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Category: information science

Classical hydrodynamics laws can be very useful for describing the behavior of systems composed of many particles (i.e., many-body systems) after they reach a local state of equilibrium. These laws are expressed by so-called hydrodynamical equations, a set of mathematical equations that describe the movement of water or other fluids.

Researchers at Oak Ridge National Laboratory and University of California, Berkeley (UC Berkeley) have recently carried out a study exploring the hydrodynamics of a quantum Heisenberg spin-1/2 chain. Their paper, published in Nature Physics, shows that the spin dynamics of a 1D Heisenberg antiferromagnet (i.e., KCuF3) could be effectively described by a dynamical exponent aligned with the so-called Kardar-Parisi-Zhang universality class.

“Joel Moore and I have known each other for many years and we both have an interest in quantum magnets as a place where we can explore and test new ideas in physics; my interests are experimental and Joel’s are theoretical,” Alan Tennant, one of the researchers who carried out the study, told Phys.org. “For a long time, we have both been interested in temperature in quantum systems, an area where a number of really new insights have come along recently, but we had not worked together on any projects.”

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MIT Technology Review Insights, in association with AI cybersecurity company Darktrace, surveyed more than 300 C-level executives, directors, and managers worldwide to understand how they’re addressing the cyberthreats they’re up against—and how to use AI to help fight against them.

Cyberattacks continue to grow in prevalence and sophistication. With the ability to disrupt business operations, wipe out critical data, and cause reputational damage, they pose an existential threat to businesses, critical services, and infrastructure. Today’s new wave of attacks is outsmarting and outpacing humans, and even starting to incorporate artificial intelligence (AI). What’s known as “offensive AI” will enable cybercriminals to direct targeted attacks at unprecedented speed and scale while flying under the radar of traditional, rule-based detection tools.

Some of the world’s largest and most trusted organizations have already fallen victim to damaging cyberattacks, undermining their ability to safeguard critical data. With offensive AI on the horizon, organizations need to adopt new defenses to fight back: the battle of algorithms has begun.

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Place one clock at the top of a mountain. Place another on the beach. Eventually, you’ll see that each clock tells a different time. Why?

In his book “The Order of Time,” Italian theoretical physicist Carlo Rovelli suggests that our perception of time — our sense that time is forever flowing forward — could be a highly subjective projection. After all, when you look at reality on the smallest scale (using equations of quantum gravity, at least), time vanishes.

“If I observe the microscopic state of things,” writes Rovelli, “then the difference between past and future vanishes … in the elementary grammar of things, there is no distinction between ‘cause’ and ‘effect.’”

So, why do we perceive time as flowing forward? Rovelli notes that, although time disappears on extremely small scales, we still obviously perceive events occur sequentially in reality. In other words, we observe entropy: Order changing into disorder; an egg cracking and getting scrambled.

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Computer scientists from Rice University have displayed an artificial intelligence (AI) software that can run on commodity processors and train deep neural networks 15 times faster than platforms based on graphics processors.

According to Anshumali Shrivastava, an assistant professor of computer science at Rice’s Brown School of Engineering, the resources spent on training are the actual bottleneck in AI. Companies are spending millions of dollars a week to train and fine-tune their AI workloads.

Deep neural networks (DNN) are a very powerful type of artificial intelligence that can outperform humans at some tasks. DNN training is a series of matrix multiplication operations and an ideal workload for graphics processing units (GPUs), which costs nearly three times more than general-purpose central processing units (CPUs).

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TAE Technologies, the California, USA-based fusion energy technology company, has announced that its proprietary beam-driven field-reversed configuration (FRC) plasma generator has produced stable plasma at over 50 million degrees Celsius. The milestone has helped the company raise USD280 million in additional funding.

Norman — TAE’s USD150 million National Laboratory-scale device named after company founder, the late Norman Rostoker — was unveiled in May 2017 and reached first plasma in June of that year. The device achieved the latest milestone as part of a “well-choreographed sequence of campaigns” consisting of over 25000 fully-integrated fusion reactor core experiments. These experiments were optimised with the most advanced computing processes available, including machine learning from an ongoing collaboration with Google (which produced the Optometrist Algorithm) and processing power from the US Department of Energy’s INCITE programme that leverages exascale-level computing.

Plasma must be hot enough to enable sufficiently forceful collisions to cause fusion and sustain itself long enough to harness the power at will. These are known as the ‘hot enough’ and ‘long enough’ milestone. TAE said it had proved the ‘long enough’ component in 2015, after more than 100000 experiments. A year later, the company began building Norman, its fifth-generation device, to further test plasma temperature increases in pursuit of ‘hot enough’.

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Rice University computer scientists have demonstrated artificial intelligence (AI) software that runs on commodity processors and trains deep neural networks 15 times faster than platforms based on graphics processors.

“The cost of training is the actual bottleneck in AI,” said Anshumali Shrivastava, an assistant professor of computer science at Rice’s Brown School of Engineering. “Companies are spending millions of dollars a week just to train and fine-tune their AI workloads.”

Shrivastava and collaborators from Rice and Intel will present research that addresses that bottleneck April 8 at the machine learning systems conference MLSys.

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Michio Kaku is a professor of theoretical physics at City College, New York, a proponent of string theory but also a well-known populariser of science, with multiple TV appearances and several bestselling books behind him. His latest book, The God Equation, is a clear and accessible examination of the quest to combine Einstein’s general relativity with quantum theory to create an all-encompassing “theory of everything” about the nature of the universe.

The physicist on Newton finding inspiration amid the great plague, how the multiverse can unite religions, and why a ‘theory of everything’ is within our grasp.

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The researchers let the cell clusters assemble in the right proportions and then used micro-manipulation tools to move or eliminate cells — essentially poking and carving them into shapes like those recommended by the algorithm. The resulting cell clusters showed the predicted ability to move over a surface in a nonrandom way.

The team dubbed these structures xenobots. While the prefix was derived from the Latin name of the African clawed frogs (Xenopus laevis) that supplied the cells, it also seemed fitting because of its relation to xenos, the ancient Greek for “strange.” These were indeed strange living robots: tiny masterpieces of cell craft fashioned by human design. And they hinted at how cells might be persuaded to develop new collective goals and assume shapes totally unlike those that normally develop from an embryo.

But that only scratched the surface of the problem for Levin, who wanted to know what might happen if embryonic frog cells were “liberated” from the constraints of both an embryonic body and researchers’ manipulations. “If we give them the opportunity to re-envision multicellularity,” Levin said, then his question was, “What is it that they will build?”

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