A suit-X trio designed to support workers: Meet MAX

A suit-X trio designed to support workers: Meet MAX



(Tech Xplore)—Not all of us park our bodies in a chair in the morning and cross our legs to do our work. In fact, just think of vast numbers of workers doing physically demanding or just physically repetitive tasks including bending and lifting.
Workers on construction sites, factories and warehouses might cope with aches and pains brought on by their work. Hopefully, the future will provide an easy answer for workers to suit up in a suitable way for them to avoid these aches and pain.
There is a new kid on the block aiming to address such a solution, and a number of tech watchers have put them in the news this month. A California-based group aptly called suit-X announced its MAX, which stands for Modular Agile Exoskeleton. The company designs and makes exoskeletons.
"MAX is designed to support workers during the repetitive tasks that most frequently cause injury," said a company release.
Will Knight in MIT Technology Review said that this essentially is " a trio of devices that use robotic technologies to enhance the abilities of able-bodied workers and prevent common workplace injuries."
Target users, for example, could include those who carry out ceiling inspections, welding, installations and repairs. "It's not only lifting 75 pounds that can hurt your back; it is also lifting 20 pounds repeatedly throughout the day that will lead to injury," said Dr. Homayoon Kazerooni, founder and CEO, suitX."The MAX solution is designed for unstructured workplaces where no robot can work as efficiently as a human worker. Our goal is to augment and support workers who perform demanding and repetitive tasks in unstructured workplaces in order to prevent and reduce injuries."
Seeker referred to the MAX system as an exoskeleton device that could potentially change the way millions of people work.
Seeker noted its advantages as workplace exoskeletons in several ways, being lightweight such that the user can walk around unimpeded. "The exoskeleton units kick in only when you need them, and they don't require any external power source."
MAX is a product with three modules. You use them independently or in combination, depending on work needs. The three modules are backX, shoulderX, and legX.
According to the company, "All modules intelligently engage when you need them, and don't impede you otherwise."
The backX (lower back) reduces forces and torques.
The shoulderX reduces forces; it "enables the wearer to perform chest-to-ceiling level tasks for longer periods of time." In a video the company defines shoulderX as "an industrial arm exoskeleton that augments its wearer by reducing gravity-induced forces at the shoulder complex."
The legX was designed to support knee joint and quadriceps. It incorporates microcomputers in each leg. They communicate with each other to determine if the person is walking, bending, or taking the stairs." Seeker said these communicate via Bluetooth, monitoring spacing and position.
Credit: suitx
A suit-X trio designed to support workers: Meet MAX
Kazerooni spoke about his company and its mission, in Seeker. "My job is easy. I sit in front of a computer. But these guys work all day long, put their bodies through abuse. We can use bionics to help them." He also said he and his team did not create this "because of science fiction movies. We were responding to numbers from the Department of Labor, which said that back, knee and shoulder injuries are the most common form of injuries among workers."
Will Knight meanwhile has reflected on the bigger picture in developments. Can they help in preventing injury on the job and help prolong workers' careers? "New materials, novel mechanical designs, and cheaper actuators and motors have enabled a new generation of cheaper, more lightweight exoskeletons to emerge in recent years," he wrote. "For instance, research groups at Harvard and SRI are developing systems that are passive and use soft, lightweight materials."
Some companies, such as BMW, said Knight, have been experimenting with exoskeletons. "The MAX is another (bionic) step toward an augmented future of work."

credit;   Nancy Owano
Cannabinoids control memory through mitochondria

Cannabinoids control memory through mitochondria


Cannabinoids and memory
Few classes of drugs have galvanized the pharmaceutical industry in recent times like the cannabinoids. This class of molecules includes not only the natural forms, but also a vast new treasury of powerful synthetic analogs with up to several hundred times the potency as measured by receptor activity and binding affinity. With the FDA now fast tracking all manner of injectables, topicals, and sprays promising everything from relief of nebulous cancer pain to anti-seizure neuroprotection, more than a few skeptics have been generated.
What inquiring minds really want to know, beyond the thorny issue of how well they actually work, is how do they work at all? If you want to understand what something is doing in the cell, one useful approach is to ask what it does to their mitochondria. With drug companies now drooling over the possibility of targeting drugs and treatments directly to these organelles by attaching mitochondrial localization sequences (MLS) or other handler molecules, answers to this kind of question are now coming into focus.
But even with satisfactory explanations in hand, there would still be one large hurdle standing in the way of cannabinoid medical bliss: Namely, even if a patient can manage to avoid operating vehicles or heavy machinery throughout the course of their treatment, how do they cope with the endemic collateral memory loss these drugs invariably cause?
A recent paper published in Nature neatly ties all these subtleties together, and even suggests a possible way out of the brain fog by toggling the sites of cannabinoid action between mitochondria and other cellular compartments. By generating a panel of cannabinoid receptor and second messenger molecules with and without the appropriate MLS tags or accessory binding proteins, the authors were able to directly link cannabinoid-controlled mitochondrial activity to memory formation.
One confounder in this line of work is that these MLSs are very fickle beasts. The 22 or so leader amino acids that make up their 'code' is not a direct addresses in any sense. While the consensus sequences that localize protease action or sort nuclear, endoplasmic reticulum, and plasma membrane proteins generally contain clearly recognizable motifs, any regularities in the MLSs have only proven visible to a computer. That is not to say that MLSs are fictions—they clearly do work—but their predictable action is only witnessed whole once their 3-dimensional vibrating structures are fully-conformed.
The authors availed themselves of two fairly sophisticated programs called Mitoprot and PSQRT to remove any guesswork in identifying a potential MLS in CN1 cannabinoid receptors. CN1s had been previously associated by immunohistochemical methods to what we might call the mitochondrial penumbra, but their presence there may have been purely incidental. This in silico analysis theoretically confirmed the presence of a putative MLS in CB1 and encouraged them to carry out further manipulations of this pathway.
Namely, the researchers took a mouse with the mitochondrial mtCB1 receptor knocked out, and then added modified versions back using viral vectors. When they applied the synthetic cannabinoid ligands (known as WIN55,212 and HU210 ) they found that mitochondrial respiration and mobility, and subsequently memory formation, remained largely intact in animals without the MLS in their receptor.
The researchers were then able to look further downstream using the same general strategy of controlling localization of the second messenger molecule protein kinase A (PKA). By fusing a constitutively active mutant form of PKA to an MLS and putting it inside using an adenovirus they were able to trace the signal cascade into the heart of the complex I of the respiratory chain.
The presence and origin of full G-protein receptor signal pathways in mitochondria is now more than just an academic question. Exactly how retroviruses and other molecular agents of sequence modification managed to re-jigger gene duplicated backups of proteins like CN1 to add alternatively spliced MLS tags is still shrouded in mystery.
Our ability to now harness these same slow evolutionary processes in real time, and bend them to our needs, will undoubtedly have implication well beyond the cannabinoid market. Together the results above suggest the tantalizing possibility of preserving some of the desired benefits of while eliminating the unintended consequences like memory loss or full blown amnesia.

credit; John Hewitt report
New 'smart metal' technology to keep bridge operational in next big quake

New 'smart metal' technology to keep bridge operational in next big quake


A bridge that bends in an strong earthquake and not only remains standing, but remains usable is making its debut in its first real-world application as part of a new exit bridge ramp on a busy downtown Seattle highway.
"We've tested new materials, memory retaining metal rods and flexible concrete composites, in a number of bridge model studies in our large-scale shake table lab, it's gratifying to see the applied for the first time in an important setting in a seismically active area with heavy traffic loads," Saiid Saiidi, civil engineering professor and researcher at the University of Nevada, Reno, said. "Using these materials substantially reduces damage and allows the bridge to remain open even after a strong earthquake."
Saiidi, who pioneered this technology, has built and destroyed, in the lab, several large-scale 200-ton bridges, single bridge columns and concrete abutments using various combinations of innovative materials, replacements for the standard steel rebar and concrete materials and design in his quest for a safer, more resilient infrastructure.
"We have solved the problem of survivability, we can keep a bridge usable after a ," Saiidi said. "With these techniques and materials, we will usher in a new era of super earthquake-resilient structures."
The University partnered with the Washington Department of Transportation and the Federal Highway Administration to implement this new technology on their massive Alaska Way Viaduct Replacement Program, the centerpiece of which is a two-mile long tunnel, but includes 31 separate projects that began in 2007 along the State Route 99 corridor through downtown Seattle.
"This is potentially a giant leap forward," Tom Baker, bridge and structures engineer for the Washington State Department of Transportation, said. "We design for no-collapse, but in the future, we could be designing for no-damage and be able to keep bridges open to emergency vehicles, commerce and the public after a strong quake."
Modern bridges are designed to not collapse during an earthquake, and this new technology takes that design a step further. In the earthquake lab tests, bridge columns built using memory-retaining nickel/titanium rods and a flexible concrete composite returned to their original shape after an earthquake as strong as a magnitude 7.5.
"The tests we've conducted on 4-span bridges leading to this point aren't possible anywhere else in the world than our large-scale structures and earthquake engineering lab," Saiidi said. "We've had great support along the way from many state highway departments and funding agencies like the National Science Foundation, the Federal Highway Administration and the U.S. Department of Transportation. Washington DOT recognized the potential of this technology and understands the need to keep infrastructure operating following a large earthquake."
In an experiment in 2015, featured in a video, one of Saiidi's 's moved more than six inches off center at the base and returned to its original position, as designed, in an upright and stable position. Using the computer-controlled hydraulics, the earthquake engineering lab can increase the intensity of the recorded . Saiidi turned the dial up to 250 percent of the design parameters and still had excellent results.
"It had an incredible 9 percent drift with little damage," Saiidi said.
The Seattle off-ramp with the innovative columns is currently under construction and scheduled for completion in spring 2017. After the new SR 99 tunnel opens, this ramp, just south of the tunnel entrance, will take northbound drivers from SR 99 to Seattle's SODO neighborhood.
A new WSDOT video describes how this innovative technology works.
"Dr. Saiidi sets the mark for the level of excellence to which the College of Engineering aspires," Manos Maragakis, dean of the University's College of Engineering, said. "His research is original and innovative and has made a seminal contribution to seismic safety around the globe."
Use drones and insect biobots to map disaster areas

Use drones and insect biobots to map disaster areas


Tech would use drones and insect biobots to map disaster areas
Credit: North Carolina State University  
Researchers at North Carolina State University have developed a combination of software and hardware that will allow them to use unmanned aerial vehicles (UAVs) and insect cyborgs, or biobots, to map large, unfamiliar areas – such as collapsed buildings after a disaster.
"The idea would be to release a swarm of sensor-equipped biobots – such as remotely controlled cockroaches – into a collapsed building or other dangerous, unmapped area," says Edgar Lobaton, an assistant professor of electrical and computer engineering at NC State and co-author of two papers describing the work.
"Using remote-control technology, we would restrict the movement of the biobots to a defined area," Lobaton says. "That area would be defined by proximity to a beacon on a UAV. For example, the biobots may be prevented from going more than 20 meters from the UAV."
The biobots would be allowed to move freely within a defined area and would signal researchers via radio waves whenever they got close to each other. Custom software would then use an algorithm to translate the biobot sensor data into a rough map of the unknown environment.
Once the program receives enough data to map the defined area, the UAV moves forward to hover over an adjacent, unexplored section. The biobots move with it, and the mapping process is repeated. The software program then stitches the new map to the previous one. This can be repeated until the entire region or structure has been mapped; that map could then be used by first responders or other authorities.
"This has utility for areas – like collapsed buildings – where GPS can't be used," Lobaton says. "A strong radio signal from the UAV could penetrate to a certain extent into a collapsed building, keeping the biobot swarm contained. And as long as we can get a signal from any part of the swarm, we are able to retrieve data on what the rest of the swarm is doing. Based on our experimental data, we know you're going to lose track of a few individuals, but that shouldn't prevent you from collecting enough data for mapping."
Co-lead author Alper Bozkurt, an associate professor of electrical and computer engineering at NC State, has previously developed functional cockroach biobots. However, to test their new mapping technology, the research team relied on inch-and-a-half-long robots that simulate cockroach behavior.
In their experiment, researchers released these robots into a maze-like space, with the effect of the UAV beacon emulated using an overhead camera and a physical boundary attached to a moving cart. The cart was moved as the robots mapped the area.
"We had previously developed proof-of-concept software that allowed us to map small areas with biobots, but this work allows us to map much larger areas and to stitch those maps together into a comprehensive overview," Lobaton says. "It would be of much more practical use for helping to locate survivors after a disaster, finding a safe way to reach survivors, or for helping responders determine how structurally safe a building may be.
"The next step is to replicate these experiments using biobots, which we're excited about."
An article on the framework for developing local maps and stitching them together, "A Framework for Mapping with Biobotic Insect Networks: From Local to Global Maps," is published in Robotics and Autonomous Systems. An article on the theory of mapping based on the proximity of mobile sensors to each other, "Geometric Learning and Topological Inference with Biobotic Networks," is published in IEEE Transactions on Signal and Information Processing over Networks.


credit;   Matt Shipman
How machine learning advances artificial intelligence

How machine learning advances artificial intelligence


Computers that learn for themselves are with us now. As they become more common in 'high-stakes' applications like robotic surgery, terrorism detection and driverless cars, researchers ask what can be done to make sure we can trust them.
There would always be a first death in a driverless car and it happened in May 2016. Joshua Brown had engaged the autopilot system in his Tesla when a tractor-trailor drove across the road in front of him. It seems that neither he nor the sensors in the autopilot noticed the white-sided truck against a brightly lit sky, with tragic results.
Of course many people die in car crashes every day – in the USA there is one fatality every 94 million miles, and according to Tesla this was the first known fatality in over 130 million miles of driving with activated autopilot. In fact, given that most road fatalities are the result of human error, it has been said that autonomous cars should make travelling safer.
Even so, the tragedy raised a pertinent question: how much do we understand – and trust – the computers in an autonomous vehicle? Or, in fact, in any machine that has been taught to carry out an activity that a human would do?
We are now in the era of machine learning. Machines can be trained to recognise certain patterns in their environment and to respond appropriately. It happens every time your digital camera detects a face and throws a box around it to focus, or the personal assistant on your smartphone answers a question, or the adverts match your interests when you search online.
Machine learning is a way to program computers to learn from experience and improve their performance in a way that resembles how humans and animals learn tasks. As machine learning techniques become more common in everything from finance to healthcare, the issue of trust is becoming increasingly important, says Zoubin Ghahramani, Professor of Information Engineering in Cambridge's Department of Engineering.
Faced with a life or death decision, would a driverless car decide to hit pedestrians, or avoid them and risk the lives of its occupants? Providing a medical diagnosis, could a machine be wildly inaccurate because it has based its opinion on a too-small sample size? In making financial transactions, should a computer explain how robust is its assessment of the volatility of the stock markets?
"Machines can now achieve near-human abilities at many cognitive tasks even if confronted with a situation they have never seen before, or an incomplete set of data," says Ghahramani. "But what is going on inside the 'black box'? If the processes by which decisions were being made were more transparent, then trust would be less of an issue."
His team builds the algorithms that lie at the heart of these technologies (the "invisible bit" as he refers to it). Trust and transparency are important themes in their work: "We really view the whole mathematics of machine learning as sitting inside a framework of understanding uncertainty. Before you see data – whether you are a baby learning a language or a scientist analysing some data – you start with a lot of uncertainty and then as you have more and more data you have more and more certainty.
"When machines make decisions, we want them to be clear on what stage they have reached in this process. And when they are unsure, we want them to tell us."
One method is to build in an internal self-evaluation or calibration stage so that the machine can test its own certainty, and report back.
Two years ago, Ghahramani's group launched the Automatic Statistician with funding from Google. The tool helps scientists analyse datasets for statistically significant patterns and, crucially, it also provides a report to explain how sure it is about its predictions.
"The difficulty with machine learning systems is you don't really know what's going on inside – and the answers they provide are not contextualised, like a human would do. The Automatic Statistician explains what it's doing, in a human-understandable form."
Where transparency becomes especially relevant is in applications like medical diagnoses, where understanding the provenance of how a decision is made is necessary to trust it.
Dr Adrian Weller, who works with Ghahramani, highlights the difficulty: "A particular issue with new (AI) systems that learn or evolve is that their processes do not clearly map to rational decision-making pathways that are easy for humans to understand." His research aims both at making these pathways more transparent, sometimes through visualisation, and at looking at what happens when systems are used in real-world scenarios that extend beyond their training environments – an increasingly common occurrence.
"We would like AI systems to monitor their situation dynamically, detect whether there has been a change in their environment and – if they can no longer work reliably – then provide an alert and perhaps shift to a safety mode." A , for instance, might decide that a foggy night in heavy traffic requires a human driver to take control.
Weller's theme of trust and transparency forms just one of the projects at the newly launched £10 million Leverhulme Centre for the Future of Intelligence (CFI). Ghahramani, who is Deputy Director of the Centre, explains: "It's important to understand how developing technologies can help rather than replace humans. Over the coming years, philosophers, social scientists, cognitive scientists and computer scientists will help guide the future of the technology and study its implications – both the concerns and the benefits to society."
CFI brings together four of the world's leading universities (Cambridge, Oxford, Berkeley and Imperial College, London) to explore the implications of AI for human civilisation. Together, an interdisciplinary community of researchers will work closely with policy-makers and industry investigating topics such as the regulation of autonomous weaponry, and the implications of AI for democracy.
Ghahramani describes the excitement felt across the field: "It's exploding in importance. It used to be an area of research that was very academic – but in the past five years people have realised these methods are incredibly useful across a wide range of societally important areas.
"We are awash with data, we have increasing computing power and we will see more and more applications that make predictions in real time. And as we see an escalation in what machines can do, they will challenge our notions of intelligence and make it all the more important that we have the means to trust what they tell us."
Artificial intelligence has the power to eradicate poverty and disease or hasten the end of human civilisation as we know it – according to a speech delivered by Professor Stephen Hawking 19 October 2016 at the launch of the Centre for the Future of Intelligence.
internet robot  investigate creativity

internet robot investigate creativity


A portrait of Benjamin Franklin manipulated by Smilevector. Credit: Smithsonian National Portrait Gallery.
Tom White, senior lecturer in Victoria's School of Design, has created Smilevector—a bot that examines images of people, then adds or removes smiles to their faces.
"It has examined hundreds of thousands of faces to learn the difference between images, by finding relations and reapplying them," says Mr White.
"When the computer finds an image it looks to identify if the person is smiling or not. If there isn't a smile, it adds one, but if there is a smile then it takes it away.
"It represents these changes as an animation, which moves parts of the face around, including crinkling and widening the eyes."
The bot can be used as a form of puppetry, says Mr White.
"These systems are domain independent, meaning you can do it with anything—from manipulating images of faces to shoes to chairs. It's really fun and interesting to work in this space. There are lots of ideas to play around with."
The creation of the bot was sparked by Mr White's research into creative intelligence.
"Machine learning and artificial intelligence are starting to have implications for people in creative industries. Some of these implications have to do with the computer's capabilities, like completing mundane tasks so that people can complete higher level tasks," says Mr White.
"I'm interested in exploring what these systems are capable of doing but also how it changes what we think of as being creative is in the first place. Once you have a system that can automate processes, is that still a creative act? If you can make something a completely push of the button operation, does its meaning change?"
Mr White says people have traditionally used creative tools by giving commands.
"However, I think we're moving toward more of a collaboration with computers—where there's an intelligent system that's making suggestions and helping steer the process.
"A lot will happen in this space in the next five to ten years, and now is the right time to progress. I also hope these techniques influence teaching over the long term as they become more mainstream. It is something that students could work with me on at Victoria University as part of our Master of Design Innovation or our new Master of Fine Arts (Creative Practice)."
The paper Sampling Generative Networks describing this research is available as an arXiv preprint. The research will also be presented as part of the Neural Information Processing Systems conference in Spain and Generative Art conference in Italy in December.

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