Lifeboat Foundation

WASHINGTON, D.C.-Today, U.S. Secretary of Energy Rick Perry announced the establishment of the DOE Artificial Intelligence and Technology Office (AITO). The Secretary has established the office to serve as the coordinating hub for the work being done across the DOE enterprise in Artificial Intelligence. This action has been taken as part of the President’s call for a national AI strategy to ensure AI technologies are developed to positively impact the lives of Americans.

DOE-fueled AI is already being used to strengthen our national security and cybersecurity, improve grid resilience, increase environmental sustainability, enable smarter cities, improve water resource management, as well as speed the discovery of new materials and compounds, and further the understanding, prediction, and treatment of disease. DOE’s National Labs are home to four of the top ten fastest supercomputers in the world, and we’re currently building three next-generation, exascale machines, which will be even faster and more AI-capable computers.

“The world is in the midst of the Golden Age of AI, and DOE’s world class scientific and computing capabilities will be critical to securing America’s dominance in this field,” said Secretary Perry. “This new office housed within the Department of Energy will concentrate our existing efforts while also facilitating partnerships and access to federal data, models and high performance computing resources for America’s AI researchers. Its mission will be to elevate, accelerate and expand DOE’s transformative work to accelerate America’s progress in AI for years to come.”

AN ASTEROID which came crashing into Earth and NASA had no idea it was coming reiterates the need to keep a closer eye on the sky in case a massive space rock comes hurtling towards our planet.

Chandrayaan 2 mission officials are trying to figure out why its rover-carrying lander went silent moments before it was to reach the moon’s surface.

At the Battery Show North America 2019, AKASOL will present a wide portfolio of new solutions to the battery community. The company’s flagship product is the new high-energy lithium-ion battery AKASystemAKM CYC, which has entered serial development for the battery module and system.

The AKAModule CYC achieves energy density of 221 Wh/kg with liquid-cooled battery modules that are scalable and can be integrated in various system designs at the pack level.

Aristo has passed an American eighth grade science test. If you are told Aristo is an earnest kid who loves to read all he can about Faraday and plays the drums you will say so what, big deal.

Aristo, though, is an artificial intelligence program and scientists would like the world to know this is a big deal, as “a benchmark in AI development,” as Melissa Locker called it in Fast Company.

We mean, just think about it. Cade Metz, in The New York Times, has thought about it. “Four years ago, more than 700 computer scientists competed in a contest to build artificial intelligence that could pass an eighth-grade science test. There was $80,000 in prize money on the line. They all flunked. Even the most sophisticated system couldn’t do better than 60% on the test. AI couldn’t match the language and logic skills that students are expected to have when they enter high school.”

The blockchain is public, yet a Bitcoin wallet can be created anonymously. So are Bitcoin transactions anonymous? Not at all…

Each transaction into and out of a wallet is a bread crumb. Following the trail is trivial. Every day, an army of armchair sleuths help the FBI. That’s how Silk Road was brought down.

The problem is that some of that money eventually interacts with the real world (a dentist is paid, a package shipped or a candy is purchased at a gas station). Even if the real-world transaction is 4 hops before or after hitting the “anonymous” wallet, it creates a forensic focal point. Next comes a tax man, an ex-spouse or a goon.

The first article linked below addresses the state of tumblers (aka “mixers”). They anonymize an open network by obfuscating the trail of bread crumbs.

Continue reading “Can Bitcoin Transactions be Made Private?” »

Quantum computing promises to revolutionize the ways in which scientists can process and manipulate information. The physical and material underpinnings for quantum technologies are still being explored, and researchers continue to look for new ways in which information can be manipulated and exchanged at the quantum level.

Researchers around the world are constantly looking for ways to enhance or transcend the capabilities of electronic devices, which seem to be reaching their theoretical limits. Undoubtedly, one of the most important advantages of electronic technology is its speed, which, albeit high, can still be surpassed by orders of magnitude through other approaches that are not yet commercially available.

A possible way of surpassing traditional electronics is through the use of antiferromagnetic (AFM) materials. The electrons of AFM materials spontaneously align themselves in such a way that the overall magnetization of the material is practically zero. In fact, the order of an AFM material can be quantified in what is known as the ‘order parameter.’ Recent studies have even shown that the AFM order parameter can be ‘switched’ (that is, changed from one known value to another, really fast) using light or electric currents, which means that AFM materials could become the building blocks of future electronic devices.

However, the dynamics of the order-switching process are not understood because it is very difficult to measure the changes in the AFM order parameter in real time with high resolution. Current approaches rely on measuring only certain phenomena during AFM order switching and trying to obtain the full picture from there, which has proven to be unreliable for understanding other more intricate phenomena in detail. Therefore, a research team lead by Prof. Takuya Satoh from Tokyo Tech and researchers from ETH Zurich, developed a method for thoroughly measuring the changes in the AFM order of an YMnO₃ crystal induced through optical excitation (that is, using a laser).

NEW YORK (Reuters Health) — A convolutional neural network trained through deep learning can accurately predict a person’s age and gender using only standard 12-lead ECG signals, researchers report.

“Our standard diagnostic tools may have far more information behind them than we’ve come to expect throughout standard approaches to diagnostic interpretation,” said Dr. Suraj Kapa from Mayo Clinic College of Medicine, in Rochester, Minnesota.

“Between this study and other prior studies showing that we can predict likelihood of having atrial fibrillation from a normal sinus ECG or the presence of a low ejection fraction, AI-enabled ECG analysis may offer new, rapid, and cost-effective insights into human health well beyond what we could have anticipated in the last two centuries since the ECG was first developed,” he told Reuters Health by email.