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Researchers from North Carolina State University and Elon University have developed a technique that allows them to remotely control the movement of soft robots, lock them into position for as long as needed and later reconfigure the robots into new shapes. The technique relies on light and magnetic fields.

“We’re particularly excited about the reconfigurability,” says Joe Tracy, a professor of materials science and engineering at NC State and corresponding author of a paper on the work. “By engineering the properties of the material, we can control the soft robot’s movement remotely; we can get it to hold a given shape; we can then return the robot to its original shape or further modify its movement; and we can do this repeatedly. All of those things are valuable, in terms of this technology’s utility in biomedical or aerospace applications.”

For this work, the researchers used soft robots made of a polymer embedded with magnetic iron microparticles. Under normal conditions, the material is relatively stiff and holds its shape. However, researchers can heat up the material using light from a light-emitting diode (LED), which makes the polymer pliable. Once pliable, researchers demonstrated that they could control the shape of the robot remotely by applying a magnetic field. After forming the desired shape, researchers could remove the LED light, allowing the robot to resume its original stiffness—effectively locking the shape in place.

As well as Gait Recognition. (Go ahead and wear a disguise.)

The mass surveillance of innocent Americans continues as George Orwell’s 1984 becomes more of a reality with each passing day. “All told, we are barreling toward a future where every ritual of public life carries implicit consent to be surveilled,” writes Sidney Fussell for The Atlantic.

A new report from Georgetown Law‘s Center on Privacy & Technology (CPT) suggests that Immigration and Customs Enforcement (ICE) may be using the rampant problem of illegal immigration as a type of cover to track and spy on Americans in violation of their Fourth Amendment rights. Three years ago, the center revealed that nearly half of all U.S. adults are already in the FBI’s facial recognition database, which is largely sourced from DMV photos.

Continue reading “Mass Surveillance: 1 in 2 Americans Are Already In A Government Facial Recognition Database” »

The phrase “positive reinforcement,” is something you hear more often in an article about child rearing than one about artificial intelligence. But according to Alice Parker, Dean’s Professor of Electrical Engineering in the Ming Hsieh Department of Electrical and Computer Engineering, a little positive reinforcement is just what our AI machines need. Parker has been building electronic circuits for over a decade to reverse-engineer the human brain to better understand how it works and ultimately build artificial systems that mimic it. Her most recent paper, co-authored with Ph.D. student Kun Yue and colleagues from UC Riverside, was just published in the journal Science Advances and takes an important step towards that ultimate goal.

The AI we rely on and read about today is modeled on traditional computers; it sees the world through the lens of binary zeros and ones. This is fine for making complex calculations but, according to Parker and Yue, we’re quickly approaching the limits of the size and complexity of problems we can solve with the platforms our AI exists on. “Since the initial deep learning revolution, the goals and progress of deep-learning based AI as we know it has been very slow,” Yue says. To reach its full potential, AI can’t simply think better—it must react and learn on its own to events in real time. And for that to happen, a massive shift in how we build AI in the first place must be conceived.

To address this problem, Parker and her colleagues are looking to the most accomplished learning system nature has ever created: the human brain. This is where positive reinforcement comes into play. Brains, unlike computers, are analog learners and biological memory has persistence. Analog signals can have multiple states (much like humans). While a binary AI built with similar types of nanotechnologies to achieve long-lasting memory might be able to understand something as good or bad, an analog brain can understand more deeply that a situation might be “very good,” “just okay,” “bad” or “very bad.” This field is called neuromorphic computing and it may just represent the future of artificial intelligence.

https://youtu.be/3lY3XK_Z6UQ

Agricultural Revolution is one of the milestones of today’s civilization. It was driven by technological innovations and inventions thousands of years ago, and it is still a very crucial part of our species’ social construct. Engineers are developing tools and machines to make farmers’ job a lot easier by technologies like automation for sustainable productivity. Here are 7 innovative ways the technology is used for agriculture.

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But critics point out that all that promise could vanish if the rush to implement A.I. tramples patient privacy rights, overlooks biases and limitations, or fails to deploy services in a way that improves health outcomes for most people.

You could be forgiven for thinking that A.I. will soon replace human physicians based on headlines such as “The A.I. Doctor Will See You Now,” “Your Future Doctor May Not Be Human,” and “This A.I. Just Beat Human Doctors on a Clinical Exam.” But experts say the reality is more of a collaboration than an ousting: Patients could soon find their lives partly in the hands of A.I. services working alongside human clinicians.

Continue reading “Will Artificial Intelligence Improve Health Care for Everyone?” »

Digital privacy activists have created a blueprint for a nationwide campaign to ban the futuristic technology—and it’s about to catch on.

Meet the classified artificial brain being developed by US intelligence programs.

Facebook has announced a breakthrough in its plan to create a device that allows people to type just by thinking.

It has funded a study that developed machine-learning algorithms capable of turning brain activity into speech

It worked on epilepsy patients who had already had recording electrodes placed on their brains to asses the origins of their seizures, ahead of surgery.

It might not look like much, but this wobbly self-driving bicycle is a symbol of growing Chinese expertise in advanced chip design.

Look, no hands: The bike not only balances itself but steers itself around obstacles and even responds to simple voice commands. But it’s the brains behind the bike that matter. It uses a new kind of computer chip, called Tianjic, that was developed by Luping Shi and colleagues at Tsinghua University, a top academic institution in Beijing.

Two in one: The Tianjic chip features a hybrid design that seeks to bring together two different architectural approaches to computing: a conventional, von Neumann design and a neurologically inspired one. The two architectures are used in cooperation to run artificial neural networks for obstacle detection, motor and balance control, and voice recognition, as well as conventional software.