Tag Archives: features

#431906 Low-Cost Soft Robot Muscles Can Lift 200 ...

Jerky mechanical robots are staples of science fiction, but to seamlessly integrate into everyday life they’ll need the precise yet powerful motor control of humans. Now scientists have created a new class of artificial muscles that could soon make that a reality.
The advance is the latest breakthrough in the field of soft robotics. Scientists are increasingly designing robots using soft materials that more closely resemble biological systems, which can be more adaptable and better suited to working in close proximity to humans.
One of the main challenges has been creating soft components that match the power and control of the rigid actuators that drive mechanical robots—things like motors and pistons. Now researchers at the University of Colorado Boulder have built a series of low-cost artificial muscles—as little as 10 cents per device—using soft plastic pouches filled with electrically insulating liquids that contract with the force and speed of mammalian skeletal muscles when a voltage is applied to them.

Three different designs of the so-called hydraulically amplified self-healing electrostatic (HASEL) actuators were detailed in two papers in the journals Science and Science Robotics last week. They could carry out a variety of tasks, from gently picking up delicate objects like eggs or raspberries to lifting objects many times their own weight, such as a gallon of water, at rapid repetition rates.
“We draw our inspiration from the astonishing capabilities of biological muscle,” Christoph Keplinger, an assistant professor at UC Boulder and senior author of both papers, said in a press release. “Just like biological muscle, HASEL actuators can reproduce the adaptability of an octopus arm, the speed of a hummingbird and the strength of an elephant.”
The artificial muscles work by applying a voltage to hydrogel electrodes on either side of pouches filled with liquid insulators, which can be as simple as canola oil. This creates an attraction between the two electrodes, pulling them together and displacing the liquid. This causes a change of shape that can push or pull levers, arms or any other articulated component.
The design is essentially a synthesis of two leading approaches to actuating soft robots. Pneumatic and hydraulic actuators that pump fluids around have been popular due to their high forces, easy fabrication and ability to mimic a variety of natural motions. But they tend to be bulky and relatively slow.
Dielectric elastomer actuators apply an electric field across a solid insulating layer to make it flex. These can mimic the responsiveness of biological muscle. But they are not very versatile and can also fail catastrophically, because the high voltages required can cause a bolt of electricity to blast through the insulator, destroying it. The likelihood of this happening increases in line with the size of their electrodes, which makes it hard to scale them up. By combining the two approaches, researchers get the best of both worlds, with the power, versatility and easy fabrication of a fluid-based system and the responsiveness of electrically-powered actuators.
One of the designs holds particular promise for robotics applications, as it behaves a lot like biological muscle. The so-called Peano-HASEL actuators are made up of multiple rectangular pouches connected in series, which allows them to contract linearly, just like real muscle. They can lift more than 200 times their weight, but being electrically powered, they exceed the flexing speed of human muscle.
As the name suggests, the HASEL actuators are also self-healing. They are still prone to the same kind of electrical damage as dielectric elastomer actuators, but the liquid insulator is able to immediately self-heal by redistributing itself and regaining its insulating properties.
The muscles can even monitor the amount of strain they’re under to provide the same kind of feedback biological systems would. The muscle’s capacitance—its ability to store an electric charge—changes as the device stretches, which makes it possible to power the arm while simultaneously measuring what position it’s in.
The researchers say this could imbue robots with a similar sense of proprioception or body-awareness to that found in plants and animals. “Self-sensing allows for the development of closed-loop feedback controllers to design highly advanced and precise robots for diverse applications,” Shane Mitchell, a PhD student in Keplinger’s lab and an author on both papers, said in an email.
The researchers say the high voltages required are an ongoing challenge, though they’ve already designed devices in the lab that use a fifth of the voltage of those features in the recent papers.
In most of their demonstrations, these soft actuators were being used to power rigid arms and levers, pointing to the fact that future robots are likely to combine both rigid and soft components, much like animals do. The potential applications for the technology range from more realistic prosthetics to much more dextrous robots that can work easily alongside humans.
It will take some work before these devices appear in commercial robots. But the combination of high-performance with simple and inexpensive fabrication methods mean other researchers are likely to jump in, so innovation could be rapid.
Image Credit: Keplinger Research Group/University of Colorado Continue reading

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#431872 AI Uses Titan Supercomputer to Create ...

You don’t have to dig too deeply into the archive of dystopian science fiction to uncover the horror that intelligent machines might unleash. The Matrix and The Terminator are probably the most well-known examples of self-replicating, intelligent machines attempting to enslave or destroy humanity in the process of building a brave new digital world.
The prospect of artificially intelligent machines creating other artificially intelligent machines took a big step forward in 2017. However, we’re far from the runaway technological singularity futurists are predicting by mid-century or earlier, let alone murderous cyborgs or AI avatar assassins.
The first big boost this year came from Google. The tech giant announced it was developing automated machine learning (AutoML), writing algorithms that can do some of the heavy lifting by identifying the right neural networks for a specific job. Now researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL), using the most powerful supercomputer in the US, have developed an AI system that can generate neural networks as good if not better than any developed by a human in less than a day.
It can take months for the brainiest, best-paid data scientists to develop deep learning software, which sends data through a complex web of mathematical algorithms. The system is modeled after the human brain and known as an artificial neural network. Even Google’s AutoML took weeks to design a superior image recognition system, one of the more standard operations for AI systems today.
Computing Power
Of course, Google Brain project engineers only had access to 800 graphic processing units (GPUs), a type of computer hardware that works especially well for deep learning. Nvidia, which pioneered the development of GPUs, is considered the gold standard in today’s AI hardware architecture. Titan, the supercomputer at ORNL, boasts more than 18,000 GPUs.
The ORNL research team’s algorithm, called MENNDL for Multinode Evolutionary Neural Networks for Deep Learning, isn’t designed to create AI systems that cull cute cat photos from the internet. Instead, MENNDL is a tool for testing and training thousands of potential neural networks to work on unique science problems.
That requires a different approach from the Google and Facebook AI platforms of the world, notes Steven Young, a postdoctoral research associate at ORNL who is on the team that designed MENNDL.
“We’ve discovered that those [neural networks] are very often not the optimal network for a lot of our problems, because our data, while it can be thought of as images, is different,” he explains to Singularity Hub. “These images, and the problems, have very different characteristics from object detection.”
AI for Science
One application of the technology involved a particle physics experiment at the Fermi National Accelerator Laboratory. Fermilab researchers are interested in understanding neutrinos, high-energy subatomic particles that rarely interact with normal matter but could be a key to understanding the early formation of the universe. One Fermilab experiment involves taking a sort of “snapshot” of neutrino interactions.
The team wanted the help of an AI system that could analyze and classify Fermilab’s detector data. MENNDL evaluated 500,000 neural networks in 24 hours. Its final solution proved superior to custom models developed by human scientists.
In another case involving a collaboration with St. Jude Children’s Research Hospital in Memphis, MENNDL improved the error rate of a human-designed algorithm for identifying mitochondria inside 3D electron microscopy images of brain tissue by 30 percent.
“We are able to do better than humans in a fraction of the time at designing networks for these sort of very different datasets that we’re interested in,” Young says.
What makes MENNDL particularly adept is its ability to define the best or most optimal hyperparameters—the key variables—to tackle a particular dataset.
“You don’t always need a big, huge deep network. Sometimes you just need a small network with the right hyperparameters,” Young says.
A Virtual Data Scientist
That’s not dissimilar to the approach of a company called H20.ai, a startup out of Silicon Valley that uses open source machine learning platforms to “democratize” AI. It applies machine learning to create business solutions for Fortune 500 companies, including some of the world’s biggest banks and healthcare companies.
“Our software is more [about] pattern detection, let’s say anti-money laundering or fraud detection or which customer is most likely to churn,” Dr. Arno Candel, chief technology officer at H2O.ai, tells Singularity Hub. “And that kind of insight-generating software is what we call AI here.”
The company’s latest product, Driverless AI, promises to deliver the data scientist equivalent of a chessmaster to its customers (the company claims several such grandmasters in its employ and advisory board). In other words, the system can analyze a raw dataset and, like MENNDL, automatically identify what features should be included in the computer model to make the most of the data based on the best “chess moves” of its grandmasters.
“So we’re using those algorithms, but we’re giving them the human insights from those data scientists, and we automate their thinking,” he explains. “So we created a virtual data scientist that is relentless at trying these ideas.”
Inside the Black Box
Not unlike how the human brain reaches a conclusion, it’s not always possible to understand how a machine, despite being designed by humans, reaches its own solutions. The lack of transparency is often referred to as the AI “black box.” Experts like Young say we can learn something about the evolutionary process of machine learning by generating millions of neural networks and seeing what works well and what doesn’t.
“You’re never going to be able to completely explain what happened, but maybe we can better explain it than we currently can today,” Young says.
Transparency is built into the “thought process” of each particular model generated by Driverless AI, according to Candel.
The computer even explains itself to the user in plain English at each decision point. There is also real-time feedback that allows users to prioritize features, or parameters, to see how the changes improve the accuracy of the model. For example, the system may include data from people in the same zip code as it creates a model to describe customer turnover.
“That’s one of the advantages of our automatic feature engineering: it’s basically mimicking human thinking,” Candel says. “It’s not just neural nets that magically come up with some kind of number, but we’re trying to make it statistically significant.”
Moving Forward
Much digital ink has been spilled over the dearth of skilled data scientists, so automating certain design aspects for developing artificial neural networks makes sense. Experts agree that automation alone won’t solve that particular problem. However, it will free computer scientists to tackle more difficult issues, such as parsing the inherent biases that exist within the data used by machine learning today.
“I think the world has an opportunity to focus more on the meaning of things and not on the laborious tasks of just fitting a model and finding the best features to make that model,” Candel notes. “By automating, we are pushing the burden back for the data scientists to actually do something more meaningful, which is think about the problem and see how you can address it differently to make an even bigger impact.”
The team at ORNL expects it can also make bigger impacts beginning next year when the lab’s next supercomputer, Summit, comes online. While Summit will boast only 4,600 nodes, it will sport the latest and greatest GPU technology from Nvidia and CPUs from IBM. That means it will deliver more than five times the computational performance of Titan, the world’s fifth-most powerful supercomputer today.
“We’ll be able to look at much larger problems on Summit than we were able to with Titan and hopefully get to a solution much faster,” Young says.
It’s all in a day’s work.
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#431866 The Technologies We’ll Have Our Eyes ...

It’s that time of year again when our team has a little fun and throws on our futurist glasses to look ahead at some of the technologies and trends we’re most anticipating next year.
Whether the implications of a technology are vast or it resonates with one of us personally, here’s the list from some of the Singularity Hub team of what we have our eyes on as we enter the new year.
For a little refresher, these were the technologies our team was fired up about at the start of 2017.
Tweet us the technology you’re excited to watch in 2018 at @SingularityHub.
Cryptocurrency and Blockchain
“Given all the noise Bitcoin is making globally in the media, it is driving droves of main street investors to dabble in and learn more about cryptocurrencies. This will continue to raise valuations and drive adoption of blockchain. From Bank of America recently getting a blockchain-based patent approved to the Australian Securities Exchange’s plan to use blockchain, next year is going to be chock-full of these stories. Coindesk even recently spotted a patent filing from Apple involving blockchain. From ‘China’s Ethereum’, NEO, to IOTA to Golem to Qtum, there are a lot of interesting cryptos to follow given the immense numbers of potential applications. Hang on, it’s going to be a bumpy ride in 2018!”
–Kirk Nankivell, Website Manager
There Is No One Technology to Watch
“Next year may be remembered for advances in gene editing, blockchain, AI—or most likely all these and more. There is no single technology to watch. A number of consequential trends are advancing and converging. This general pace of change is exciting, and it also contributes to spiking anxiety. Technology’s invisible lines of force are extending further and faster into our lives and subtly subverting how we view the world and each other in unanticipated ways. Still, all the near-term messiness and volatility, the little and not-so-little dramas, the hype and disillusion, the controversies and conflict, all that smooths out a bit when you take a deep breath and a step back, and it’s my sincere hope and belief the net result will be more beneficial than harmful.”
–Jason Dorrier, Managing Editor
‘Fake News’ Fighting Technology
“It’s been a wild ride for the media this year with the term ‘fake news’ moving from the public’s peripheral and into mainstream vocabulary. The spread of ‘fake news’ is often blamed on media outlets, but social media platforms and search engines are often responsible too. (Facebook still won’t identify as a media company—maybe next year?) Yes, technology can contribute to spreading false information, but it can also help stop it. From technologists who are building in-article ‘trust indicator’ features, to artificial intelligence systems that can both spot and shut down fake news early on, I’m hopeful we can create new solutions to this huge problem. One step further: if publishers step up to fix this we might see some faith restored in the media.”
–Alison E. Berman, Digital Producer
Pay-as-You-Go Home Solar Power
“People in rural African communities are increasingly bypassing electrical grids (which aren’t even an option in many cases) and installing pay-as-you-go solar panels on their homes. The companies offering these services are currently not subject to any regulations, though they’re essentially acting as a utility. As demand for power grows, they’ll have to come up with ways to efficiently scale, and to balance the humanitarian and capitalistic aspects of their work. It’s fascinating to think traditional grids may never be necessary in many areas of the continent thanks to this technology.”
–Vanessa Bates Ramirez, Associate Editor
Virtual Personal Assistants
“AI is clearly going to rule our lives, and in many ways it already makes us look like clumsy apes. Alexa, Siri, and Google Assistant are promising first steps toward a world of computers that understand us and relate to us on an emotional level. I crave the day when my Apple Watch coaches me into healthier habits, lets me know about new concerts nearby, speaks to my self-driving Lyft on my behalf, and can help me respond effectively to aggravating emails based on communication patterns. But let’s not brush aside privacy concerns and the implications of handing over our personal data to megacorporations. The scariest thing here is that privacy laws and advertising ethics do not accommodate this level of intrusive data hoarding.”
–Matthew Straub, Director of Digital Engagement (Hub social media)
Solve for Learning: Educational Apps for Children in Conflict Zones
“I am most excited by exponential technology when it is used to help solve a global grand challenge. Educational apps are currently being developed to help solve for learning by increasing accessibility to learning opportunities for children living in conflict zones. Many children in these areas are not receiving an education, with girls being 2.5 times more likely than boys to be out of school. The EduApp4Syria project is developing apps to help children in Syria and Kashmir learn in their native languages. Mobile phones are increasingly available in these areas, and the apps are available offline for children who do not have consistent access to mobile networks. The apps are low-cost, easily accessible, and scalable educational opportunities.
–Paige Wilcoxson, Director, Curriculum & Learning Design
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#431828 This Self-Driving AI Is Learning to ...

I don’t have to open the doors of AImotive’s white 2015 Prius to see that it’s not your average car. This particular Prius has been christened El Capitan, the name written below the rear doors, and two small cameras are mounted on top of the car. Bundles of wire snake out from them, as well as from the two additional cameras on the car’s hood and trunk.
Inside is where things really get interesting, though. The trunk holds a computer the size of a microwave, and a large monitor covers the passenger glove compartment and dashboard. The center console has three switches labeled “Allowed,” “Error,” and “Active.”
Budapest-based AImotive is working to provide scalable self-driving technology alongside big players like Waymo and Uber in the autonomous vehicle world. On a highway test ride with CEO Laszlo Kishonti near the company’s office in Mountain View, California, I got a glimpse of just how complex that world is.
Camera-Based Feedback System
AImotive’s approach to autonomous driving is a little different from that of some of the best-known systems. For starters, they’re using cameras, not lidar, as primary sensors. “The traffic system is visual and the cost of cameras is low,” Kishonti said. “A lidar can recognize when there are people near the car, but a camera can differentiate between, say, an elderly person and a child. Lidar’s resolution isn’t high enough to recognize the subtle differences of urban driving.”
Image Credit: AImotive
The company’s aiDrive software uses data from the camera sensors to feed information to its algorithms for hierarchical decision-making, grouped under four concurrent activities: recognition, location, motion, and control.
Kishonti pointed out that lidar has already gotten more cost-efficient, and will only continue to do so.
“Ten years ago, lidar was best because there wasn’t enough processing power to do all the calculations by AI. But the cost of running AI is decreasing,” he said. “In our approach, computer vision and AI processing are key, and for safety, we’ll have fallback sensors like radar or lidar.”
aiDrive currently runs on Nvidia chips, which Kishonti noted were originally designed for graphics, and are not terribly efficient given how power-hungry they are. “We’re planning to substitute lower-cost, lower-energy chips in the next six months,” he said.
Testing in Virtual Reality
Waymo recently announced its fleet has now driven four million miles autonomously. That’s a lot of miles, and hard to compete with. But AImotive isn’t trying to compete, at least not by logging more real-life test miles. Instead, the company is doing 90 percent of its testing in virtual reality. “This is what truly differentiates us from competitors,” Kishonti said.
He outlined the three main benefits of VR testing: it can simulate scenarios too dangerous for the real world (such as hitting something), too costly (not every company has Waymo’s funds to run hundreds of cars on real roads), or too time-consuming (like waiting for rain, snow, or other weather conditions to occur naturally and repeatedly).
“Real-world traffic testing is very skewed towards the boring miles,” he said. “What we want to do is test all the cases that are hard to solve.”
On a screen that looked not unlike multiple games of Mario Kart, he showed me the simulator. Cartoon cars cruised down winding streets, outfitted with all the real-world surroundings: people, trees, signs, other cars. As I watched, a furry kangaroo suddenly hopped across one screen. “Volvo had an issue in Australia,” Kishonti explained. “A kangaroo’s movement is different than other animals since it hops instead of running.” Talk about cases that are hard to solve.
AImotive is currently testing around 1,000 simulated scenarios every night, with a steadily-rising curve of successful tests. These scenarios are broken down into features, and the car’s behavior around those features fed into a neural network. As the algorithms learn more features, the level of complexity the vehicles can handle goes up.
On the Road
After Kishonti and his colleagues filled me in on the details of their product, it was time to test it out. A safety driver sat in the driver’s seat, a computer operator in the passenger seat, and Kishonti and I in back. The driver maintained full control of the car until we merged onto the highway. Then he flicked the “Allowed” switch, his copilot pressed the “Active” switch, and he took his hands off the wheel.
What happened next, you ask?
A few things. El Capitan was going exactly the speed limit—65 miles per hour—which meant all the other cars were passing us. When a car merged in front of us or cut us off, El Cap braked accordingly (if a little abruptly). The monitor displayed the feed from each of the car’s cameras, plus multiple data fields and a simulation where a blue line marked the center of the lane, measured by the cameras tracking the lane markings on either side.
I noticed El Cap wobbling out of our lane a bit, but it wasn’t until two things happened in a row that I felt a little nervous: first we went under a bridge, then a truck pulled up next to us, both bridge and truck casting a complete shadow over our car. At that point El Cap lost it, and we swerved haphazardly to the right, narrowly missing the truck’s rear wheels. The safety driver grabbed the steering wheel and took back control of the car.
What happened, Kishonti explained, was that the shadows made it hard for the car’s cameras to see the lane markings. This was a new scenario the algorithm hadn’t previously encountered. If we’d only gone under a bridge or only been next to the truck for a second, El Cap may not have had so much trouble, but the two events happening in a row really threw the car for a loop—almost literally.
“This is a new scenario we’ll add to our testing,” Kishonti said. He added that another way for the algorithm to handle this type of scenario, rather than basing its speed and positioning on the lane markings, is to mimic nearby cars. “The human eye would see that other cars are still moving at the same speed, even if it can’t see details of the road,” he said.
After another brief—and thankfully uneventful—hands-off cruise down the highway, the safety driver took over, exited the highway, and drove us back to the office.
Driving into the Future
I climbed out of the car feeling amazed not only that self-driving cars are possible, but that driving is possible at all. I squint when driving into a tunnel, swerve to avoid hitting a stray squirrel, and brake gradually at stop signs—all without consciously thinking to do so. On top of learning to steer, brake, and accelerate, self-driving software has to incorporate our brains’ and bodies’ unconscious (but crucial) reactions, like our pupils dilating to let in more light so we can see in a tunnel.
Despite all the progress of machine learning, artificial intelligence, and computing power, I have a wholly renewed appreciation for the thing that’s been in charge of driving up till now: the human brain.
Kishonti seemed to feel similarly. “I don’t think autonomous vehicles in the near future will be better than the best drivers,” he said. “But they’ll be better than the average driver. What we want to achieve is safe, good-quality driving for everyone, with scalability.”
AImotive is currently working with American tech firms and with car and truck manufacturers in Europe, China, and Japan.
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#431733 Why Humanoid Robots Are Still So Hard to ...

Picture a robot. In all likelihood, you just pictured a sleek metallic or chrome-white humanoid. Yet the vast majority of robots in the world around us are nothing like this; instead, they’re specialized for specific tasks. Our cultural conception of what robots are dates back to the coining of the term robots in the Czech play, Rossum’s Universal Robots, which originally envisioned them as essentially synthetic humans.
The vision of a humanoid robot is tantalizing. There are constant efforts to create something that looks like the robots of science fiction. Recently, an old competitor in this field returned with a new model: Toyota has released what they call the T-HR3. As humanoid robots go, it appears to be pretty dexterous and have a decent grip, with a number of degrees of freedom making the movements pleasantly human.
This humanoid robot operates mostly via a remote-controlled system that allows the user to control the robot’s limbs by exerting different amounts of pressure on a framework. A VR headset completes the picture, allowing the user to control the robot’s body and teleoperate the machine. There’s no word on a price tag, but one imagines a machine with a control system this complicated won’t exactly be on your Christmas list, unless you’re a billionaire.

Toyota is no stranger to robotics. They released a series of “Partner Robots” that had a bizarre affinity for instrument-playing but weren’t often seen doing much else. Given that they didn’t seem to have much capability beyond the automaton that Leonardo da Vinci made hundreds of years ago, they promptly vanished. If, as the name suggests, the T-HR3 is a sequel to these robots, which came out shortly after ASIMO back in 2003, it’s substantially better.
Slightly less humanoid (and perhaps the more useful for it), Toyota’s HSR-2 is a robot base on wheels with a simple mechanical arm. It brings to mind earlier machines produced by dream-factory startup Willow Garage like the PR-2. The idea of an affordable robot that could simply move around on wheels and pick up and fetch objects, and didn’t harbor too-lofty ambitions to do anything else, was quite successful.
So much so that when Robocup, the international robotics competition, looked for a platform for their robot-butler competition @Home, they chose HSR-2 for its ability to handle objects. HSR-2 has been deployed in trial runs to care for the elderly and injured, but has yet to be widely adopted for these purposes five years after its initial release. It’s telling that arguably the most successful multi-purpose humanoid robot isn’t really humanoid at all—and it’s curious that Toyota now seems to want to return to a more humanoid model a decade after they gave up on the project.
What’s unclear, as is often the case with humanoid robots, is what, precisely, the T-HR3 is actually for. The teleoperation gets around the complex problem of control by simply having the machine controlled remotely by a human. That human then handles all the sensory perception, decision-making, planning, and manipulation; essentially, the hardest problems in robotics.
There may not be a great deal of autonomy for the T-HR3, but by sacrificing autonomy, you drastically cut down the uses of the robot. Since it can’t act alone, you need a convincing scenario where you need a teleoperated humanoid robot that’s less precise and vastly more expensive than just getting a person to do the same job. Perhaps someday more autonomy will be developed for the robot, and the master maneuvering system that allows humans to control it will only be used in emergencies to control the robot if it gets stuck.
Toyota’s press release says it is “a platform with capabilities that can safely assist humans in a variety of settings, such as the home, medical facilities, construction sites, disaster-stricken areas and even outer space.” In reality, it’s difficult to see such a robot being affordable or even that useful in the home or in medical facilities (unless it’s substantially stronger than humans). Equally, it certainly doesn’t seem robust enough to be deployed in disaster zones or outer space. These tasks have been mooted for robots for a very long time and few have proved up to the challenge.
Toyota’s third generation humanoid robot, the T-HR3. Image Credit: Toyota
Instead, the robot seems designed to work alongside humans. Its design, standing 1.5 meters tall, weighing 75 kilograms, and possessing 32 degrees of freedom in its body, suggests it is built to closely mimic a person, rather than a robot like ATLAS which is robust enough that you can imagine it being useful in a war zone. In this case, it might be closer to the model of the collaborative robots or co-bots developed by Rethink Robotics, whose tons of safety features, including force-sensitive feedback for the user, reduce the risk of terrible PR surrounding killer robots.
Instead the emphasis is on graceful precision engineering: in the promo video, the robot can be seen balancing on one leg before showing off a few poised, yoga-like poses. This perhaps suggests that an application in elderly care, which Toyota has ventured into before and which was the stated aim of their simple HSR-2, might be more likely than deployment to a disaster zone.
The reason humanoid robots remain so elusive and so tempting is probably because of a simple cognitive mistake. We make two bad assumptions. First, we assume that if you build a humanoid robot, give its joints enough flexibility, throw in a little AI and perhaps some pre-programmed behaviors, then presto, it will be able to do everything humans can. When you see a robot that moves well and looks humanoid, it seems like the hardest part is done; surely this robot could do anything. The reality is never so simple.

We also make the reverse assumption: we assume that when we are finally replaced, it will be by perfect replicas of our own bodies and brains that can fulfill all the functions we used to fulfill. Perhaps, in reality, the future of robots and AI is more like its present: piecemeal, with specialized algorithms and specialized machines gradually learning to outperform humans at every conceivable task without ever looking convincingly human.
It may well be that the T-HR3 is angling towards this concept of machine learning as a platform for future research. Rather than trying to program an omni-capable robot out of the box, it will gradually learn from its human controllers. In this way, you could see the platform being used to explore the limits of what humans can teach robots to do simply by having them mimic sequences of our bodies’ motion, in the same way the exploitation of neural networks is testing the limits of training algorithms on data. No one machine will be able to perform everything a human can, but collectively, they will vastly outperform us at anything you’d want one to do.
So when you see a new android like Toyota’s, feel free to marvel at its technical abilities and indulge in the speculation about whether it’s a PR gimmick or a revolutionary step forward along the road to human replacement. Just remember that, human-level bots or not, we’re already strolling down that road.
Image Credit: Toyota Continue reading

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