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#431175 Servosila introduces Mobile Robots ...

Servosila introduces a new member of the family of Servosila “Engineer” robots, a UGV called “Radio Engineer”. This new variant of the well-known backpack-transportable robot features a Software Defined Radio (SDR) payload module integrated into the robotic vehicle.

“Several of our key customers had asked us to enable an Electronic Warfare (EW) or Cognitive Radio applications in our robots”, – says a spokesman for the company, “By integrating a Software Defined Radio (SDR) module into our robotic platforms we cater to both requirements. Radio spectrum analysis, radio signal detection, jamming, and radio relay are important features for EOD robots such as ours. Servosila continues to serve the customers by pushing the boundaries of what their Servosila robots can do. Our partners in the research world and academia shall also greatly benefit from the new functionality that gives them more means of achieving their research goals.”
Photo Credit: Servosila – www.servosila.com
Coupling a programmable mobile robot with a software-defined radio creates a powerful platform for developing innovative applications that mix mobility and artificial intelligence with modern radio technologies. The new robotic radio applications include localized frequency hopping pattern analysis, OFDM waveform recognition, outdoor signal triangulation, cognitive mesh networking, automatic area search for radio emitters, passive or active mobile robotic radars, mobile base stations, mobile radio scanners, and many others.

A rotating head of the robot with mounts for external antennae acts as a pan-and-tilt device thus enabling various scanning and tracking applications. The neck of the robotic head is equipped with a pair of highly accurate Servosila-made servos with a pointing precision of 3.0 angular minutes. This means that the robot can point its antennae with an unprecedented accuracy.

Researchers and academia can benefit from the platform’s support for GnuRadio, an open source software framework for developing SDR applications. An on-board Intel i7 computer capable of executing OpenCL code, is internally connected to the SDR payload module. This makes it possible to execute most existing GnuRadio applications directly on the robot’s on-board computer. Other sensors of the robot such as a GPS sensor, an IMU or a thermal vision camera contribute into sensor fusion algorithms.

Since Servosila “Engineer” mobile robots are primarily designed for outdoor use, the SDR module is fully enclosed into a hardened body of the robot which provides protection in case of dust, rain, snow or impacts with obstacles while the robot is on the move. The robot and its SDR payload module are both powered by an on-board battery thus making the entire robotic radio platform independent of external power supplies.

Servosila plans to start shipping the SDR-equipped robots to international customers in October, 2017.

Web: https://www.servosila.com
YouTube: https://www.youtube.com/user/servosila/videos

About the Company
Servosila is a robotics technology company that designs, produces and markets a range of mobile robots, robotic arms, servo drives, harmonic reduction gears, robotic control systems as well as software packages that make the robots intelligent. Servosila provides consulting, training and operations support services to various customers around the world. The company markets its products and services directly or through a network of partners who provide tailored and localized services that meet specific procurement, support or operational needs.
Press Release above is by: Servosila
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#431171 SceneScan: Real-Time 3D Depth Sensing ...

Nerian Introduces a High-Performance Successor for the Proven SP1 System
Stereo vision, which is the three-dimensional perception of our environment with two sensors likeour eyes, is a well-known technology. As a passive method – there is no need to emit light in thevisible or invisible spectral range – this technology can open up new possibilities for three dimensional perception, even under difficult conditions.
But as often, the devil is in the details: for most applications, the software implementation withstandard PCs, but also with graphics processors, is too slow. Another complicating factor is thatthese hardware platforms are expensive and not energy-efficient. The solution is to instead usespecialized hardware for image processing. A programmable logic device – a so-called FPGA – cangreatly accelerate the image processing.
As a technology leader, Nerian Vision Technologies has been following this path successfully forthe past two years with the SP1 stereo vision system, which has enabled completely newapplications in the fields of robotics, automation technology, medical technology, autonomousdriving and other domains. Now the company introduces two successors:
SceneScan and SceneScan Pro. Real eye-catchers in a double sense: stereo vision in an elegant design!But more important is, of course, the significantly improved inner workings of the two new modelsin comparison to their predecessor. The new hardware allows processing rates of up to 100 framesper second at resolutions of up to 3 megapixels, which leaves the SP1 far behind:
Photo Credit: Nerian Vision Technologies – www.nerian.com

The table illustrates the difference: while SceneScan Pro has the highest possible computing powerand is designed for the most demanding applications, SceneScan has been cost-reduced forapplications with lower requirements. The customer can thus optimize his embedded vision solution both in terms of costs and technology.
The new duo is completed by Nerian’s proven Karmin stereo cameras. Of course, industrialUSB3Vision cameras by other manufacturers are also supported.This combination not only supports the above-mentioned applications even better, but alsofacilitates completely new and innovative ones. If required, customer-specific adaptations are alsopossible.
ContactNerian Vision TechnologiesOwner: Dr. Konstantin SchauweckerGotenstr. 970771 Leinfelden-EchterdingenGermanyPhone: +49 711 / 2195 9414Email: service@nerian.comWebsite: http://nerian.com
Press Release Authored By: Nerian Vision Technologies
Photo Credit: Nerian Vision Technologies – www.nerian.com
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#431170 This Week’s Awesome Stories From ...

AUGMENTED REALITY
ZED Mini Turns Rift and Vive Into an AR Headset From the FutureBen Lang | Road to VR“When attached, the camera provides stereo pass-through video and real-time depth and environment mapping, turning the headsets into dev kits emulating the capabilities of high-end AR headsets of the future. The ZED Mini will launch in November.”
ROBOTICS
Life-Size Humanoid Robot Is Designed to Fall Over (and Over and Over)Evan Ackerman | IEEE Spectrum “The researchers came up with a new strategy for not worrying about falls: not worrying about falls. Instead, they’ve built their robot from the ground up with an armored structure that makes it totally okay with falling over and getting right back up again.”
SPACE
Russia Will Team up With NASA to Build a Lunar Space StationAnatoly Zak | Popular Mechanics “NASA and its partner agencies plan to begin the construction of the modular habitat known as the Deep-Space Gateway in orbit around the Moon in the early 2020s. It will become the main destination for astronauts for at least a decade, extending human presence beyond the Earth’s orbit for the first time since the end of the Apollo program in 1972. Launched on NASA’s giant SLS rocket and serviced by the crews of the Orion spacecraft, the outpost would pave the way to a mission to Mars in the 2030s.”
TRANSPORTATION
Dubai Starts Testing Crewless Two-Person ‘Flying Taxis’Thuy Ong | The Verge“The drone was uncrewed and hovered 200 meters high during the test flight, according to Reuters. The AAT, which is about two meters high, was supplied by specialist German manufacturer Volocopter, known for its eponymous helicopter drone hybrid with 18 rotors…Dubai has a target for autonomous transport to account for a quarter of total trips by 2030.”
AUTONOMOUS CARS
Toyota Is Trusting a Startup for a Crucial Part of Its Newest Self-Driving CarsJohana Bhuiyan | Recode “Toyota unveiled the next generation of its self-driving platform today, which features more accurate object detection technology and mapping, among other advancements. These test cars—which Toyota is testing on both a closed driving course and on some public roads—will also be using Luminar’s lidar sensors, or radars that use lasers to detect the distance to an object.”
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#431165 Intel Jumps Into Brain-Like Computing ...

The brain has long inspired the design of computers and their software. Now Intel has become the latest tech company to decide that mimicking the brain’s hardware could be the next stage in the evolution of computing.
On Monday the company unveiled an experimental “neuromorphic” chip called Loihi. Neuromorphic chips are microprocessors whose architecture is configured to mimic the biological brain’s network of neurons and the connections between them called synapses.
While neural networks—the in vogue approach to artificial intelligence and machine learning—are also inspired by the brain and use layers of virtual neurons, they are still implemented on conventional silicon hardware such as CPUs and GPUs.
The main benefit of mimicking the architecture of the brain on a physical chip, say neuromorphic computing’s proponents, is energy efficiency—the human brain runs on roughly 20 watts. The “neurons” in neuromorphic chips carry out the role of both processor and memory which removes the need to shuttle data back and forth between separate units, which is how traditional chips work. Each neuron also only needs to be powered while it’s firing.

At present, most machine learning is done in data centers due to the massive energy and computing requirements. Creating chips that capture some of nature’s efficiency could allow AI to be run directly on devices like smartphones, cars, and robots.
This is exactly the kind of application Michael Mayberry, managing director of Intel’s research arm, touts in a blog post announcing Loihi. He talks about CCTV cameras that can run image recognition to identify missing persons or traffic lights that can track traffic flow to optimize timing and keep vehicles moving.
There’s still a long way to go before that happens though. According to Wired, so far Intel has only been working with prototypes, and the first full-size version of the chip won’t be built until November.
Once complete, it will feature 130,000 neurons and 130 million synaptic connections split between 128 computing cores. The device will be 1,000 times more energy-efficient than standard approaches, according to Mayberry, but more impressive are claims the chip will be capable of continuous learning.
Intel’s newly launched self-learning neuromorphic chip.
Normally deep learning works by training a neural network on giant datasets to create a model that can then be applied to new data. The Loihi chip will combine training and inference on the same chip, which will allow it to learn on the fly, constantly updating its models and adapting to changing circumstances without having to be deliberately re-trained.
A select group of universities and research institutions will be the first to get their hands on the new chip in the first half of 2018, but Mayberry said it could be years before it’s commercially available. Whether commercialization happens at all may largely depend on whether early adopters can get the hardware to solve any practically useful problems.
So far neuromorphic computing has struggled to gain traction outside the research community. IBM released a neuromorphic chip called TrueNorth in 2014, but the device has yet to showcase any commercially useful applications.
Lee Gomes summarizes the hurdles facing neuromorphic computing excellently in IEEE Spectrum. One is that deep learning can run on very simple, low-precision hardware that can be optimized to use very little power, which suggests complicated new architectures may struggle to find purchase.
It’s also not easy to transfer deep learning approaches developed on conventional chips over to neuromorphic hardware, and even Intel Labs chief scientist Narayan Srinivasa admitted to Forbes Loihi wouldn’t work well with some deep learning models.
Finally, there’s considerable competition in the quest to develop new computer architectures specialized for machine learning. GPU vendors Nvidia and AMD have pivoted to take advantage of this newfound market and companies like Google and Microsoft are developing their own in-house solutions.
Intel, for its part, isn’t putting all its eggs in one basket. Last year it bought two companies building chips for specialized machine learning—Movidius and Nervana—and this was followed up with the $15 billion purchase of self-driving car chip- and camera-maker Mobileye.
And while the jury is still out on neuromorphic computing, it makes sense for a company eager to position itself as the AI chipmaker of the future to have its fingers in as many pies as possible. There are a growing number of voices suggesting that despite its undoubted power, deep learning alone will not allow us to imbue machines with the kind of adaptable, general intelligence humans possess.
What new approaches will get us there are hard to predict, but it’s entirely possible they will only work on hardware that closely mimics the one device we already know is capable of supporting this kind of intelligence—the human brain.
Image Credit: Intel Continue reading

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#431159 How Close Is Turing’s Dream of ...

The quest for conversational artificial intelligence has been a long one.
When Alan Turing, the father of modern computing, racked his considerable brains for a test that would truly indicate that a computer program was intelligent, he landed on this area. If a computer could convince a panel of human judges that they were talking to a human—if it could hold a convincing conversation—then it would indicate that artificial intelligence had advanced to the point where it was indistinguishable from human intelligence.
This gauntlet was thrown down in 1950 and, so far, no computer program has managed to pass the Turing test.
There have been some very notable failures, however: Joseph Weizenbaum, as early as 1966—when computers were still programmed with large punch-cards—developed a piece of natural language processing software called ELIZA. ELIZA was a machine intended to respond to human conversation by pretending to be a psychotherapist; you can still talk to her today.
Talking to ELIZA is a little strange. She’ll often rephrase things you’ve said back at you: so, for example, if you say “I’m feeling depressed,” she might say “Did you come to me because you are feeling depressed?” When she’s unsure about what you’ve said, ELIZA will usually respond with “I see,” or perhaps “Tell me more.”
For the first few lines of dialogue, especially if you treat her as your therapist, ELIZA can be convincingly human. This was something Weizenbaum noticed and was slightly alarmed by: people were willing to treat the algorithm as more human than it really was. Before long, even though some of the test subjects knew ELIZA was just a machine, they were opening up with some of their deepest feelings and secrets. They were pouring out their hearts to a machine. When Weizenbaum’s secretary spoke to ELIZA, even though she knew it was a fairly simple computer program, she still insisted Weizenbaum leave the room.
Part of the unexpected reaction ELIZA generated may be because people are more willing to open up to a machine, feeling they won’t be judged, even if the machine is ultimately powerless to do or say anything to really help. The ELIZA effect was named for this computer program: the tendency of humans to anthropomorphize machines, or think of them as human.

Weizenbaum himself, who later became deeply suspicious of the influence of computers and artificial intelligence in human life, was astonished that people were so willing to believe his script was human. He wrote, “I had not realized…that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people.”

“Consciously, you know you’re talking to a big block of code stored somewhere out there in the ether. But subconsciously, you might feel like you’re interacting with a human.”

The ELIZA effect may have disturbed Weizenbaum, but it has intrigued and fascinated others for decades. Perhaps you’ve noticed it in yourself, when talking to an AI like Siri, Alexa, or Google Assistant—the occasional response can seem almost too real. Consciously, you know you’re talking to a big block of code stored somewhere out there in the ether. But subconsciously, you might feel like you’re interacting with a human.
Yet the ELIZA effect, as enticing as it is, has proved a source of frustration for people who are trying to create conversational machines. Natural language processing has proceeded in leaps and bounds since the 1960s. Now you can find friendly chatbots like Mitsuku—which has frequently won the Loebner Prize, awarded to the machines that come closest to passing the Turing test—that aim to have a response to everything you might say.
In the commercial sphere, Facebook has opened up its Messenger program and provided software for people and companies to design their own chatbots. The idea is simple: why have an app for, say, ordering pizza when you can just chatter to a robot through your favorite messenger app and make the order in natural language, as if you were telling your friend to get it for you?
Startups like Semantic Machines hope their AI assistant will be able to interact with you just like a secretary or PA would, but with an unparalleled ability to retrieve information from the internet. They may soon be there.
But people who engineer chatbots—both in the social and commercial realm—encounter a common problem: the users, perhaps subconsciously, assume the chatbots are human and become disappointed when they’re not able to have a normal conversation. Frustration with miscommunication can often stem from raised initial expectations.
So far, no machine has really been able to crack the problem of context retention—understanding what’s been said before, referring back to it, and crafting responses based on the point the conversation has reached. Even Mitsuku will often struggle to remember the topic of conversation beyond a few lines of dialogue.

“For everything you say, there could be hundreds of responses that would make sense. When you travel a layer deeper into the conversation, those factors multiply until you end up with vast numbers of potential conversations.”

This is, of course, understandable. Conversation can be almost unimaginably complex. For everything you say, there could be hundreds of responses that would make sense. When you travel a layer deeper into the conversation, those factors multiply until—like possible games of Go or chess—you end up with vast numbers of potential conversations.
But that hasn’t deterred people from trying, most recently, tech giant Amazon, in an effort to make their AI voice assistant, Alexa, friendlier. They have been running the Alexa Prize competition, which offers a cool $500,000 to the winning AI—and a bonus of a million dollars to any team that can create a ‘socialbot’ capable of sustaining a conversation with human users for 20 minutes on a variety of themes.
Topics Alexa likes to chat about include science and technology, politics, sports, and celebrity gossip. The finalists were recently announced: chatbots from universities in Prague, Edinburgh, and Seattle. Finalists were chosen according to the ratings from Alexa users, who could trigger the socialbots into conversation by saying “Hey Alexa, let’s chat,” although the reviews for the socialbots weren’t always complimentary.
By narrowing down the fields of conversation to a specific range of topics, the Alexa Prize has cleverly started to get around the problem of context—just as commercially available chatbots hope to do. It’s much easier to model an interaction that goes a few layers into the conversational topic if you’re limiting those topics to a specific field.
Developing a machine that can hold almost any conversation with a human interlocutor convincingly might be difficult. It might even be a problem that requires artificial general intelligence to truly solve, rather than the previously-employed approaches of scripted answers or neural networks that associate inputs with responses.
But a machine that can have meaningful interactions that people might value and enjoy could be just around the corner. The Alexa Prize winner is announced in November. The ELIZA effect might mean we will relate to machines sooner than we’d thought.
So, go well, little socialbots. If you ever want to discuss the weather or what the world will be like once you guys take over, I’ll be around. Just don’t start a therapy session.
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