Lifeboat Foundation

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.

(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 machine learning.

“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.”

Continue reading “How quantum effects could improve artificial intelligence” »

With Google Assistant, Google is far ahead of the competition when it comes to artificial intelligence.

Its called Singularity.

This new technology could change what we know about computers.

Everything is about cloud computing these days. In fact, there is such an emphasis on stuffing all your applications into the cloud that we’ve managed to create a situation where now we’re having performance issues. So then the tech world came up with another concept called fog computing which means we take everything out of the cloud and move it “to the edge”. It’s only a matter of time before we decide that edge computing isn’t centralized enough and then start moving everything back up to the cloud. All the while, highly paid data consultants are laughing all the way to the bank. The truth is though that cloud based solutions (also called software-as-a-service or SaaS) are here to stay. In many cases, the technology on offer is so complex and resource intensive that it only works with a centralized model. Quantum computing is a good example of this. So is IBM’s Watson cognitive computing solution. The company we’re going to talk about in this article, Cognitive Scale, is taking IBM Watson and making cognitive computing available to anyone via the cloud.

Founded in 2013, Texas based startup Cognitive Scale took in $25 million in funding just last week from investors that included Intel, Microsoft, and IBM. Probably the most compelling thing about Cognitive Scale is the pedigree of their leadership. The Company Chairman, Manoj Saxena, was responsible for commercializing IBM’s Watson with a $1 billion investment from IBM. He ended up at IBM because a company he founded called Webify was acquired by IBM in 2006. In fact, he founded and sold two venture-backed software companies in just 5 years’ time. The founder and CTO of Cognitive Scale, Matt Sanchez, was the 3rd employee and Chief Architect of Webify and was responsible for founding the R&D arm of IBM Watson called IBM Watson Labs. See how this all fits together?

DeepMind, an artificial intelligence firm that was acquired by Google in 2014 and is now under the Alphabet umbrella, has developed a computer than can refer to its own memory to learn facts and use that knowledge to answer questions.

That’s huge, because it means that future AI could respond to queries from humans without being taught every possible correct answer.

TNW Momentum is our New York technology event for anyone interested in helping their company grow.

Continue reading “DeepMind’s new computer can learn from its own memory” »

A dozen years ago, an auto accident left Nathan Copeland paralyzed, without any feeling in his fingers. Now that feeling is back, thanks to a robotic hand wired up to a brain implant.

“I can feel just about every finger – it’s a really weird sensation,” the 28-year-old Pennsylvanian told doctors a month after his surgery.

Continue reading “Brain implant provides sense of touch with robotic hand – and that’s just the start” »

If there is any organization on the planet that has had a closer view of the coming demise of Moore’s Law, it is the Institute of Electrical and Electronics Engineers (IEEE). Since its inception in the 1960s, the wide range of industry professionals have been able to trace a steady trajectory for semiconductors, but given the limitations ahead, it is time to look to a new path—or several forks, to be more accurate.

This realization about the state of computing for the next decade and beyond has spurred action from a subgroup, led by Georgia Tech professor Tom Conte and superconducting electronics researcher, Elie Track called “Rebooting Computing,” which produces reports based on invite-only deep dives on a wide range of post-Moore’s Law technologies, many of which were cited here this week via Europe’s effort to pinpoint future post-exascale architectures. The Rebooting Computing effort is opening its doors next week for a wider-reaching, open forum in San Diego to bring together new ideas in novel architectures and modes of computing as well as on the applications and algorithm development fronts.

According to co-chair of the Rebooting Computing effort, Elie Track, a former Yale physicist who has turned his superconducting circuits work toward high efficiency solar cells in his role at startup Nvizix, Moore’s Law is unquestionably dead. “There is no known technology that can keep packing more density and features into a given space and further, the real issue is power dissipation. We just cannot keep reducing things further; a fresh perspective is needed.” The problem with gaining that view, however, is that for now it means taking a broad, sweeping look across many emerging areas; from quantum and neuromorphic devices, approximate computing, and a wide range of other technologies. “It might seem frustrating that this is general, but there is no clear way forward yet. What we all agree on is that we need exponential growth in computing engines.”

Continue reading “IEEE Reboots, Scans for Future Architectures” »

My guess is there is some QC help in this picture.

Artificial neural networks — systems patterned after the arrangement and operation of neurons in the human brain — excel at tasks that require pattern recognition, but are woefully limited when it comes to carrying out instructions that require basic logic and reasoning. This is a problem for scientists working toward the creation of Artificial Intelligence (AI) systems capable of performing complex tasks with minimal human supervision.

Continue reading “Google Creates New, Smarter AI” »