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Category: robotics/AI – Page 1,314

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.

In support of the SkyRange initiative, Block 20 and 30 RQ-4B Global Hawk aircraft are being transferred to TRMC to be reconfigured into RangeHawks. The conversion will integrate advanced payloads to equip the aircraft with the capability to support the testing of hypersonic vehicles and other long-range weapons. RangeHawks provide over-the-horizon altitude, endurance and flexibility, which are critical for collecting telemetry and other data to monitor the vehicle during flight tests. Increasing the capacity of hypersonic vehicle testing furthers research and development necessary to remain competitive in the global landscape.

“Our RQ-4 RangeHawks will support the emerging class of hypersonic weapons and provide a combination of range, endurance and payload capacity,” said Jane Bishop, vice president and general manager, global surveillance, Northrop Grumman. “These aircraft will continue their role in vital national security missions while enabling us to bring premier aircraft design, modification, operations and sustainment work to the Grand Forks community.”

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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 and robots that are safe and comfortable for direct physical interaction with humans and for use in fragile environments. Unlike rigid electronics, soft and 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 , prostheses, soft tools for surgery, drug delivery devices, and artificial organ function.

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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 ,” 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.

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Researchers from the University of Pennsylvania demonstrated in a proof-of-concept study that a hands-free device could successfully automate the treatment and removal of dental plaque and bacteria that cause tooth decay.

In the future, a shape-shifting robotic microswarm may serve as a toothbrush, rinse, and dental floss all in one. The technology, created by a multidisciplinary team at the University of Pennsylvania, has the potential to provide a brand-new, automated method for carrying out the repetitive but important daily duties of brushing and flossing. For people who lack the manual dexterity to efficiently clean their teeth alone, this system could be extremely helpful.

These microrobots are composed of iron oxide nanoparticles with catalytic and magnetic properties. Researchers were able to control their movement and configuration using a magnetic field to either produce bristle-like structures that remove dental plaque from the wide surfaces of teeth or elongated threads that can slide between teeth like a piece of floss. In both situations, the nanoparticles are driven by a catalytic reaction to release antimicrobials that eliminate harmful oral bacteria on site.

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Robert Long is a research fellow at the Future of Humanity Institute. His work is at the intersection of the philosophy of AI Safety and consciousness of AI. We talk about the recent LaMDA controversy, Ilya Sutskever’s slightly conscious tweet, the metaphysics and philosophy of consciousness, artificial sentience, and how a future filled with digital minds could get really weird.

Audio & transcript: https://theinsideview.ai/roblong.
Michaël: https://twitter.com/MichaelTrazzi.
Robert: https://twitter.com/rgblong.

Robert’s blog: https://experiencemachines.substack.com.

OUTLINE
00:00:00 Intro.
00:01:11 The LaMDA Controversy.
00:07:06 Defining AGI And Consciousness.
00:10:30 The Slightly Conscious Tweet.
00:13:16 Could Large Language Models Become Conscious?
00:18:03 Blake Lemoine Does Not Negotiate With Terrorists.
00:25:58 Could We Actually Test Artificial Consciousness?
00:29:33 From Metaphysics To Illusionism.
00:35:30 How We Could Decide On The Moral Patienthood Of Language Models.
00:42:00 Predictive Processing, Global Workspace Theories and Integrated Information Theory.
00:49:46 Have You Tried DMT?
00:51:13 Is Valence Just The Reward in Reinforcement Learning?
00:54:26 Are Pain And Pleasure Symetrical?
01:04:25 From Charismatic AI Systems to Artificial Sentience.
01:15:07 Sharing The World With Digital Minds.
01:24:33 Why AI Alignment Is More Pressing Than Artificial Sentience.
01:39:48 Why Moral Personhood Could Require Memory.
01:42:41 Last thoughts And Further Readings.

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Aug. 24, 2022 — Training a quantum neural network requires only a small amount of data, according to a new proof that upends previous assumptions stemming from classical computing’s huge appetite for data in machine learning, or artificial intelligence. The theorem has several direct applications, including more efficient compiling for quantum computers and distinguishing phases of matter for materials discovery.

“Many people believe that quantum machine learning will require a lot of data. We have rigorously shown that for many relevant problems, this is not the case,” said Lukasz Cincio, a quantum theorist at Los Alamos National Laboratory and co-author of the paper containing the proof published in the journal Nature Communications. “This provides new hope for quantum machine learning. We’re closing the gap between what we have today and what’s needed for quantum advantage, when quantum computers outperform classical computers.”

“The need for large data sets could have been a roadblock to quantum AI, but our work removes this roadblock. While other issues for quantum AI could still exist, at least now we know that the size of the data set is not an issue,” said Patrick Coles, a quantum theorist at the Laboratory and co-author of the paper.

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Researchers at the University of Toronto and the Barcelona Institute of Science and Technology have recently created new solution-processed perovskite photodetectors that exhibit remarkable efficiencies and response times. These photodetectors, introduced in a paper published in Nature Electronics, have a unique design that prevents the formation of defects between its different layers.

“There is growing interest in 3D range imaging for autonomous driving and consumer electronics,” Edward H. Sargent told TechXplore. “We have worked as a team for years on finding new materials that enable light sensing technologies such as next-generation image sensors and striving to take these in a direction that could have a commercial and societal impact.”

Photodetectors, sensing devices that detect or respond to light, can have numerous highly valuable applications. For instance, they can be integrated in robotic systems, autonomous vehicles, , environmental sensing technology, fiber optic communication systems and security systems.

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This week our guest is author and technologist, David Weinberger, who has spent years lecturing at Harvard as well as acting as a fellow and senior researcher at the renowned Berkman Klein Center for Internet & Society. And just prior to covid, David released his latest book, Everyday Chaos: Technology, Complexity, and How We’re Thriving in a New World of Possibility. In this episode, David and I explore some of the key ideas he focused on in Everyday Chaos. This includes looking at the ways in which we have historically used reductionist thinking to make generalizations for society, products, and technology, and how the latest technologies like the internet and Machine learning are revealing how much more we can thrive when we embrace chaos and customization. This means letting individuals and data tell us what people want by exploring all the possibilities rather than attempting to predict and shape outcomes beforehand.

** Find out more about David at his website weinberger.org and buy his book at everydaychaosbook.com.

55 MINS

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