sad series of images ...

is this the breaktrough that we were waiting for?

for the geeks ... and Anna&Jai

Interesting overview paper by Massa from Trient on reconfigurable metamaterials … makes me wonder if it is time for us to look closer into it or already too late …

finally a consumer application for fuel cells?

true?

tesla announcing home batteries at 350$/kW

Does any of you know this Berlin based Design Research Lab - they seem to do some interesting "stuff"

looks like an application of laser filamentation ...

interesting read - also on the approach taken ... again thanks to Snowden

and what an effort they make ..

revealing - though not surprising, but nicely detailed; opportunities for really secure comm via space?

stronger than steel - small teeth of snail like creature ...

might look a bit scary ...

electrically charged spider web ...

enjoy ....

interesting story with many juicy details on how they proceed ... (similarly interesting nickname for the "operation" chosen by our british friends) "The spies used the IP addresses they had associated with the engineers as search terms to sift through their surveillance troves, and were quickly able to find what they needed to confirm the employees' identities and target them individually with malware. The confirmation came in the form of Google, Yahoo, and LinkedIn "cookies," tiny unique files that are automatically placed on computers to identify and sometimes track people browsing the Internet, often for advertising purposes. GCHQ maintains a huge repository named MUTANT BROTH that stores billions of these intercepted cookies, which it uses to correlate with IP addresses to determine the identity of a person. GCHQ refers to cookies internally as "target detection identifiers." Top-secret GCHQ documents name three male Belgacom engineers who were identified as targets to attack. The Intercept has confirmed the identities of the men, and contacted each of them prior to the publication of this story; all three declined comment and requested that their identities not be disclosed. GCHQ monitored the browsing habits of the engineers, and geared up to enter the most important and sensitive phase of the secret operation. The agency planned to perform a so-called "Quantum Insert" attack, which involves redirecting people targeted for surveillance to a malicious website that infects their computers with malware at a lightning pace. In this case, the documents indicate that GCHQ set up a malicious page that looked like LinkedIn to trick the Belgacom engineers. (The NSA also uses Quantum Inserts to target people, as The Intercept has previously reported.) A GCHQ document reviewing operations conducted between January and March 2011 noted that the hack on Belgacom was successful, and stated that the agency had obtained access to the company's

love this one ... it seems to take physicist to explain to the AI crowd what they are actually doing ... Deep learning is a broad set of techniques that uses multiple layers of representation to automatically learn relevant features directly from structured data. Recently, such techniques have yielded record-breaking results on a diverse set of difficult machine learning tasks in computer vision, speech recognition, and natural language processing. Despite the enormous success of deep learning, relatively little is understood theoretically about why these techniques are so successful at feature learning and compression. Here, we show that deep learning is intimately related to one of the most important and successful techniques in theoretical physics, the renormalization group (RG). RG is an iterative coarse-graining scheme that allows for the extraction of relevant features (i.e. operators) as a physical system is examined at different length scales. We construct an exact mapping from the variational renormalization group, first introduced by Kadanoff, and deep learning architectures based on Restricted Boltzmann Machines (RBMs). We illustrate these ideas using the nearest-neighbor Ising Model in one and two-dimensions. Our results suggests that deep learning algorithms may be employing a generalized RG-like scheme to learn relevant features from data.

where real power sits ...

not for space but the air breathing battery still is a nice concept - would make robots more "human" if they have to breath :-)

anybody wanna try? surprising to me