Ein Hacker namens BuggiCorp hat eine schwerwiegende Sicherheitslücke in allen Windows-Versionen seit Windows 2000 entdeckt. Über den Bug kann ein Hacker seiner Malware die höchsten Zugriff auf die höchste Systemsicherheits-Stufe verschaffen.
"Today a group of 25 scientists officially announced their plan to build a human genome from scratch within the next 10 years. They have also given more details about their intended applications for the synthetic DNA – but not everyone is convinced by their approach. The team bills this grand challenge as a natural extension of the Human Genome Project. If that was about reading – or sequencing – the code of life, this new project proposes to write it, chemically synthesising each letter or base pair. Poring over our DNA has limitations, the team argues. “Reading the genome can only get you so far,” says Susan Rosser, a co-author on the paper and the director of the Mammalian Synthetic Biology Research Centre at the University of Edinburgh, UK. “At some point you have to build it.” The team, which is led by maverick geneticist George Church at Harvard University and Andrew Hessler of design software company Autodesk, says it is aiming to launch the ambitious initiative – known as The Human Genome Project–Write – this year, depending on raising an initial £100 million."
At a glance, it seems that DRIDEX has dwindled its activities or operation, appearing only for a few days this May. This is quite unusual given that in the past five months or so, this prevalent online banking threat has always been active in the computing landscape. Last May 25, 2016, we observed a sudden spike in DRIDEX–related spam emails after its seeming ‘hiatus.’ This spam campaign mostly affected users in the United States, Brazil, China, Germany, and Japan.
Welcome to the new world of artificial intelligence. Soon, we won't program computers. We'll train them. Like dolphins. Or dogs. Or humans.
Over the past several years, the biggest tech companies in Silicon Valley have aggressively pursued an approach to computing called machine learning. In traditional programming, an engineer writes explicit, step-by-step instructions for the computer to follow. With machine learning, programmers don’t encode computers with instructions. They train them. If you want to teach a neural network to recognize a cat, for instance, you don’t tell it to look for whiskers, ears, fur, and eyes. You simply show it thousands and thousands of photos of cats, and eventually it works things out. If it keeps misclassifying foxes as cats, you don’t rewrite the code. You just keep coaching it.
This approach is not new—it’s been around for decades—but it has recently become immensely more powerful, thanks in part to the rise of deep neural networks, massively distributed computational systems that mimic the multilayered connections of neurons in the brain. And already, whether you realize it or not, machine learning powers large swaths of our online activity. Facebook uses it to determine which stories show up in your News Feed, and Google Photos uses it to identify faces.
Machine learning runs Microsoft’s Skype Translator, which converts speech to different languages in real time. Self-driving cars use machine learning to avoid accidents. Even Google’s search engine—for so many years a towering edifice of human-written rules—has begun to rely on these deep neural networks. In February the company replaced its longtime head of search with machine-learning expert John Giannandrea, and it has initiated a major program to retrain its engineers in these new techniques. “By building learning systems,” Giannandrea told reporters this fall, “we don’t have to write these rules anymore.”
A lonely spacecraft is nearing Pluto after a three billion-mile journey lasting almost nine years. Nasa’s New Horizons probe awoke from hibernation on December 6 and is preparing to explore the Solar System’s mysterious ‘ninth planet’.
(Phys.org)—Light behaves both as a particle and as a wave. Since the days of Einstein, scientists have been trying to directly observe both of these aspects of light at the same time.
Quantum mechanics tells us that light can behave simultaneously as a particle or a wave. However, there has never been an experiment able to capture both natures of light at the same time; the closest we have come is seeing either wave or particle, but always at different times. Taking a radically different experimental approach, EPFL scientists have now been able to take the first ever snapshot of light behaving both as a wave and as a particle. The breakthrough work is published in Nature Communications.
When UV light hits a metal surface, it causes an emission of electrons. Albert Einstein explained this "photoelectric" effect by proposing that light – thought to only be a wave – is also a stream of particles. Even though a variety of experiments have successfully observed both the particle- and wave-like behaviors of light, they have never been able to observe both at the same time.
A research team led by Fabrizio Carbone at EPFL has now carried out an experiment with a clever twist: using electrons to image light. The researchers have captured, for the first time ever, a single snapshot of light behaving simultaneously as both a wave and a stream of particles.
The experiment is set up like this: A pulse of laser light is fired at a tiny metallic nanowire. The laser adds energy to the charged particles in the nanowire, causing them to vibrate. Light travels along this tiny wire in two possible directions, like cars on a highway. When waves traveling in opposite directions meet each other they form a new wave that looks like it is standing in place. Here, this standing wave becomes the source of light for the experiment, radiating around the nanowire.
Witness or accomplice? At a congress in Berlin, historians have been debating Germany’s role in the genocide of Armenians 100 years ago. New findings show that Germany’s complicity is greater than previously assumed.
In order to determine how fast quantum technologies can ultimately operate, physicists have established the concept of "quantum speed limits." Quantum speed limits impose limitations on how fast a quantum system can transition from one state to another, so that such a transition requires a minimum amount of time (typically on the order of nanoseconds). This means, for example, that a future quantum computer will not be able to perform computations faster than a certain time determined by these limits.
For a long time dismissed as "junk DNA", we now know that also the regions between the genes fulfil vital functions. Mutations in those DNA regions can severely impair development in humans and may lead to serious diseases later in life. Until now, however, regulatory DNA regions have been hard to find. Scientists around Prof. Julien Gagneur, Professor for Computational Biology at the Technical University of Munich (TUM) and Prof. Patrick Cramer at the Max Planck Institute (MPI) for Biophysical Chemistry in Göttingen have now developed a method to find regulatory DNA regions which are active and controlling genes.
So will we ever be able to model something as complex as the human brain using computers? After all, biological systems use symmetry and interaction to do things that even the most powerful computers cannot do – like surviving, adapting and reproducing. This is one reason why binary logic often falls short of describing how living things or human intelligence work. But our new research suggests there are alternatives: by using the mathematics that describe biological networks in the computers of the future, we may be able to make them more complex and similar to living systems like the brain.
How the hidden mathematics of living cells could help us decipher the brain
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