches It Sold

A display case containing the Apple Watch Sport at the company's flagship store in San Francisco, on June 17, 2015.

Car Security: Remotely disabling brakes on a Jeep

Hackers Remotely Kill a Jeep on the Highway—With Me in It

Video Live Hacking Jeep

Jonathan Ive – The Shape of Things to Come



Chris Urmson – Leader of Google Self Driving Car Project on TED

0:11 So in 1885, Karl Benz invented the automobile. Later that year, he took it out for the first public test drive, and — true story — crashed into a wall. For the last 130 years, we’ve been working around that least reliable part of the car, the driver. We’ve made the car stronger. We’ve added seat belts, we’ve added air bags, and in the last decade, we’ve actually started trying to make the car smarter to fix that bug, the driver.

0:40 Now, today I’m going to talk to you a little bit about the difference between patching around the problem with driver assistance systems and actually having fully self-driving cars and what they can do for the world. I’m also going to talk to you a little bit about our car and allow you to see how it sees the world and how it reacts and what it does, but first I’m going to talk a little bit about the problem. And it’s a big problem: 1.2 million people are killed on the world’s roads every year. In America alone, 33,000 people are killed each year. To put that in perspective, that’s the same as a 737 falling out of the sky every working day. It’s kind of unbelievable. Cars are sold to us like this, but really, this is what driving’s like. Right? It’s not sunny, it’s rainy, and you want to do anything other than drive. And the reason why is this: Traffic is getting worse. In America, between 1990 and 2010, the vehicle miles traveled increased by 38 percent. We grew by six percent of roads, so it’s not in your brains. Traffic really is substantially worse than it was not very long ago.

1:49 And all of this has a very human cost. So if you take the average commute time in America, which is about 50 minutes, you multiply that by the 120 million workers we have, that turns out to be about six billion minutes wasted in commuting every day. Now, that’s a big number, so let’s put it in perspective. You take that six billion minutes and you divide it by the average life expectancy of a person, that turns out to be 162 lifetimes spent every day, wasted, just getting from A to B. It’s unbelievable. And then, there are those of us who don’t have the privilege of sitting in traffic. So this is Steve. He’s an incredibly capable guy, but he just happens to be blind, and that means instead of a 30-minute drive to work in the morning, it’s a two-hour ordeal of piecing together bits of public transit or asking friends and family for a ride. He doesn’t have that same freedom that you and I have to get around. We should do something about that.

2:48 Now, conventional wisdom would say that we’ll just take these driver assistance systems and we’ll kind of push them and incrementally improve them, and over time, they’ll turn into self-driving cars. Well, I’m here to tell you that’s like me saying that if I work really hard at jumping, one day I’ll be able to fly. We actually need to do something a little different. And so I’m going to talk to you about three different ways that self-driving systems are different than driver assistance systems. And I’m going to start with some of our own experience.

3:17 So back in 2013, we had the first test of a self-driving car where we let regular people use it. Well, almost regular — they were 100 Googlers, but they weren’t working on the project. And we gave them the car and we allowed them to use it in their daily lives. But unlike a real self-driving car, this one had a big asterisk with it: They had to pay attention, because this was an experimental vehicle. We tested it a lot, but it could still fail. And so we gave them two hours of training, we put them in the car, we let them use it, and what we heard back was something awesome, as someone trying to bring a product into the world. Every one of them told us they loved it. In fact, we had a Porsche driver who came in and told us on the first day, „This is completely stupid. What are we thinking?“ But at the end of it, he said, „Not only should I have it, everyone else should have it, because people are terrible drivers.“ So this was music to our ears, but then we started to look at what the people inside the car were doing, and this was eye-opening. Now, my favorite story is this gentleman who looks down at his phone and realizes the battery is low, so he turns around like this in the car and digs around in his backpack, pulls out his laptop, puts it on the seat, goes in the back again, digs around, pulls out the charging cable for his phone, futzes around, puts it into the laptop, puts it on the phone. Sure enough, the phone is charging. All the time he’s been doing 65 miles per hour down the freeway. Right? Unbelievable. So we thought about this and we said, it’s kind of obvious, right? The better the technology gets, the less reliable the driver is going to get. So by just making the cars incrementally smarter, we’re probably not going to see the wins we really need.

4:59 Let me talk about something a little technical for a moment here. So we’re looking at this graph, and along the bottom is how often does the car apply the brakes when it shouldn’t. You can ignore most of that axis, because if you’re driving around town, and the car starts stopping randomly, you’re never going to buy that car. And the vertical axis is how often the car is going to apply the brakes when it’s supposed to to help you avoid an accident. Now, if we look at the bottom left corner here, this is your classic car. It doesn’t apply the brakes for you, it doesn’t do anything goofy, but it also doesn’t get you out of an accident. Now, if we want to bring a driver assistance system into a car, say with collision mitigation braking, we’re going to put some package of technology on there, and that’s this curve, and it’s going to have some operating properties, but it’s never going to avoid all of the accidents, because it doesn’t have that capability. But we’ll pick some place along the curve here, and maybe it avoids half of accidents that the human driver misses, and that’s amazing, right? We just reduced accidents on our roads by a factor of two. There are now 17,000 less people dying every year in America.

6:01 But if we want a self-driving car, we need a technology curve that looks like this. We’re going to have to put more sensors in the vehicle, and we’ll pick some operating point up here where it basically never gets into a crash. They’ll happen, but very low frequency. Now you and I could look at this and we could argue about whether it’s incremental, and I could say something like „80-20 rule,“ and it’s really hard to move up to that new curve. But let’s look at it from a different direction for a moment. So let’s look at how often the technology has to do the right thing. And so this green dot up here is a driver assistance system. It turns out that human drivers make mistakes that lead to traffic accidents about once every 100,000 miles in America. In contrast, a self-driving system is probably making decisions about 10 times per second, so order of magnitude, that’s about 1,000 times per mile. So if you compare the distance between these two, it’s about 10 to the eighth, right? Eight orders of magnitude. That’s like comparing how fast I run to the speed of light. It doesn’t matter how hard I train, I’m never actually going to get there. So there’s a pretty big gap there.

7:10 And then finally, there’s how the system can handle uncertainty. So this pedestrian here might be stepping into the road, might not be. I can’t tell, nor can any of our algorithms, but in the case of a driver assistance system, that means it can’t take action, because again, if it presses the brakes unexpectedly, that’s completely unacceptable. Whereas a self-driving system can look at that pedestrian and say, I don’t know what they’re about to do, slow down, take a better look, and then react appropriately after that.

7:38 So it can be much safer than a driver assistance system can ever be. So that’s enough about the differences between the two. Let’s spend some time talking about how the car sees the world.

7:48 So this is our vehicle. It starts by understanding where it is in the world, by taking a map and its sensor data and aligning the two, and then we layer on top of that what it sees in the moment. So here, all the purple boxes you can see are other vehicles on the road, and the red thing on the side over there is a cyclist, and up in the distance, if you look really closely, you can see some cones. Then we know where the car is in the moment, but we have to do better than that: we have to predict what’s going to happen. So here the pickup truck in top right is about to make a left lane change because the road in front of it is closed, so it needs to get out of the way. Knowing that one pickup truck is great, but we really need to know what everybody’s thinking, so it becomes quite a complicated problem. And then given that, we can figure out how the car should respond in the moment, so what trajectory it should follow, how quickly it should slow down or speed up. And then that all turns into just following a path: turning the steering wheel left or right, pressing the brake or gas. It’s really just two numbers at the end of the day. So how hard can it really be?

8:49 Back when we started in 2009, this is what our system looked like. So you can see our car in the middle and the other boxes on the road, driving down the highway. The car needs to understand where it is and roughly where the other vehicles are. It’s really a geometric understanding of the world. Once we started driving on neighborhood and city streets, the problem becomes a whole new level of difficulty. You see pedestrians crossing in front of us, cars crossing in front of us, going every which way, the traffic lights, crosswalks. It’s an incredibly complicated problem by comparison. And then once you have that problem solved, the vehicle has to be able to deal with construction. So here are the cones on the left forcing it to drive to the right, but not just construction in isolation, of course. It has to deal with other people moving through that construction zone as well. And of course, if anyone’s breaking the rules, the police are there and the car has to understand that that flashing light on the top of the car means that it’s not just a car, it’s actually a police officer. Similarly, the orange box on the side here, it’s a school bus, and we have to treat that differently as well.

9:49 When we’re out on the road, other people have expectations: So, when a cyclist puts up their arm, it means they’re expecting the car to yield to them and make room for them to make a lane change. And when a police officer stood in the road, our vehicle should understand that this means stop, and when they signal to go, we should continue.

10:08 Now, the way we accomplish this is by sharing data between the vehicles. The first, most crude model of this is when one vehicle sees a construction zone, having another know about it so it can be in the correct lane to avoid some of the difficulty. But we actually have a much deeper understanding of this. We could take all of the data that the cars have seen over time, the hundreds of thousands of pedestrians, cyclists, and vehicles that have been out there and understand what they look like and use that to infer what other vehicles should look like and other pedestrians should look like. And then, even more importantly, we could take from that a model of how we expect them to move through the world. So here the yellow box is a pedestrian crossing in front of us. Here the blue box is a cyclist and we anticipate that they’re going to nudge out and around the car to the right. Here there’s a cyclist coming down the road and we know they’re going to continue to drive down the shape of the road. Here somebody makes a right turn, and in a moment here, somebody’s going to make a U-turn in front of us, and we can anticipate that behavior and respond safely.

11:04 Now, that’s all well and good for things that we’ve seen, but of course, you encounter lots of things that you haven’t seen in the world before. And so just a couple of months ago, our vehicles were driving through Mountain View, and this is what we encountered. This is a woman in an electric wheelchair chasing a duck in circles on the road. (Laughter) Now it turns out, there is nowhere in the DMV handbook that tells you how to deal with that, but our vehicles were able to encounter that, slow down, and drive safely. Now, we don’t have to deal with just ducks. Watch this bird fly across in front of us. The car reacts to that. Here we’re dealing with a cyclist that you would never expect to see anywhere other than Mountain View. And of course, we have to deal with drivers, even the very small ones. Watch to the right as someone jumps out of this truck at us. And now, watch the left as the car with the green box decides he needs to make a right turn at the last possible moment. Here, as we make a lane change, the car to our left decides it wants to as well. And here, we watch a car blow through a red light and yield to it. And similarly, here, a cyclist blowing through that light as well. And of course, the vehicle responds safely. And of course, we have people who do I don’t know what sometimes on the road, like this guy pulling out between two self-driving cars. You have to ask, „What are you thinking?“ (Laughter)

12:27 Now, I just fire-hosed you with a lot of stuff there, so I’m going to break one of these down pretty quickly. So what we’re looking at is the scene with the cyclist again, and you might notice in the bottom, we can’t actually see the cyclist yet, but the car can: it’s that little blue box up there, and that comes from the laser data. And that’s not actually really easy to understand, so what I’m going to do is I’m going to turn that laser data and look at it, and if you’re really good at looking at laser data, you can see a few dots on the curve there, right there, and that blue box is that cyclist. Now as our light is red, the cyclist’s light has turned yellow already, and if you squint, you can see that in the imagery. But the cyclist, we see, is going to proceed through the intersection. Our light has now turned green, his is solidly red, and we now anticipate that this bike is going to come all the way across. Unfortunately the other drivers next to us were not paying as much attention. They started to pull forward, and fortunately for everyone, this cyclists reacts, avoids, and makes it through the intersection. And off we go.

13:25 Now, as you can see, we’ve made some pretty exciting progress, and at this point we’re pretty convinced this technology is going to come to market. We do three million miles of testing in our simulators every single day, so you can imagine the experience that our vehicles have. We are looking forward to having this technology on the road, and we think the right path is to go through the self-driving rather than driver assistance approach because the urgency is so large. In the time I have given this talk today, 34 people have died on America’s roads.

13:55 How soon can we bring it out? Well, it’s hard to say because it’s a really complicated problem, but these are my two boys. My oldest son is 11, and that means in four and a half years, he’s going to be able to get his driver’s license. My team and I are committed to making sure that doesn’t happen.

14:13 Thank you.

14:15 (Laughter) (Applause) Chris Anderson: Chris, I’ve got a question for you.

14:22 Chris Urmson: Sure.

14:25 CA: So certainly, the mind of your cars is pretty mind-boggling. On this debate between driver-assisted and fully driverless — I mean, there’s a real debate going on out there right now. So some of the companies, for example, Tesla, are going the driver-assisted route. What you’re saying is that that’s kind of going to be a dead end because you can’t just keep improving that route and get to fully driverless at some point, and then a driver is going to say, „This feels safe,“ and climb into the back, and something ugly will happen.

14:58 CU: Right. No, that’s exactly right, and it’s not to say that the driver assistance systems aren’t going to be incredibly valuable. They can save a lot of lives in the interim, but to see the transformative opportunity to help someone like Steve get around, to really get to the end case in safety, to have the opportunity to change our cities and move parking out and get rid of these urban craters we call parking lots, it’s the only way to go.

15:20 CA: We will be tracking your progress with huge interest. Thanks so much, Chris. CU: Thank you.

Humans to become ‚pets‘ of AI robots, says Apple co-founder Wozniak

If you needed just one more reason to trash your iPhone, this is it. Apple co-founder Steve Wozniak recently told a crowd of techies in Austin, Texas, that the future of humanity will predicate on artificially-intelligent (AI) robots keeping people as „pets“ – and Wozniak says he’s actually looking forward to this grim, robot-dominated future.

Building upon Apple’s „Siri“ concept, which is AI in its infancy, Wozniak’s vision for 100 years from today is that humans will be literally owned by AI robots, much like how humans currently own dogs or cats. Robots will be in charge, in other words, and humans will be their slaves. And all of this will somehow be „really good for humans,“ in Wozniak’s view.

Speaking at the Freescale Technology Forum 2015, Wozniak told eager listeners that putting robots in charge is a good thing because, by that point (100 years from now), they’ll have the capacity to become good stewards of nature, „and humans are part of nature,“ he says. Expressing comfort by this thought, Wozniak stated that he „got over [his] fear“ of becoming a robot slave.

„Computers are going to take over from humans, no question,“ Wozniak told the Australian Financial Review during a recent interview, affirming what many others in the tech industry, including Tesla CEO Elon Musk, believe will commence once AI technology really gets off the ground.

Since Wozniak treats his own dogs „really nice,“ he says he isn’t concerned about AI robots taking over

Echoing the concerns of Musk, Microsoft founder Bill Gates, physicist Stephen Hawking, and others, Wozniak does acknowledge some of the risks involved with developing AI technologies. But these risks aren’t necessarily a deal breaker because AI robots, in his view, will probably treat humans kindly just like most people treat their own pets.

„Will we be the gods? Will we be the family pets? Or will we be ants that get stepped on? I don’t know about that,“ he stated. „But when I got that thinking in my head about if I’m going to be treated in the future as a pet to these smart machines … well I’m going to treat my own pet dog really nice.“

Well, phew! It’s all settled then. Because Wozniak happens to be kind to his own dog, it’s perfectly fine, in his view, to unleash an army of advanced robots that are „smarter“ than humans and capable of destroying them because maybe they’ll choose instead to be kind to humans.

Wozniak: Let’s just unleash AI robots in order to find out how they’ll treat humans


It’s a lot like the infamously absurd words of House Minority leader Nancy Pelosi, who stated prior to voting for Obamacare that „we have to pass the bill so that you can find out what is in it.“ Concerning AI robots, Wozniak’s message is essentially the same: We just have to create them first in order to find out what they’ll do to humanity.

But if a recent „Google Brain“ study is any indicator of how AI robots think, humans would be lucky to be treated as kindly as a family pet. An experimental AI robot „interviewed“ by Google researchers revealed that such technology is both amoral and hostile to humans. When asked „what is immoral?“ the robot responded, „the fact that you have a child,“ expressing enmity against human reproduction.

You can read the full paper here: Neural Conversational Model
„Everyone on the planet has much to fear from the unregulated development of super-intelligent machines,“ stated James Barrat, a documentary filmmaker and author of the book Our Final Invention: Artificial Intelligence and the End of the Human Era, during a recent interview with Smithsonian. „They will be machines that kill, unsupervised by humans.“


Windows Phone is as good as dead



Image: Mashable composite, Microsoft


With Wednesday’s layoffs, Microsoft, saddled with the losing mobile hand that is Windows Phone, has essentially folded. The bulk of the 7,800 people let go are from the company’s phone division, a tacit admission that its big plans for Windows Phone haven’t exactly worked out.

The company’s not leaving the casino, though: Windows Phone, the platform, isn’t going anywhere, even as Microsoft greatly scales back its hardware ambitions. The company has labored for years to create both a full-featured mobile operating system as well as an ecosystem of devices — PC, phone, tablet and more — that all use the same code base. It would be silly to just abandon its mobile platform, especially as people spend more and more of their time on smartphones.

In fact, if you’re not one of the 7,800 people losing their jobs, there’s actually a lot to like in Satya Nadella’s explanation: Microsoft will continue to build Windows Lumia handsets, but only three types: flagships, business-focused enterprise phones and low-end budget devices.


They’re retreating from being a mainstream player

They’re retreating from being a mainstream player,“ says Martin Reynolds, vice president at Gartner Research. „They’ll continue to bring products to market, but not particularly aggressively.“The move represents a clear refocusing, putting Microsoft’s phones in the arenas where they might actually score a few punches before Android and the iPhone walk away with all the market share. It also rightly ditches the current strategy of offering several different Lumias, each with region-specific models, which led to a muddled brand and a confusing market strategy message to consumers.

Retreating forward

Even without the model shake-up, trimming the fat on the handset business Microsoft acquired from Nokia was probably inevitable. With a few exceptions (hello, curved screens), smartphone design and technology have more or less plateaued — it’s no coincidence that both iOS and Android have essentially taken a „bye“ in 2015, with few feature updates. Big hardware teams aren’t really needed to build good smartphones in 2015, as illustrated by upstart Chinese companies like Xiaomi and OnePlus.

„Things have changed in the last few years,“ says Reynolds. „You don’t have [to be a] big company to run a small phone business. They certainly don’t need the design teams and manufacturing people going forward.“

Still, there are new Lumias — and certainly a new flagship — coming soon. A couple of months after Windows 10 debuts, Windows 10 for phones will arrive, and, as Nadella suggested in his letter to employees, those phones will emphasize the big differentiators in the Windows ecosystem. Commentators like Daniel Rubino at Windows Central almost have you believing that a leaner-and-meaner Microsoft mobile division will be poised to succeed, albeit with lowered expectations, once Windows 10 is fully formed.


That point of view overlooks the crux of the matter: Windows Phone’s fate was never in the hands of Microsoft. What the company does in mobile at this point is virtually irrelevant. It designed a beautiful (and influential!) user interface, offered sweeter deals to developers than competitors, and helped engineer some of the most sophisticated cameras ever seen on mobile.

None of it mattered. Developers and consumers didn’t respond, locked in a deadly catch-22: If the apps weren’t there, consumers wouldn’t buy the phones; if there weren’t enough people on the platform, developers wouldn’t bother creating apps.

„Windows Phone is not even a blip on [developers‘] radar,“ says Richard Hay, a longtime Microsoft observer and contributor to SuperSite for Windows. „They’re not going to start flocking to it, because what’s the draw? You’re still going to have the app gap.“

The „app gap“ ultimately dug Windows Phone’s grave, and even though it’s only got one foot in it, today’s news will be widely perceived as an admission that the other will soon follow. If there were other Windows Phone manufacturers, it might be a different story, but Microsoft makes 97% of the Windows Phones being sold, according to Ad Duplex. If they’re scaling back, who’s going to step up?

Windows 10 and mobile

If there’s a way Microsoft can resuscitate Windows Phone, it’s with Windows 10. Its ecosystem strategy doesn’t depend on it, but with the new OS, Windows Phones will be more connected to the platform than before, sharing all the same code and development tools.

„The universal Windows platform helps,“ says Hay. „Will that persuade developers to develop for handsets and smaller tablets? Is it enough to come back from the edge? i’m just not sure it is.“

That means Windows developers will be able to create Windows Phone versions of their apps with minimal effort, and some of Windows Phone’s key differentiators, like Cortana, will get a chance to shine on PCs, which could ultimately have a positive impact on the platform.

Finally, there’s Continuum — the feature that allows Windows apps to adapt from PC to tablet to phone seamlessly and lets a Windows Phone theoretically act as your PC when it’s plugged into an external screen. And although there will always be performance concerns when trying to do PC with a mobile processor, it’s a pretty cool trick.

Continuum, though, has only the slimmest chance of being the ace in the hole that wins the day — any day — for Windows Phone. Even for enterprise customers, it’s hard to picture any of Windows 10’s differentiators winning over users, especially now that we’re firmly in a BYOD and single-device world.


Even if Continuum ends up being an X factor, who’s left to give Windows Phones a chance?

Even if Continuum ends up being an X factor, who’s left to give Windows Phones a chance? Microsoft is certainly hoping today’s belt-tightening and the Windows 10 launch will lead to some kind of success in mobile, albeit on redefined terms. But it’s not acting in a vacuum. Today’s retreat — or rather the perception of it — may have sealed Windows Phone’s fate. Who would believe the recommendation of a Windows handset after today?Without those new users, developers will have even less incentive to create apps. And without those experiences, Windows Phone will be even more of shell than it is now. What then?

It’s admirable that Microsoft is taking painful steps to preserve what it’s built, but it’s hard not to see its Windows Phone restructuring as delaying the inevitable. Yes, by reducing its ambitions, it’s no longer losing on big bets. But in mobile, there really isn’t a low-stakes table.


Drones for Maintenance

One of the drones used in Nokia’s network testing. Image: Nokia

Nokia has showed off how it plans to use drones to replace some of the inspection and maintenance work on cell towers which is currently done by human technicians.

Amazon hopes drones will one day be used to bring packages to Prime customers, free from the constraints of road traffic and costly human delivery drivers. In the same vein, postal services in France and Switzerland have launched drone trials to see whether the unmanned aerial vehicles could be used to deliver the post in the next five years.

Now Nokia is examining whether it should require human technicians to climb cell towers to inspect them when a drone can do the job faster and without the risk of falling.

For a recent trial of the drones, Nokia teamed up with operator du in the United Arab Emirates. While inspecting the towers, the drones carried smartphones to help with radio planning and line of sight testing between radio towers.

The proof of concept was conducted in the contained environment of the Dubai International Stadium, a sports stadium that seats 25,000 people.

According to Nokia, its telco drones offered a number of advantages over humans, including covering manual walk tests faster than humans. Thanks to a network testing app installed on the smartphones which the drones carried, they were able to automatically send test data for processing at Nokia’s global delivery centre.

The drones could be used to cut down the frequency of technicians climbing up and down a telecoms tower, which Nokia says can be very dangerous in bad weather conditions. In addition, with a single passover the drones could generate a panoramic view of the lattice tower and offer the potential to remotely monitor installations.

The drones were useful for helping engineers design the network, and detecting if trees interfered with a frequency being used, determining power requirements, and latency simulation.

Nokia and its partners employed Secutronic INSPIRE1 drones for network optimisation at the stadium and MICRODRONES d4-1000 models for tower inspection, line of sight testing, and radio site planning.

Nokia isn’t the first to think of drones for network maintenance. Fluke Networks last year launched a drone edition of its Wireless Work Advisor with a focus on safety and efficiency.