© Sören Boyn / CNRS/Thales physics joint research unit.
Artist’s impression of the electronic synapse: the particles represent electrons circulating through oxide, by analogy with neurotransmitters in biological synapses. The flow of electrons depends on the oxide’s ferroelectric domain structure, which is controlled by electric voltage pulses.
Download the press release : 
Arranging employees in an office is like creating a 13-dimensional matrix that triangulates human wants, corporate needs, and the cold hard laws of physics: Joe needs to be near Jane but Jane needs natural light, and Jim is sensitive to smells and can’t be near the kitchen but also needs to work with the product ideation and customer happiness team—oh, and Jane hates fans. Enter Autodesk’s Project Discover. Not only does the software apply the principles of generative design to a workspace, using algorithms to determine all possible paths to your #officegoals, but it was also the architect (so to speak) behind the firm’s newly opened space in Toronto.
That project, overseen by design firm The Living, first surveyed the 300 employees who would be moving in. What departments would you like to sit near? Are you a head-down worker or an interactive one? Project Discover generated 10,000 designs, exploring different combinations of high- and low-traffic areas, communal and private zones, and natural-light levels. Then it matched as many of the 300 workers as possible with their specific preferences, all while taking into account the constraints of the space itself. “Typically this kind of fine-resolution evaluation doesn’t make it into the design of an office space,” says Living founder David Benjamin. OK, humans—you got what you wanted. Now don’t screw it up.
Four years ago, Google started to see the real potential for deploying neural networks to support a large number of new services. During that time it was also clear that, given the existing hardware, if people did voice searches for three minutes per day or dictated to their phone for short periods, Google would have to double the number of datacenters just to run machine learning models.
The need for a new architectural approach was clear, Google distinguished hardware engineer, Norman Jouppi, tells The Next Platform, but it required some radical thinking. As it turns out, that’s exactly what he is known for. One of the chief architects of the MIPS processor, Jouppi has pioneered new technologies in memory systems and is one of the most recognized names in microprocessor design. When he joined Google over three years ago, there were several options on the table for an inference chip to churn services out from models trained on Google’s CPU and GPU hybrid machines for deep learning but ultimately Jouppi says he never excepted to return back to what is essentially a CISC device.
We are, of course, talking about Google’s Tensor Processing Unit (TPU), which has not been described in much detail or benchmarked thoroughly until this week. Today, Google released an exhaustive comparison of the TPU’s performance and efficiencies compared with Haswell CPUs and Nvidia Tesla K80 GPUs. We will cover that in more detail in a separate article so we can devote time to an in-depth exploration of just what’s inside the Google TPU to give it such a leg up on other hardware for deep learning inference. You can take a look at the full paper, which was just released, and read on for what we were able to glean from Jouppi that the paper doesn’t reveal.
Big Dog marches again.
The US Marine Corps is preparing to resume testing on its four-legged robot, “Spot.”
A project of the Corps’ Warfighting Lab, the dog-sized device is slated to re-enter developmental testing in the fall.
Capt. Mike Malandra, who heads the Warfighting Lab’s science and technology branch, said that Spot’s hydraulic legs may make it more maneuverable than the small, unmanned Modular Advanced Armed Robotic System, which features treads similar to a tank rather than limbs.
Taking a cue from the Marvel Universe, researchers report that they have developed a self-healing polymeric material with an eye toward electronics and soft robotics that can repair themselves. The material is stretchable and transparent, conducts ions to generate current and could one day help your broken smartphone go back together again.
The researchers will present their work today at the 253rd National Meeting & Exposition of the American Chemical Society (ACS).
“When I was young, my idol was Wolverine from the X-Men,” Chao Wang, Ph.D., says. “He could save the world, but only because he could heal himself. A self-healing material, when carved into two parts, can go back together like nothing has happened, just like our human skin. I’ve been researching making a self-healing lithium ion battery, so when you drop your cell phone, it could fix itself and last much longer.”
Posted in robotics/AI
Ray is not worried about A.I. though he does not dismiss the dangers.
James Bedsol interviewed Ray Kurzweil, one of the world’s leading minds on artificial intelligence, technology and futurism, in his Google office in Mountain View, CA, February 15, 2017.
Who is Raymond “Ray” Kurzweil?
Kurzweil is one of the world’s leading minds on artificial intelligence, technology and futurism. He is the author of five national best-selling books, including “The Singularity is Near” and “How to Create a Mind.”
A team of researchers has developed artificial synapses that are capable of learning autonomously and can improve how fast artificial neural networks learn.
Developments and advances in artificial intelligence (AI) have been due in large part to technologies that mimic how the human brain works. In the world of information technology, such AI systems are called neural networks. These contain algorithms that can be trained, among other things, to imitate how the brain recognizes speech and images. However, running an Artificial Neural Network consumes a lot of time and energy.
Now, researchers from the National Center for Scientific Research (CNRS) in Thales, the University of Bordeaux in Paris-Sud, and Evry have developed an artificial synapse called a memristor directly on a chip. It paves the way for intelligent systems that required less time and energy to learn, and it can learn autonomously.
