Lifeboat Foundation

NASA and the Defense Advanced Research Projects Agency (DARPA) have signed an interagency agreement to collaborate on a satellite servicing demonstration in geosynchronous Earth orbit, where hundreds of satellites provide communications, meteorological, national security, and other vital functions.

Under this agreement, NASA will provide subject matter expertise to DARPA’s Robotic Servicing of Geosynchronous Satellites (RSGS) program to help complete the technology development, integration, testing, and demonstration. The RSGS servicing spacecraft will advance in-orbit satellite inspection, repair, and upgrade capabilities.

It reaffirms Tesla’s goal to launch the company’s self-driving technology in two new markets early next year.

About 63% of the world population access the internet [Source: Statista] and a majority of them experience the internet through webpages. As such, the general population refers to the internet and the web pages interchangeably. Of course, those in the technology arena do know the difference but may or may not remember when and where the world wide web (WWW) was invented. Without its invention, the internet experience of today will not be the same.

100% of all living creatures experience something automatically and that is their “mass”, interchangeably and inaccurately referred to as “weight” by the general population. Of course, those who remember their physics know the difference. While material mass is taken for granted in general physics, there is a field of physics that tries to explain what gives materials their mass. The existence of the mass-giving field was confirmed when the Higgs boson particle was discovered.

The organization that is behind both the WWW invention and the Higgs boson discovery and many other remarkable inventions is CERN. The World Wide Web was invented in 1989 by Tim Berners-Lee while working at CERN. The existence of the mass-giving field was confirmed in 2012, when the Higgs boson particle was discovered at CERN.

Researchers use AI and models to improve EV battery safety:

One of the electric vehicles’ most critical safety concerns is keeping their batteries cool, as temperature spikes can lead to dangerous consequences.

New research led by a University of Arizona doctoral student proposes a way to predict and prevent temperature spikes in the lithium-ion batteries commonly used to power such vehicles.

Continue reading “New machine learning model developed to prevent EV battery fires” »

Butterflies can see more of the world than humans, including more colors and the field oscillation direction, or polarization, of light. This special ability enables them to navigate with precision, forage for food and communicate with one another. Other species, like the mantis shrimp, can sense an even wider spectrum of light, as well as the circular polarization, or spinning states, of light waves. They use this capability to signal a “love code,” which helps them find and be discovered by mates.

Inspired by these abilities in the animal kingdom, a team of researchers at the Penn State College of Engineering has developed an ultrathin optical element known as a metasurface, which can attach to a conventional camera and encode the spectral and polarization data of images captured in a snapshot or video through tiny, antenna-like nanostructures that tailor light properties. A machine learning framework, also developed by the team, then decodes this multi-dimensional visual information in real-time on a standard laptop.

The researchers have published their work in Science Advances.

Scientists in China have developed a tensor processing unit (TPU) that uses carbon-based transistors instead of silicon – and they say it’s extremely energy efficient.

Organoid Intelligence is the fusion of stem cell-derived mini-organs and AI. It promises more human-like AI but also ethical questions.

The new AI system can estimate cosmological parameters with stunning precision, and it could help astronomers unpick one of the thorniest problems in the field.

A clever use of machine learning guides researchers to a missing term that’s needed to accurately describe the dynamics of a complex fluid system.

Physical theories and machine-learning (ML) models are both judged on their ability to predict results in unseen scenarios. However, the bar for the former is much higher. To become accepted knowledge, a theory must conform to known physical laws and—crucially—be interpretable. An interpretable theory is capable of explaining why phenomena occur rather than simply predicting their form. Having such an interpretation can inform the scope of a new theory, allowing it to be applied in new contexts, while also connecting it to and incorporating prior knowledge. To date, researchers have largely struggled to get ML models (or any automated optimization process) to produce new theories that meet these standards. Jonathan Colen and Vincenzo Vitelli of the University of Chicago and their colleagues now show success at harnessing ML not as a stand-in for a researcher but rather as a guide to aid building a model of a complex system [1].