Tag Archives: computer

#430761 How Robots Are Getting Better at Making ...

The multiverse of science fiction is populated by robots that are indistinguishable from humans. They are usually smarter, faster, and stronger than us. They seem capable of doing any job imaginable, from piloting a starship and battling alien invaders to taking out the trash and cooking a gourmet meal.
The reality, of course, is far from fantasy. Aside from industrial settings, robots have yet to meet The Jetsons. The robots the public are exposed to seem little more than over-sized plastic toys, pre-programmed to perform a set of tasks without the ability to interact meaningfully with their environment or their creators.
To paraphrase PayPal co-founder and tech entrepreneur Peter Thiel, we wanted cool robots, instead we got 140 characters and Flippy the burger bot. But scientists are making progress to empower robots with the ability to see and respond to their surroundings just like humans.
Some of the latest developments in that arena were presented this month at the annual Robotics: Science and Systems Conference in Cambridge, Massachusetts. The papers drilled down into topics that ranged from how to make robots more conversational and help them understand language ambiguities to helping them see and navigate through complex spaces.
Improved Vision
Ben Burchfiel, a graduate student at Duke University, and his thesis advisor George Konidaris, an assistant professor of computer science at Brown University, developed an algorithm to enable machines to see the world more like humans.
In the paper, Burchfiel and Konidaris demonstrate how they can teach robots to identify and possibly manipulate three-dimensional objects even when they might be obscured or sitting in unfamiliar positions, such as a teapot that has been tipped over.
The researchers trained their algorithm by feeding it 3D scans of about 4,000 common household items such as beds, chairs, tables, and even toilets. They then tested its ability to identify about 900 new 3D objects just from a bird’s eye view. The algorithm made the right guess 75 percent of the time versus a success rate of about 50 percent for other computer vision techniques.
In an email interview with Singularity Hub, Burchfiel notes his research is not the first to train machines on 3D object classification. How their approach differs is that they confine the space in which the robot learns to classify the objects.
“Imagine the space of all possible objects,” Burchfiel explains. “That is to say, imagine you had tiny Legos, and I told you [that] you could stick them together any way you wanted, just build me an object. You have a huge number of objects you could make!”
The infinite possibilities could result in an object no human or machine might recognize.
To address that problem, the researchers had their algorithm find a more restricted space that would host the objects it wants to classify. “By working in this restricted space—mathematically we call it a subspace—we greatly simplify our task of classification. It is the finding of this space that sets us apart from previous approaches.”
Following Directions
Meanwhile, a pair of undergraduate students at Brown University figured out a way to teach robots to understand directions better, even at varying degrees of abstraction.
The research, led by Dilip Arumugam and Siddharth Karamcheti, addressed how to train a robot to understand nuances of natural language and then follow instructions correctly and efficiently.
“The problem is that commands can have different levels of abstraction, and that can cause a robot to plan its actions inefficiently or fail to complete the task at all,” says Arumugam in a press release.
In this project, the young researchers crowdsourced instructions for moving a virtual robot through an online domain. The space consisted of several rooms and a chair, which the robot was told to manipulate from one place to another. The volunteers gave various commands to the robot, ranging from general (“take the chair to the blue room”) to step-by-step instructions.
The researchers then used the database of spoken instructions to teach their system to understand the kinds of words used in different levels of language. The machine learned to not only follow instructions but to recognize the level of abstraction. That was key to kickstart its problem-solving abilities to tackle the job in the most appropriate way.
The research eventually moved from virtual pixels to a real place, using a Roomba-like robot that was able to respond to instructions within one second 90 percent of the time. Conversely, when unable to identify the specificity of the task, it took the robot 20 or more seconds to plan a task about 50 percent of the time.
One application of this new machine-learning technique referenced in the paper is a robot worker in a warehouse setting, but there are many fields that could benefit from a more versatile machine capable of moving seamlessly between small-scale operations and generalized tasks.
“Other areas that could possibly benefit from such a system include things from autonomous vehicles… to assistive robotics, all the way to medical robotics,” says Karamcheti, responding to a question by email from Singularity Hub.
More to Come
These achievements are yet another step toward creating robots that see, listen, and act more like humans. But don’t expect Disney to build a real-life Westworld next to Toon Town anytime soon.
“I think we’re a long way off from human-level communication,” Karamcheti says. “There are so many problems preventing our learning models from getting to that point, from seemingly simple questions like how to deal with words never seen before, to harder, more complicated questions like how to resolve the ambiguities inherent in language, including idiomatic or metaphorical speech.”
Even relatively verbose chatbots can run out of things to say, Karamcheti notes, as the conversation becomes more complex.
The same goes for human vision, according to Burchfiel.
While deep learning techniques have dramatically improved pattern matching—Google can find just about any picture of a cat—there’s more to human eyesight than, well, meets the eye.
“There are two big areas where I think perception has a long way to go: inductive bias and formal reasoning,” Burchfiel says.
The former is essentially all of the contextual knowledge people use to help them reason, he explains. Burchfiel uses the example of a puddle in the street. People are conditioned or biased to assume it’s a puddle of water rather than a patch of glass, for instance.
“This sort of bias is why we see faces in clouds; we have strong inductive bias helping us identify faces,” he says. “While it sounds simple at first, it powers much of what we do. Humans have a very intuitive understanding of what they expect to see, [and] it makes perception much easier.”
Formal reasoning is equally important. A machine can use deep learning, in Burchfiel’s example, to figure out the direction any river flows once it understands that water runs downhill. But it’s not yet capable of applying the sort of human reasoning that would allow us to transfer that knowledge to an alien setting, such as figuring out how water moves through a plumbing system on Mars.
“Much work was done in decades past on this sort of formal reasoning… but we have yet to figure out how to merge it with standard machine-learning methods to create a seamless system that is useful in the actual physical world.”
Robots still have a lot to learn about being human, which should make us feel good that we’re still by far the most complex machines on the planet.
Image Credit: Alex Knight via Unsplash Continue reading

Posted in Human Robots

#430743 Teaching Machines to Understand, and ...

We humans are swamped with text. It’s not just news and other timely information: Regular people are drowning in legal documents. The problem is so bad we mostly ignore it. Every time a person uses a store’s loyalty rewards card or connects to an online service, his or her activities are governed by the equivalent of hundreds of pages of legalese. Most people pay no attention to these massive documents, often labeled “terms of service,” “user agreement,” or “privacy policy.”
These are just part of a much wider societal problem of information overload. There is so much data stored—exabytes of it, as much stored as has ever been spoken by people in all of human history—that it’s humanly impossible to read and interpret everything. Often, we narrow down our pool of information by choosing particular topics or issues to pay attention to. But it’s important to actually know the meaning and contents of the legal documents that govern how our data is stored and who can see it.
As computer science researchers, we are working on ways artificial intelligence algorithms could digest these massive texts and extract their meaning, presenting it in terms regular people can understand.
Can computers understand text?
Computers store data as 0s and 1s—data that cannot be directly understood by humans. They interpret these data as instructions for displaying text, sound, images, or videos that are meaningful to people. But can computers actually understand the language, not only presenting the words but also their meaning?
One way to find out is to ask computers to summarize their knowledge in ways that people can understand and find useful. It would be best if AI systems could process text quickly enough to help people make decisions as they are needed—for example, when you’re signing up for a new online service and are asked to agree with the site’s privacy policy.
What if a computerized assistant could digest all that legal jargon in a few seconds and highlight key points? Perhaps a user could even tell the automated assistant to pay particular attention to certain issues, like when an email address is shared, or whether search engines can index personal posts. Companies could use this capability, too, to analyze contracts or other lengthy documents.
To do this sort of work, we need to combine a range of AI technologies, including machine learning algorithms that take in large amounts of data and independently identify connections among them; knowledge representation techniques to express and interpret facts and rules about the world; speech recognition systems to convert spoken language to text; and human language comprehension programs that process the text and its context to determine what the user is telling the system to do.
Examining privacy policies
A modern internet-enabled life today more or less requires trusting for-profit companies with private information (like physical and email addresses, credit card numbers and bank account details) and personal data (photos and videos, email messages and location information).
These companies’ cloud-based systems typically keep multiple copies of users’ data as part of backup plans to prevent service outages. That means there are more potential targets—each data center must be securely protected both physically and electronically. Of course, internet companies recognize customers’ concerns and employ security teams to protect users’ data. But the specific and detailed legal obligations they undertake to do that are found in their impenetrable privacy policies. No regular human—and perhaps even no single attorney—can truly understand them.
In our study, we ask computers to summarize the terms and conditions regular users say they agree to when they click “Accept” or “Agree” buttons for online services. We downloaded the publicly available privacy policies of various internet companies, including Amazon AWS, Facebook, Google, HP, Oracle, PayPal, Salesforce, Snapchat, Twitter, and WhatsApp.
Summarizing meaning
Our software examines the text and uses information extraction techniques to identify key information specifying the legal rights, obligations and prohibitions identified in the document. It also uses linguistic analysis to identify whether each rule applies to the service provider, the user or a third-party entity, such as advertisers and marketing companies. Then it presents that information in clear, direct, human-readable statements.
For example, our system identified one aspect of Amazon’s privacy policy as telling a user, “You can choose not to provide certain information, but then you might not be able to take advantage of many of our features.” Another aspect of that policy was described as “We may also collect technical information to help us identify your device for fraud prevention and diagnostic purposes.”

We also found, with the help of the summarizing system, that privacy policies often include rules for third parties—companies that aren’t the service provider or the user—that people might not even know are involved in data storage and retrieval.
The largest number of rules in privacy policies—43 percent—apply to the company providing the service. Just under a quarter of the rules—24 percent—create obligations for users and customers. The rest of the rules govern behavior by third-party services or corporate partners, or could not be categorized by our system.

The next time you click the “I Agree” button, be aware that you may be agreeing to share your data with other hidden companies who will be analyzing it.
We are continuing to improve our ability to succinctly and accurately summarize complex privacy policy documents in ways that people can understand and use to access the risks associated with using a service.

This article was originally published on The Conversation. Read the original article. Continue reading

Posted in Human Robots

#430686 This Week’s Awesome Stories From ...

ARTIFICIAL INTELLIGENCE
DeepMind’s AI Is Teaching Itself Parkour, and the Results Are AdorableJames Vincent | The Verge“The research explores how reinforcement learning (or RL) can be used to teach a computer to navigate unfamiliar and complex environments. It’s the sort of fundamental AI research that we’re now testing in virtual worlds, but that will one day help program robots that can navigate the stairs in your house.”
VIRTUAL REALITY
Now You Can Broadcast Facebook Live Videos From Virtual RealityDaniel Terdiman | Fast Company“The idea is fairly simple. Spaces allows up to four people—each of whom must have an Oculus Rift VR headset—to hang out together in VR. Together, they can talk, chat, draw, create new objects, watch 360-degree videos, share photos, and much more. And now, they can live-broadcast everything they do in Spaces, much the same way that any Facebook user can produce live video of real life and share it with the world.”
ROBOTICS
I Watched Two Robots Chat Together on Stage at a Tech EventJon Russell | TechCrunch“The robots in question are Sophia and Han, and they belong to Hanson Robotics, a Hong Kong-based company that is developing and deploying artificial intelligence in humanoids. The duo took to the stage at Rise in Hong Kong with Hanson Robotics’ Chief Scientist Ben Goertzel directing the banter. The conversation, which was partially scripted, wasn’t as slick as the human-to-human panels at the show, but it was certainly a sight to behold for the packed audience.”
BIOTECH
Scientists Used CRISPR to Put a GIF Inside a Living Organism’s DNAEmily Mullin | MIT Technology Review“They delivered the GIF into the living bacteria in the form of five frames: images of a galloping horse and rider, taken by English photographer Eadweard Muybridge…The researchers were then able to retrieve the data by sequencing the bacterial DNA. They reconstructed the movie with 90 percent accuracy by reading the pixel nucleotide code.”
DIGITAL MEDIA
AI Creates Fake ObamaCharles Q. Choi | IEEE Spectrum“In the new study, the neural net learned what mouth shapes were linked to various sounds. The researchers took audio clips and dubbed them over the original sound files of a video. They next took mouth shapes that matched the new audio clips and grafted and blended them onto the video. Essentially, the researchers synthesized videos where Obama lip-synched words he said up to decades beforehand.”
Stock Media provided by adam121 / Pond5 Continue reading

Posted in Human Robots

#430630 CORE2 consumer robot controller by ...

Hardware, software and cloud for fast robot prototyping and development
Kraków, Poland, June 27th, 2017 – Robotic development platform creator Husarion has launched its next-generation dedicated robot controller CORE2. Available now at the Crowd Supply crowdfunding platform, CORE2 enables the rapid prototyping and development of consumer and service robots. It’s especially suitable for engineers designing commercial appliances and robotics students or hobbyists. Whether the next robotic idea is a tiny rover that penetrates tunnels, a surveillance drone, or a room-sized 3D printer, the CORE2 can serve as the brains behind it.
Photo Credit: Husarionwww.husarion.com
Husarion’s platform greatly simplifies robot development, making it as easy as creating a website. It provides engineers with embedded hardware, preconfigured software and easy online management. From the simple, proof-of-concept prototypes made with LEGO® Mindstorms to complex designs ready for mass manufacturing, the core technology stays the same throughout the process, shortening the time to market significantly. It’s designed as an innovation for the consumer robotics industry similar to what Arduino or Raspberry PI were to the Maker Movement.

“We are on the verge of a consumer robotics revolution”, says Dominik Nowak, CEO of Husarion. “Big industrial businesses have long been utilizing robots, but until very recently the consumer side hasn’t seen that many of them. This is starting to change now with the democratization of tools, the Maker Movement and technology maturing. We believe Husarion is uniquely positioned for the upcoming boom, offering robot developers a holistic solution and lowering the barrier of entry to the market.”

The hardware part of the platform is the Husarion CORE2 board, a computer that interfaces directly with motors, servos, encoders or sensors. It’s powered by an ARM® CORTEX-M4 CPU, features 42x I/O ports and can support up to 4x DC motors and 6x servomechanisms. Wireless connectivity is provided by a built-in Wi-Fi module.
Photo Credit: Husarion – www.husarion.com
The Husarion CORE2-ROS is an alternative configuration with a Raspberry Pi 3 ARMv8-powered board layered on top, with a preinstalled Robot Operating System (ROS) custom Linux distribution. It allows users to tap into the rich sets of modules and building tools already available for ROS. Real-time capabilities and high computing power enable advanced use cases, such as fully autonomous devices.

Developing software for CORE2-powered robots is easy. Husarion provides Web IDE, allowing engineers to program their connected robots directly from within the browser. There’s also an offline SDK and a convenient extension for Visual Studio Code. The open-source library hFramework based on Real Time Operating System masks the complexity of interface communication behind an elegant, easy-to-use API.

CORE2 also works with Arduino libraries, which can be used with no modifications at all through the compatibility layer of the hFramework API.
Photo Credit: Husarion – www.husarion.com
For online access, programming and control, Husarion provides its dedicated Cloud. By registering the CORE2-powerd robot at https://cloud.husarion.com, developers can update firmware online, build a custom Web control UI and share controls of their device with anyone.

Starting at $89, Husarion CORE2 and CORE2-ROS controllers are now on sale through Crowd Supply.

Husarion also offers complete development kits, extra servo controllers and additional modules for compatibility with LEGO® Mindstorms or Makeblock® mechanics. For more information, please visit: https://www.crowdsupply.com/husarion/core2.

Key points:
A dedicated robot hardware controller, with built-in interfaces for sensors, servos, DC motors and encoders

Programming with free tools: online (via Husarion Cloud Web IDE) or offline (Visual Studio Code extension)
Compatible with ROS, provides C++ 11 open-source programming framework based on RTOS
Husarion Cloud: control, program and share robots, with customizable control UI
Allows faster development and more advanced robotics than general maker boards like Arduino or Raspberry Pi

About Husarion
Husarion was founded in 2013 in Kraków, Poland. In 2015, Husarion successfully financed a Kickstarter campaign for RoboCORE, the company’s first-generation controller. The company delivers a fast prototyping platform for consumer robots. Thanks to Husarion’s hardware modules, efficient programming tools and cloud management, engineers can rapidly develop and iterate on their robot ideas. Husarion simplifies the development of connected, commercial robots ready for mass production and provides kits for academic education.

For more information, visit: https://husarion.com/.
Photo Credit: Husarion – www.husarion.com

Photo Credit: Husarion – www.husarion.com

Media contact:

Piotr Sarotapublic relations consultant
SAROTA PR – public relations agencyphone: +48 12 684 12 68mobile: +48 606 895 326email: piotr(at)sarota.pl
http://www.sarota.pl/
Jakub Misiurapublic relations specialist
phone: +48 12 349 03 52mobile: +48 696 778 568email: jakub.misiura(at)sarota.pl

Photo Credit: Husarion – www.husarion.com
Photo Credit: Husarion – www.husarion.com
Photo Credit: Husarion – www.husarion.com

The post CORE2 consumer robot controller by Husarion appeared first on Roboticmagazine. Continue reading

Posted in Human Robots

#430556 Forget Flying Cars, the Future Is ...

Flying car concepts have been around nearly as long as their earthbound cousins, but no one has yet made them a commercial success. MIT engineers think we’ve been coming at the problem from the wrong direction; rather than putting wings on cars, we should be helping drones to drive.
The team from the university’s Computer Science and Artificial Intelligence Laboratory (CSAIL) added wheels to a fleet of eight mini-quadcopters and tested driving and flying them around a tiny toy town made out of cardboard and fabric.
Adding the ability to drive reduced the distance the drone could fly by 14 percent compared to a wheel-less version. But while driving was slower, the drone could travel 150 percent further than when flying. The result is a vehicle that combines the speed and mobility of flying with the energy-efficiency of driving.

CSAIL director Daniela Rus told MIT News their work suggested that when looking to create flying cars, it might make more sense to build on years of research into drones rather than trying to simply “put wings on cars.”
Historically, flying car concepts have looked like someone took apart a Cessna light aircraft and a family sedan, mixed all the parts up, and bolted them back together again. Not everyone has abandoned this approach—two of the most developed flying car designs from Terrafugia and AeroMobil are cars with folding wings that need an airstrip to take off.
But flying car concepts are looking increasingly drone-like these days, with multiple small rotors, electric propulsion and vertical take-off abilities. Take the eHang 184 autonomous aerial vehicle being developed in China, the Kitty Hawk all-electric aircraft backed by Google founder Larry Page, which is little more than a quadcopter with a seat, the AirQuadOne designed by UK consortium Neva Aerospace, or Lilium Aviation’s Jet.
The attraction is obvious. Electric-powered drones are more compact, maneuverable, and environmentally friendly, making them suitable for urban environments.
Most of these vehicles are not quite the same as those proposed by the MIT engineers, as they’re pure flying machines. But a recent Airbus concept builds on the same principle that the future of urban mobility is vehicles that can both fly and drive. Its Pop.Up design is a two-passenger pod that can either be clipped to a set of wheels or hang under a quadcopter.
Importantly, they envisage their creation being autonomous in both flight and driving modes. And they’re not the only ones who think the future of flying cars is driverless. Uber has committed to developing a network of autonomous air taxis within a decade. This spring, Dubai announced it would launch a pilotless passenger drone service using the Ehang 184 as early as next month (July).
While integrating fully-fledged autonomous flying cars into urban environments will be far more complex, the study by Rus and her colleagues provides a good starting point for the kind of 3D route-planning and collision avoidance capabilities this would require.
The team developed multi-robot path planning algorithms that were able to control all eight drones as they flew and drove around their mock up city, while also making sure they didn’t crash into each other and avoided no-fly zones.
“This work provides an algorithmic solution for large-scale, mixed-mode transportation and shows its applicability to real-world problems,” Jingjin Yu, a computer science professor at Rutgers University who was not involved in the research, told MIT News.
This vision of a driverless future for flying cars might be a bit of a disappointment for those who’d envisaged themselves one day piloting their own hover car just like George Jetson. But autonomy and Uber-like ride-hailing business models are likely to be attractive, as they offer potential solutions to three of the biggest hurdles drone-like passenger vehicles face.
Firstly, it makes the vehicles accessible to anyone by removing the need to learn how to safely pilot an aircraft. Secondly, battery life still limits most electric vehicles to flight times measured in minutes. For personal vehicles this could be frustrating, but if you’re just hopping in a driverless air taxi for a five minute trip across town it’s unlikely to become apparent to you.
Operators of the service simply need to make sure they have a big enough fleet to ensure a charged vehicle is never too far away, or they’ll need a way to swap out batteries easily, such as the one suggested by the makers of the Volocopter electric helicopter.
Finally, there has already been significant progress in developing technology and regulations needed to integrate autonomous drones into our airspace that future driverless flying cars can most likely piggyback off of.
Safety requirements will inevitably be more stringent, but adding more predictable and controllable autonomous drones to the skies is likely to be more attractive to regulators than trying to license and police thousands of new amateur pilots.
Image Credit: Lilium Continue reading

Posted in Human Robots