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Enrique Latorres
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This Stealth Warship Runs On Linux and Doesn't Need Humans to Defend Itself

In the ongoing fight between Macs and PCs, it's hard to deny that Linux has the biggest actual firepower (not to mention controls the most super computers too). Case in point: the USS Zumwalt, the most advanced surface ship in existence, which weighs in at over 10,000 tons and features 80 missile silos (its Tomahawk missiles can cover a distance of 1,550 miles), as well as a main gun that fires rocket-assisted, GPS-guided rounds (which can hit within 30 inches of a target roughly 72 miles away). What's really interesting, though, is its ability to detect, analyze, and respond to potential threats, all without the need for human intervention at all. This is where Linux comes in.

Equipped with a top-of-the-line server farm running on Linus Torvalds' adaptable operating system, the Zumwalt is essentially a huge, floating drone—left to its own devices, its impressive computer power allows it to act autonomously during missions in order to function with ease as everything, from lights to electrics and emergency responses, are all completely controlled by the warship's powerful computer brain.

See http://www.outerplaces.com/science/item/16624-zumwaltz-navy-stealth-ship-linux-server-farm
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Artificial neural networks decode brain activity during performed and imagined movements

Artificial intelligence has far outpaced human intelligence in certain tasks. Several groups from the Freiburg excellence cluster BrainLinks-BrainTools led by neuroscientist private lecturer Dr. Tonio Ball are showing how ideas from computer science could revolutionize brain research. In the scientific journal Human Brain Mapping, they illustrate how a self-learning algorithm decodes human brain signals that were measured by an electroencephalogram (EEG). It included performed movements, but also hand and foot movements that were merely thought or an imaginary rotation of objects. Even though the algorithm was not given any characteristics ahead of time, it works as quickly and precisely as traditional systems that have been created to solve certain tasks based on predetermined brain signal characteristics, which are therefore not appropriate for every situation. The demand for such diverse intersections between man and machine is huge: At the University Hospital Freiburg, for instance, it could be used for early detection of epileptic seizures. It could also be used to improve communication possibilities for severely paralyzed patients or an automatic neurological diagnosis. "Our software is based on brain-inspired models that have proven to be most helpful to decode various natural signals such as phonetic sounds," says computer scientist Robin Tibor Schirrmeister. The researcher is using it to rewrite methods that the team has used for decoding EEG data: So-called artificial neural networks are the heart of the current project at BrainLinks-BrainTools. "The great thing about the program is we needn't predetermine any characteristics. The information is processed layer for layer, that is in multiple steps with the help of a non-linear function. The system learns to recognize and differentiate between certain behavioral patterns from various movements as it goes along," explains Schirrmeister. The model is based on the connections between nerve cells in the human body in which electric signals from synapses are directed from cellular protuberances to the cell's core and back again. "Theories have been in circulation for decades, but it wasn't until the emergence of today's computer processing power that the model has become feasible," comments Schirrmeister.
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