Tag Archives: university

#435822 The Internet Is Coming to the Rest of ...

Posted on September 26, 2019 by Android

People surf it. Spiders crawl it. Gophers navigate it.

Now, a leading group of cognitive biologists and computer scientists want to make the tools of the Internet accessible to the rest of the animal kingdom.

Dubbed the Interspecies Internet, the project aims to provide intelligent animals such as elephants, dolphins, magpies, and great apes with a means to communicate among each other and with people online.

And through artificial intelligence, virtual reality, and other digital technologies, researchers hope to crack the code of all the chirps, yips, growls, and whistles that underpin animal communication.

Oh, and musician Peter Gabriel is involved.

“We can use data analysis and technology tools to give non-humans a lot more choice and control,” the former Genesis frontman, dressed in his signature Nehru-style collar shirt and loose, open waistcoat, told IEEE Spectrum at the inaugural Interspecies Internet Workshop, held Monday in Cambridge, Mass. “This will be integral to changing our relationship with the natural world.”

The workshop was a long time in the making.

Eighteen years ago, Gabriel visited a primate research center in Atlanta, Georgia, where he jammed with two bonobos, a male named Kanzi and his half-sister Panbanisha. It was the first time either bonobo had sat at a piano before, and both displayed an exquisite sense of musical timing and melody.

Gabriel seemed to be speaking to the great apes through his synthesizer. It was a shock to the man who once sang “Shock the Monkey.”

“It blew me away,” he says.

Add in the bonobos’ ability to communicate by pointing to abstract symbols, Gabriel notes, and “you’d have to be deaf, dumb, and very blind not to notice language being used.”

Gabriel eventually teamed up with Internet protocol co-inventor Vint Cerf, cognitive psychologist Diana Reiss, and IoT pioneer Neil Gershenfeld to propose building an Interspecies Internet. Presented in a 2013 TED Talk as an “idea in progress,” the concept proved to be ahead of the technology.

“It wasn’t ready,” says Gershenfeld, director of MIT’s Center for Bits and Atoms. “It needed to incubate.”

So, for the past six years, the architects of the Dolittlesque initiative embarked on two small pilot projects, one for dolphins and one for chimpanzees.

At her Hunter College lab in New York City, Reiss developed what she calls the D-Pad—a touchpad for dolphins.

Reiss had been trying for years to create an underwater touchscreen with which to probe the cognition and communication skills of bottlenose dolphins. But “it was a nightmare coming up with something that was dolphin-safe and would work,” she says.

Her first attempt emitted too much heat. A Wii-like system of gesture recognition proved too difficult to install in the dolphin tanks.

Eventually, she joined forces with Rockefeller University biophysicist Marcelo Magnasco and invented an optical detection system in which images and infrared sensors are projected through an underwater viewing window onto a glass panel, allowing the dolphins to play specially designed apps, including one dubbed Whack-a-Fish.

Meanwhile, in the United Kingdom, Gabriel worked with Alison Cronin, director of the ape rescue center Monkey World, to test the feasibility of using FaceTime with chimpanzees.

The chimps engaged with the technology, Cronin reported at this week’s workshop. However, our hominid cousins proved as adept at videotelephonic discourse as my three-year-old son is at video chatting with his grandparents—which is to say, there was a lot of pass-the-banana-through-the-screen and other silly games, and not much meaningful conversation.

“We can use data analysis and technology tools to give non-humans a lot more choice and control.”
—Peter Gabriel

The buggy, rudimentary attempt at interspecies online communication—what Cronin calls her “Max Headroom experiment”—shows that building the Interspecies Internet will not be as simple as giving out Skype-enabled tablets to smart animals.

“There are all sorts of problems with creating a human-centered experience for another animal,” says Gabriel Miller, director of research and development at the San Diego Zoo.

Miller has been working on animal-focused sensory tools such as an “Elephone” (for elephants) and a “Joybranch” (for birds), but it’s not easy to design efficient interactive systems for other creatures—and for the Interspecies Internet to be successful, Miller points out, “that will be super-foundational.”

Researchers are making progress on natural language processing of animal tongues. Through a non-profit organization called the Earth Species Project, former Firefox designer Aza Raskin and early Twitter engineer Britt Selvitelle are applying deep learning algorithms developed for unsupervised machine translation of human languages to fashion a Rosetta Stone–like tool capable of interpreting the vocalizations of whales, primates, and other animals.

Inspired by the scientists who first documented the complex sonic arrangements of humpback whales in the 1960s—a discovery that ushered in the modern marine conservation movement—Selvitelle hopes that an AI-powered animal translator can have a similar effect on environmentalism today.

“A lot of shifts happen when someone who doesn’t have a voice gains a voice,” he says.

A challenge with this sort of AI software remains verification and validation. Normally, machine-learning algorithms are benchmarked against a human expert, but who is to say if a cybernetic translation of a sperm whale’s clicks is accurate or not?

One could back-translate an English expression into sperm whale-ese and then into English again. But with the great apes, there might be a better option.

According to primatologist Sue Savage-Rumbaugh, expertly trained bonobos could serve as bilingual interpreters, translating the argot of apes into the parlance of people, and vice versa.

Not just any trained ape will do, though. They have to grow up in a mixed Pan/Homo environment, as Kanzi and Panbanisha were.

“If I can have a chat with a cow, maybe I can have more compassion for it.”
—Jeremy Coller

Those bonobos were raised effectively from birth both by Savage-Rumbaugh, who taught the animals to understand spoken English and to communicate via hundreds of different pictographic “lexigrams,” and a bonobo mother named Matata that had lived for six years in the Congolese rainforests before her capture.

Unlike all other research primates—which are brought into captivity as infants, reared by human caretakers, and have limited exposure to their natural cultures or languages—those apes thus grew up fluent in both bonobo and human.

Panbanisha died in 2012, but Kanzi, aged 38, is still going strong, living at an ape sanctuary in Des Moines, Iowa. Researchers continue to study his cognitive abilities—Francine Dolins, a primatologist at the University of Michigan-Dearborn, is running one study in which Kanzi and other apes hunt rabbits and forage for fruit through avatars on a touchscreen. Kanzi could, in theory, be recruited to check the accuracy of any Google Translate–like app for bonobo hoots, barks, grunts, and cries.

Alternatively, Kanzi could simply provide Internet-based interpreting services for our two species. He’s already proficient at video chatting with humans, notes Emily Walco, a PhD student at Harvard University who has personally Skyped with Kanzi. “He was super into it,” Walco says.

And if wild bonobos in Central Africa can be coaxed to gather around a computer screen, Savage-Rumbaugh is confident Kanzi could communicate with them that way. “It can all be put together,” she says. “We can have an Interspecies Internet.”

“Both the technology and the knowledge had to advance,” Savage-Rumbaugh notes. However, now, “the techniques that we learned could really be extended to a cow or a pig.”

That’s music to the ears of Jeremy Coller, a private equity specialist whose foundation partially funded the Interspecies Internet Workshop. Coller is passionate about animal welfare and has devoted much of his philanthropic efforts toward the goal of ending factory farming.

At the workshop, his foundation announced the creation of the Coller Doolittle Prize, a US $100,000 award to help fund further research related to the Interspecies Internet. (A working group also formed to synthesize plans for the emerging field, to facilitate future event planning, and to guide testing of shared technology platforms.)

Why would a multi-millionaire with no background in digital communication systems or cognitive psychology research want to back the initiative? For Coller, the motivation boils to interspecies empathy.

“If I can have a chat with a cow,” he says, “maybe I can have more compassion for it.”

An abridged version of this post appears in the September 2019 print issue as “Elephants, Dolphins, and Chimps Need the Internet, Too.” Continue reading →

Posted in Human Robots

#435816 This Light-based Nervous System Helps ...

Posted on September 26, 2019 by Android

Last night, way past midnight, I stumbled onto my porch blindly grasping for my keys after a hellish day of international travel. Lights were low, I was half-asleep, yet my hand grabbed the keychain, found the lock, and opened the door.

If you’re rolling your eyes—yeah, it’s not exactly an epic feat for a human. Thanks to the intricate wiring between our brain and millions of sensors dotted on—and inside—our skin, we know exactly where our hand is in space and what it’s touching without needing visual confirmation. But this combined sense of the internal and the external is completely lost to robots, which generally rely on computer vision or surface mechanosensors to track their movements and their interaction with the outside world. It’s not always a winning strategy.

What if, instead, we could give robots an artificial nervous system?

This month, a team led by Dr. Rob Shepard at Cornell University did just that, with a seriously clever twist. Rather than mimicking the electric signals in our nervous system, his team turned to light. By embedding optical fibers inside a 3D printed stretchable material, the team engineered an “optical lace” that can detect changes in pressure less than a fraction of a pound, and pinpoint the location to a spot half the width of a tiny needle.

The invention isn’t just an artificial skin. Instead, the delicate fibers can be distributed both inside a robot and on its surface, giving it both a sense of tactile touch and—most importantly—an idea of its own body position in space. Optical lace isn’t a superficial coating of mechanical sensors; it’s an entire platform that may finally endow robots with nerve-like networks throughout the body.

Eventually, engineers hope to use this fleshy, washable material to coat the sharp, cold metal interior of current robots, transforming C-3PO more into the human-like hosts of Westworld. Robots with a “bodily” sense could act as better caretakers for the elderly, said Shepard, because they can assist fragile people without inadvertently bruising or otherwise harming them. The results were published in Science Robotics.

An Unconventional Marriage
The optical lace is especially creative because it marries two contrasting ideas: one biological-inspired, the other wholly alien.

The overarching idea for optical lace is based on the animal kingdom. Through sight, hearing, smell, taste, touch, and other senses, we’re able to interpret the outside world—something scientists call exteroception. Thanks to our nervous system, we perform these computations subconsciously, allowing us to constantly “perceive” what’s going on around us.

Our other perception is purely internal. Proprioception (sorry, it’s not called “inception” though it should be) is how we know where our body parts are in space without having to look at them, which lets us perform complex tasks when blind. Although less intuitive than exteroception, proprioception also relies on stretching and other deformations within the muscles and tendons and receptors under the skin, which generate electrical currents that shoot up into the brain for further interpretation.

In other words, in theory it’s possible to recreate both perceptions with a single information-carrying system.

Here’s where the alien factor comes in. Rather than using electrical properties, the team turned to light as their data carrier. They had good reason. “Compared with electricity, light carries information faster and with higher data densities,” the team explained. Light can also transmit in multiple directions simultaneously, and is less susceptible to electromagnetic interference. Although optical nervous systems don’t exist in the biological world, the team decided to improve on Mother Nature and give it a shot.

Optical Lace
The construction starts with engineering a “sheath” for the optical nerve fibers. The team first used an elastic polyurethane—a synthetic material used in foam cushioning, for example—to make a lattice structure filled with large pores, somewhat like a lattice pie crust. Thanks to rapid, high-resolution 3D printing, the scaffold can have different stiffness from top to bottom. To increase sensitivity to the outside world, the team made the top of the lattice soft and pliable, to better transfer force to mechanical sensors. In contrast, the “deeper” regions held their structure better, and kept their structure under pressure.

Now the fun part. The team next threaded stretchable “light guides” into the scaffold. These fibers transmit photons, and are illuminated with a blue LED light. One, the input light guide, ran horizontally across the soft top part of the scaffold. Others ran perpendicular to the input in a “U” shape, going from more surface regions to deeper ones. These are the output guides. The architecture loosely resembles the wiring in our skin and flesh.

Normally, the output guides are separated from the input by a small air gap. When pressed down, the input light fiber distorts slightly, and if the pressure is high enough, it contacts one of the output guides. This causes light from the input fiber to “leak” to the output one, so that it lights up—the stronger the pressure, the brighter the output.

“When the structure deforms, you have contact between the input line and the output lines, and the light jumps into these output loops in the structure, so you can tell where the contact is happening,” said study author Patricia Xu. “The intensity of this determines the intensity of the deformation itself.”

Double Perception
As a proof-of-concept for proprioception, the team made a cylindrical lace with one input and 12 output channels. They varied the stiffness of the scaffold along the cylinder, and by pressing down at different points, were able to calculate how much each part stretched and deformed—a prominent precursor to knowing where different regions of the structure are moving in space. It’s a very rudimentary sort of proprioception, but one that will become more sophisticated with increasing numbers of strategically-placed mechanosensors.

The test for exteroception was a whole lot stranger. Here, the team engineered another optical lace with 15 output channels and turned it into a squishy piano. When pressed down, an Arduino microcontroller translated light output signals into sound based on the position of each touch. The stronger the pressure, the louder the volume. While not a musical masterpiece, the demo proved their point: the optical lace faithfully reported the strength and location of each touch.

A More Efficient Robot
Although remarkably novel, the optical lace isn’t yet ready for prime time. One problem is scalability: because of light loss, the material is limited to a certain size. However, rather than coating an entire robot, it may help to add optical lace to body parts where perception is critical—for example, fingertips and hands.

The team sees plenty of potential to keep developing the artificial flesh. Depending on particular needs, both the light guides and scaffold can be modified for sensitivity, spatial resolution, and accuracy. Multiple optical fibers that measure for different aspects—pressure, pain, temperature—can potentially be embedded in the same region, giving robots a multitude of senses.

In this way, we hope to reduce the number of electronics and combine signals from multiple sensors without losing information, the authors said. By taking inspiration from biological networks, it may even be possible to use various inputs through an optical lace to control how the robot behaves, closing the loop from sensation to action.

Image Credit: Cornell Organic Robotics Lab. A flexible, porous lattice structure is threaded with stretchable optical fibers containing more than a dozen mechanosensors and attached to an LED light. When the lattice structure is pressed, the sensors pinpoint changes in the photon flow. Continue reading →

Posted in Human Robots

#435791 To Fly Solo, Racing Drones Have a Need ...

Posted on September 21, 2019 by Android

Drone racing’s ultimate vision of quadcopters weaving nimbly through obstacle courses has attracted far less excitement and investment than self-driving cars aimed at reshaping ground transportation. But the U.S. military and defense industry are betting on autonomous drone racing as the next frontier for developing AI so that it can handle high-speed navigation within tight spaces without human intervention.

The autonomous drone challenge requires split-second decision-making with six degrees of freedom instead of a car’s mere two degrees of road freedom. One research team developing the AI necessary for controlling autonomous racing drones is the Robotics and Perception Group at the University of Zurich in Switzerland. In late May, the Swiss researchers were among nine teams revealed to be competing in the two-year AlphaPilot open innovation challenge sponsored by U.S. aerospace company Lockheed Martin. The winning team will walk away with up to $2.25 million for beating other autonomous racing drones and a professional human drone pilot in head-to-head competitions.

“I think it is important to first point out that having an autonomous drone to finish a racing track at high speeds or even beating a human pilot does not imply that we can have autonomous drones [capable of] navigating in real-world, complex, unstructured, unknown environments such as disaster zones, collapsed buildings, caves, tunnels or narrow pipes, forests, military scenarios, and so on,” says Davide Scaramuzza, a professor of robotics and perception at the University of Zurich and ETH Zurich. “However, the robust and computationally efficient state estimation algorithms, control, and planning algorithms developed for autonomous drone racing would represent a starting point.”

The nine teams that made the cut—from a pool of 424 AlphaPilot applicants—will compete in four 2019 racing events organized under the Drone Racing League’s Artificial Intelligence Robotic Racing Circuit, says Keith Lynn, program manager for AlphaPilot at Lockheed Martin. To ensure an apples-to-apples comparison of each team’s AI secret sauce, each AlphaPilot team will upload its AI code into identical, specially-built drones that have the NVIDIA Xavier GPU at the core of the onboard computing hardware.

“Lockheed Martin is offering mentorship to the nine AlphaPilot teams to support their AI tech development and innovations,” says Lynn. The company “will be hosting a week-long Developers Summit at MIT in July, dedicated to workshopping and improving AlphaPilot teams’ code,” he added. He notes that each team will retain the intellectual property rights to its AI code.

The AlphaPilot challenge takes inspiration from older autonomous drone racing events hosted by academic researchers, Scaramuzza says. He credits Hyungpil Moon, a professor of robotics and mechanical engineering at Sungkyunkwan University in South Korea, for having organized the annual autonomous drone racing competition at the International Conference on Intelligent Robots and Systems since 2016.

It’s no easy task to create and train AI that can perform high-speed flight through complex environments by relying on visual navigation. One big challenge comes from how drones can accelerate sharply, take sharp turns, fly sideways, do zig-zag patterns and even perform back flips. That means camera images can suddenly appear tilted or even upside down during drone flight. Motion blur may occur when a drone flies very close to structures at high speeds and camera pixels collect light from multiple directions. Both cameras and visual software can also struggle to compensate for sudden changes between light and dark parts of an environment.

To lend AI a helping hand, Scaramuzza’s group recently published a drone racing dataset that includes realistic training data taken from a drone flown by a professional pilot in both indoor and outdoor spaces. The data, which includes complicated aerial maneuvers such as back flips, flight sequences that cover hundreds of meters, and flight speeds of up to 83 kilometers per hour, was presented at the 2019 IEEE International Conference on Robotics and Automation.

The drone racing dataset also includes data captured by the group’s special bioinspired event cameras that can detect changes in motion on a per-pixel basis within microseconds. By comparison, ordinary cameras need milliseconds (each millisecond being 1,000 microseconds) to compare motion changes in each image frame. The event cameras have already proven capable of helping drones nimbly dodge soccer balls thrown at them by the Swiss lab’s researchers.

The Swiss group’s work on the racing drone dataset received funding in part from the U.S. Defense Advanced Research Projects Agency (DARPA), which acts as the U.S. military’s special R&D arm for more futuristic projects. Specifically, the funding came from DARPA’s Fast Lightweight Autonomy program that envisions small autonomous drones capable of flying at high speeds through cluttered environments without GPS guidance or communication with human pilots.

Such speedy drones could serve as military scouts checking out dangerous buildings or alleys. They could also someday help search-and-rescue teams find people trapped in semi-collapsed buildings or lost in the woods. Being able to fly at high speed without crashing into things also makes a drone more efficient at all sorts of tasks by making the most of limited battery life, Scaramuzza says. After all, most drone battery life gets used up by the need to hover in flight and doesn’t get drained much by flying faster.

Even if AI manages to conquer the drone racing obstacle courses, that would be the end of the beginning of the technology’s development. What would still be required? Scaramuzza specifically singled out the need to handle low-visibility conditions involving smoke, dust, fog, rain, snow, fire, hail, as some of the biggest challenges for vision-based algorithms and AI in complex real-life environments.

“I think we should develop and release datasets containing smoke, dust, fog, rain, fire, etc. if we want to allow using autonomous robots to complement human rescuers in saving people lives after an earthquake or natural disaster in the future,” Scaramuzza says. Continue reading →

Posted in Human Robots

#435779 This Robot Ostrich Can Ride Around on ...

Posted on September 20, 2019 by Android

Proponents of legged robots say that they make sense because legs are often required to go where humans go. Proponents of wheeled robots say, “Yeah, that’s great but watch how fast and efficient my robot is, compared to yours.” Some robots try and take advantage of wheels and legs with hybrid designs like whegs or wheeled feet, but a simpler and more versatile solution is to do what humans do, and just take advantage of wheels when you need them.

We’ve seen a few experiments with this. The University of Michigan managed to convince Cassie to ride a Segway, with mostly positive (but occasionally quite negative) results. A Segway, and hoverboard-like systems, can provide wheeled mobility for legged robots over flat terrain, but they can’t handle things like stairs, which is kind of the whole point of having a robot with legs anyway.

Image: UC Berkeley

From left, a Segway, a hovercraft, and hovershoes, with complexity in terms of user control increasing from left to right.

At UC Berkeley’s Hybrid Robotics Lab, led by Koushil Sreenath, researchers have taken things a step further. They are teaching their Cassie bipedal robot (called Cassie Cal) to wheel around on a pair of hovershoes. Hovershoes are like hoverboards that have been chopped in half, resulting in a pair of motorized single-wheel skates. You balance on the skates, and control them by leaning forwards and backwards and left and right, which causes each skate to accelerate or decelerate in an attempt to keep itself upright. It’s not easy to get these things to work, even for a human, but by adding a sensor package to Cassie the UC Berkeley researchers have managed to get it to zip around campus fully autonomously.

Remember, Cassie is operating autonomously here—it’s performing vSLAM (with an Intel RealSense) and doing all of its own computation onboard in real time. Watching it jolt across that cracked sidewalk is particularly impressive, especially considering that it only has pitch control over its ankles and can’t roll its feet to maintain maximum contact with the hovershoes. But you can see the advantage that this particular platform offers to a robot like Cassie, including the ability to handle stairs. Stairs in one direction, anyway.

It’s a testament to the robustness of UC Berkeley’s controller that they were willing to let the robot operate untethered and outside, and it sounds like they’re thinking long-term about how legged robots on wheels would be real-world useful:

Our feedback control and autonomous system allow for swift movement through urban environments to aid in everything from food delivery to security and surveillance to search and rescue missions. This work can also help with transportation in large factories and warehouses.

For more details, we spoke with the UC Berkeley students (Shuxiao Chen, Jonathan Rogers, and Bike Zhang) via email.

IEEE Spectrum: How representative of Cassie’s real-world performance is what we see in the video? What happens when things go wrong?

Cassie’s real-world performance is similar to what we see in the video. Cassie can ride the hovershoes successfully all around the campus. Our current controller allows Cassie to robustly ride the hovershoes and rejects various perturbations. At present, one of the failure modes is when the hovershoe rolls to the side—this happens when it goes sideways down a step or encounters a large obstacle on one side of it, causing it to roll over. Under these circumstances, Cassie doesn’t have sufficient control authority (due to the thin narrow feet) to get the hovershoe back on its wheel.

The Hybrid Robotics Lab has been working on robots that walk over challenging terrain—how do wheeled platforms like hovershoes fit in with that?

Surprisingly, this research is related to our prior work on walking on discrete terrain. While locomotion using legs is efficient when traveling over rough and discrete terrain, wheeled locomotion is more efficient when traveling over flat continuous terrain. Enabling legged robots to ride on various micro-mobility platforms will offer multimodal locomotion capabilities, improving the efficiency of locomotion over various terrains.

Our current research furthers the locomotion ability for bipedal robots over continuous terrains by using a wheeled platform. In the long run, we would like to develop multi-modal locomotion strategies based on our current and prior work to allow legged robots to robustly and efficiently locomote in our daily life.

Photo: UC Berkeley

In their experiments, the UC Berkeley researchers say Cassie proved quite capable of riding the hovershoes over rough and uneven terrain, including going down stairs.

How long did it take to train Cassie to use the hovershoes? Are there any hovershoe skills that Cassie is better at than an average human?

We spent about eight months to develop our whole system, including a controller, a path planner, and a vision system. This involved developing mathematical models of Cassie and the hovershoes, setting up a dynamical simulation, figuring out how to interface and communicate with various sensors and Cassie, and doing several experiments to slowly improve performance. In contrast, a human with a good sense of balance needs a few hours to learn to use the hovershoes. A human who has never used skates or skis will probably need a longer time.

A human can easily turn in place on the hovershoes, while Cassie cannot do this motion currently due to our algorithm requiring a non-zero forward speed in order to turn. However, Cassie is much better at riding the hovershoes over rough and uneven terrain including riding the hovershoes down some stairs!

What would it take to make Cassie faster or more agile on the hovershoes?

While Cassie can currently move at a decent pace on the hovershoes and navigate obstacles, Cassie’s ability to avoid obstacles at rapid speeds is constrained by the sensing, the controller, and the onboard computation. To enable Cassie to dynamically weave around obstacles at high speeds exhibiting agile motions, we need to make progress on different fronts.

We need planners that take into account the entire dynamics of the Cassie-Hovershoe system and rapidly generate dynamically-feasible trajectories; we need controllers that tightly coordinate all the degrees-of-freedom of Cassie to dynamically move while balancing on the hovershoes; we need sensors that are robust to motion-blur artifacts caused due to fast turns; and we need onboard computation that can execute our algorithms at real-time speeds.

What are you working on next?

We are working on enabling more aggressive movements for Cassie on the hovershoes by fully exploiting Cassie’s dynamics. We are working on approaches that enable us to easily go beyond hovershoes to other challenging micro-mobility platforms. We are working on enabling Cassie to step onto and off from wheeled platforms such as hovershoes. We would like to create a future of multi-modal locomotion strategies for legged robots to enable them to efficiently help people in our daily life.

“Feedback Control for Autonomous Riding of Hovershoes by a Cassie Bipedal Robot,” by Shuxiao Chen, Jonathan Rogers, Bike Zhang, and Koushil Sreenath from the Hybrid Robotics Lab at UC Berkeley, has been submitted to IEEE Robotics and Automation Letters with option to be presented at the 2019 IEEE RAS International Conference on Humanoid Robots. Continue reading →

Posted in Human Robots

#435765 The Four Converging Technologies Giving ...

Posted on September 20, 2019 by Android

How each of us sees the world is about to change dramatically.

For all of human history, the experience of looking at the world was roughly the same for everyone. But boundaries between the digital and physical are beginning to fade.

The world around us is gaining layer upon layer of digitized, virtually overlaid information—making it rich, meaningful, and interactive. As a result, our respective experiences of the same environment are becoming vastly different, personalized to our goals, dreams, and desires.

Welcome to Web 3.0, or the Spatial Web. In version 1.0, static documents and read-only interactions limited the internet to one-way exchanges. Web 2.0 provided quite an upgrade, introducing multimedia content, interactive web pages, and participatory social media. Yet, all this was still mediated by two-dimensional screens.

Today, we are witnessing the rise of Web 3.0, riding the convergence of high-bandwidth 5G connectivity, rapidly evolving AR eyewear, an emerging trillion-sensor economy, and powerful artificial intelligence.

As a result, we will soon be able to superimpose digital information atop any physical surrounding—freeing our eyes from the tyranny of the screen, immersing us in smart environments, and making our world endlessly dynamic.

In the third post of our five-part series on augmented reality, we will explore the convergence of AR, AI, sensors, and blockchain and dive into the implications through a key use case in manufacturing.

A Tale of Convergence
Let’s deconstruct everything beneath the sleek AR display.

It all begins with graphics processing units (GPUs)—electric circuits that perform rapid calculations to render images. (GPUs can be found in mobile phones, game consoles, and computers.)

However, because AR requires such extensive computing power, single GPUs will not suffice. Instead, blockchain can now enable distributed GPU processing power, and blockchains specifically dedicated to AR holographic processing are on the rise.

Next up, cameras and sensors will aggregate real-time data from any environment to seamlessly integrate physical and virtual worlds. Meanwhile, body-tracking sensors are critical for aligning a user’s self-rendering in AR with a virtually enhanced environment. Depth sensors then provide data for 3D spatial maps, while cameras absorb more surface-level, detailed visual input. In some cases, sensors might even collect biometric data, such as heart rate and brain activity, to incorporate health-related feedback in our everyday AR interfaces and personal recommendation engines.

The next step in the pipeline involves none other than AI. Processing enormous volumes of data instantaneously, embedded AI algorithms will power customized AR experiences in everything from artistic virtual overlays to personalized dietary annotations.

In retail, AIs will use your purchasing history, current closet inventory, and possibly even mood indicators to display digitally rendered items most suitable for your wardrobe, tailored to your measurements.

In healthcare, smart AR glasses will provide physicians with immediately accessible and maximally relevant information (parsed from the entirety of a patient’s medical records and current research) to aid in accurate diagnoses and treatments, freeing doctors to engage in the more human-centric tasks of establishing trust, educating patients and demonstrating empathy.

Image Credit: PHD Ventures.
Convergence in Manufacturing
One of the nearest-term use cases of AR is manufacturing, as large producers begin dedicating capital to enterprise AR headsets. And over the next ten years, AR will converge with AI, sensors, and blockchain to multiply manufacturer productivity and employee experience.

(1) Convergence with AI
In initial application, digital guides superimposed on production tables will vastly improve employee accuracy and speed, while minimizing error rates.

Already, the International Air Transport Association (IATA) — whose airlines supply 82 percent of air travel — recently implemented industrial tech company Atheer’s AR headsets in cargo management. And with barely any delay, IATA reported a whopping 30 percent improvement in cargo handling speed and no less than a 90 percent reduction in errors.

With similar success rates, Boeing brought Skylight’s smart AR glasses to the runway, now used in the manufacturing of hundreds of airplanes. Sure enough—the aerospace giant has now seen a 25 percent drop in production time and near-zero error rates.

Beyond cargo management and air travel, however, smart AR headsets will also enable on-the-job training without reducing the productivity of other workers or sacrificing hardware. Jaguar Land Rover, for instance, implemented Bosch’s Re’flekt One AR solution to gear technicians with “x-ray” vision: allowing them to visualize the insides of Range Rover Sport vehicles without removing any dashboards.

And as enterprise capabilities continue to soar, AIs will soon become the go-to experts, offering support to manufacturers in need of assembly assistance. Instant guidance and real-time feedback will dramatically reduce production downtime, boost overall output, and even help customers struggling with DIY assembly at home.

Perhaps one of the most profitable business opportunities, AR guidance through centralized AI systems will also serve to mitigate supply chain inefficiencies at extraordinary scale. Coordinating moving parts, eliminating the need for manned scanners at each checkpoint, and directing traffic within warehouses, joint AI-AR systems will vastly improve workflow while overseeing quality assurance.

After its initial implementation of AR “vision picking” in 2015, leading courier company DHL recently announced it would continue to use Google’s newest smart lens in warehouses across the world. Motivated by the initial group’s reported 15 percent jump in productivity, DHL’s decision is part of the logistics giant’s $300 million investment in new technologies.

And as direct-to-consumer e-commerce fundamentally transforms the retail sector, supply chain optimization will only grow increasingly vital. AR could very well prove the definitive step for gaining a competitive edge in delivery speeds.

As explained by Vital Enterprises CEO Ash Eldritch, “All these technologies that are coming together around artificial intelligence are going to augment the capabilities of the worker and that’s very powerful. I call it Augmented Intelligence. The idea is that you can take someone of a certain skill level and by augmenting them with artificial intelligence via augmented reality and the Internet of Things, you can elevate the skill level of that worker.”

Already, large producers like Goodyear, thyssenkrupp, and Johnson Controls are using the Microsoft HoloLens 2—priced at $3,500 per headset—for manufacturing and design purposes.

Perhaps the most heartening outcome of the AI-AR convergence is that, rather than replacing humans in manufacturing, AR is an ideal interface for human collaboration with AI. And as AI merges with human capital, prepare to see exponential improvements in productivity, professional training, and product quality.

(2) Convergence with Sensors
On the hardware front, these AI-AR systems will require a mass proliferation of sensors to detect the external environment and apply computer vision in AI decision-making.

To measure depth, for instance, some scanning depth sensors project a structured pattern of infrared light dots onto a scene, detecting and analyzing reflected light to generate 3D maps of the environment. Stereoscopic imaging, using two lenses, has also been commonly used for depth measurements. But leading technology like Microsoft’s HoloLens 2 and Intel’s RealSense 400-series camera implement a new method called “phased time-of-flight” (ToF).

In ToF sensing, the HoloLens 2 uses numerous lasers, each with 100 milliwatts (mW) of power, in quick bursts. The distance between nearby objects and the headset wearer is then measured by the amount of light in the return beam that has shifted from the original signal. Finally, the phase difference reveals the location of each object within the field of view, which enables accurate hand-tracking and surface reconstruction.

With a far lower computing power requirement, the phased ToF sensor is also more durable than stereoscopic sensing, which relies on the precise alignment of two prisms. The phased ToF sensor’s silicon base also makes it easily mass-produced, rendering the HoloLens 2 a far better candidate for widespread consumer adoption.

To apply inertial measurement—typically used in airplanes and spacecraft—the HoloLens 2 additionally uses a built-in accelerometer, gyroscope, and magnetometer. Further equipped with four “environment understanding cameras” that track head movements, the headset also uses a 2.4MP HD photographic video camera and ambient light sensor that work in concert to enable advanced computer vision.

For natural viewing experiences, sensor-supplied gaze tracking increasingly creates depth in digital displays. Nvidia’s work on Foveated AR Display, for instance, brings the primary foveal area into focus, while peripheral regions fall into a softer background— mimicking natural visual perception and concentrating computing power on the area that needs it most.

Gaze tracking sensors are also slated to grant users control over their (now immersive) screens without any hand gestures. Conducting simple visual cues, even staring at an object for more than three seconds, will activate commands instantaneously.

And our manufacturing example above is not the only one. Stacked convergence of blockchain, sensors, AI and AR will disrupt almost every major industry.

Take healthcare, for example, wherein biometric sensors will soon customize users’ AR experiences. Already, MIT Media Lab’s Deep Reality group has created an underwater VR relaxation experience that responds to real-time brain activity detected by a modified version of the Muse EEG. The experience even adapts to users’ biometric data, from heart rate to electro dermal activity (inputted from an Empatica E4 wristband).

Now rapidly dematerializing, sensors will converge with AR to improve physical-digital surface integration, intuitive hand and eye controls, and an increasingly personalized augmented world. Keep an eye on companies like MicroVision, now making tremendous leaps in sensor technology.

While I’ll be doing a deep dive into sensor applications across each industry in our next blog, it’s critical to first discuss how we might power sensor- and AI-driven augmented worlds.

(3) Convergence with Blockchain
Because AR requires much more compute power than typical 2D experiences, centralized GPUs and cloud computing systems are hard at work to provide the necessary infrastructure. Nonetheless, the workload is taxing and blockchain may prove the best solution.

A major player in this pursuit, Otoy aims to create the largest distributed GPU network in the world, called the Render Network RNDR. Built specifically on the Ethereum blockchain for holographic media, and undergoing Beta testing, this network is set to revolutionize AR deployment accessibility.

Alphabet Chairman Eric Schmidt (an investor in Otoy’s network), has even said, “I predicted that 90% of computing would eventually reside in the web based cloud… Otoy has created a remarkable technology which moves that last 10%—high-end graphics processing—entirely to the cloud. This is a disruptive and important achievement. In my view, it marks the tipping point where the web replaces the PC as the dominant computing platform of the future.”

Leveraging the crowd, RNDR allows anyone with a GPU to contribute their power to the network for a commission of up to $300 a month in RNDR tokens. These can then be redeemed in cash or used to create users’ own AR content.

In a double win, Otoy’s blockchain network and similar iterations not only allow designers to profit when not using their GPUs, but also democratize the experience for newer artists in the field.

And beyond these networks’ power suppliers, distributing GPU processing power will allow more manufacturing companies to access AR design tools and customize learning experiences. By further dispersing content creation across a broad network of individuals, blockchain also has the valuable potential to boost AR hardware investment across a number of industry beneficiaries.

On the consumer side, startups like Scanetchain are also entering the blockchain-AR space for a different reason. Allowing users to scan items with their smartphone, Scanetchain’s app provides access to a trove of information, from manufacturer and price, to origin and shipping details.

Based on NEM (a peer-to-peer cryptocurrency that implements a blockchain consensus algorithm), the app aims to make information far more accessible and, in the process, create a social network of purchasing behavior. Users earn tokens by watching ads, and all transactions are hashed into blocks and securely recorded.

The writing is on the wall—our future of brick-and-mortar retail will largely lean on blockchain to create the necessary digital links.

Final Thoughts
Integrating AI into AR creates an “auto-magical” manufacturing pipeline that will fundamentally transform the industry, cutting down on marginal costs, reducing inefficiencies and waste, and maximizing employee productivity.

Bolstering the AI-AR convergence, sensor technology is already blurring the boundaries between our augmented and physical worlds, soon to be near-undetectable. While intuitive hand and eye motions dictate commands in a hands-free interface, biometric data is poised to customize each AR experience to be far more in touch with our mental and physical health.

And underpinning it all, distributed computing power with blockchain networks like RNDR will democratize AR, boosting global consumer adoption at plummeting price points.

As AR soars in importance—whether in retail, manufacturing, entertainment, or beyond—the stacked convergence discussed above merits significant investment over the next decade. The augmented world is only just getting started.

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This article originally appeared on Diamandis.com

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