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Oct 18, 2016

Google’s ‘DeepMind’ AI platform can now learn without human input

Posted by in category: robotics/AI

Bow to your robot overlords. Google’s parent company, Alphabet, now possesses a smart AI capable of learning without the need for human input.

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Oct 18, 2016

Apple’s new director of AI research will speak at EmTech MIT 2016

Posted by in categories: computing, robotics/AI

Ruslan Salakhutdinov, a deep-learning expert at Carnegie Mellon, is exploring smart ways for computers to learn about the world.

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Oct 18, 2016

Shailesh Prasad Photo

Posted by in category: futurism

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Oct 18, 2016

Samsung’s 10nm node, SoCs now in mass production

Posted by in category: computing

Samsung announced today that it is rolling out 10nm technology for mass manufacturing, with hardware expected in early 2017.

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Oct 18, 2016

Graphene and Quantum Dots Come Together to Create “Hybrid” Tech

Posted by in categories: materials, quantum physics

In Brief:

This new development in photoelectronics makes the technology more cost (and quantum) efficient. This opens ways for graphene to be further integrated in the field of photoelectronics.

EICREA professors Frank Koppens and Gerasimos Konstantatos led researchers in the ICFO in developing a hybrid photodetector that is better-performing in terms of speed, accuracy and range, and operates in the visible spectrum, near infrared (NIR) and short-wave infrared (SWIR), with wavelengths ranging from 400 to 3000 nm.

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Oct 18, 2016

Antimatter and the Sail

Posted by in categories: materials, space travel

Interesting!


An antimatter probe to a nearby star? The idea holds enormous appeal, given the colossal energies obtained when normal matter annihilates in contact with its antimatter equivalent. But as we’ve seen through the years on Centauri Dreams, such energies are all but impossible to engineer. Antimatter production is infinitesimal, the by-product of accelerators designed with a much different agenda. Moreover, antimatter storage is hellishly difficult, so that maintaining large quantities in a stable condition requires multiple breakthroughs.

All of which is why I became interested in the work Gerald Jackson and Steve Howe were doing at Hbar Technologies. Howe, in fact, became a key source when I put together the original book from which this site grew. This was back in 2002–2003, and I was captivated with the idea of what could be called an ‘antimatter sail.’ The idea, now part of a new Kickstarter campaign being launched by Jackson and Howe, is to work with mere milligrams of antimatter, allowing antiprotons to be released from the spacecraft into a uranium-enriched, five-meter sail.

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Oct 17, 2016

A paralyzed man used his mind-controlled robotic hand to shake hands with President Obama at a Pittsburgh tech event

Posted by in categories: biotech/medical, robotics/AI

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Oct 17, 2016

Self-Learning AI: This New Neuro-Inspired Computer Trains Itself

Posted by in categories: computing, robotics/AI

In Brief:

  • Using reservoir computing and backpropagation, researchers were able to push an analog computer past its own boundaries.
  • By combining established technologies with new innovations, we can speed up development with tech having the ability to improve itself.

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Oct 17, 2016

The High-end VR Room of the Future Looks Like This — By Sarah Downey | UploadVR

Posted by in category: virtual reality

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“Let’s start from the ground up. Forget the room scale debate: the VR setup of the future moves with you.”

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Oct 17, 2016

How quantum effects could improve artificial intelligence

Posted by in categories: computing, encryption, quantum physics, robotics/AI, sustainability

(Phys.org)—Over the past few decades, quantum effects have greatly improved many areas of information science, including computing, cryptography, and secure communication. More recently, research has suggested that quantum effects could offer similar advantages for the emerging field of quantum machine learning (a subfield of artificial intelligence), leading to more intelligent machines that learn quickly and efficiently by interacting with their environments.

In a new study published in Physical Review Letters, Vedran Dunjko and coauthors have added to this research, showing that quantum effects can likely offer significant benefits to .

“The progress in machine learning critically relies on processing power,” Dunjko, a physicist at the University of Innsbruck in Austria, told Phys.org. “Moreover, the type of underlying information processing that many aspects of machine learning rely upon is particularly amenable to quantum enhancements. As quantum technologies emerge, quantum machine learning will play an instrumental role in our society—including deepening our understanding of climate change, assisting in the development of new medicine and therapies, and also in settings relying on learning through interaction, which is vital in automated cars and smart factories.”

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