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They say size isn’t everything, but when it comes to delta robots it seems like it’s pretty important.
The speed and precision of these machines sees them employed in delicate pick-and-place tasks in all kinds of factories, as well as to control 3D printer heads. But Harvard researchers have found that scaling them down to millimeter scale makes them even faster and more precise, opening up applications in everything from microsurgery to manipulating tiny objects like circuit board components or even living cells.
Unlike the industrial robots you’re probably more familiar with, delta robots consist of three individually controlled arms supporting a platform. Different combinations of movements can move the platform in three directions, and a variety of tools can be attached to this platform.
The benefit of this design is that unlike a typical robotic arm, all the motors are housed at the base rather than at the joints, which reduces their mechanical complexity, but also—importantly—the weight of the arms. That means they can move and accelerate faster and with greater precision.
It’s been known for a while that the physics of these robots means the smaller you can make them, the more pronounced these advantages become, but scientists had struggled to build them at scales below tens of centimeters.
In a recent paper in the journal Science Robotics, the researchers describe how they used an origami-inspired micro-fabrication approach that relies on folding flat sheets of composite materials to create a robot measuring just 15 millimeters by 15 millimeters by 20 millimeters.
The robot dubbed “milliDelta” features joints that rely on a flexible polymer core to bend—a simplified version of the more complicated joints found in large-scale delta robots. The machine was powered by three piezoelectric actuators, which flex when a voltage is applied, and could perform movements at frequencies 15 to 20 times higher than current delta robots, with precisions down to roughly 5 micrometers.
One potential use for the device is to cancel out surgeons’ hand tremors as they carry out delicate microsurgery procedures, such as operations on the eye’s retina. The researchers actually investigated this application in their paper. They got volunteers to hold a toothpick and measured the movement of the tip to map natural hand tremors. They fed this data to the milliDelta, which was able to match the movements and therefore cancel them out.
In an email to Singularity Hub, the researchers said that adding the robot to the end of a surgical tool could make it possible to stabilize needles or scalpels, though this would require some design optimization. For a start, the base would have to be redesigned to fit on a surgical tool, and sensors would have to be added to the robot to allow it to measure tremors in real time.
Another promising application for the device would be placing components on circuit boards at very high speeds, which could prove valuable in electronics manufacturing. The researchers even think the device’s precision means it could be used for manipulating living cells in research and clinical laboratories.
The researchers even said it would be feasible to integrate the devices onto microrobots to give them similarly impressive manipulation capabilities, though that would require considerable work to overcome control and sensing challenges.
Image Credit: Wyss institute / Harvard Continue reading
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.
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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
“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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The tech industry touts its ability to automate tasks and remove slow and expensive humans from the equation. But in the background, a lot of the legwork training machine learning systems, solving problems software can’t, and cleaning up its mistakes is still done by people.
This was highlighted recently when Expensify, which promises to automatically scan photos of receipts to extract data for expense reports, was criticized for sending customers’ personally identifiable receipts to workers on Amazon’s Mechanical Turk (MTurk) crowdsourcing platform.
The company uses text analysis software to read the receipts, but if the automated system falls down then the images are passed to a human for review. While entrusting this job to random workers on MTurk was maybe not so wise—and the company quickly stopped after the furor—the incident brought to light that this kind of human safety net behind AI-powered services is actually very common.
As Wired notes, similar services like Ibotta and Receipt Hog that collect receipt information for marketing purposes also use crowdsourced workers. In a similar vein, while most users might assume their Facebook newsfeed is governed by faceless algorithms, the company has been ramping up the number of human moderators it employs to catch objectionable content that slips through the net, as has YouTube. Twitter also has thousands of human overseers.
Humans aren’t always witting contributors either. The old text-based reCAPTCHA problems Google used to use to distinguish humans from machines was actually simultaneously helping the company digitize books by getting humans to interpret hard-to-read text.
“Every product that uses AI also uses people,” Jeffrey Bigham, a crowdsourcing expert at Carnegie Mellon University, told Wired. “I wouldn’t even say it’s a backstop so much as a core part of the process.”
Some companies are not shy about their use of crowdsourced workers. Startup Eloquent Labs wants to insert them between customer service chatbots and human agents who step in when the machines fail. Many times the AI is pretty certain what particular work means, and an MTurk worker can step in and quickly classify them faster and cheaper than a service agent.
Fashion retailer Gilt provides “pre-emptive shipping,” which uses data analytics to predict what people will buy to get products to them faster. The company uses MTurk workers to provide subjective critiques of clothing that feed into their models.
MTurk isn’t the only player. Companies like Cloudfactory and Crowdflower provide crowdsourced human manpower tailored to particular niches, and some companies prefer to maintain their own communities of workers. Unlabel uses an army of 50,000 humans to check and edit the translations its artificial intelligence system produces for customers.
Most of the time these human workers aren’t just filling in the gaps, they’re also helping to train the machine learning component of these companies’ services by providing new examples of how to solve problems. Other times humans aren’t used “in-the-loop” with AI systems, but to prepare data sets they can learn from by labeling images, text, or audio.
It’s even possible to use crowdsourced workers to carry out tasks typically tackled by machine learning, such as large-scale image analysis and forecasting.
Zooniverse gets citizen scientists to classify images of distant galaxies or videos of animals to help academics analyze large data sets too complex for computers. Almanis creates forecasts on everything from economics to politics with impressive accuracy by giving those who sign up to the website incentives for backing the correct answer to a question. Researchers have used MTurkers to power a chatbot, and there’s even a toolkit for building algorithms to control this human intelligence called TurKit.
So what does this prominent role for humans in AI services mean? Firstly, it suggests that many tools people assume are powered by AI may in fact be relying on humans. This has obvious privacy implications, as the Expensify story highlighted, but should also raise concerns about whether customers are really getting what they pay for.
One example of this is IBM’s Watson for oncology, which is marketed as a data-driven AI system for providing cancer treatment recommendations. But an investigation by STAT highlighted that it’s actually largely driven by recommendations from a handful of (admittedly highly skilled) doctors at Memorial Sloan Kettering Cancer Center in New York.
Secondly, humans intervening in AI-run processes also suggests AI is still largely helpless without us, which is somewhat comforting to know among all the doomsday predictions of AI destroying jobs. At the same time, though, much of this crowdsourced work is monotonous, poorly paid, and isolating.
As machines trained by human workers get better at all kinds of tasks, this kind of piecemeal work filling in the increasingly small gaps in their capabilities may get more common. While tech companies often talk about AI augmenting human intelligence, for many it may actually end up being the other way around.
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