Iranian researchers built a device that can automatically give injections to patients from distance.
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Intel has planted some solid stakes in the ground for the future of deep learning over the last month with its acquisition of deep learning chip startup, Nervana Systems, and most recently, mobile and embedded machine learning company, Movidius.
These new pieces will snap into Intel’s still-forming puzzle for capturing the supposed billion-plus dollar market ahead for deep learning, which is complemented by its own Knights Mill effort and software optimization work on machine learning codes and tooling. At the same time, just down the coast, Nvidia is firming up the market for its own GPU training and inference chips as well as its own hardware outfitted with the latest Pascal GPUs and requisite deep learning libraries.
While Intel’s efforts have garnered significant headlines recently with that surprising pair of acquisitions, a move which is pushing Nvidia harder to demonstrate how GPU acceleration (thus far the dominant compute engine for model training), they still have some work to do to capture mindshare for this emerging market. Further complicating this is the fact that the last two years have brought a number of newcomers to the field—deep learning chip upstarts touting the idea that general purpose architectures (including GPUs) cannot compare to a low precision, fixed point, specialized approach. In fact, we could be moving into a “Cambrian explosion” for computer architecture–one that is brought about by the new requirements of deep learning. Assuming, of course, there are really enough applications and users in a short enough window that the chip startups don’t fall over waiting for their big bang.
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Meet the iPhone 7
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Dobby Selfie-Drone: Hands-On
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In an attempt to bring the next generation of computers to life, teams around the globe have been working with carbon nanotubes — one of the most conductive materials ever discovered. Now, for the first time ever, scientists made a transistor using carbon nanotubes that beats silicon.
For the first time, scientists have built a transistor out of carbon nanotubes that can run almost twice as fast as its silicon counterparts.
This is big, because for decades, scientists have been trying to figure out how to build the next generation of computers using carbon nanotube components, because their unique properties could form the basis of faster devices that consume way less power.
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A Spanish publishing house has finally been given permission to make exact copies of the Voynich Manuscript — a 15th century book written in a mysterious coded language that no one has cracked.
For centuries, scientists have been trying to decipher the text. Some of the world’s best cryptographers have dedicated their lives to solving the puzzle — but no one’s even gotten close. Now, with almost 900 copies about to go into circulation, we might finally get some answers.
“The Voynich Manuscript has led some of the smartest people down rabbit holes for centuries,” Bill Sherman from the Folger Shakespeare Library told The Washington Post in 2014, when he was about to open an exhibit on the text.
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Researchers design aqueous battery that stores solar energy better than current lithium technology.
Batteries based on water that can store the electricity that we generate from solar technology? It can now be done.
Researchers at Ohio State University have designed a device with an aqueous flow battery that is based on water as opposed to the standard lithium design of your average rechargeable batteries. It is the first aqueous flow battery to work with a solar cell and it is 20 percent more efficient than the lithium design.
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