Lifeboat Foundation

Innovations expert Sandy Plunkett joins panellists in criticising government’s controversial NBN model, but Wyatt Roy says it’s about separating ‘rhetoric and reality’.

DARPA’s new “Spectrum Collaboration Challenge” with a $2million prize for who can motivate a machine learning approach to dynamically sharing the RF Spectrum.

WASHINGTON, March 28, 2016 /PRNewswire-iReach/ — On March 23rd, 2016 DARPA announced its next Grand Challenge at the International Wireless Conference Expo in Las Vegas, Nevada. Program Manager, Paul Tilghman of DARPA’s Microsystems Technology Office (MTO), made the announcement to industry leaders following the conferences Dynamic Spectrum Sharing Summit. The challenge will motivate a machine learning approach to dynamically sharing the RF Spectrum and has been named the “Spectrum Collaboration Challenge.” A top prize of $2million dollars has been announced.

While mostly transparent to the typical cell phone or Wi-Fi user, the problem of spectrum congestion has been a long standing issue for both the commercial sector and Department of Defense. The insatiable appetite for wireless connectivity over the last 30 years has grown at such a hurried pace that within the RF community the term spectrum scarcity has been coined. RF bandwidth, the number of frequencies available to communicate information over, is a relatively fixed resource, and advanced communication systems like LTE and military communications systems consume a lot of it. As spectrum planners prepare for the next big wave of connected devices, dubbed the Internet of Things, they wonder where they will find the spectrum bandwidth they need to support these billions of new devices. Equally challenging, is the military’s desire to connect every soldier on the battlefield, while using these very same frequencies.

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Looking for partners.

[Via Satellite 03-28-2016] The U.S. Defense Advanced Research Projects Agency (DARPA) is reviving its in-orbit servicing efforts through a new public-private partnership program called Robotic Servicing of Geosynchronous Satellites (RSGS). Under the RSGS vision, the partners would join a DARPA-developed modular toolkit, including hardware and software, to a privately developed spacecraft to create a commercially owned and operated Robotic Servicing Vehicle (RSV). DARPA would contribute the robotics technology, such as the previously developed Front End Robotic Enabling Near-Term Demonstration (FREND) robotic arm, expertise, and a government-provided launch. The commercial partner would contribute the satellite to carry the robotic payload, integration of the payload, and the mission operations center and staff.

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Awesome!

Even the simplest networks of neurons in the brain are composed of millions of connections, and examining these vast networks is critical to understanding how the brain works. An international team of researchers, led by R. Clay Reid, Wei Chung Allen Lee and Vincent Bonin from the Allen Institute for Brain Science, Harvard Medical School and Neuro-Electronics Research Flanders (NERF), respectively, has published the largest network to date of connections between neurons in the cortex, where high-level processing occurs, and have revealed several crucial elements of how networks in the brain are organized. The results are published in the journal Nature.

“This is a culmination of a research program that began almost ten years ago. Brain networks are too large and complex to understand piecemeal, so we used high-throughput techniques to collect huge data sets of brain activity and brain wiring,” says R. Clay Reid, M.D., Ph.D., Senior Investigator at the Allen Institute for Brain Science. “But we are finding that the effort is absolutely worthwhile and that we are learning a tremendous amount about the structure of networks in the brain, and ultimately how the brain’s structure is linked to its function.”

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Discover & Share this Dog GIF with everyone you know. GIPHY is how you search, share, discover, and create GIFs.

Welcome to Beauty.AI, the first beauty contest judged by an all-robot jury. Robots and humans are welcome to join.

You know that scene in the classic film Bruce Almighty when Jim Carrey uses his God-like powers to mess with Steve Carrell’s character while he’s giving a live news broadcast? That’s what this video looks like (kind of), except replace Steve Carrell for George W. Bush, Donald Trump, Vladimir Putin, and Barack Obama, and Jim Carrey for a team of wily researchers.

The system was designed by researchers from the University of Erlangen-Nuremberg, Max-Planck-Institude for Informatics, and Stanford University. The same team worked on a similar facial expression transfer project last year, but that involved controlling the expressions of someone in the same room. This time they’re doing it with YouTube videos. First, the “target actor” (that’s Bush, Trump, Putin, and Obama) is rendered with a neutral expression. Then, the expressions of the source actor (that’s the other guy) are captured via webcam, and those expressions control the animation in the YouTube video.

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Deep neural networks (DNNs) can be taught nearly anything, including how to beat us at our own games. The problem is that training AI systems ties up big-ticket supercomputers or data centers for days at a time. Scientists from IBM’s T.J. Watson Research Center think they can cut the horsepower and learning times drastically using “resistive processing units,” theoretical chips that combine CPU and non-volatile memory. Those could accelerate data speeds exponentially, resulting in systems that can do tasks like “natural speech recognition and translation between all world languages,” according to the team.

So why does it take so much computing power and time to teach AI? The problem is that modern neural networks like Google’s DeepMind or IBM Watson must perform billions of tasks in in parallel. That requires numerous CPU memory calls, which quickly adds up over billions of cycles. The researchers debated using new storage tech like resistive RAM that can permanently store data with DRAM-like speeds. However, they eventually came up with the idea for a new type of chip called a resistive processing unit (RPU) that puts large amounts of resistive RAM directly onto a CPU.

Annual sales of drones in the U.S. will hit 2.5 million this year and swell to 7 million by 2020, according to a projection from the Federal Aviation Administration.

Unmanned aircraft purchases are growing both for hobbyists and for commercial ventures that perform inspections, assist farmers, and survey construction sites, according to the agency’s annual forecast of aviation activity, released on Thursday.