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#432051 What Roboticists Are Learning From Early ...

You might not have heard of Hanson Robotics, but if you’re reading this, you’ve probably seen their work. They were the company behind Sophia, the lifelike humanoid avatar that’s made dozens of high-profile media appearances. Before that, they were the company behind that strange-looking robot that seemed a bit like Asimo with Albert Einstein’s head—or maybe you saw BINA48, who was interviewed for the New York Times in 2010 and featured in Jon Ronson’s books. For the sci-fi aficionados amongst you, they even made a replica of legendary author Philip K. Dick, best remembered for having books with titles like Do Androids Dream of Electric Sheep? turned into films with titles like Blade Runner.

Hanson Robotics, in other words, with their proprietary brand of life-like humanoid robots, have been playing the same game for a while. Sometimes it can be a frustrating game to watch. Anyone who gives the robot the slightest bit of thought will realize that this is essentially a chat-bot, with all the limitations this implies. Indeed, even in that New York Times interview with BINA48, author Amy Harmon describes it as a frustrating experience—with “rare (but invariably thrilling) moments of coherence.” This sensation will be familiar to anyone who’s conversed with a chatbot that has a few clever responses.

The glossy surface belies the lack of real intelligence underneath; it seems, at first glance, like a much more advanced machine than it is. Peeling back that surface layer—at least for a Hanson robot—means you’re peeling back Frubber. This proprietary substance—short for “Flesh Rubber,” which is slightly nightmarish—is surprisingly complicated. Up to thirty motors are required just to control the face; they manipulate liquid cells in order to make the skin soft, malleable, and capable of a range of different emotional expressions.

A quick combinatorial glance at the 30+ motors suggests that there are millions of possible combinations; researchers identify 62 that they consider “human-like” in Sophia, although not everyone agrees with this assessment. Arguably, the technical expertise that went into reconstructing the range of human facial expressions far exceeds the more simplistic chat engine the robots use, although it’s the second one that allows it to inflate the punters’ expectations with a few pre-programmed questions in an interview.

Hanson Robotics’ belief is that, ultimately, a lot of how humans will eventually relate to robots is going to depend on their faces and voices, as well as on what they’re saying. “The perception of identity is so intimately bound up with the perception of the human form,” says David Hanson, company founder.

Yet anyone attempting to design a robot that won’t terrify people has to contend with the uncanny valley—that strange blend of concern and revulsion people react with when things appear to be creepily human. Between cartoonish humanoids and genuine humans lies what has often been a no-go zone in robotic aesthetics.

The uncanny valley concept originated with roboticist Masahiro Mori, who argued that roboticists should avoid trying to replicate humans exactly. Since anything that wasn’t perfect, but merely very good, would elicit an eerie feeling in humans, shirking the challenge entirely was the only way to avoid the uncanny valley. It’s probably a task made more difficult by endless streams of articles about AI taking over the world that inexplicably conflate AI with killer humanoid Terminators—which aren’t particularly likely to exist (although maybe it’s best not to push robots around too much).

The idea behind this realm of psychological horror is fairly simple, cognitively speaking.

We know how to categorize things that are unambiguously human or non-human. This is true even if they’re designed to interact with us. Consider the popularity of Aibo, Jibo, or even some robots that don’t try to resemble humans. Something that resembles a human, but isn’t quite right, is bound to evoke a fear response in the same way slightly distorted music or slightly rearranged furniture in your home will. The creature simply doesn’t fit.

You may well reject the idea of the uncanny valley entirely. David Hanson, naturally, is not a fan. In the paper Upending the Uncanny Valley, he argues that great art forms have often resembled humans, but the ultimate goal for humanoid roboticists is probably to create robots we can relate to as something closer to humans than works of art.

Meanwhile, Hanson and other scientists produce competing experiments to either demonstrate that the uncanny valley is overhyped, or to confirm it exists and probe its edges.

The classic experiment involves gradually morphing a cartoon face into a human face, via some robotic-seeming intermediaries—yet it’s in movement that the real horror of the almost-human often lies. Hanson has argued that incorporating cartoonish features may help—and, sometimes, that the uncanny valley is a generational thing which will melt away when new generations grow used to the quirks of robots. Although Hanson might dispute the severity of this effect, it’s clearly what he’s trying to avoid with each new iteration.

Hiroshi Ishiguro is the latest of the roboticists to have dived headlong into the valley.

Building on the work of pioneers like Hanson, those who study human-robot interaction are pushing at the boundaries of robotics—but also of social science. It’s usually difficult to simulate what you don’t understand, and there’s still an awful lot we don’t understand about how we interpret the constant streams of non-verbal information that flow when you interact with people in the flesh.

Ishiguro took this imitation of human forms to extreme levels. Not only did he monitor and log the physical movements people made on videotapes, but some of his robots are based on replicas of people; the Repliee series began with a ‘replicant’ of his daughter. This involved making a rubber replica—a silicone cast—of her entire body. Future experiments were focused on creating Geminoid, a replica of Ishiguro himself.

As Ishiguro aged, he realized that it would be more effective to resemble his replica through cosmetic surgery rather than by continually creating new casts of his face, each with more lines than the last. “I decided not to get old anymore,” Ishiguro said.

We love to throw around abstract concepts and ideas: humans being replaced by machines, cared for by machines, getting intimate with machines, or even merging themselves with machines. You can take an idea like that, hold it in your hand, and examine it—dispassionately, if not without interest. But there’s a gulf between thinking about it and living in a world where human-robot interaction is not a field of academic research, but a day-to-day reality.

As the scientists studying human-robot interaction develop their robots, their replicas, and their experiments, they are making some of the first forays into that world. We might all be living there someday. Understanding ourselves—decrypting the origins of empathy and love—may be the greatest challenge to face. That is, if you want to avoid the valley.

Image Credit: Anton Gvozdikov / Shutterstock.com Continue reading

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#431928 How Fast Is AI Progressing? Stanford’s ...

When? This is probably the question that futurists, AI experts, and even people with a keen interest in technology dread the most. It has proved famously difficult to predict when new developments in AI will take place. The scientists at the Dartmouth Summer Research Project on Artificial Intelligence in 1956 thought that perhaps two months would be enough to make “significant advances” in a whole range of complex problems, including computers that can understand language, improve themselves, and even understand abstract concepts.
Sixty years later, and these problems are not yet solved. The AI Index, from Stanford, is an attempt to measure how much progress has been made in artificial intelligence.
The index adopts a unique approach, and tries to aggregate data across many regimes. It contains Volume of Activity metrics, which measure things like venture capital investment, attendance at academic conferences, published papers, and so on. The results are what you might expect: tenfold increases in academic activity since 1996, an explosive growth in startups focused around AI, and corresponding venture capital investment. The issue with this metric is that it measures AI hype as much as AI progress. The two might be correlated, but then again, they may not.
The index also scrapes data from the popular coding website Github, which hosts more source code than anyone in the world. They can track the amount of AI-related software people are creating, as well as the interest levels in popular machine learning packages like Tensorflow and Keras. The index also keeps track of the sentiment of news articles that mention AI: surprisingly, given concerns about the apocalypse and an employment crisis, those considered “positive” outweigh the “negative” by three to one.
But again, this could all just be a measure of AI enthusiasm in general.
No one would dispute the fact that we’re in an age of considerable AI hype, but the progress of AI is littered by booms and busts in hype, growth spurts that alternate with AI winters. So the AI Index attempts to track the progress of algorithms against a series of tasks. How well does computer vision perform at the Large Scale Visual Recognition challenge? (Superhuman at annotating images since 2015, but they still can’t answer questions about images very well, combining natural language processing and image recognition). Speech recognition on phone calls is almost at parity.
In other narrow fields, AIs are still catching up to humans. Translation might be good enough that you can usually get the gist of what’s being said, but still scores poorly on the BLEU metric for translation accuracy. The AI index even keeps track of how well the programs can do on the SAT test, so if you took it, you can compare your score to an AI’s.
Measuring the performance of state-of-the-art AI systems on narrow tasks is useful and fairly easy to do. You can define a metric that’s simple to calculate, or devise a competition with a scoring system, and compare new software with old in a standardized way. Academics can always debate about the best method of assessing translation or natural language understanding. The Loebner prize, a simplified question-and-answer Turing Test, recently adopted Winograd Schema type questions, which rely on contextual understanding. AI has more difficulty with these.
Where the assessment really becomes difficult, though, is in trying to map these narrow-task performances onto general intelligence. This is hard because of a lack of understanding of our own intelligence. Computers are superhuman at chess, and now even a more complex game like Go. The braver predictors who came up with timelines thought AlphaGo’s success was faster than expected, but does this necessarily mean we’re closer to general intelligence than they thought?
Here is where it’s harder to track progress.
We can note the specialized performance of algorithms on tasks previously reserved for humans—for example, the index cites a Nature paper that shows AI can now predict skin cancer with more accuracy than dermatologists. We could even try to track one specific approach to general AI; for example, how many regions of the brain have been successfully simulated by a computer? Alternatively, we could simply keep track of the number of professions and professional tasks that can now be performed to an acceptable standard by AI.

“We are running a race, but we don’t know how to get to the endpoint, or how far we have to go.”

Progress in AI over the next few years is far more likely to resemble a gradual rising tide—as more and more tasks can be turned into algorithms and accomplished by software—rather than the tsunami of a sudden intelligence explosion or general intelligence breakthrough. Perhaps measuring the ability of an AI system to learn and adapt to the work routines of humans in office-based tasks could be possible.
The AI index doesn’t attempt to offer a timeline for general intelligence, as this is still too nebulous and confused a concept.
Michael Woodridge, head of Computer Science at the University of Oxford, notes, “The main reason general AI is not captured in the report is that neither I nor anyone else would know how to measure progress.” He is concerned about another AI winter, and overhyped “charlatans and snake-oil salesmen” exaggerating the progress that has been made.
A key concern that all the experts bring up is the ethics of artificial intelligence.
Of course, you don’t need general intelligence to have an impact on society; algorithms are already transforming our lives and the world around us. After all, why are Amazon, Google, and Facebook worth any money? The experts agree on the need for an index to measure the benefits of AI, the interactions between humans and AIs, and our ability to program values, ethics, and oversight into these systems.
Barbra Grosz of Harvard champions this view, saying, “It is important to take on the challenge of identifying success measures for AI systems by their impact on people’s lives.”
For those concerned about the AI employment apocalypse, tracking the use of AI in the fields considered most vulnerable (say, self-driving cars replacing taxi drivers) would be a good idea. Society’s flexibility for adapting to AI trends should be measured, too; are we providing people with enough educational opportunities to retrain? How about teaching them to work alongside the algorithms, treating them as tools rather than replacements? The experts also note that the data suffers from being US-centric.
We are running a race, but we don’t know how to get to the endpoint, or how far we have to go. We are judging by the scenery, and how far we’ve run already. For this reason, measuring progress is a daunting task that starts with defining progress. But the AI index, as an annual collection of relevant information, is a good start.
Image Credit: Photobank gallery / Shutterstock.com Continue reading

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#431902 Old dog, new tricks: Sony unleashes ...

As Japan celebrates the year of the dog, electronics giant Sony on Thursday unleashed its new robot canine companion, packed with artificial intelligence and internet connectivity. Continue reading

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#431862 Want Self-Healing Robots and Tires? ...

We all have scars, and each one tells a story. Tales of tomfoolery, tales of haphazardness, or in my case, tales of stupidity.
Whether the cause of your scar was a push-bike accident, a lack of concentration while cutting onions, or simply the byproduct of an active lifestyle, the experience was likely extremely painful and distressing. Not to mention the long and vexatious recovery period, stretching out for weeks and months after the actual event!
Cast your minds back to that time. How you longed for instant relief from your discomfort! How you longed to have your capabilities restored in an instant!
Well, materials that can heal themselves in an instant may not be far from becoming a reality—and a family of them known as elastomers holds the key.
“Elastomer” is essentially a big, fancy word for rubber. However, elastomers have one unique property—they are capable of returning to their original form after being vigorously stretched and deformed.
This unique property of elastomers has caught the eye of many scientists around the world, particularly those working in the field of robotics. The reason? Elastomer can be encouraged to return to its original shape, in many cases by simply applying heat. The implication of this is the quick and cost-effective repair of “wounds”—cuts, tears, and punctures to the soft, elastomer-based appendages of a robot’s exoskeleton.

Researchers from Vrije University in Brussels, Belgium have been toying with the technique, and with remarkable success. The team built a robotic hand with fingers made of a type of elastomer. They found that cuts and punctures were indeed able to repair themselves simply by applying heat to the affected area.
How long does the healing process take? In this instance, about a day. Now that’s a lot shorter than the weeks and months of recovery time we typically need for a flesh wound, during which we are unable to write, play the guitar, or do the dishes. If you consider the latter to be a bad thing…
However, it’s not the first time scientists have played around with elastomers and examined their self-healing properties. Another team of scientists, headed up by Cheng-Hui Li and Chao Wang, discovered another type of elastomer that exhibited autonomous self-healing properties. Just to help you picture this stuff, the material closely resembles animal muscle— strong, flexible, and elastic. With autogenetic restorative powers to boot.
Advancements in the world of self-healing elastomers, or rubbers, may also affect the lives of everyday motorists. Researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a self-healing rubber material that could be used to make tires that repair their own punctures.
This time the mechanism of self-healing doesn’t involve heat. Rather, it is related to a physical phenomenon associated with the rubber’s unique structure. Normally, when a large enough stress is applied to a typical rubber, there is catastrophic failure at the focal point of that stress. The self-healing rubber the researchers created, on the other hand, distributes that same stress evenly over a network of “crazes”—which are like cracks connected by strands of fiber.
Here’s the interesting part. Not only does this unique physical characteristic of the rubber prevent catastrophic failure, it facilitates self-repair. According to Harvard researchers, when the stress is released, the material snaps back to its original form and the crazes heal.
This wonder material could be used in any number of rubber-based products.
Professor Jinrong Wu, of Sichuan University, China, and co-author of the study, happened to single out tires: “Imagine that we could use this material as one of the components to make a rubber tire… If you have a cut through the tire, this tire wouldn’t have to be replaced right away. Instead, it would self-heal while driving, enough to give you leeway to avoid dramatic damage,” said Wu.
So where to from here? Well, self-healing elastomers could have a number of different applications. According to the article published by Quartz, cited earlier, the material could be used on artificial limbs. Perhaps it will provide some measure of structural integrity without looking like a tattered mess after years of regular use.
Or perhaps a sort of elastomer-based hybrid skin is on the horizon. A skin in which wounds heal instantly. And recovery time, unlike your regular old human skin of yesteryear, is significantly slashed. Furthermore, this future skin might eliminate those little reminders we call scars.
For those with poor judgment skills, this spells an end to disquieting reminders of our own stupidity.
Image Credit: Vrije Universiteit Brussel / Prof. Dr. ir. Bram Vanderborght Continue reading

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#431859 Digitized to Democratized: These Are the ...

“The Six Ds are a chain reaction of technological progression, a road map of rapid development that always leads to enormous upheaval and opportunity.”
–Peter Diamandis and Steven Kotler, Bold
We live in incredible times. News travels the globe in an instant. Music, movies, games, communication, and knowledge are ever-available on always-connected devices. From biotechnology to artificial intelligence, powerful technologies that were once only available to huge organizations and governments are becoming more accessible and affordable thanks to digitization.
The potential for entrepreneurs to disrupt industries and corporate behemoths to unexpectedly go extinct has never been greater.
One hundred or fifty or even twenty years ago, disruption meant coming up with a product or service people needed but didn’t have yet, then finding a way to produce it with higher quality and lower costs than your competitors. This entailed hiring hundreds or thousands of employees, having a large physical space to put them in, and waiting years or even decades for hard work to pay off and products to come to fruition.

“Technology is disrupting traditional industrial processes, and they’re never going back.”

But thanks to digital technologies developing at exponential rates of change, the landscape of 21st-century business has taken on a dramatically different look and feel.
The structure of organizations is changing. Instead of thousands of employees and large physical plants, modern start-ups are small organizations focused on information technologies. They dematerialize what was once physical and create new products and revenue streams in months, sometimes weeks.
It no longer takes a huge corporation to have a huge impact.
Technology is disrupting traditional industrial processes, and they’re never going back. This disruption is filled with opportunity for forward-thinking entrepreneurs.
The secret to positively impacting the lives of millions of people is understanding and internalizing the growth cycle of digital technologies. This growth cycle takes place in six key steps, which Peter Diamandis calls the Six Ds of Exponentials: digitization, deception, disruption, demonetization, dematerialization, and democratization.
According to Diamandis, cofounder and chairman of Singularity University and founder and executive chairman of XPRIZE, when something is digitized it begins to behave like an information technology.

Newly digitized products develop at an exponential pace instead of a linear one, fooling onlookers at first before going on to disrupt companies and whole industries. Before you know it, something that was once expensive and physical is an app that costs a buck.
Newspapers and CDs are two obvious recent examples. The entertainment and media industries are still dealing with the aftermath of digitization as they attempt to transform and update old practices tailored to a bygone era. But it won’t end with digital media. As more of the economy is digitized—from medicine to manufacturing—industries will hop on an exponential curve and be similarly disrupted.
Diamandis’s 6 Ds are critical to understanding and planning for this disruption.
The 6 Ds of Exponential Organizations are Digitized, Deceptive, Disruptive, Demonetized, Dematerialized, and Democratized.

Diamandis uses the contrasting fates of Kodak and Instagram to illustrate the power of the six Ds and exponential thinking.
Kodak invented the digital camera in 1975, but didn’t invest heavily in the new technology, instead sticking with what had always worked: traditional cameras and film. In 1996, Kodak had a $28 billion market capitalization with 95,000 employees.
But the company didn’t pay enough attention to how digitization of their core business was changing it; people were no longer taking pictures in the same way and for the same reasons as before.
After a downward spiral, Kodak went bankrupt in 2012. That same year, Facebook acquired Instagram, a digital photo sharing app, which at the time was a startup with 13 employees. The acquisition’s price tag? $1 billion. And Instagram had been founded only 18 months earlier.
The most ironic piece of this story is that Kodak invented the digital camera; they took the first step toward overhauling the photography industry and ushering it into the modern age, but they were unwilling to disrupt their existing business by taking a risk in what was then uncharted territory. So others did it instead.
The same can happen with any technology that’s just getting off the ground. It’s easy to stop pursuing it in the early part of the exponential curve, when development appears to be moving slowly. But failing to follow through only gives someone else the chance to do it instead.
The Six Ds are a road map showing what can happen when an exponential technology is born. Not every phase is easy, but the results give even small teams the power to change the world in a faster and more impactful way than traditional business ever could.
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