Lifeboat Foundation

WASHINGTON — Israel Aerospace Industries will debut REX MK II, its newest ground robot set to be fielded to British troops, at DSEI, the London-based defense exhibition, which begins Tuesday. IAI signed a contract with the UK in the second quarter of the calendar year and has sold the vehicle to other European countries, Rani Avni, IAI’s deputy general manager for robotics and autonomous systems, told Defense News in a Sept. 12 interview ahead of the show. He declined to identify the other countries.

Israeli defense company unveils its latest ground robot at DSEI as market for light robotic combat vehicles heats up in the European theater.

The website is eye-catching for its simplicity. Against a white backdrop, a giant blue button invites visitors to upload a picture of a face. Below the button, four AI-generated faces allow you to test the service. Above it, the tag line boldly proclaims the purpose: turn anyone into a porn star by using deepfake technology to swap the person’s face into an adult video. All it requires is the picture and the push of a button.

MIT Technology Review has chosen not to name the service, which we will call Y, or use any direct quotes and screenshots of its contents, to avoid driving traffic to the site. It was discovered and brought to our attention by deepfake researcher Henry Ajder, who has been tracking the evolution and rise of synthetic media online.

California-based startup Relativity Space is manufacturing rockets using giant Westworld-esque 3D printers, a process they say could drastically shorten the rocket-making process from years to weeks. Tim Ellis, the company’s 30-year-old CEO, explains how the high degree of automation in Relativity’s factory has enabled them to build rockets remotely during the Covid-19 pandemic.

#Coronavirus #Space #HelloWorld.
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Continue reading “The SpaceX Competitor is Printing Its Rockets” »

Technically, several attempts have been made to automate text replacement in still images based on principles of deep style transfer. The research group is including this progress and their research to tackle the problem of text replacement in videos. Videotext replacement is not an easy task. It must meet the challenges faced in still images while also accounting for time and effects such as lighting changes, blur caused by camera motion or object movement.

One approach to solve video-test replacement could be to train an image-based text style transfer module on individual frames while incorporating temporal consistency constraints in the network loss. But with this approach, the network performing text style transfer will be additionally burdened with handling geometric and motion-induced effects encountered in the video.

Continue reading “AI Researchers From Amazon, NEC, Stanford Unveil The First Deep Videos Text-Replacement Method, ‘STRIVE’” »

With complex structures – including a strong, flexible mechanical arm carrying various tools – the splash created by a deep sea mining robot was akin to that of humans. But unlike a free-fall diver, the robot was lowered by a rope and the swing caused by wind and waves added uncertainty to its motion, according to the researchers.

Robots are perfecting their diving skills in preparation for the serious business of tapping into mineral resources in the seabed.

NASA and Joby’s eVTOL craft could be the weird plane/chopper fusion of the future.

The National Aeronautics and Space Administration is also America’s civilian aerospace research organization. In that role, it has been instrumental in developing new technologies ranging from rocket engines to aircraft control systems.

Continue reading “Look: NASA’s futuristic eVTOL helicopter is ready for flight tests” »

Are there any limits to large neural networks?

OpenAI will release GPT-4 in the next few years. It will have around 100 trillion parameters.

Continue reading “GPT-4 Will Have 100 Trillion Parameters — 500x the Size of GPT-3” »

Our reign as sole understanders of the cosmos is rapidly coming to an end. We should not be afraid of this. The revolution that has just begun may be understood as a continuation of the process whereby the Earth nurtures the understanders, the beings that will lead the cosmos to self-knowledge. What is revolutionary about this moment is that the understanders of the future will not be humans but cyborgs that will have designed and built themselves from the artificial intelligence systems we have already constructed. These will soon become thousands then millions of times more intelligent than us.

“The dream of predicting a protein shape just from its gene sequence is now a reality,” said Paul Adams, Associate Laboratory Director for Biosciences at Berkeley Lab. For Adams and other structural biologists who study proteins, predicting their shape offers a key to understanding their function and accelerating treatments for diseases like cancer and COVID-19.

The current approaches to accurately mapping that shape, however, usually rely on complex experiments at synchrotrons. But even these sophisticated processes have their limitations—the data and quality aren’t always sufficient to understand a protein at an atomic level. By applying powerful machine learning methods to the large library of protein structures it is now possible to predict a protein’s shape from its gene sequence.

Researchers in Berkeley Lab’s Molecular Biophysics & Integrated Bioimaging Division joined an international effort led by the University of Washington to produce a computer software tool called RoseTTAFold. The algorithm simultaneously takes into account patterns, distances, and coordinates of amino acids. As these data inputs flow in, the tool assesses relationships within and between structures, eventually helping to build a very detailed picture of a protein’s shape.