A reader shares a report: A team of theoretical physicists from Oxford University in the UK has shown that life and reality cannot be merely simulations generated by a massive extraterrestrial computer. The finding — an unexpectedly definite one — arose from the discovery of a novel link between gravitational anomalies and computational complexity. In a paper published in the journal Science Advances, Zohar Ringel and Dmitry Kovrizhi show that constructing a computer simulation of a particular quantum phenomenon that occurs in metals is impossible — not just practically, but in principle. The pair initially set out to see whether it was possible to use a technique known as quantum Monte Carlo to study the quantum Hall effect — a phenomenon in physical systems that exhibit strong magnetic fields and very low temperatures, and manifests as an energy current that runs across the temperature gradient. The phenomenon indicates an anomaly in the underlying space-time geometry. They discovered that the complexity of the simulation increased exponentially with the number of particles being simulated. If the complexity grew linearly with the number of particles being simulated, then doubling the number of partices would mean doubling the computing power required. If, however, the complexity grows on an exponential scale — where the amount of computing power has to double every time a single particle is added — then the task quickly becomes impossible. Read more of this story at Slashdot.
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We’re Not Living in a Computer Simulation, New Research Shows
sciencehabit shares a report from Science Magazine: The Equifax breach is reason for concern, of course, but if a hacker wants to access your online data by simply guessing your password, you’re probably toast in less than an hour. Now, there’s more bad news: Scientists have harnessed the power of artificial intelligence (AI) to create a program that, combined with existing tools, figured more than a quarter of the passwords from a set of more than 43 million LinkedIn profiles. Researchers at Stevens Institute of Technology in Hoboken, New Jersey, started with a so-called generative adversarial network, or GAN, which comprises two artificial neural networks. A “generator” attempts to produce artificial outputs (like images) that resemble real examples (actual photos), while a “discriminator” tries to detect real from fake. They help refine each other until the generator becomes a skilled counterfeiter. The Stevens team created a GAN it called PassGAN and compared it with two versions of hashCat and one version of John the Ripper. The scientists fed each tool tens of millions of leaked passwords from a gaming site called RockYou, and asked them to generate hundreds of millions of new passwords on their own. Then they counted how many of these new passwords matched a set of leaked passwords from LinkedIn, as a measure of how successful they’d be at cracking them. On its own, PassGAN generated 12% of the passwords in the LinkedIn set, whereas its three competitors generated between 6% and 23%. But the best performance came from combining PassGAN and hashCat. Together, they were able to crack 27% of passwords in the LinkedIn set, the researchers reported this month in a draft paper posted on arXiv. Even failed passwords from PassGAN seemed pretty realistic: saddracula, santazone, coolarse18. Read more of this story at Slashdot.