Lifeboat Foundation

Enterprises are seeking ways to modernize their operations. Emerging as a critical element in the digital transformation tech stack are cloud databases.

Having the right cloud database can help address the range of applications companies depend on and also those that they build, from the cloud to mobile and edge. For companies aspiring to provide better and more personalized customer experiences, implementing a DBaaS (database as a service) should be a key consideration.

Grid operators should embrace renewable integration, microgrids, climate resilience, last-mile challenges, and data over powerlines.

The future of neural network computing could be a little soggier than we were expecting.

A team of physicists has successfully developed an ionic circuit – a processor based on the movements of charged atoms and molecules in an aqueous solution, rather than electrons in a solid semiconductor.

Since this is closer to the way the brain transports information, they say, their device could be the next step forward in brain-like computing.

A joint letter from Sweden and Denmark to the UN Security Council said “several hundred kilos” of explosives caused the damage in a “deliberate” act.

Our position in the Milky Way makes it difficult for Earth to be detected using photometric microlensing.

Physicists at the Princeton Plasma Physics Laboratory (PPPL) have proposed that the formation of “hills and valleys” in magnetic field lines could be the source of sudden collapses of heat ahead of disruptions that can damage doughnut-shaped tokamak fusion facilities. Their discovery could help overcome a critical challenge facing such facilities.

The research, published in a Physics of Plasmas paper in July, traced the collapse to the 3D disordering of the strong magnetic fields used to contain the hot, charged plasma gas. “We proposed a novel way to understand the [disordered] field lines, which was usually ignored or poorly modelled in the previous studies,” said Min-Gu Yoo, a post-doctoral researcher at PPPL and lead author of the paper.

Fusion is the process that powers the Sun and stars as hydrogen atoms fuse together to form helium, and matter is converted into energy. Capturing the process on Earth could create a clean, carbon-free and almost inexhaustible source of power to generate electricity, but comes with many engineering challenges: in stars, massive gravitational forces create the right conditions for fusion. On Earth those conditions are much harder to achieve.

A prototype computer built using a magnetic material called a skyrmion has been programmed to recognise handwritten digits. The approach could be particularly energy-efficient.

Juncal Arbelaiz Mugica is a native of Spain, where octopus is a common menu item. However, Arbelaiz appreciates octopus and similar creatures in a different way, with her research into soft-robotics theory.

More than half of an octopus’ nerves are distributed through its eight arms, each of which has some degree of autonomy. This distributed sensing and information processing system intrigued Arbelaiz, who is researching how to design decentralized intelligence for human-made systems with embedded sensing and computation. At MIT, Arbelaiz is an applied math student who is working on the fundamentals of optimal distributed control and estimation in the final weeks before completing her PhD this fall.

Continue reading “Wiggling toward bio-inspired machine intelligence” »

The past may be a fixed and immutable point, but with the help of machine learning, the future can at times be more easily divined.

Using a new type of machine learning method called next generation reservoir computing, researchers at The Ohio State University have recently found a new way to predict the behavior of spatiotemporal chaotic systems—such as changes in Earth’s weather—that are particularly complex for scientists to forecast.

The study, published today in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science, utilizes a new and highly efficient algorithm that, when combined with next generation reservoir computing, can learn spatiotemporal chaotic systems in a fraction of the time of other machine learning algorithms.