Lifeboat Foundation

Encoding information into light, and transmitting it through optical fibers lies at the core of optical communications. With an incredibly low loss of 0.2 dB/km, optical fibers made from silica have laid the foundations of today’s global telecommunication networks and our information society.

Such ultralow optical loss is equally essential for integrated photonics, which enable the synthesis, processing and detection of optical signals using on-chip waveguides. Today, a number of innovative technologies are based on integrated photonics, including semiconductor lasers, modulators, and photodetectors, and are used extensively in data centers, communications, sensing and computing.

Integrated photonic chips are usually made from silicon that is abundant and has good optical properties. But silicon can’t do everything we need in integrated photonics, so new material platforms have emerged. One of these is silicon nitride (Si3N4), whose exceptionally low optical loss (orders of magnitude lower than that of silicon), has made it the material of choice for applications for which low loss is critical, such as narrow-linewidth lasers, photonic delay lines, and nonlinear photonics.

Spin waves could unlock the next generation of computer technology, a new component allows physicists to control them.

Researchers at Aalto University have developed a new device for spintronics. The results have been published in the journal Nature Communications, and mark a step towards the goal of using spintronics to make computer chips and devices for data processing and communication technology that are small and powerful.

Traditional electronics uses electrical charge to carry out computations that power most of our day-to-day technology. However, engineers are unable to make electronics do calculations faster, as moving charge creates heat, and we’re at the limits of how small and fast chips can get before overheating. Because electronics can’t be made smaller, there are concerns that computers won’t be able to get more powerful and cheaper at the same rate they have been for the past 7 decades. This is where spintronics comes in.

Nobel laureate in physics Richard Feynman once described turbulence as “the most important unsolved problem of classical physics.”

Understanding turbulence in classical fluids like water and air is difficult partly because of the challenge in identifying the vortices swirling within those fluids. Locating vortex tubes and tracking their motion could greatly simplify the modeling of turbulence.

But that challenge is easier in quantum fluids, which exist at low enough temperatures that quantum mechanics — which deals with physics on the scale of atoms or subatomic particles — govern their behavior.

Russian officials stated the Russian Army would be creating its first unit with strike robots, which will operate five Uran-9 tanks or 20 combat vehicles.

March 28, 2021

While it is time to move ahead with advanced robotics in war, governments are yet to answer ethical questions and biased disadvantages that robotic warfare leverages.

January 25, 2021

CAMBRIDGE, England, Jan. 25, 2021 — Riverlane, a quantum software company, today announces that it has raised $20m in Series A funding to build Deltaflow, its operating system for quantum computers. Over the past year, Riverlane has signed up 20% of the world’s quantum hardware manufacturers to use Deltaflow and will use the funding to expand internationally to the US, Europe and beyond.

The round was led by European technology venture capital fund Draper Esprit, and supported by existing investors, Cambridge Innovation Capital, Amadeus Capital Partners, and the University of Cambridge.

Continue reading “Quantum Computing Software Specialist Riverlane Secures $20M in Series A Funding” »

The speed at which new autonomous weapons are being created and improved is impressive but also staggering.

By Kris Osborn

Drone fleets, robotic vehicles, and multi-domain manned-unmanned connectivity are changing the future of warfare.

https://youtube.com/watch?v=eNOGqNCbcV8

Around a century ago when film stocks and photographs were first coming to light, they faced a number of challenges in capturing the essence of an image. In addition to the black and white limitation, photography and film methods also struggled to capture other various elements of the color spectrum, rendering many images of famous figures appearing differently than they may have actually looked.

Now, a new AI imaging technique uses color to restyle old photographs in a way that could almost pass for modern day photographs. This colorization method mitigates the main obstacles of cameras and lenses from the olden days—namely, the orthochromatic nature of those tools, meaning that the photo capture device in question incorporated all detected light into the image without discrimination. The inclusion of all of this light resulted in photos that appeared grainy and noisy, leading to renowned figures such as U.S. president Abraham Lincoln looking far older and wrinklier than he actually was.

Continue reading “New photo colorizing technique uses skin reaction to light for life-like results” »

Whatever business a company may be in, software plays an increasingly vital role, from managing inventory to interfacing with customers. Software developers, as a result, are in greater demand than ever, and that’s driving the push to automate some of the easier tasks that take up their time.

Productivity tools like Eclipse and Visual Studio suggest snippets of code that developers can easily drop into their work as they write. These automated features are powered by sophisticated language models that have learned to read and write computer code after absorbing thousands of examples. But like other deep learning models trained on big datasets without explicit instructions, language models designed for code-processing have baked-in vulnerabilities.

“Unless you’re really careful, a hacker can subtly manipulate inputs to these models to make them predict anything,” says Shashank Srikant, a graduate student in MIT’s Department of Electrical Engineering and Computer Science. “We’re trying to study and prevent that.”