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Will The Hyperloop Arise—In Slovakia?

A train faster than any other on earth is being explored by the government of the Slovak Republic and Hyperloop Transportation Technologies (HTT), a Southern California startup.

“Explore” is the restrained word used in the joint press release, which set no deadlines and mentioned no sum of money. Even so, this represents the first solid evidence that anyone might build a large-scale version of the Hyperloop, Elon Musk’s 2013 concept for shooting passenger-carrying pods through partially evacuated tubes at just-barely subsonic speeds.

HTT is building a small, 8-kilometer (5 mile) test track along a highway that runs between Los Angeles and San Francisco. 

The release speculated on several possible routes originating in the capital city of Bratislava, in western Slovakia. One might run the 400 km (250 mile) to Košice, in the eastern part of the country. HTT says the trip would take only 25 minutes at full speed—a bit more than 1,200 km per hour (760 mph). A 70-km westward route to Vienna, Austria, would take 8 minutes. 

“Hyperloop in Europe would cut distances substantially and network cities in unprecedented ways,” said Vazil Hudak, Minister of Economy of the Slovak Republic, in the statement. “A transportation system of this kind would redefine the concept of commuting and boost cross-border cooperation in Europe.”

Travel times depends not only on the top speed of the system but also on the g-forces that passengers can comfortably withstand. The stress comes not only at the beginning and end of a trip but also at every bend along the route. HTT puts the maximum force at 1 g, arguing that “the experience is similar to a performance car.”

Editor’s note: this article has been corrected: Bratislava is indeed west of Košice.

AlphaGo Wins Game One Against World Go Champion

Last night Google’s AI AlphaGo won the first in a five-game series against the world’s best Go player, in Seoul, South Korea. The success comes just five months after a slightly less experienced version of the same program became the first machine to defeat any Go professional by winning five games against the European champion.

This victory was far more impressive though because it came at the expense of Lee Sedol, 33, who has dominated the ancient Chinese game for a decade. The European champion, Fan Hui, is ranked only 663rd in the world.

And the machine, by all accounts, played a noticeably stronger game than it did back in October, evidence that it has learned much since then. Describing their research in the journal Nature, AlphaGo’s programmers insist that it now studies mostly on its own, tuning its deep neural networks by playing millions of games against itself.

The object of Go is to surround and capture territory on a 19-by-19 board; each player alternates to place a lozenge-shaped white or black piece, called a stone, on the intersections of the lines. Unlike in chess, the player of the black stones moves first.

The neural networks judge the position, and do so well enough to play a good game. But AlphaGo rises one level further by yoking its networks to a system that generates a “tree” of analysis that represents the many branching possibilities that the game might follow. Because so many moves are possible the branches quickly become an impenetrable thicket, one reason why Go programmers haven’t had the same success as chess programmers when using this “brute force” method alone. Chess has a far lower branching factor than Go.

It seems that AlphaGo’s self-improving capability largely explains its quick rise to world mastery. By contrast, chess programs’ brute-force methods required endless fine-tuning by engineers working together with chess masters. That partly explains why programs took nine years to progress from the first defeat of a grandmaster in a single game, back in 1988, to defeating then World Champion Garry Kasparov, in a six-game match, in 1997.

Even that crowning achievement—garnered with worldwide acclaim by IBM’s Deep Blue machine—came only on the second attempt. The previous year Deep Blue had managed to win only one game in the match—the first. Kasparov then exploited weaknesses he’d spotted in the computer’s game to win three and draw four subsequent games.

Sedol appears to face longer odds of staging a comeback. Unlike Deep Blue, AlphaGo can play numerous games against itself during the 24 hours until Game Two (to be streamed live tonight at 11 pm EST, 4 am GMT). The machine can study ceaselessly, unclouded by worry, ambition, fear, or hope.

Sedol, the king of the Go world, must spend much of his time sleeping—if he can. Uneasy lies the head that wears a crown.

Scientists Flip Switch on Genes With a Magnet

Matching the brain’s machinery to behaviors and emotions was risky business throughout much of medical history. It was achievable, more or less, only through clumsy techniques such as lobotomies. Examiners who removed chunks of the brain could observe the surgery’s effects, but patients had to live with the results.

The rise of optogenetics, in which light in the form of lasers is used to manipulate individual neurons, has improved the situation slightly. But this technique still works only in regions of the brain where it’s easy to shine light. Neuroscientists must physically insert a fiber optic cable to study anything that isn’t easily accessible.

Now a team from the University of Virginia has shown that it’s possible to use a magnet to control neurons embedded deep in the brains of mice. This technique could offer a non-invasive alternative to optogenetics and aid researchers eager to understand the underpinnings of emotions or more clearly identify the origins of cognitive disorders.

By using this technique to flip genes on and off, researchers could trace neural circuits and determine which behaviors or feelings are affiliated with specific pathways in the brain. Ali Guler, a biochemist, led the group that published the results of this proof-of-concept research in the 7 March edition of Nature Neuroscience.

Guler, aware of the limits of other examination methods, wanted to find a way to remotely control neurons. His idea was to create a genetic analog to techniques used to alter cellular functions. If simple adjustments to calcium ion channels can change important processes such as muscle contraction and hormone secretion, he reasoned, why can’t we manipulate hard-to-reach areas of the brain, but with genetic switching as the trigger?

With this strategy in mind, he created a tool that linked the gene for a protein called TRPV4 (which serves as a gatekeeper for ion channels) with a gene for an iron-fixing protein called ferritin. Connecting the two genes in this way enabled his team to tug the ion channels open or push them closed simply by moving the nearby iron with a magnet.

"It's essentially a biological nanomagnet,” Guler says. He dubbed the creation “Magneto” for the Marvel comic book character capable of generating magnetic fields at will.

In one experiment, the Virginia researchers inserted the specially-designed Magneto genes in a virus, which acted as the transport medium to ferry the magnetic field–susceptible gene product to the striata of six mice. The aim: to see if they could switch ion channels open and closed in a way that might mimic the pleasurable effects of dopamine. The striatum, which processes rewards, is buried beneath the wrinkly bulk of the forebrain and has proven difficult to reach by other methods. If the technique worked, they figured, they could use Magneto in other parts of the brain to mimic different hormones and neurotransmitters. Six other mice formed the control group.

The researchers put all 12 mice into a chamber that was magnetized at one end. Their hypothesis: that the Magneto-carrying mice would scramble for the magnetized side because the open ion channels in their striata would give them a dopamine-like rush of pleasure. Indeed, they found that all six of the Magneto mice preferred to spend their time on the magnetized side of the chamber while all but one of the control mice kept to the non-magnetized end.

When Guler measured the rate at which the mice’s neurons fired, he found that the neurons in Magneto mice at the magnetized end fired more frequently than those in the untreated mice—an effect he would expect to see with true dopamine.

In the future, Guler says, this technique could be used to map neural pathways, tinker with behaviors, and compare neurons in different parts of the brain. “Similar to the optogenetic strategies, you can manipulate any group of neurons that you would like to control,” he says.

If that sounds eerie, rest assured that this power will stay confined to the lab for the time being. The synthetic genes that responded to Guler’s magnet were specially designed and built for this purpose. Magnets would not have the same effect on normal neurons in mice or people.

Biodegradable Power Generators Could Power Medical Implants

Biodegradable devices that generate energy from the same effect behind most static electricity could help power transient electronic implants that dissolve in the body, researchers say.

Implantable electronic devices now help treat everything from damaged hearts to traumatic brain injuries. For example, pacemakers can help keep hearts beating properly, while brain sensors can monitor patients for potentially dangerous swelling in the brain.

However, when standard electronic implants run out of power, they need to be removed lest they eventually become sites of infection. But their surgical removal can result in potentially dangerous complications. Scientists are developing transient implantable electronics that dissolve once they are no longer needed, but these mostly rely on external sources of power, limiting their applications.

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Forty Years Later, Turing Prize Winners Devoted to Digital Privacy and Nuclear Activism

Martin Hellman was one of two computer scientists who won the prestigious Turing Award this week for pioneering work in encryption and digital security published nearly 40 years ago. But Hellman’s priorities have little to do with cryptography these days. Instead, he spends the bulk of his time warning the public of society’s potential nuclear demise.

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Nervana Systems Puts Deep Learning AI in the Cloud

Deep learning is Silicon Valley’s latest and greatest attempt at training artificial intelligence to understand the world by sifting through huge amounts of data. A startup called Nervana Systems aims to make AI based on deep learning neural networks even more widely available by turning it into a cloud service for any industry that has Big Data problems to solve.

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Quantum Computer Comes Closer to Cracking RSA Encryption

Quantum computers are often heralded as the future of smarter searching and lightning fast performance. But their amazing mathematical skills may also create grave security risks for data that has long been safely guarded by the premise that certain math problems are simply too complex for computers to solve.

Now computer scientists at MIT and the University of Innsbruck say they've assembled the first five quantum bits (qubits) of a quantum computer that could someday factor any number, and thereby crack the security of traditional encryption schemes.

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Monkeys Navigate a Wheelchair With Their Thoughts

Scientists at Duke University have demonstrated a wireless brain-machine interface (BMI) that allows monkeys to navigate a robotic wheelchair using their thoughts. This is the first long-term wireless BMI implant that has given high-quality signals to precisely control a wheelchair’s movements in real time.

“This is the first wireless brain-machine interface for whole-body locomotion,” says Miguel Nicolelis, professor of neuroscience at Duke who led the work published in the journal Scientific Reports. “Even severely disabled patients who cannot move any part of their body could be placed on a wheelchair and be able to use this device for mobility.”

Nicolelis and his colleagues pioneered brain-machine interfaces in a 1999 study on rats. Since then, researchers have done several demonstrations of primates using brain signals to control prosthetic arms, advanced devices, and computers, and even receive haptic signals

Despite those exciting advances, reliable, long-lasting implants that give high-quality signals have been lacking for human trials and use. 

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EUV Lithography's Prospects Are Brightening

After a hard slog, extreme ultraviolet (EUV) lithography seems to be closing in on a long-sought quarry: a light source bright enough to pattern chips cheaply and keep Moore’s Law marching along.

The technology, which uses 13.5-nanometer light instead of today’s 193-nanometer light, could—at least in the short term—allow chipmakers to create finer features without having to expose chips multiple times, a process that can add significantly to the expense of the manufacturing process.

But for years, EUV’s prospects were limited by the dimness of its light source. Unlike conventional lithography, which uses an ultraviolet laser, EUV generates its invisible light—just at the edge of the x-ray part of the spectrum—by turning tin into a plasma. ASML, which is developing EUV machines for the semiconductor industry, has put its support behind a particular approach called laser-produced plasma, which creates light by shooting 50,000 microscopic molten tin droplets per second across a vacuum chamber and vaporizing each one with a pulse of CO2 laser light.

At the SPIE Advanced Lithography conference in San Jose last week, ASML said it has pushed the limit of that light source to 200 W and aims to reach 250 W by the end of the year.

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