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#433634 This Robotic Skin Makes Inanimate ...
In Goethe’s poem “The Sorcerer’s Apprentice,” made world-famous by its adaptation in Disney’s Fantasia, a lazy apprentice, left to fetch water, uses magic to bewitch a broom into performing his chores for him. Now, new research from Yale has opened up the possibility of being able to animate—and automate—household objects by fitting them with a robotic skin.
Yale’s Soft Robotics lab, the Faboratory, is led by Professor Rebecca Kramer-Bottiglio, and has long investigated the possibilities associated with new kinds of manufacturing. While the typical image of a robot is hard, cold steel and rigid movements, soft robotics aims to create something more flexible and versatile. After all, the human body is made up of soft, flexible surfaces, and the world is designed for us. Soft, deformable robots could change shape to adapt to different tasks.
When designing a robot, key components are the robot’s sensors, which allow it to perceive its environment, and its actuators, the electrical or pneumatic motors that allow the robot to move and interact with its environment.
Consider your hand, which has temperature and pressure sensors, but also muscles as actuators. The omni-skins, as the Science Robotics paper dubs them, combine sensors and actuators, embedding them into an elastic sheet. The robotic skins are moved by pneumatic actuators or memory alloy that can bounce back into shape. If this is then wrapped around a soft, deformable object, moving the skin with the actuators can allow the object to crawl along a surface.
The key to the design here is flexibility: rather than adding chips, sensors, and motors into every household object to turn them into individual automatons, the same skin can be used for many purposes. “We can take the skins and wrap them around one object to perform a task—locomotion, for example—and then take them off and put them on a different object to perform a different task, such as grasping and moving an object,” said Kramer-Bottiglio. “We can then take those same skins off that object and put them on a shirt to make an active wearable device.”
The task is then to dream up applications for the omni-skins. Initially, you might imagine demanding a stuffed toy to fetch the remote control for you, or animating a sponge to wipe down kitchen surfaces—but this is just the beginning. The scientists attached the skins to a soft tube and camera, creating a worm-like robot that could compress itself and crawl into small spaces for rescue missions. The same skins could then be worn by a person to sense their posture. One could easily imagine this being adapted into a soft exoskeleton for medical or industrial purposes: for example, helping with rehabilitation after an accident or injury.
The initial motivating factor for creating the robots was in an environment where space and weight are at a premium, and humans are forced to improvise with whatever’s at hand: outer space. Kramer-Bottoglio originally began the work after NASA called out for soft robotics systems for use by astronauts. Instead of wasting valuable rocket payload by sending up a heavy metal droid like ATLAS to fetch items or perform repairs, soft robotic skins with modular sensors could be adapted for a range of different uses spontaneously.
By reassembling components in the soft robotic skin, a crumpled ball of paper could provide the chassis for a robot that performs repairs on the spaceship, or explores the lunar surface. The dynamic compression provided by the robotic skin could be used for g-suits to protect astronauts when they rapidly accelerate or decelerate.
“One of the main things I considered was the importance of multi-functionality, especially for deep space exploration where the environment is unpredictable. The question is: How do you prepare for the unknown unknowns? … Given the design-on-the-fly nature of this approach, it’s unlikely that a robot created using robotic skins will perform any one task optimally,” Kramer-Bottiglio said. “However, the goal is not optimization, but rather diversity of applications.”
There are still problems to resolve. Many of the videos of the skins indicate that they can rely on an external power supply. Creating new, smaller batteries that can power wearable devices has been a focus of cutting-edge materials science research for some time. Much of the lab’s expertise is in creating flexible, stretchable electronics that can be deformed by the actuators without breaking the circuitry. In the future, the team hopes to work on streamlining the production process; if the components could be 3D printed, then the skins could be created when needed.
In addition, robotic hardware that’s capable of performing an impressive range of precise motions is quite an advanced technology. The software to control those robots, and enable them to perform a variety of tasks, is quite another challenge. With soft robots, it can become even more complex to design that control software, because the body itself can change shape and deform as the robot moves. The same set of programmed motions, then, can produce different results depending on the environment.
“Let’s say I have a soft robot with four legs that crawls along the ground, and I make it walk up a hard slope,” Dr. David Howard, who works on robotics at CSIRO in Australia, explained to ABC.
“If I make that slope out of gravel and I give it the same control commands, the actual body is going to deform in a different way, and I’m not necessarily going to know what that is.”
Despite these and other challenges, research like that at the Faboratory still hopes to redefine how we think of robots and robotics. Instead of a robot that imitates a human and manipulates objects, the objects themselves will become programmable matter, capable of moving autonomously and carrying out a range of tasks. Futurists speculate about a world where most objects are automated to some degree and can assemble and repair themselves, or are even built entirely of tiny robots.
The tale of the Sorcerer’s Apprentice was first written in 1797, at the dawn of the industrial revolution, over a century before the word “robot” was even coined. Yet more and more roboticists aim to prove Arthur C Clarke’s maxim: any sufficiently advanced technology is indistinguishable from magic.
Image Credit: Joran Booth, The Faboratory Continue reading
Posted in Human Robots
#430286 Artificial Intelligence Predicts Death ...
Do not go gentle into that good night, Old age should burn and rave at close of day; Rage, rage against the dying of the light.
Welsh poet Dylan Thomas’ famous lines are a passionate plea to fight against the inevitability of death. While the sentiment is poetic, the reality is far more prosaic. We are all going to die someday at a time and place that will likely remain a mystery to us until the very end.
Or maybe not.
Researchers are now applying artificial intelligence, particularly machine learning and computer vision, to predict when someone may die. The ultimate goal is not to play the role of Grim Reaper, like in the macabre sci-fi Machine of Death universe, but to treat or even prevent chronic diseases and other illnesses.
The latest research into this application of AI to precision medicine used an off-the-shelf machine-learning platform to analyze 48 chest CT scans. The computer was able to predict which patients would die within five years with 69 percent accuracy. That’s about as good as any human doctor.
The results were published in the Nature journal Scientific Reports by a team led by the University of Adelaide.
In an email interview with Singularity Hub, lead author Dr. Luke Oakden-Rayner, a radiologist and PhD student, says that one of the obvious benefits of using AI in precision medicine is to identify health risks earlier and potentially intervene.
Less obvious, he adds, is the promise of speeding up longevity research.
“Currently, most research into chronic disease and longevity requires long periods of follow-up to detect any difference between patients with and without treatment, because the diseases progress so slowly,” he explains. “If we can quantify the changes earlier, not only can we identify disease while we can intervene more effectively, but we might also be able to detect treatment response much sooner.”
That could lead to faster and cheaper treatments, he adds. “If we could cut a year or two off the time it takes to take a treatment from lab to patient, that could speed up progress in this area substantially.”
AI has a heart
In January, researchers at Imperial College London published results that suggested AI could predict heart failure and death better than a human doctor. The research, published in the journal Radiology, involved creating virtual 3D hearts of about 250 patients that could simulate cardiac function. AI algorithms then went to work to learn what features would serve as the best predictors. The system relied on MRIs, blood tests, and other data for its analyses.
In the end, the machine was faster and better at assessing risk of pulmonary hypertension—about 73 percent versus 60 percent.
The researchers say the technology could be applied to predict outcomes of other heart conditions in the future. “We would like to develop the technology so it can be used in many heart conditions to complement how doctors interpret the results of medical tests,” says study co-author Dr. Tim Dawes in a press release. “The goal is to see if better predictions can guide treatment to help people to live longer.”
AI getting smarter
These sorts of applications with AI to precision medicine are only going to get better as the machines continue to learn, just like any medical school student.
Oakden-Rayner says his team is still building its ideal dataset as it moves forward with its research, but have already improved predictive accuracy by 75 to 80 percent by including information such as age and sex.
“I think there is an upper limit on how accurate we can be, because there is always going to be an element of randomness,” he says, replying to how well AI will be able to pinpoint individual human mortality. “But we can be much more precise than we are now, taking more of each individual’s risks and strengths into account. A model combining all of those factors will hopefully account for more than 80 percent of the risk of near-term mortality.”
Others are even more optimistic about how quickly AI will transform this aspect of the medical field.
“Predicting remaining life span for people is actually one of the easiest applications of machine learning,” Dr. Ziad Obermeyer tells STAT News. “It requires a unique set of data where we have electronic records linked to information about when people died. But once we have that for enough people, you can come up with a very accurate predictor of someone’s likelihood of being alive one month out, for instance, or one year out.”
Obermeyer co-authored a paper last year with Dr. Ezekiel Emanuel in the New England Journal of Medicine called “Predicting the Future—Big Data, Machine Learning, and Clinical Medicine.”
AI still has much to learn
Experts like Obermeyer and Oakden-Rayner agree that advances will come swiftly, but there is still much work to be done.
For one thing, there’s plenty of data out there to mine, but it’s still a bit of a mess. For example, the images needed to train machines still need to be processed to make them useful. “Many groups around the world are now spending millions of dollars on this task, because this appears to be the major bottleneck for successful medical AI,” Oakden-Rayner says.
In the interview with STAT News, Obermeyer says data is fragmented across the health system, so linking information and creating comprehensive datasets will take time and money. He also notes that while there is much excitement about the use of AI in precision medicine, there’s been little activity in testing the algorithms in a clinical setting.
“It’s all very well and good to say you’ve got an algorithm that’s good at predicting. Now let’s actually port them over to the real world in a safe and responsible and ethical way and see what happens,” he says in STAT News.
AI is no accident
Preventing a fatal disease is one thing. But preventing fatal accidents with AI?
That’s what US and Indian researchers set out to do when they looked over the disturbing number of deaths occurring from people taking selfies. The team identified 127 people who died while posing for a self-taken photo over a two-year period.
Based on a combination of text, images and location, the machine learned to identify a selfie as potentially dangerous or not. Running more than 3,000 annotated selfies collected on Twitter through the software resulted in 73 percent accuracy.
“The combination of image-based and location-based features resulted in the best accuracy,” they reported.
What’s next? A sort of selfie early warning system. “One of the directions that we are working on is to have the camera give the user information about [whether or not a particular location is] dangerous, with some score attached to it,” says Ponnurangam Kumaraguru, a professor at Indraprastha Institute of Information Technology in Delhi, in a story by Digital Trends.
AI and the future
This discussion begs the question: Do we really want to know when we’re going to die?
According to at least one paper published in Psychology Review earlier this year, the answer is a resounding “no.” Nearly nine out of 10 people in Germany and Spain who were quizzed about whether they would want to know about their future, including death, said they would prefer to remain ignorant.
Obermeyer sees it differently, at least when it comes to people living with life-threatening illness.
“[O]ne thing that those patients really, really want and aren’t getting from doctors is objective predictions about how long they have to live,” he tells Marketplace public radio. “Doctors are very reluctant to answer those kinds of questions, partly because, you know, you don’t want to be wrong about something so important. But also partly because there’s a sense that patients don’t want to know. And in fact, that turns out not to be true when you actually ask the patients.”
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Posted in Human Robots