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Category: robotics/AI – Page 1891

The other gaming was that computers don’t really understand words… So you ask, “Who was the sixteenth president of the United States.”? The computer doesn’t know what “sixteenth” and “president of the United States” mean. But it can go and rummage through Wikipedia-like sources and find those words and match them to a president, Abraham Lincoln and come back with “‘Who’ was Abraham Lincoln.”

But then you put anything in that’s like a pun or a joke or a riddle or sarcasm, that you can’t look up in Wikipedia, and computers are helpless. For example, in the first round, one of the final Jeopardy clues was, “Its largest airport is named for a World War II hero; its second largest for a World War II battle.” And the correct answer was “Chicago.” And Watson guessed “Toronto,” apparently because it was confused in the second part of that sentence, what “it” referred to. And that is a common problem with computers. (See: Why did Watson think Toronto was in the U.S.A.?)

Terry Winograd is a computer scientist at Stanford and he thought up this test of computer knowledge. The question is, “What does ‘it’ refer to in this sentence?”

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China has declared its ambition to dominate the technology sector from 5G and artificial intelligence to robotics and quantum computing. Joining the infrastructure firms on this year’s list are technology firms such as Alibaba, http://JD.com/, Tencent, Xiaomi, and BOE. Huawei was not included as it is a private entity.

China’s ambition to dominate the technology sector from 5G and artificial intelligence to robotics and quantum computing is bearing fruit.

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Given that going viral on the Internet is often cyclical, it should come as no surprise that an app that made its debut in 2017 has once again surged in popularity. FaceApp applies various transformations to the image of any face, but the option that ages facial features has been especially popular. However, the fun has been accompanied by controversy; since biometric systems are replacing access passwords, is it wise to freely offer up our image and our personal data? The truth is that today the face is ceasing to be as non-transferable as it used to be, and in just a few years it could be more hackable than the password of a lifetime.

Our countenance is the most recognisable key to social relationships. We might have doubts when hearing a voice on the phone, but never when looking at the face of a familiar person. In the 1960s, a handful of pioneering researchers began training computers to recognise human faces, although it was not until the 1990s that this technology really began to take off. Facial recognition algorithms have improved to such an extent that since 1993 their error rate has been halved every two years. When it comes to recognising unfamiliar faces in laboratory experiments, today’s systems outperform human capabilities.

Nowadays these systems are among the most widespread applications of Artificial Intelligence (AI). Every day, our laptops, smartphones and tablets greet us by name as they recognise our facial features, but at the same time, the uses of this technology have set off alarm bells over invasion of privacy concerns. In China, the world leader in facial recognition systems, the introduction of this technology associated with surveillance cameras to identify even pedestrians has been viewed by the West as another step towards the Big Brother dystopia, the eye of the all-watching state, as George Orwell portrayed in 1984.

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The US Army will field 50 kilowatt (kW)-class lasers on a platoon of four Stryker vehicles in 2022. They will support the Maneuver-Short Range Air Defense (M-SHORAD) mission. The directed energy M-SHORAD capability is intended to protect maneuvering Brigade Combat Teams from unmanned aerial systems (UAS), rotary-wing aircraft, and rockets, artillery and mortar (RAM).

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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 ’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 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 . 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.

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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.

ICE has apparently requested special access to Department of Motor Vehicles (DMV) databases in at least three states – Utah, Washington State, and Vermont – which the federal agency plans to use in conjunction with facial recognition technology to scan people’s drivers’ license photos and match them against criminal and residency databases, all without their knowledge or consent.

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 . 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 . This is where 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 and it may just represent the future of artificial intelligence.

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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.

There is no shortage of optimism about A.I. in the medical community. But many also caution the hype surrounding A.I. has yet to be realized in real clinical settings. There are also different visions for how A.I. services could make the biggest impact. And it’s still unclear whether A.I. will improve the lives of patients or just the bottom line for Silicon Valley companies, health care organizations, and insurers.

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