With all our global instability and still-nascent grasp on tech, adding in ASI would be lighting a match next to a fireworks factory.
Category: robotics/AI – Page 1215
Why do we think that reducing risks from AI is one of the most pressing issues of our time? There are technical safety issues that we believe could, in the worst case, lead to an existential threat to humanity.
Human languages are notoriously complex, and linguists have long thought it would be impossible to teach a machine how to analyze speech sounds and word structures in the way human investigators do.
But researchers at MIT, Cornell University, and McGill University have taken a step in this direction. They have demonstrated an artificial intelligence system that can learn the rules and patterns of human languages on its own.
When given words and examples of how those words change to express different grammatical functions (like tense, case, or gender) in one language, this machine-learning model comes up with rules that explain why the forms of those words change. For instance, it might learn that the letter “a” must be added to end of a word to make the masculine form feminine in Serbo-Croatian.
How to create a Robotic Arm under the control of the new KRIA Robotic Starter Kit By Adam Taylor.
Researchers have developed a new chip-based beam steering technology that provides a promising route to small, cost-effective, and high-performance lidar systems. Lidar, or light detection and ranging, uses laser pulses to acquire 3D information about a scene or object. It is used in a wide range of applications such as autonomous driving, 3D holography, biomedical sensing, free-space optical communications, and virtual reality.
“Optical beam steering is a key technology for lidar systems, but conventional mechanical-based beam steering systems are bulky, expensive, sensitive to vibration, and limited in speed,” said research team leader Hao Hu from the Technical University of Denmark. “Although devices known as chip-based optical phased arrays (OPAs) can quickly and precisely steer light in a non-mechanical way, so far, these devices have had poor beam quality and a field of view typically below 100 degrees.”
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No more dark side of the Moon?
An international research team headed by ETH Zurich has investigated the permanently shadowed regions of the Moon with the use of artificial intelligence. Future lunar missions will be able to find acceptable spots thanks to the knowledge they have gained about the region’s physical properties.
The research was published in Geophysical Research Letters on August 26.
We produce the first high-signal-to-noise ratio and-resolution orbital images over 44 shadowed regions within the Artemis exploration zone using an AI tool.
It will be reconfigured to meet testing needs.
The giant drone, RQ-4 RangeHawk, will soon be used to support the development of hypersonic missiles in the U.S., its manufacturer, Northrop Grumman, said in a press release.
Hypersonic missiles are the newest frontier in the weapons race, with countries like Russia and North Korea laying claims to have successfully demonstrated this technology. The U.S. hypersonic missile program has faced a few hiccups with repetitive test failures. Last month, the U.S. Air Force confirmed that its Air-launched Rapid Response Weapon (ARRW) had been successfully tested, almost after a year after similar claims from Russia.
GRAND FORKS, N.D. – Aug. 24, 2022 – Northrop Grumman Corporation’s (NYSE: NOC) RQ-4 RangeHawk is poised to support the SkyRange program’s U.S. hypersonic missile flight tests from its Grand Sky facility near Grand Forks, North Dakota. SkyRange is the Department of Defense Test Resource Management Center’s (TRMC) unmanned high-altitude, long-endurance, responsive mobile flight test system.
Carnegie Mellon mechanical engineering researchers have developed a new scalable and reproducible manufacturing technique that could accelerate the mainstream adoption and commercialization of soft and stretchable electronics.
The next generation of robotic technology will produce soft machines and robots that are safe and comfortable for direct physical interaction with humans and for use in fragile environments. Unlike rigid electronics, soft and stretchable electronics can be used to create wearable technologies and implantable electronics where safe physical contact with biological tissue and other delicate materials is essential.
Soft robots that safely handle delicate fruits and vegetables can improve food safety by preventing cross-contamination. Robots made from soft materials can brave the unexplored depths of the sea to collect delicate marine specimens. And the many biomedical applications for soft robots include wearable and assistive devices, prostheses, soft tools for surgery, drug delivery devices, and artificial organ function.
A breakthrough low-memory technique by Rice University computer scientists could put one of the most resource-intensive forms of artificial intelligence—deep-learning recommendation models (DLRM)—within reach of small companies.
DLRM recommendation systems are a popular form of AI that learns to make suggestions users will find relevant. But with top-of-the-line training models requiring more than a hundred terabytes of memory and supercomputer-scale processing, they’ve only been available to a short list of technology giants with deep pockets.
Rice’s “random offset block embedding array,” or ROBE Array, could change that. It’s an algorithmic approach for slashing the size of DLRM memory structures called embedding tables, and it will be presented this week at the Conference on Machine Learning and Systems (MLSys 2022) in Santa Clara, California, where it earned Outstanding Paper honors.