Lifeboat Foundation

D-Wave Systems Inc., the world’s first quantum computing company, announced that Los Alamos National Laboratory will acquire and install the latest D-Wave quantum computer, the 1000+ qubit D-Wave 2X™ system. Los Alamos, a multidisciplinary research institution engaged in strategic science on behalf of national security, will lead a collaboration within the Department of Energy and with select university partners to explore the capabilities and applications of quantum annealing technology, consistent with the goals of the government-wide National Strategic Computing Initiative. The National Strategic Computing Initiative, created by executive order of President Barack Obama in late July, is intended “to maximize [the] benefits of high-performance computing (HPC) research, development, and deployment.”

“Eventually Moore’s Law (that predicted that the number of transistors on an integrated circuit would double every two years) will come to an end,” said John Sarrao, associate director for Theory, Simulation, and Computation at Los Alamos. “Dennard Scaling (that predicted that performance per watt of computing would grow exponentially at roughly the same rate) already has. Beyond these two observations lies the end of the current ‘conventional’ computing era, so new technologies and ideas are needed.”

“As conventional computers reach their limits in terms of scaling and performance per watt, we need to investigate new technologies to support our mission,” said Mark Anderson of the Laboratory’s Weapons Physics Directorate. “Researching and evaluating quantum annealing as the basis for new approaches to address intractable problems is an essential and powerful step, and will enable a new generation of forward thinkers to influence its evolution in a direction most beneficial to the nation.”

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Nvidia shared some more details on its upcoming Pascal architecture for 2016 — the new GPU will offer 1TB/s of memory bandwidth and up to 16GB of VRAM.

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A team of quantum physicists in Martinis Lab have come a step closer in creating the circuitry that would allow them to process super computing done by quantum computers. The revolution is promised by the new quantum bits (qubits) compared to the previously done classical computing. Qubits infuse the system with high levels of reliability and speed, thus building foundations for large scale superconducting quantum computers.

Till now computing has been done by classical methods in which the bits were either in states 0 or 1, but qubits exist at all the positions simultaneously, in different dimensions. This special property of being omnipresent is called ‘superpositioning’. However, one of the difficulties is keeping the qubits stable to reproduce same result each time. This superpositioning characteristic makes qubits prone to ‘flipping’, therefore making it difficult to work with.

Julian Kelly, graduate student researcher and co-lead author of a research paper that was published in the journal Nature said:

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A computer simulation of a cognitive model entirely made up of artificial neurons learns to communicate through dialog starting from a state of tabula rasa —

A group of researchers from the University of Sassari (Italy) and the University of Plymouth (UK) has developed a cognitive model, made up of two million interconnected artificial neurons, able to learn to communicate using human language starting from a state of ‘tabula rasa’, only through communication with a human interlocutor. The model is called ANNABELL (Artificial Neural Network with Adaptive Behavior Exploited for Language Learning) and it is described in an article published in PLOS ONE. This research sheds light on the neural processes that underlie the development of language.

How does our brain develop the ability to perform complex cognitive functions, such as those needed for language and reasoning? This is a question that certainly we are all asking ourselves, to which the researchers are not yet able to give a complete answer. We know that in the human brain there are about one hundred billion neurons that communicate by means of electrical signals. We learned a lot about the mechanisms of production and transmission of electrical signals among neurons. There are also experimental techniques, such as functional magnetic resonance imaging, which allow us to understand which parts of the brain are most active when we are involved in different cognitive activities. But a detailed knowledge of how a single neuron works and what are the functions of the various parts of the brain is not enough to give an answer to the initial question.

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“The subtitle to this post is a variation of William Gibson’s famous remark: “The future is already here — it’s just not very evenly distributed.” An obvious follow up question is: if the future is already here, where can I find it?”

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Because of its unique chemical and physical properties, graphene has helped scientists design new gadgets from tiny computer chips to salt water filters. Now a team of researchers from MIT has found a new use for the 2D wonder material: in infrared sensors that could replace bulky night-vision goggles, or even add night vision capabilities to high-tech windshields or smartphone cameras. The study was published last week in Nano Letters.

Night vision technology picks up on infrared wavelengths, energy usually emitted in the form of heat that humans can’t see with the naked eye. Researchers have known for years that because of how it conducts electricity, graphene is an excellent infrared detector, and they wanted to see if they could create something less bulky than current night-vision goggles. These goggles rely on cryogenic cooling to reduce the amount of excess heat that might muddle the image. To create the sensor, the researchers integrated graphene with tiny silicon-based devices called MEMS. Then, they suspended this chip over an air pocket so that it picks up on incoming heat and eliminates the need for the cooling mechanisms found in other infrared-sensing devices. That signal is then transmitted to another part of the device that creates a visible image. When the researchers tested their sensor, they found that it clearly and successfully picked up the image of a human hand.

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It‘s older, but interesting!

The year is 2050 and super-intelligent robots have emerged as the masters of Earth. Unfortunately, you have no idea of that fact because we are immersed in a computer simulation set decades ago. Everything you see and touch has now been created and programmed by machines that use mankind for their own benefit. This radical theory, demonstrated in numerous books and science fiction films, has been, and is currently regarded by science as possible; Moreover, scientists are taking this theory to a cosmic level and even believe that if only one extraterrestrial civilization in the universe go the technological level to “emulate” an entire “multiverse,” then even our probes and space telescopes, which are out there exploring the universe, belong to that “creepy simulation.”

Continue reading “Researchers suggest that the universe could be a computer simulation: Is it possible?” »

Feeling like the typical four or eight (Extreme Edition) cores in your current Core i7 processor are holding you back? Well, you’re in luck. Intel is going to offer up their very first Core i7 with ten processing cores before the end of next summer.

While it’ll be the first desktop-class CPU with that many cores, it won’t actually be Intel’s first 10-core processor. They’ve been making Xeon chips with at least 10 cores since 2011, and some with as many as 15. They’re aimed primarily at servers and enterprise-class workstations, though. Next year, however, they’ll finally offer up a deca-core processor for the consumer market.

That chip will be the Core i7-6950X, a 10-core beast with Hyper-threading support that allows it to handle 20 independent instructions at any given time. It’s based on Intel’s new 14nm process, down from 22nm on Ivy Bridge and Haswell. The 6950X should be clocked at 3GHz, but it’s not yet known where Turbo Boost will top out.

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In the past couple of years, Google has been trying to improve more and more of its services with artificial intelligence. Google also happens to own a quantum computer — a system capable of performing certain computations faster than classical computers.

It would be reasonable to think that Google would try running AI workloads on the quantum computer it got from startup D-Wave, which is kept at NASA’s Ames Research Center in Mountain View, California, right near Google headquarters.

Google is keen on advancing its capabilities in a type of AI called deep learning, which involves training artificial neural networks on a large supply of data and then getting them to make inferences about new data.

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