Hiển thị các bài đăng có nhãn future. Hiển thị tất cả bài đăng
Hiển thị các bài đăng có nhãn future. Hiển thị tất cả bài đăng

Thứ Ba, 21 tháng 2, 2017

The Future is here: Terahertz chips a New Way of Seeing through Matter

Princeton University researchers have drastically shrunk the equipment for producing terahertz -- important electromagnetic pulses lasting one millionth of a millionth of a second -- from a tabletop setup with lasers and mirrors to a pair.

Electromagnetic pulses lasting one millionth of a millionth of a second may hold the key to advances in medical imaging, communications and drug development. But the pulses, called terahertz waves, have long required elaborate and expensive equipment to use.

Now, researchers at Princeton University have drastically shrunk much of that equipment: moving from a tabletop setup with lasers and mirrors to a pair of microchips small enough to fit on a fingertip.

In two articles recently published in the IEEE Journal of Solid State Circuits, the researchers describe one microchip that can generate terahertz waves, and a second chip that can capture and read intricate details of these waves.


In two recently published articles, researchers Kaushik Sengupta (left), an assistant professor of electrical engineering, and Xue Wu (right), a Princeton graduate student in computer science, describe one microchip that can generate.

"The system is realized in the same silicon chip technology that powers all modern electronic devices from smartphones to tablets, and therefore costs only a few dollars to make on a large scale" said lead researcher Kaushik Sengupta, a Princeton assistant professor of electrical engineering.

Terahertz waves are part of the electromagnetic spectrum—the broad class of waves that includes radio, X-rays and visible light—and sit between the microwave and infrared light wavebands. The waves have some unique characteristics that make them interesting to science. For one, they pass through most non-conducting material, so they could be used to peer through clothing or boxes for security purposes, and because they have less energy than X-rays, they don't damage human tissue or DNA.

Terahertz waves also interact in distinct ways with different chemicals, so they can be used to characterize specific substances. Known as spectroscopy, the ability to use light waves to analyze material is one of the most promising—and the most challenging—applications of terahertz technology, Sengupta said.

To do it, scientists shine a broad range of terahertz waves on a target then observe how the waves change after interacting with it. The human eye performs a similar type of spectroscopy with visible light—we see a leaf as green because light in the green light frequency bounces off the chlorophyll-laden leaf.

The challenge has been that generating a broad range of terahertz waves and interpreting their interaction with a target requires a complex array of equipment such as bulky terahertz generators or ultrafast lasers. The equipment's size and expense make the technology impractical for most applications.

Researchers have been working for years to simplify these systems. In September, Sengupta's team reported a way to reduce the size of the terahertz generator and the apparatus that interprets the returning waves to a millimeter-sized chip. The solution lies in re-imaging how an antenna functions. When terahertz waves interact with a metal structure inside the chip, they create a complex distribution of electromagnetic fields that are unique to the incident signal. Typically, these subtle fields are ignored, but the researchers realized that they could read the patterns as a sort of signature to identify the waves. The entire process can be accomplished with tiny devices inside the microchip that read terahertz waves.



"Instead of directly reading the waves, we are interpreting the patterns created by the waves," Sengupta said. "It is somewhat like looking for a pattern of raindrops by the ripples they make in a pond."

Daniel Mittleman, a professor of engineering at Brown University, said the development was "a very innovative piece of work, and it potentially has a lot of impact." Mittleman, who is the vice chair of the International Society for Infrared Millimeter and Terahertz Waves, said scientists still have work to do before the terahertz band can begin to be used in everyday devices, but the developments are promising.
"It is a very big puzzle with many pieces, and this is just one, but it is a very important one," said Mittleman, who is familiar with the work but had no role in it.

On the terahertz-generation end, much of the challenge is creating a wide range of wavelengths within the terahertz band, particularly in a microchip. The researchers realized they could overcome the problem by generating multiple wavelengths on the chip. They then used precise timing to combine these wavelengths and create very sharp terahertz pulses.

In an article published Dec. 14 in the IEEE Journal of Solid State Circuits, the researchers explained how they created a chip to generate the terahertz waves. The next step, the researchers said, is to extend the work farther along the terahertz band. "Right now we are working with the lower part of the terahertz band," said Xue Wu, a Princeton doctoral student in electrical engineering and an author on both papers.

"What can you do with a billion transistors operating at terahertz frequencies?" Sengupta asked. "Only by re-imagining these complex electromagnetic interactions from fundamental principles can we invent game-changing new technology."
Provided by: Princeton University

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Thứ Hai, 16 tháng 1, 2017

We can see the future of Quantum Systems

Scientists at the University of Sydney have demonstrated the ability to "see" the future of quantum systems, and used that knowledge to preempt their demise, in a major achievement that could help bring the strange and powerful world of quantum technology closer to reality.

The applications of quantum-enabled technologies are compelling, and already demonstrating significant impacts - especially in the realm of sensing and metrology. And the potential to build exceptionally powerful quantum computers using quantum bits, or qubits, is driving investment from the world's largest companies.

However a significant obstacle to building reliable quantum technologies has been the randomization of quantum systems by their environments, or de-coherence, which effectively destroys the useful quantum character.

The physicists have taken a technical quantum leap in addressing this, using techniques from big data to predict how quantum systems will change and then, preventing the system's breakdown from occurring.



The research is published today in Nature Communications.
"Much the way the individual components in mobile phones will eventually fail, so too do quantum systems," said the paper's senior author Professor Michael J. Biercuk.
"But in quantum Technology the lifetime is generally measured in fractions of a second, rather than years."

Professor Biercuk, from the University of Sydney's School of Physics and a chief investigator at the Australian Research Council's Centre for Engineered Quantum Systems, said his group had demonstrated it was possible to suppress de-coherence in a preventive manner. The key was to develop a technique to predict how the system would disintegrate.

Professor Biercuk highlighted the challenges of making predictions in a quantum world: "Humans routinely employ predictive techniques in our daily experience; for instance, when we play tennis we predict where the ball will end up based on observations of the airborne ball," he said.

This works because the rules that govern how the ball will move, like gravity, are regular and known. But what if the rules changed randomly while the ball was on its way to you? In that case it's next to impossible to predict the future behavior of that ball.
"And yet this situation is exactly what we had to deal with because the disintegration of quantum systems is random. Moreover, in the quantum realm observation erases ‘quantumness’, so our team needed to be able to guess how and when the system would randomly break.

"We effectively needed to swing at the randomly moving tennis ball while blindfolded."

The team turned to machine learning for help in keeping their quantum systems - qubits realized in trapped atoms - from breaking.



What might look like random behavior actually contained enough information for a computer program to guess how the system would change in the future. It could then predict the future without direct observation, which would otherwise erase the system's useful characteristics.

The predictions were remarkably accurate, allowing the team to use their guesses preemptively to compensate for the anticipated changes.

Doing this in real time allowed the team to prevent the disintegration of the quantum character, extending the useful lifetime of the qubits.
"We know that building real quantum technologies will require major advances in our ability to control and stabilize qubits - to make them useful in applications," Professor Biercuk said.

Our techniques apply to any qubit, built in any technology, including the special superconducting circuits being used by major corporations.
"We're excited to be developing new capabilities that turn quantum systems from novelties into useful technologies. The quantum future is looking better all the time," Professor Biercuk said.

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Thứ Hai, 8 tháng 8, 2016

Robotics: The Future in here

Robots are expanding skills, moving up the corporate ladder, showing awesome productivity and retention rates, and increasingly shoving aside their human counterparts.



You might have heard the word Robotics very often by many people in areas where there is some extraordinary thing going on. Yes it is true that for those who don’t know what robotics actually is, it might seem something like magic to them which is always involved with something fascinating all the time. Well you can now know what Robotics is and what it does to have a full concept of what’s actually going on behind the scenes when you see those robots working out in the form of humans or in different machines and limb looking mechanisms.

What is Robotics?


Robotics is a field of science rather a branch of technology that deals with the working, operation, building, designing, implementation, challenges, issues and just about everything related to robots.



You should however keep in mind that robots are not only those seen in cartoons as human forms working for people but all those machines that work intelligently to perform different tasks are also robots and can be found in other forms too.

What Are the Different Applications of Robotics?


Robots perform different information processing types and produce different results and responses according to the field for which they have been developed. They also use sensory decisions in them sometimes too. The different applications include surgical, bio, rehabilitation robots like the very following:
-Industrial operations to produce or assemble different things

-Perform tasks that are difficult to be done in an environment that is tough or hazardous to be done by humans

-For doing physical tasks that are impossible to be done with human force

-For taking decisions in a field based on different precise mathematical operations and past experiences

-In the military when soldiers cannot be that multi-tasked and skilled as robots

-In the medical fields of different experimental trials and scans for producing related observations

What Are the Effects of Robotics?


While every field has its different pros and cons, the most affecting results include:
-Cost

It is not easy to make or build a robot, as it requires lots of money through many funding plans besides the approvals needed to make something that can be revolutionary.

-Maintenance
When robots are doing things so effective which remain unmatched in any areas then they need high maintenance as well. Hence the high maintenance might be very no-attractive as well



-Power
Depending on the amount of work entrusted to them, these robots can consume a lot of power and therefore need a lot of power supply.

-Jobs
Due to the application of robots, many people can lose their jobs as one robot can replace a whole strength of staff.

-Fascinating Facts About Robots
Robot is a Czech word from Robota meaning Drudgery
There are more than a million robots in the world while half of them are used in Japan
The real Mars robot ‘Spirit and Opportunity’ has been three years across the Red Planet already
Robot has sold more than 2 million robotic vacuum cleaners with great environment sensing technology.



Hence now you are completely enlightened about the robotics word whenever someone mentions the name anywhere!

Source: New Mind Journal

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Chủ Nhật, 17 tháng 7, 2016

Teaching Machines to Predict the Future

Deep-learning vision system from the Computer Science and Artificial Intelligence Lab, anticipates human interactions using videos of TV shows.



When we see two people meet, we can often predict what happens next: a handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences.

Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations.



This week researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have made an important new breakthrough in predictive vision, developing an algorithm that can anticipate interactions more accurately than ever before.

Trained on YouTube videos and TV shows such as “The Office” and “Desperate Housewives,” the system can predict whether two individuals will hug, kiss, shake hands or slap five. In a second scenario, it could also anticipate what object is likely to appear in a video five seconds later.

While human greetings may seem like arbitrary actions to predict, the task served as a more easily controllable test case for the researchers to study.

“Humans automatically learn to anticipate actions through experience, which is what made us interested in trying to imbue computers with the same sort of common sense,” says CSAIL PhD student Carl Vondrick, who is first author on a related paper that he will present this week at the International Conference on Computer Vision and Pattern Recognition (CVPR). “We wanted to show that just by watching large amounts of video computers can gain enough knowledge to consistently make predictions about their surroundings.”

Vondrick’s co-authors include MIT Professor Antonio Torralba and former postdoctoral Hamed Pirsiavash, now a professor at the University of Maryland.

Past attempts at predictive computer-vision have generally taken one of two approaches.

The first method is to look at an image’s individual pixels and use that knowledge to create a photorealistic “future” image, pixel by pixel — a task that Vondrick describes as “difficult for a professional painter, much less an algorithm.” The second is to have humans label the scene for the computer in advance, which is impractical for being able to predict actions on a large scale.



The CSAIL team instead created an algorithm that can predict “visual representations,” which are basically freeze-frames showing different versions of what the scene might look like.

Rather than saying that one pixel value is blue, the next one is red, and so on, visual representations reveal information about the larger image, such as a certain collection of pixels that represents a human face,” Vondrick says.

The team’s algorithm employs techniques from deep-learning, a field of artificial intelligence that uses systems called “neural networks” to teach computers to pore over massive amounts of data to find patterns on their own.

Each of the algorithm’s networks predicts a representation is automatically classified as one of the four actions — in this case, a hug, handshake, high-five, or kiss. The system then merges those actions into one that it uses as its prediction. For example, three networks might predict a kiss, while another might use the fact that another person has entered the frame as a rationale for predicting a hug instead.

“A video isn’t like a ‘Choose Your Own Adventure’ book where you can see all of the potential paths,” says Vondrick. “The future is inherently ambiguous, so it’s exciting to challenge ourselves to develop a system that uses these representations to anticipate all of the possibilities.”



After training the algorithm on 600 hours of unlabeled video, the team tested it on new videos showing both actions and objects.

When shown a video of people who are one second away from performing one of the four actions, the algorithm correctly predicted the action more than 43 percent of the time, which compares to existing algorithms that could only do 36 percent of the time.

In a second study, the algorithm was shown a frame from a video and asked to predict what object will appear five seconds later. For example, seeing someone open a microwave might suggest the future presence of a coffee mug. The algorithm predicted the object in the frame 30 percent more accurately than baseline measures, though the researchers caution that it still only has an average precision of 11 percent.

It’s worth noting that even humans make mistakes on these tasks: for example, human subjects were only able to correctly predict the action 71 percent of the time.

“There’s a lot of subtlety to understanding and forecasting human interactions,” says Vondrick. “We hope to be able to work off of this example to be able to soon predict even more complex tasks.”

While the algorithms aren’t yet accurate enough for practical applications, Vondrick says that future versions could be used for everything from robots that develop better action plans to security cameras that can alert emergency responders when someone who has fallen or gotten injured.



“I’m excited to see how much better the algorithms get if we can feed them a lifetime’s worth of videos,” says Vondrick. “We might see some significant improvements that would get us closer to using predictive-vision in real-world situations.”

Source: Adam Conner-Simons | Rachel Gordon | CSAIL

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Thứ Hai, 11 tháng 7, 2016

Is your Job secured?

Soon 95 million jobs going to Robots in the next 10 to 20 years



Sooner or later, the robot uprising and the ultimate downfall of man will be upon us. Or, at least, that’s how robot doomsayers tell it.



Robots will play more and more important roles in our lives in the future, likely becoming essential components of our daily routines. In the process, they may end up taking over the world, but not in a Terminator or Matrix kind of way, but rather in a WALL-E kind of fashion. We might end up using robots for various tasks that will only grow in complexity as robotics advance to the point they can replace humans for plenty of jobs, including tasks that require plenty of creativity. These smarter robots might put 50% of jobs at risk in the U.S. and the U.K., a new report show.

The Bank of England believes that machines might take over 80 million American and 15 million British jobs over the next 10 to 20 years, “CNN Money report”, or 50% of the workforce in each of the two countries.



“These machines are different,” the bank’s chief economist Andy Haldane said. “Unlike in the past, they have the potential to substitute for human brains as well as hands.”

According to the bank, administrative, clerical and production workers might be the first to be replaced by robots in the coming years. That’s not to say unemployment will suddenly rise. Humans will “adapt their skills to the tasks where they continue to have a comparative advantage over machines.”

A recent Oxford University study says that the jobs at risk of being replaced by robots include loan officers, receptionists, paralegals, salespeople, drivers, security guards, fast food cooks, and bartenders.

Other jobs including marketers, journalists and lawyers might also be added to the list in the future, founder of Webbmedia Group Amy Webb said at the Milken Global Conference this year.



Haldane says that unlike during the Industrial Revolution, where manual laborers were forced to improve their skills and adapt to more sophisticated jobs, robots will simply replace humans this time around. The more intelligent machines would be able to take over mid-skilled jobs, leaving low-skilled or very high-skilled jobs for humans.

“The smarter machines become, the greater the likelihood that the space remaining for uniquely-human skills could shrink further,” he warned.

Just to give an example of how the Robotic will affect the future, let us introduce Pepper the robot, one of the smartest AI creations on the market today, is capable of recognizing human facial expressions and interacting with them accordingly. It’s because of this that Pepper has found use as a ‘companion’ robot for elderly people and children, primarily.

Now, however, Pepper is being used as a receptionist in two Belgian hospitals, offering the first insight of how it fares in a dedicated healthcare environment.

While Softbank has been making Pepper in batches of 1,000 for less than $2,000 each, that’s not quite the same model that will be gracing the reception desks in those hospitals. Instead, that’s an upgraded $34,000 model, according to the BBC.



Among Pepper’s special abilities is its understanding of 20 different languages, and being able whether its talking to a man, woman or child.

According to the Beeb, in one hospital, Pepper will remain in reception, but in another trial location, he’ll be accompanying visitors to other departments and wards. Meanwhile in Singapore, Pepper’s been employed to take food at some specialty Pizza Hut stores.

The future is very nearly here, people.

Source: Chris Smith

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