Inspired by mammals’ eyes, University of Wisconsin-Madison electrical engineers have created the fastest, most responsive flexible silicon phototransistor ever made.
Category: computing – Page 927
Australian scientists have designed a 3D silicon chip architecture based on single atom quantum bits, which is compatible with atomic-scale fabrication techniques — providing a blueprint to build a large-scale quantum computer.
To many people, the introduction of the first Macintosh computer and its graphical user interface in 1984 is viewed as the dawn of creative computing. But if you ask Dr. Nick Montfort, a poet, computer scientist, and assistant professor of Digital Media at MIT, he’ll offer a different direction and definition for creative computing and its origins.
Defining Creative
“Creative Computing was the name of a computer magazine that ran from 1974 through 1985. Even before micro-computing there was already this magazine extolling the capabilities of the computer to teach, to help people learn, help people explore and help them do different types of creative work, in literature, the arts, music and so on,” Montfort said.
“It was a time when people had a lot of hope that computing would enable people personally as artists and creators to do work. It was actually a different time than we’re in now. There are a few people working in those areas, but it’s not as widespread as hoped in the late 70’s or early 80s.”
These days, Montfort notes that many people use the term “artificial intelligence” interchangeably with creative computing. While there are some parallels, Montfort said what is classically called AI isn’t the same as computational creativity. The difference, he says, is in the results.
“A lot of the ways in which AI is understood is the ability to achieve a particular known objective,” Montfort said. “In computational creativity, you’re trying to develop a system that will surprise you. If it does something you already knew about then, by definition, it’s not creative.”
Given that, Montfort quickly pointed out that creative computing can still come from known objectives.
“A lot of good creative computer work comes from doing things we already know computers can do well,” he said. “As a simple example, the difference between a computer as a producer of poetic language and person as a producer of poetic language is, the computer can just do it forever. The computer can just keep reproducing and, (with) that capability to bring it together with images to produce a visual display, now you’re able to do something new. There’s no technical accomplishment, but it’s beautiful nonetheless.”
Models of Creativity
As a poet himself, another area of creative computing that Montfort keeps an eye on is the study of models of creativity used to imitate human creativity. While the goal may be to replicate human creativity, Montfort has a greater appreciation for the end results that don’t necessarily appear human-like.
“Even if you’re using a model of human creativity the way it’s done in computational creativity, you don’t have to try to make something human-like, (even though) some people will try to make human-like poetry,” Montfort said. “I’d much rather have a system that is doing something radically different than human artistic practice and making these bizarre combinations than just seeing the results of imitative work.”
To further illustrate his point, Montfort cited a recent computer generated novel contest that yielded some extraordinary, and unusual, results. Those novels were nothing close to what a human might have written, he said, but depending on the eye of the beholder, it at least bodes well for the future.
“A lot of the future of creative computing is individual engagement with creative types of programs,” Montfort said. “That’s not just using drawing programs or other facilities to do work or using prepackaged apps that might assist creatively in the process of composition or creation, but it’s actually going and having people work to code themselves, which they can do with existing programs, modifying them, learning about code and developing their abilities in very informal ways.”
That future of creative computing lies not in industrial creativity or video games, but rather a sharing of information and revisioning of ideas in the multiple hands and minds of connected programmers, Montfort believes.
“One doesn’t have to get a computer science degree or even take a formal class. I think the perspective of free software and open source is very important to the future of creative programming,” Montfort said. “…If people take an academic project and provide their work as free software, that’s great for all sorts of reasons. It allows people to replicate your results, it allows people to build on your research, but also, people might take the work that you’ve done and inflect it in different types of artistic and creative ways.”
Trong Toan Tran states: “Ultimately we want to build a ‘plug and play’ device that can generate single photons on demand…” #QuantumComputing.
A team of UTS researchers has made a major breakthrough that could pave the way for the next generation of quantum communications.
Who needs a peep hole when a wifi network will do? Researchers from MIT have developed technology that uses wireless signals to see your silhouette through a wall—and it can even tell you apart from other people, too.
The team from MIT’s Computer Science and Artificial Intelligence Lab are no strangers to using wireless signals to see what’s happening on the other side of a wall. In 2013, they showed off software that could use variations in wifi signal to detect the presence of human motion from the other side of a wall. But in the last two years they’ve been busy developing the technique, and now they’ve unveiled the obvious — if slightly alarming — natural progression: they can use the wireless reflections bouncing off a human body to see the silhouette of a person standing behind a wall.
Not only that, the team’s technique, known is RF-Capture, is accurate enough to track the hand of a human and, with some repeated measurements, the system can even be trained to recognise different people based just on their wifi silhouette. The research, which is to be presented at SIGGRAPH Asia next month, was published this morning on the research group’s website.
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Even D-Wave’s detractors are starting to feel like quantum computers are getting close, though only for some applications.
Theoretical physicists have proposed a scalable quantum computer architecture. The new model, developed by Wolfgang Lechner, Philipp Hauke and Peter Zoller, overcomes fundamental limitations of programmability in current approaches that aim at solving real-world general optimization problems by exploiting quantum mechanics.
Within the last several years, considerable progress has been made in developing a quantum computer, which holds the promise of solving problems a lot more efficiently than a classical computer. Physicists are now able to realize the basic building blocks, the quantum bits (qubits) in a laboratory, control them and use them for simple computations. For practical application, a particular class of quantum computers, the so-called adiabatic quantum computer, has recently generated a lot of interest among researchers and industry. It is designed to solve real-world optimization problems conventional computers are not able to tackle. All current approaches for adiabatic quantum computation face the same challenge: The problem is encoded in the interaction between qubits; to encode a generic problem, an all-to-all connectivity is necessary, but the locality of the physical quantum bits limits the available interactions.
“The programming language of these systems is the individual interaction between each physical qubit. The possible input is determined by the hardware. This means that all these approaches face a fundamental challenge when trying to build a fully programmable quantum computer,” explains Wolfgang Lechner from the Institute for Quantum Optics and Quantum Information (IQOQI) at the Austrian Academy of Sciences in Innsbruck.
You might not be able to control the 7Bot robotic arm with your mind or your eyes, but at least it’ll only cost you around $350 — cheaper than an iPhone, its creators point out — to get one. Even better, you don’t need to know how to code to program it: just physically guide the arm or use a gesture control device like a Kinect or a Leap motion sensor to make it mimic your movements. In the video below the fold and on its Kickstarter page, you can see it doing calligraphy after a team member’s grandfather physically taught it how. The team also managed make it paint cherry blossoms and do basic mathematics, and we’ll bet you can teach it other productive things, like how to terrorize your cat.
If you prefer the more hands-off approach, you can remotely control it using its 3D visualization app on a computer. And, in case you’re more tech-savvy than the average user, you can program it using the C and C++ open source APIs the 7Bot team provides. In addition to the basic model, the team also offers packages with more features, such as a version with two arms and one that comes with a 3D printer, though they’re also understandably more expensive. According to its campaign page, rewards should start shipping out as soon as January 2016, but as always, it’s best not to treat Kickstarter and other crowdfunding websites as a store.