A NASA team plans to build the first integrated-photonics modem, using an emerging, potentially revolutionary technology that could transform everything from telecommunications, medical imaging, advanced manufacturing to national defense.
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Stanford University PhD candidate, Song Han, who works under advisor and networking pioneer, Dr. Bill Dally, responded in a most soft-spoken and thoughtful way to the question of whether the coupled software and hardware architecture he developed might change the world.
In fact, instead of answering the question directly, he pointed to the range of applications, both in the present and future, that will be driven by near real-time inference for complex deep neural networks—all a roundabout way of showing not just why what he is working toward is revolutionary, but why the missing pieces he is filling in have kept neural network-fed services at a relative constant.
There is one large barrier to that future Han considers imminent—one pushed by an existing range of neural network-driven applications powering all aspects of the consumer economy and, over time, the enterprise. And it’s less broadly technical than it is efficiency-driven. After all, considering the mode of service delivery of these applications, often lightweight, power-aware devices, how much computation can be effectively packed into the memory of such devices—and at what cost to battery life or overall power? Devices aside, these same concerns, at a grander level of scale, are even more pertinent at the datacenter where some bulk of the inference is handled.
What if computers could recognize objects as well as the human brain could? Electrical engineers at the University of California, San Diego have taken an important step toward that goal by developing a pedestrian detection system that performs in near real-time (2−4 frames per second) and with higher accuracy (close to half the error) compared to existing systems. The technology, which incorporates deep learning models, could be used in “smart” vehicles, robotics and image and video search systems.
Another step forward for Quantum — The Quantum Current. US Dept. of Energy has a new method to generate very low-resistance electric (Quantum) current which will improve our methods for energy, quantum computing, and medical imaging, and possibly even a new mechanism for inducing superconductivity—the ability of some materials (zirconium pentatelluride) to carry current with no energy loss.
Carnegie Mellon University is embarking on a five-year, $12 million research effort to reverse-engineer the brain and “make computers think more like humans,” funded by the U.S. Intelligence Advanced Research Projects Activity (IARPA). The research is led by Tai Sing Lee, a professor in the Computer Science Department and the Center for the Neural Basis of Cognition (CNBC).
The research effort, through IARPA’s Machine Intelligence from Cortical Networks (MICrONS) research program, is part of the U.S. BRAIN Initiative to revolutionize the understanding of the human brain.
By now, everyone is probably familiar with holographic performances from such artists as Elvis, Micheal Jackson and the one that started it all, Tupac Shakur at Coachella. However, the real pioneers of performance holograms were that quirky cartoon band, Gorillaz. The costs of a holographic setup would make your toes curl, but the technology itself is fairly straight forward. Projecting onto a specialized screen which is as close to invisible as one can get. There are two main players in the space, Holo-gauze and Musion.
When it comes to digital artists, there is one name that stands out from all others. Hatsune Miku. What makes Miku so unique when compared to holograms of dead celebrities or even the animated Gorillaz with Blur frontman, Damon Albarn, is that she is entirely computer generated. A software instrument manifest as an anime character who has become as much of a ‘real’ celebrity as anyone currently in the charts.
Continue reading “Could Digital Artists Replace Real Artists?” »
As I said this morning; there is something definitely going with Quantum today. Maybe it’s the planet alignment (I saw there was something going on with the alignment with Aquaris today) — this is awesome news.
Rigetti Computing is working on designs for quantum-powered chips to perform previously impossible feats that advance chemistry and machine learning.
Wham! Another headline; 2 new companies (Rigetti and Q Branch) trying to capture the Quantum Platform crown from D-Wave. Now, we can say a real industry race is on.
Based on a recent analysis of our most popular articles, investors seem to have a strong interest in quantum computing. The problem for investors is that there aren’t any pure play opportunities to invest in quantum computing at the moment. The main reason for this is that there aren’t many companies working on quantum computing. In fact, there’s just one company right now that’s actually selling a quantum computer; Canadian based startup D-Wave.
D-Wave has actually released a controversial “quantum computer”, and is working with big names like Google, NASA, and Lockheed. D-Wave received some major credibility recently when Google announced that they solved an optimization problem in seconds that would normally take 10,000 years with a conventional computer. There is one way to get exposure to D-Wave, but it’s hardly a pure-play and doesn’t seem overly promising. While there are very few companies other than D-Wave directly involved in quantum computing, we did find two companies that quantum computing investors should keep an eye on.
Continue reading “2 Quantum Computing Companies That Are Not D-Wave” »
In quantum physics, the creation of a state of entanglement in particles any larger and more complex than photons usually requires temperatures close to absolute zero and the application of enormously powerful magnetic fields to achieve. Now scientists working at the University of Chicago (UChicago) and the Argonne National Laboratory claim to have created this entangled state at room temperature on a semiconductor chip, using atomic nuclei and the application of relatively small magnetic fields.
When two particles, such as photons, are entangled – that is, when they interact physically and are then forcibly separated – the spin direction imparted to each is directly opposite to the other. However, when one of the entangled particles has its spin direction measured, the other particle will immediately display the reverse spin direction, no matter how great a distance they are apart. This is the “spooky action at a distance” phenomenon (as Albert Einstein put it) that has already seen the rise of applications once considered science fiction, such as ultra-safe cryptography and a new realm of quantum computing.
Ordinarily, quantum entanglement is a rarely observed occurence in the natural world, as particles coupled in this way first need to be in a highly ordered state before they can be entangled. In essence, this is because thermodynamic entropy dictates that a general chaos of particles is the standard state of things at the atomic level and makes such alignments exceedingly rare. Going up a scale to the macro level, and the sheer number of particles involved makes entanglement an exceptionally difficult state to achieve.
“Full exploitation of this information is a major challenge,” officials with the Defense Advanced Research Projects Agency (DARPA) wrote in a 2009 brief on “deep learning.”
“Human observation and analysis of [intelligence, surveillance and reconnaissance] assets is essential, but the training of humans is both expensive and time-consuming. Human performance also varies due to individuals’ capabilities and training, fatigue, boredom, and human attentional capacity.”
Working with a team of researchers at MIT, DARPA is hoping to take all of that human know-how and shrink it down into processing unit no bigger than your cellphone, using a microchip known as “Eyeriss.” The concept relies on “neural networks;” computerized memory networks based on the workings of the human brain.
