Lifeboat Foundation

The job advertisement was highly specific: applicants had to be passionate about computer games and live in the UK. Oh, and they also had to be amputees who were interested in wearing a futuristic prosthetic limb.

James Young knew straight away he had a better shot than most. After losing an arm and a leg in a rail accident in 2012, the 25-year-old Londoner had taught himself to use a video-game controller with one hand and his teeth. “How many amputee gamers can there be?” he asked himself.

In the end, more than 60 people replied to the ad, which was looking for a games-mad amputee to become the recipient of a bespoke high-tech prosthetic arm inspired by Metal Gear Solid, one of the world’s best-selling computer games. Designed and built by a team of 10 experts led by London-based prosthetic sculptor Sophie de Oliveira Barata, the £60,000 carbon-fibre limb is part art project, part engineering marvel.

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The trolls have gone retro.

This is how Aurora are produced…

Sharing is appreciated!

Google Brain software engineer Quoc Le shares his belief that artificial intelligence and deep learning will shape the future of the world.

The “unbeatable” bot still has much to learn from human players.

Help start a revolution in heathcare!

Sign onto our new letter of support and let your Senator know — the time is NOW to fund the first ever FDA approved research to target ALL the diseases of aging at once.

http://tame.healthspanpolicy.org/

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Whether in the brain or in code, neural networks are shaping up to be one of the most critical areas of research in both neuroscience and computer science. An increasing amount of attention, funding, and development has been pushed toward technologies that mimic the brain in both hardware and software to create more efficient, high performance systems capable of advanced, fast learning.

One aspect of all the efforts toward more scalable, efficient, and practical neural networks and deep learning frameworks we have been tracking here at The Next Platform is how such systems might be implemented in research and enterprise over the next ten years. One of the missing elements, at least based on the conversations that make their way into various pieces here, for such eventual end users is reducing the complexity of the training process for neural networks to make them more practically useful–and without all of the computational overhead and specialized systems training requires now. Crucial then, is a whittling down of how neural networks are trained and implemented. And not surprisingly, the key answers lie in the brain, and specifically, functions in the brain and how it “trains” its own network that are still not completely understood, even by top neuroscientists.

In many senses, neural networks, cognitive hardware and software, and advances in new chip architectures are shaping up to be the next important platform. But there are still some fundamental gaps in knowledge about our own brains versus what has been developed in software to mimic them that are holding research at bay. Accordingly, the Intelligence Advanced Research Projects Activity (IARPA) in the U.S. is getting behind an effort spearheaded by Tai Sing Lee, a computer science professor at Carnegie Mellon University’s Center for the Neural Basis of Cognition, and researchers at Johns Hopkins University, among others, to make new connections between the brain’s neural function and how those same processes might map to neural networks and other computational frameworks. The project called the Machine Intelligence from Cortical Networks (MICRONS).

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Russia’s NextGen Robots are coming.

Russian military institutions are working on a program of robotization of the army that will introduce combat robots able to act independently on the battlefield, deputy head of the Defense Ministry Pavel Popov said in an interview with the Moskovsky Komsomolets newspaper.

Special military units of robots will be operated by a united control system, Popov said, adding that many robotic and pilotless vehicles are already employed in the Russian military.

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Human and machine forecasting.

The agency’s Hybrid Forecasting Competition is intended to improve how humans and computers interact on geopolitical and geoeconomic analysis.