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Category: computing – Page 820

New method for creating smaller switches for QC identified and making smaller and more efficient QC systems possible.

Edmonton nanotechnology researchers working with atom-sized materials have made a breakthrough that could lead to smaller, ultraefficient computers.

The team, led by Robert Wolkow, together with collaborators at the Max Planck Institute in Hamburg, have developed a way to create atomic switches for electricity nearly 100 times smaller than the smallest switches, or transistors, on the market today. Their findings appeared in the Oct. 26 edition of the scientific publication Nature Communications.

“What we’re showing in this new paper is one part in a bigger scheme … that allows us to make ultralow power consuming electronic devices,” said Wolkow, a physics professor at the University of Alberta and the principal research officer at Edmonton’s National Institute for Nanotechnology. He’s also chief technology officer at spinoff company Quantum Silicon Inc.

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Talk about downsizing – researchers at the University of California in Santa Barbara have developed a design for a 50 nanometer square computer, the university announced Oct. 27.

For now, that size is entirely theoretical. It could be managed by a novel kind of logic that enables the computer to process data inside a three-dimensional structure.

“In a regular computer, data processing and memory storage are separated, which slows down computation. Processing data directly inside a three-dimensional memory structure would allow more data to be stored and processed much faster,” said Gina Adam, a postdoctoral researcher and the lead author of the paper.

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In the TU Wien design, quantum cascade heterostructures are arrayed within concentric ring-shaped waveguides (top; diameter of outer ring is 400 microns), and can act as both sources and detectors of light on the same chip. In the specific setup tested by the lab (bottom), one of the ring structures (on the right), acting in QCL mode, sends its light through a chamber containing the gas to be analyzed. The beam is reflected by a mirror (on the left) and sent back through the chamber, to be picked up by the other ring structure, acting in detector mode. [Image: TU Wien]

Quantum cascade lasers (QCL) excel as mid-infrared light sources, a characteristic that has made them a linchpin in many environmental and industrial gas-sensing applications. But though QCLs themselves can be quite small, actually setting up a sensor system requires other elements beyond the laser, which can make it tough to design compact devices ready for field use.

A team of scientists from the Vienna University of Technology (TU Wien), Austria, now offers a concept that the group believes could make designing handheld QCL-based sensors a lot easier. The key: a clever scheme that combines the laser and the detector on a single chip less than half a millimeter across (ACS Photon., doi: 10.1021/acsphotonics.6b00603).

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Abstract: In 1959 renowned physicist Richard Feynman, in his talk “Plenty of Room at the Bottom,” spoke of a future in which tiny machines could perform huge feats. Like many forward-looking concepts, his molecule and atom-sized world remained for years in the realm of science fiction.

And then, scientists and other creative thinkers began to realize Feynman’s nanotechnological visions.

In the spirit of Feynman’s insight, and in response to the challenges he issued as a way to inspire scientific and engineering creativity, electrical and computer engineers at UC Santa Barbara have developed a design for a functional nanoscale computing device. The concept involves a dense, three-dimensional circuit operating on an unconventional type of logic that could, theoretically, be packed into a block no bigger than 50 nanometers on any side.

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15 years ago, Ray Kurzweil published one of the most significant essays in the history of futurism: “The Law of Accelerating Returns.” This piece showcased the immense power of exponential technology versus linear technology and became a pivotal concept for anyone trying to anticipate what the future held.

The essay predicted advances in business and technology with eerie precision, including how exponential growth would ripple through any technology that became an information technology, such as computing, biotechnology, or energy.

[ Go here to learn more about the law of accelerating returns ]

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I argued in my 2015 paper “Why it matters that you realize you’re in a Computer Simulation” that if our universe is indeed a computer simulation, then that particular discovery should be commonplace among the intelligent lifeforms throughout the universe. The simple calculus of it all being (a) if intelligence is in part equivalent to detecting the environment (b) the environment is a computer simulation © eventually nearly all intelligent lifeforms should discover that their environment is a computer simulation. I called this the Savvy Inevitability. In simple terms, if we’re really in a Matrix, we’re supposed to eventually figure that out.

Silicon Valley, tech culture, and most nerds the world over are familiar with the real world version of the question are we living in a Matrix? The paper that’s likely most frequently cited is Nick Bostrom’s Are you living in a Computer Simulation? Whether or not everyone agrees about certain simulation ideas, everyone does seem to have an opinion about them.

Recently, the Internet heated up over Elon Musk’s comments at a Vox event on hot tub musings of the simulation hypothesis. Even Bank of America published an analysis of the simulation hypothesis, and, according to Tad Friend in an October 10, 2016 article published in New Yorker, “two tech billionaires have gone so far as to secretly engage scientists to work on breaking us out of the simulation.”

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Story just in time for Halloween.

The prospect of artificial intelligence is scary enough for some, but Manuel Cebrian Ramos at CSIRO’s Data61 is teaching machines how to terrify humans on purpose.

Dr Cebrian and his colleagues Pinar Yanardag and Iyad Rahwan at the Massachusetts Institute of Technology have developed the Nightmare Machine.

This is an artificial intelligence algorithm that is teaching a new generation of computers not only what terrifies human beings, but also how to create new images to scare us.

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In Brief:

  • Researchers have demonstrated how electrons travel on different elliptical paths by using a quantum crystal kept at low temperatures.
  • The discovery could lead to a new class of microchips far beyond the capabilities of today’s silicon chips.

New developments from Princeton University and the University of Texas-Austin have revealed odd behavior in electrons that could lay the foundation for a new generation of faster microchips, according to a study published in Science.

Electronic systems react in different ways depending on surfaces they’re exposed to, and the study demonstrated when electrons on crystal bismuth are kept at very low temperatures, they spontaneously travel in identical elliptical paths.

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Anyone who does not have QC as part of their 5+Yr Roadmap for IT are truly exposing their company as well as shareholders and customers. China, Russia, Cartels, DarkNet, etc. will use the technology to extort victims, destroy companies, economies, and complete countries where folks have not planned, budget, skilled up, and prep for full replacement of their infrastructure and Net access. Not to mention companies who have this infrastructure will provide better services/ CCE to svc. consumers.

In a recent article, we highlighted a smart beta ETF called the “Sprott BUZZ Social Media Insights ETF” that uses artificial intelligence (AI) to select and weight stocks. If we stop and think about that for a moment, that’s a pretty cool use of AI that seems well ahead of its time. Now we’re not saying that you should go out and buy this smart beta ETF right away. It uses social media data. We know that on social media, everyone’s an expert and many of the opinions that are stated are just that, opinions. However some of the signals may be legitimate. Someone who just bought Apple is likely to go on telling everyone how bullish they are on Apple shares. Bullish behavior is often accompanied by bullish rhetoric. And maybe that’s exactly the point, but the extent to which we’re actually using artificial intelligence here is not that meaningful. Simple scripting tools go out and scrape all this public data and then we use natural language processing (NLP) algorithms to determine if the data artifacts have a positive or negative sentiment. That’s not that intelligent, is it? This made us start to think about what it would take to create a truly “intelligent” smart beta ETF.

What is Smart Beta?

We have talked before about how people that work in finance love to obfuscate the simplicity of what they do with obscure acronyms and terminology. Complex nomenclature is suited for sophisticated scientific domains like synthetic biology or quantum computing but such language is hardly merited for use in the world of finance. We told you before what beta is. Smart beta is just another way of saying “rules based investing” which has in fact been around for centuries, but of course we act like it’s new and start publishing all kinds of research papers on it. In fact, a poll offered up by S&P Capital IQ shows that even 1 out of 4 finance professionals recognizes the term “smart beta” to be little more than a marketing gimmick:

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