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#433634 This Robotic Skin Makes Inanimate ...
In Goethe’s poem “The Sorcerer’s Apprentice,” made world-famous by its adaptation in Disney’s Fantasia, a lazy apprentice, left to fetch water, uses magic to bewitch a broom into performing his chores for him. Now, new research from Yale has opened up the possibility of being able to animate—and automate—household objects by fitting them with a robotic skin.
Yale’s Soft Robotics lab, the Faboratory, is led by Professor Rebecca Kramer-Bottiglio, and has long investigated the possibilities associated with new kinds of manufacturing. While the typical image of a robot is hard, cold steel and rigid movements, soft robotics aims to create something more flexible and versatile. After all, the human body is made up of soft, flexible surfaces, and the world is designed for us. Soft, deformable robots could change shape to adapt to different tasks.
When designing a robot, key components are the robot’s sensors, which allow it to perceive its environment, and its actuators, the electrical or pneumatic motors that allow the robot to move and interact with its environment.
Consider your hand, which has temperature and pressure sensors, but also muscles as actuators. The omni-skins, as the Science Robotics paper dubs them, combine sensors and actuators, embedding them into an elastic sheet. The robotic skins are moved by pneumatic actuators or memory alloy that can bounce back into shape. If this is then wrapped around a soft, deformable object, moving the skin with the actuators can allow the object to crawl along a surface.
The key to the design here is flexibility: rather than adding chips, sensors, and motors into every household object to turn them into individual automatons, the same skin can be used for many purposes. “We can take the skins and wrap them around one object to perform a task—locomotion, for example—and then take them off and put them on a different object to perform a different task, such as grasping and moving an object,” said Kramer-Bottiglio. “We can then take those same skins off that object and put them on a shirt to make an active wearable device.”
The task is then to dream up applications for the omni-skins. Initially, you might imagine demanding a stuffed toy to fetch the remote control for you, or animating a sponge to wipe down kitchen surfaces—but this is just the beginning. The scientists attached the skins to a soft tube and camera, creating a worm-like robot that could compress itself and crawl into small spaces for rescue missions. The same skins could then be worn by a person to sense their posture. One could easily imagine this being adapted into a soft exoskeleton for medical or industrial purposes: for example, helping with rehabilitation after an accident or injury.
The initial motivating factor for creating the robots was in an environment where space and weight are at a premium, and humans are forced to improvise with whatever’s at hand: outer space. Kramer-Bottoglio originally began the work after NASA called out for soft robotics systems for use by astronauts. Instead of wasting valuable rocket payload by sending up a heavy metal droid like ATLAS to fetch items or perform repairs, soft robotic skins with modular sensors could be adapted for a range of different uses spontaneously.
By reassembling components in the soft robotic skin, a crumpled ball of paper could provide the chassis for a robot that performs repairs on the spaceship, or explores the lunar surface. The dynamic compression provided by the robotic skin could be used for g-suits to protect astronauts when they rapidly accelerate or decelerate.
“One of the main things I considered was the importance of multi-functionality, especially for deep space exploration where the environment is unpredictable. The question is: How do you prepare for the unknown unknowns? … Given the design-on-the-fly nature of this approach, it’s unlikely that a robot created using robotic skins will perform any one task optimally,” Kramer-Bottiglio said. “However, the goal is not optimization, but rather diversity of applications.”
There are still problems to resolve. Many of the videos of the skins indicate that they can rely on an external power supply. Creating new, smaller batteries that can power wearable devices has been a focus of cutting-edge materials science research for some time. Much of the lab’s expertise is in creating flexible, stretchable electronics that can be deformed by the actuators without breaking the circuitry. In the future, the team hopes to work on streamlining the production process; if the components could be 3D printed, then the skins could be created when needed.
In addition, robotic hardware that’s capable of performing an impressive range of precise motions is quite an advanced technology. The software to control those robots, and enable them to perform a variety of tasks, is quite another challenge. With soft robots, it can become even more complex to design that control software, because the body itself can change shape and deform as the robot moves. The same set of programmed motions, then, can produce different results depending on the environment.
“Let’s say I have a soft robot with four legs that crawls along the ground, and I make it walk up a hard slope,” Dr. David Howard, who works on robotics at CSIRO in Australia, explained to ABC.
“If I make that slope out of gravel and I give it the same control commands, the actual body is going to deform in a different way, and I’m not necessarily going to know what that is.”
Despite these and other challenges, research like that at the Faboratory still hopes to redefine how we think of robots and robotics. Instead of a robot that imitates a human and manipulates objects, the objects themselves will become programmable matter, capable of moving autonomously and carrying out a range of tasks. Futurists speculate about a world where most objects are automated to some degree and can assemble and repair themselves, or are even built entirely of tiny robots.
The tale of the Sorcerer’s Apprentice was first written in 1797, at the dawn of the industrial revolution, over a century before the word “robot” was even coined. Yet more and more roboticists aim to prove Arthur C Clarke’s maxim: any sufficiently advanced technology is indistinguishable from magic.
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#433288 The New AI Tech Turning Heads in Video ...
A new technique using artificial intelligence to manipulate video content gives new meaning to the expression “talking head.”
An international team of researchers showcased the latest advancement in synthesizing facial expressions—including mouth, eyes, eyebrows, and even head position—in video at this month’s 2018 SIGGRAPH, a conference on innovations in computer graphics, animation, virtual reality, and other forms of digital wizardry.
The project is called Deep Video Portraits. It relies on a type of AI called generative adversarial networks (GANs) to modify a “target” actor based on the facial and head movement of a “source” actor. As the name implies, GANs pit two opposing neural networks against one another to create a realistic talking head, right down to the sneer or raised eyebrow.
In this case, the adversaries are actually working together: One neural network generates content, while the other rejects or approves each effort. The back-and-forth interplay between the two eventually produces a realistic result that can easily fool the human eye, including reproducing a static scene behind the head as it bobs back and forth.
The researchers say the technique can be used by the film industry for a variety of purposes, from editing facial expressions of actors for matching dubbed voices to repositioning an actor’s head in post-production. AI can not only produce highly realistic results, but much quicker ones compared to the manual processes used today, according to the researchers. You can read the full paper of their work here.
“Deep Video Portraits shows how such a visual effect could be created with less effort in the future,” said Christian Richardt, from the University of Bath’s motion capture research center CAMERA, in a press release. “With our approach, even the positioning of an actor’s head and their facial expression could be easily edited to change camera angles or subtly change the framing of a scene to tell the story better.”
AI Tech Different Than So-Called “Deepfakes”
The work is far from the first to employ AI to manipulate video and audio. At last year’s SIGGRAPH conference, researchers from the University of Washington showcased their work using algorithms that inserted audio recordings from a person in one instance into a separate video of the same person in a different context.
In this case, they “faked” a video using a speech from former President Barack Obama addressing a mass shooting incident during his presidency. The AI-doctored video injects the audio into an unrelated video of the president while also blending the facial and mouth movements, creating a pretty credible job of lip synching.
A previous paper by many of the same scientists on the Deep Video Portraits project detailed how they were first able to manipulate a video in real time of a talking head (in this case, actor and former California governor Arnold Schwarzenegger). The Face2Face system pulled off this bit of digital trickery using a depth-sensing camera that tracked the facial expressions of an Asian female source actor.
A less sophisticated method of swapping faces using a machine learning software dubbed FakeApp emerged earlier this year. Predictably, the tech—requiring numerous photos of the source actor in order to train the neural network—was used for more juvenile pursuits, such as injecting a person’s face onto a porn star.
The application gave rise to the term “deepfakes,” which is now used somewhat ubiquitously to describe all such instances of AI-manipulated video—much to the chagrin of some of the researchers involved in more legitimate uses.
Fighting AI-Created Video Forgeries
However, the researchers are keenly aware that their work—intended for benign uses such as in the film industry or even to correct gaze and head positions for more natural interactions through video teleconferencing—could be used for nefarious purposes. Fake news is the most obvious concern.
“With ever-improving video editing technology, we must also start being more critical about the video content we consume every day, especially if there is no proof of origin,” said Michael Zollhöfer, a visiting assistant professor at Stanford University and member of the Deep Video Portraits team, in the press release.
Toward that end, the research team is training the same adversarial neural networks to spot video forgeries. They also strongly recommend that developers clearly watermark videos that are edited through AI or otherwise, and denote clearly what part and element of the scene was modified.
To catch less ethical users, the US Department of Defense, through the Defense Advanced Research Projects Agency (DARPA), is supporting a program called Media Forensics. This latest DARPA challenge enlists researchers to develop technologies to automatically assess the integrity of an image or video, as part of an end-to-end media forensics platform.
The DARPA official in charge of the program, Matthew Turek, did tell MIT Technology Review that so far the program has “discovered subtle cues in current GAN-manipulated images and videos that allow us to detect the presence of alterations.” In one reported example, researchers have targeted eyes, which rarely blink in the case of “deepfakes” like those created by FakeApp, because the AI is trained on still pictures. That method would seem to be less effective to spot the sort of forgeries created by Deep Video Portraits, which appears to flawlessly match the entire facial and head movements between the source and target actors.
“We believe that the field of digital forensics should and will receive a lot more attention in the future to develop approaches that can automatically prove the authenticity of a video clip,” Zollhöfer said. “This will lead to ever-better approaches that can spot such modifications even if we humans might not be able to spot them with our own eyes.
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#432882 Why the Discovery of Room-Temperature ...
Superconductors are among the most bizarre and exciting materials yet discovered. Counterintuitive quantum-mechanical effects mean that, below a critical temperature, they have zero electrical resistance. This property alone is more than enough to spark the imagination.
A current that could flow forever without losing any energy means transmission of power with virtually no losses in the cables. When renewable energy sources start to dominate the grid and high-voltage transmission across continents becomes important to overcome intermittency, lossless cables will result in substantial savings.
What’s more, a superconducting wire carrying a current that never, ever diminishes would act as a perfect store of electrical energy. Unlike batteries, which degrade over time, if the resistance is truly zero, you could return to the superconductor in a billion years and find that same old current flowing through it. Energy could be captured and stored indefinitely!
With no resistance, a huge current could be passed through the superconducting wire and, in turn, produce magnetic fields of incredible power.
You could use them to levitate trains and produce astonishing accelerations, thereby revolutionizing the transport system. You could use them in power plants—replacing conventional methods which spin turbines in magnetic fields to generate electricity—and in quantum computers as the two-level system required for a “qubit,” in which the zeros and ones are replaced by current flowing clockwise or counterclockwise in a superconductor.
Arthur C. Clarke famously said that any sufficiently advanced technology is indistinguishable from magic; superconductors can certainly seem like magical devices. So, why aren’t they busy remaking the world? There’s a problem—that critical temperature.
For all known materials, it’s hundreds of degrees below freezing. Superconductors also have a critical magnetic field; beyond a certain magnetic field strength, they cease to work. There’s a tradeoff: materials with an intrinsically high critical temperature can also often provide the largest magnetic fields when cooled well below that temperature.
This has meant that superconductor applications so far have been limited to situations where you can afford to cool the components of your system to close to absolute zero: in particle accelerators and experimental nuclear fusion reactors, for example.
But even as some aspects of superconductor technology become mature in limited applications, the search for higher temperature superconductors moves on. Many physicists still believe a room-temperature superconductor could exist. Such a discovery would unleash amazing new technologies.
The Quest for Room-Temperature Superconductors
After Heike Kamerlingh Onnes discovered superconductivity by accident while attempting to prove Lord Kelvin’s theory that resistance would increase with decreasing temperature, theorists scrambled to explain the new property in the hope that understanding it might allow for room-temperature superconductors to be synthesized.
They came up with the BCS theory, which explained some of the properties of superconductors. It also predicted that the dream of technologists, a room-temperature superconductor, could not exist; the maximum temperature for superconductivity according to BCS theory was just 30 K.
Then, in the 1980s, the field changed again with the discovery of unconventional, or high-temperature, superconductivity. “High temperature” is still very cold: the highest temperature for superconductivity achieved was -70°C for hydrogen sulphide at extremely high pressures. For normal pressures, -140°C is near the upper limit. Unfortunately, high-temperature superconductors—which require relatively cheap liquid nitrogen, rather than liquid helium, to cool—are mostly brittle ceramics, which are expensive to form into wires and have limited application.
Given the limitations of high-temperature superconductors, researchers continue to believe there’s a better option awaiting discovery—an incredible new material that checks boxes like superconductivity approaching room temperature, affordability, and practicality.
Tantalizing Clues
Without a detailed theoretical understanding of how this phenomenon occurs—although incremental progress happens all the time—scientists can occasionally feel like they’re taking educated guesses at materials that might be likely candidates. It’s a little like trying to guess a phone number, but with the periodic table of elements instead of digits.
Yet the prospect remains, in the words of one researcher, tantalizing. A Nobel Prize and potentially changing the world of energy and electricity is not bad for a day’s work.
Some research focuses on cuprates, complex crystals that contain layers of copper and oxygen atoms. Doping cuprates with various different elements, such exotic compounds as mercury barium calcium copper oxide, are amongst the best superconductors known today.
Research also continues into some anomalous but unexplained reports that graphite soaked in water can act as a room-temperature superconductor, but there’s no indication that this could be used for technological applications yet.
In early 2017, as part of the ongoing effort to explore the most extreme and exotic forms of matter we can create on Earth, researchers managed to compress hydrogen into a metal.
The pressure required to do this was more than that at the core of the Earth and thousands of times higher than that at the bottom of the ocean. Some researchers in the field, called condensed-matter physics, doubt that metallic hydrogen was produced at all.
It’s considered possible that metallic hydrogen could be a room-temperature superconductor. But getting the samples to stick around long enough for detailed testing has proved tricky, with the diamonds containing the metallic hydrogen suffering a “catastrophic failure” under the pressure.
Superconductivity—or behavior that strongly resembles it—was also observed in yttrium barium copper oxide (YBCO) at room temperature in 2014. The only catch was that this electron transport lasted for a tiny fraction of a second and required the material to be bombarded with pulsed lasers.
Not very practical, you might say, but tantalizing nonetheless.
Other new materials display enticing properties too. The 2016 Nobel Prize in Physics was awarded for the theoretical work that characterizes topological insulators—materials that exhibit similarly strange quantum behaviors. They can be considered perfect insulators for the bulk of the material but extraordinarily good conductors in a thin layer on the surface.
Microsoft is betting on topological insulators as the key component in their attempt at a quantum computer. They’ve also been considered potentially important components in miniaturized circuitry.
A number of remarkable electronic transport properties have also been observed in new, “2D” structures—like graphene, these are materials synthesized to be as thick as a single atom or molecule. And research continues into how we can utilize the superconductors we’ve already discovered; for example, some teams are trying to develop insulating material that prevents superconducting HVDC cable from overheating.
Room-temperature superconductivity remains as elusive and exciting as it has been for over a century. It is unclear whether a room-temperature superconductor can exist, but the discovery of high-temperature superconductors is a promising indicator that unconventional and highly useful quantum effects may be discovered in completely unexpected materials.
Perhaps in the future—through artificial intelligence simulations or the serendipitous discoveries of a 21st century Kamerlingh Onnes—this little piece of magic could move into the realm of reality.
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#432880 Google’s Duplex Raises the Question: ...
By now, you’ve probably seen Google’s new Duplex software, which promises to call people on your behalf to book appointments for haircuts and the like. As yet, it only exists in demo form, but already it seems like Google has made a big stride towards capturing a market that plenty of companies have had their eye on for quite some time. This software is impressive, but it raises questions.
Many of you will be familiar with the stilted, robotic conversations you can have with early chatbots that are, essentially, glorified menus. Instead of pressing 1 to confirm or 2 to re-enter, some of these bots would allow for simple commands like “Yes” or “No,” replacing the buttons with limited ability to recognize a few words. Using them was often a far more frustrating experience than attempting to use a menu—there are few things more irritating than a robot saying, “Sorry, your response was not recognized.”
Google Duplex scheduling a hair salon appointment:
Google Duplex calling a restaurant:
Even getting the response recognized is hard enough. After all, there are countless different nuances and accents to baffle voice recognition software, and endless turns of phrase that amount to saying the same thing that can confound natural language processing (NLP), especially if you like your phrasing quirky.
You may think that standard customer-service type conversations all travel the same route, using similar words and phrasing. But when there are over 80,000 ways to order coffee, and making a mistake is frowned upon, even simple tasks require high accuracy over a huge dataset.
Advances in audio processing, neural networks, and NLP, as well as raw computing power, have meant that basic recognition of what someone is trying to say is less of an issue. Soundhound’s virtual assistant prides itself on being able to process complicated requests (perhaps needlessly complicated).
The deeper issue, as with all attempts to develop conversational machines, is one of understanding context. There are so many ways a conversation can go that attempting to construct a conversation two or three layers deep quickly runs into problems. Multiply the thousands of things people might say by the thousands they might say next, and the combinatorics of the challenge runs away from most chatbots, leaving them as either glorified menus, gimmicks, or rather bizarre to talk to.
Yet Google, who surely remembers from Glass the risk of premature debuts for technology, especially the kind that ask you to rethink how you interact with or trust in software, must have faith in Duplex to show it on the world stage. We know that startups like Semantic Machines and x.ai have received serious funding to perform very similar functions, using natural-language conversations to perform computing tasks, schedule meetings, book hotels, or purchase items.
It’s no great leap to imagine Google will soon do the same, bringing us closer to a world of onboard computing, where Lens labels the world around us and their assistant arranges it for us (all the while gathering more and more data it can convert into personalized ads). The early demos showed some clever tricks for keeping the conversation within a fairly narrow realm where the AI should be comfortable and competent, and the blog post that accompanied the release shows just how much effort has gone into the technology.
Yet given the privacy and ethics funk the tech industry finds itself in, and people’s general unease about AI, the main reaction to Duplex’s impressive demo was concern. The voice sounded too natural, bringing to mind Lyrebird and their warnings of deepfakes. You might trust “Do the Right Thing” Google with this technology, but it could usher in an era when automated robo-callers are far more convincing.
A more human-like voice may sound like a perfectly innocuous improvement, but the fact that the assistant interjects naturalistic “umm” and “mm-hm” responses to more perfectly mimic a human rubbed a lot of people the wrong way. This wasn’t just a voice assistant trying to sound less grinding and robotic; it was actively trying to deceive people into thinking they were talking to a human.
Google is running the risk of trying to get to conversational AI by going straight through the uncanny valley.
“Google’s experiments do appear to have been designed to deceive,” said Dr. Thomas King of the Oxford Internet Institute’s Digital Ethics Lab, according to Techcrunch. “Their main hypothesis was ‘can you distinguish this from a real person?’ In this case it’s unclear why their hypothesis was about deception and not the user experience… there should be some kind of mechanism there to let people know what it is they are speaking to.”
From Google’s perspective, being able to say “90 percent of callers can’t tell the difference between this and a human personal assistant” is an excellent marketing ploy, even though statistics about how many interactions are successful might be more relevant.
In fact, Duplex runs contrary to pretty much every major recommendation about ethics for the use of robotics or artificial intelligence, not to mention certain eavesdropping laws. Transparency is key to holding machines (and the people who design them) accountable, especially when it comes to decision-making.
Then there are the more subtle social issues. One prominent effect social media has had is to allow people to silo themselves; in echo chambers of like-minded individuals, it’s hard to see how other opinions exist. Technology exacerbates this by removing the evolutionary cues that go along with face-to-face interaction. Confronted with a pair of human eyes, people are more generous. Confronted with a Twitter avatar or a Facebook interface, people hurl abuse and criticism they’d never dream of using in a public setting.
Now that we can use technology to interact with ever fewer people, will it change us? Is it fair to offload the burden of dealing with a robot onto the poor human at the other end of the line, who might have to deal with dozens of such calls a day? Google has said that if the AI is in trouble, it will put you through to a human, which might help save receptionists from the hell of trying to explain a concept to dozens of dumbfounded AI assistants all day. But there’s always the risk that failures will be blamed on the person and not the machine.
As AI advances, could we end up treating the dwindling number of people in these “customer-facing” roles as the buggiest part of a fully automatic service? Will people start accusing each other of being robots on the phone, as well as on Twitter?
Google has provided plenty of reassurances about how the system will be used. They have said they will ensure that the system is identified, and it’s hardly difficult to resolve this problem; a slight change in the script from their demo would do it. For now, consumers will likely appreciate moves that make it clear whether the “intelligent agents” that make major decisions for us, that we interact with daily, and that hide behind social media avatars or phone numbers are real or artificial.
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