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Last summer, the National Security Commission on Artificial Intelligence asked to hear original, creative ideas about how the United States would maintain global leadership in a future enabled by artificial intelligence. RAND researchers stepped up to the challenge.

“Send us your ideas!” That was the open call for submissions about emerging technology’s role in global order put out last summer by the National Security Commission on Artificial Intelligence (NSCAI). RAND researchers stepped up to the challenge, and a wide range of ideas were submitted. Ten essays were ultimately accepted for publication.

The NSCAI, co-chaired by Eric Schmidt, the former chief executive of Alphabet (Google’s parent company), and Robert Work, the former deputy secretary of defense, is a congressionally mandated, independent federal commission set up last year “to consider the methods and means necessary to advance the development of artificial intelligence, machine learning, and associated technologies by the United States to comprehensively address the national security and defense needs of the United States.”

The commission’s ultimate role is to elevate awareness and to inform better legislation. As part of its mission, the commission is tasked with helping the Department of Defense better understand and prepare for a world where AI might impact national security in unexpected ways.

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Rice University computer scientists have overcome a major obstacle in the burgeoning artificial intelligence industry by showing it is possible to speed up deep learning technology without specialized acceleration hardware like graphics processing units (GPUs).

Computer scientists from Rice, supported by collaborators from Intel, will present their results today at the Austin Convention Center as a part of the machine learning systems conference MLSys.

Many companies are investing heavily in GPUs and other specialized hardware to implement deep learning, a powerful form of artificial intelligence that’s behind digital assistants like Alexa and Siri, facial recognition, product recommendation systems and other technologies. For example, Nvidia, the maker of the industry’s gold-standard Tesla V100 Tensor Core GPUs, recently reported a 41% increase in its fourth quarter revenues compared with the previous year.

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If it works, they would be able to input quantum information into one “black hole” circuit, which would scramble, then consume it. After a little while, that information would pop out of the second circuit, already unscrambled and decrypted. That sets it apart from existing quantum teleportation techniques, Quanta reports, as transmitted information emerges still fully scrambled and then needs to be decrypted, making the process take longer and be less accurate as an error-prone quantum computer tries to recreate the original message.

While the idea of entangled black holes and wormholes conjures sci-fi notions of intrepid explorers warping throughout the cosmos, that’s not quite what’s happening here.

Rather, it’s an evocative way to improve quantum computing technology. Recreating and entangling the bizarre properties of black holes, University of California, Berkely researcher Norman Yao told Quanta, would “allow teleportation on the fastest possible timescale.”

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A new discovery could be a clue for us to see if life could emerge elsewhere in the Solar System. Using a new analysis technique, scientists think they have found an extraterrestrial protein, tucked inside a meteorite that fell to Earth 30 years ago.

If their results can be replicated, it will be the first protein ever identified that didn’t originate here on Earth.

“This paper characterises the first protein to be discovered in a meteorite,” the researchers wrote in a paper uploaded to preprint server arXiv. Their work is yet to be peer reviewed, but the implications of this finding are noteworthy.

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