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Submission Summary: 0 pending, 3 declined, 6 accepted (9 total, 66.67% accepted)

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Submission + - AI Is Transforming Google Search. The Rest of the Web Is Next

catchblue22 writes: Yesterday, the 46-year-old Google veteran who oversees its search engine, Amit Singhal, announced his retirement. For much of his tenure, Singhai believed that Google’s search engine should be driven by algorithms based on definite rules that automatically generate a response to each query. His replacement, John Giannandrea is a proponent of using deep learning instead of hard coded algorithms; the "RankBrain" project uses deep neural networks to produce search results. Some at engineers at Google have shown skepticism of using neural networks for search: "...it’s hard to explain and ascertain why a particular search result ranks more highly than another result for a given query...It’s difficult to directly tweak a machine learning-based system to boost the importance of certain signals over others.”

Giannandrea's appointment shows that deep learning has arrived at Google Search. "By building learning systems, we don’t have to write these rules anymore...Increasingly, we’re discovering that if we can learn things rather than writing code, we can scale these things much better.”

Submission + - How brain architecture leads to abstract thought

catchblue22 writes: UMass Amherst scientists have used data analysis of fMRI data to link brain architecture with consciousness and abstract thought.

"We momentarily thought our research failed when we saw that each cognitive behavior showed activity through many network depths. Then we realized that cognition is far richer, it wasn't the simple hierarchy that everyone was looking for. So, we developed our geometrical 'slope' algorithm."

"With a slope identifier, behaviors could now be ordered by their relative depth activity with no human intervention or bias," she adds. They ranked slopes for all cognitive behaviors from the fMRI databases from negative to positive and found that they ordered from more tangible to highly abstract."

"Deep learning is a computational system employing a multi-layered neural net...the brain's processing dynamic is far richer and less constrained because it has recurrent interconnection, sometimes called feedback loops" Her lab is now creating a "massively recurrent deep learning network," she says, for a more brain-like and superior learning AI.

Submission + - New test uses a single drop of blood to reveal entire history of viral infection

catchblue22 writes: A cheap and rapid new test gives doctors a list of the viruses that have infected, or continue to infect, patients even when they have not caused any obvious symptoms. The technology means that GPs could screen patients for all of the viruses capable of infecting people. It could transform the detection of serious infections such as hepatitis C and HIV, which people can carry for years without knowing. The $25 (£16) test draws on advances in synthetic biology and rapid gene sequencing to analyse more than 1000 strains of human viruses in one pass.

Submission + - New Chips Could Bring Deep Learning Algorithms to Your Smartphone

catchblue22 writes: At the Embedded Vision Summit on Tuesday, a company called Synopsys, showed off a new image-processor core tailored for deep learning. It is expected to be added to chips that power smartphones, cameras, and cars.

Synopsys showed a demo in which the new design recognized speed-limit signs in footage from a car. The company also presented results from using the chip to run a deep-learning network trained to recognize faces. A spokesperson said that it didn’t hit the accuracy levels of the best research results, which have been achieved on powerful computers, but it came pretty close. “For applications like video surveillance it performs very well,” he said. Being able to use deep learning on mobile chips will be vital to helping robots navigate and interact with the world, he said, and to efforts to develop autonomous cars.

Submission + - Baidu's Artificial-Intelligence Supercomputer Beats Google at Image Recognition 1

catchblue22 writes: Chinese search giant Baidu says it has invented a powerful supercomputer that brings new muscle to an artificial-intelligence technique giving software more power to understand speech, images, and written language.

The new computer, called Minwa and located in Beijing, has 72 powerful processors and 144 graphics processors, known as GPUs. Late Monday, Baidu released a paper claiming that the computer had been used to train machine-learning software that set a new record for recognizing images, beating a previous mark set by Google.

Submission + - Inexpensive Electric Cars May Arrive Sooner Than You Think

catchblue22 writes: According to an article in MIT Technology Review, a new peer reviewed study suggests that battery-powered vehicles are close to being cost-effective for most people:

Electric cars may seem like a niche product that only wealthy people can afford, but a new analysis suggests that they may be close to competing with or even beating gas cars on cost. The authors of the new study concluded that the battery packs used by market-leading EV manufacturers like Tesla and Nissan cost as little as $300 per kilowatt-hour of energy in 2014. That’s lower than the most optimistic published projections for 2015, and even below the average published projection for 2020. The authors found that batteries appear on track to reach $230 per kilowatt-hour by 2018. The authors found that batteries appear on track to reach $230 per kilowatt-hour by 2018. If that’s true, it would push EVs across a meaningful threshold.

Submission + - Will ultrasound-on-a-chip make medical imaging so cheap that anyone can do it?

catchblue22 writes: MIT Technology Review has an article describing a potentially groundbreaking invention:

A scanner the size of an iPhone that you could hold up to a person’s chest and see a vivid, moving, 3-D image of what’s inside is being developed by entrepreneur Jonathan Rothberg.

Rothberg says he has raised $100 million to create a medical imaging device that’s nearly “as cheap as a stethoscope” and will “make doctors 100 times as effective.” The technology, which according to patent documents relies on a new kind of ultrasound chip, could eventually lead to new ways to destroy cancer cells with heat, or deliver information to brain cells.

Submission + - Microsoft's Quantum Mechanics

catchblue22 writes: MIT Technology Review has an excellent article summarizing the current state of quantum computing. It focuses on the efforts of Microsoft and Alcatel-Lucent's Bell Labs to build stable qubits over the past few years.

In 2012, physicists in the Netherlands announced a discovery in particle physics that started chatter about a Nobel Prize. Inside a tiny rod of semiconductor crystal chilled cooler than outer space, they had caught the first glimpse of a strange particle called the Majorana fermion, finally confirming a prediction made in 1937. It was an advance seemingly unrelated to the challenges of selling office productivity software or competing with Amazon in cloud computing, but Craig Mundie, then heading Microsoft’s technology and research strategy, was delighted. The abstruse discovery—partly underwritten by Microsoft—was crucial to a project at the company aimed at making it possible to build immensely powerful computers that crunch data using quantum physics. “It was a pivotal moment,” says Mundie. “This research was guiding us toward a way of realizing one of these systems.”

Submission + - The Gods Strike Back: Wall Street's Risky Hubris (economist.com)

catchblue22 writes: The Economist has an interesting essay that gives some perspective on our economic situation. From the article,

THE revolutionary idea that defines the boundary between modern times and the past is the mastery of risk: the notion that the future is more than a whim of the gods and that men and women are not passive before nature. So wrote Peter Bernstein in his seminal history of risk,"Against the Gods".

Wall Street quants have claimed over the past decade that they had the ability to quantify financial risk down to several decimal places. The Great Recession has shown their claims to be absurd. Wall Street's supposed mastery of risk allowed financial institutions to package risky loans as derivatives that ostensibly had definite and identifiable risk profiles. Our recent financial collapse shows the limits of our beautiful and elegant mathematical models of the economy.

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