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Category: robotics/AI – Page 1184

Since we began space exploration in the mid-20th century, space agencies have relied on sending humans and robots into space. But should we leave space exploration entirely to robots?

Or should we consider sending humans to explore a new space world instead of robots? You are about to find satisfying answers to these curious questions.

Unlike traveling from one destination to another on Earth, exploring space comes with greater responsibilities. Space agencies hoping to explore a new space world must make a lot of planning to guarantee their success.

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“Robo Sapien” taken from the album “The Machinists Of Joy”.
Directed by: Jay Gillian.
Camera OP and Computer Animation: Shane Williams.
Produced by Cinematek Film & Television.
Robo Sapien provided by: JG and the Robots www.JGandtheRobots.com.

http://www.facebook.com/diekruppsofficial.
http://www.twitter.com/diekruppsband.
http://www.diekrupps.com

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Bishop: They can still be computationally very expensive. Additionally, emulators learn from data, so they’re typically not more accurate than the data used to train them. Moreover, they may give insufficiently accurate results when presented with scenarios that are markedly different from those on which they’re trained.

“I believe in “use-inspired basic research”—[like] the work of Pasteur. He was a consultant for the brewing industry. Why did this beer keep going sour? He basically founded the whole field of microbiology.” —Chris Bishop, Microsoft Research.

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Twitter executives misled federal regulators and the company’s own board about “extreme and egregious shortcomings” in its defenses against hackers and its meager efforts to combat bots, said a former chief security officer Peiter Zatko.

What happens inside Twitter?

The document describes Twitter as a chaotic and aimless company beset by infighting, unable to adequately protect its 238 million daily users, which include government agencies, heads of state and other influential public figures.

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Google’s parent firm, Alphabet, has long been working on multipurpose robots.

The fleet of “Everyday Robots,” as they are colloquially called, has recently been upgraded with sophisticated AI language systems so that they can better comprehend human speech.

Unlike other robots, which can only understand clear directions like “bring me a drink of water,” the fleet can now understand and act on more subtle ones.

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In recent years, deep learning algorithms have achieved remarkable results in a variety of fields, including artistic disciplines. In fact, many computer scientists worldwide have successfully developed models that can create artistic works, including poems, paintings and sketches.

Researchers at Seoul National University have recently introduced a new artistic framework, which is designed to enhance the skills of a sketching . Their framework, introduced in a paper presented at ICRA 2022 and pre-published on arXiv, allows a sketching robot to learn both stroke-based rendering and motor control simultaneously.

“The primary motivation for our research was to make something cool with non-rule-based mechanisms such as deep learning; we thought drawing is a cool thing to show if the drawing performer is a learned robot instead of human,” Ganghun Lee, the first author of the paper, told TechXplore. “Recent deep learning techniques have shown astonishing results in the artistic area, but most of them are about generative models which yield whole pixel outcomes at once.”

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Someone taps your shoulder. The organized touch receptors in your skin send a message to your brain, which processes the information and directs you to look left, in the direction of the tap. Now, Penn State and U.S. Air Force researchers have harnessed this processing of mechanical information and integrated it into engineered materials that “think”.

The work, published today in Nature, hinges on a novel, reconfigurable alternative to integrated . Integrated circuits are typically composed of multiple electronic components housed on a single semiconductor material, usually silicon, and they run all types of modern electronics, including phones, cars and robots. Integrated circuits are scientists’ realization of information processing similar to the brain’s role in the . According to principal investigator Ryan Harne, James F. Will Career Development Associate Professor of Mechanical Engineering at Penn State, integrated circuits are the core constituent needed for scalable computing of signals and information but have never before been realized by scientists in any composition other than silicon semiconductors.

His team’s discovery revealed the opportunity for nearly any material around us to act like its own integrated circuit: being able to “think” about what’s happening around it.

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MIT researchers unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles. Since they’ve proven to be productive test beds for safely trying out dangerous driving scenarios, hyper-realistic virtual worlds have been heralded as the best driving schools for autonomous vehicles (AVs). Tesla, Waymo, and other self-driving companies all rely heavily on data to enable expensive and proprietary photorealistic simulators, because testing and gathering nuanced I-almost-crashed data usually isn’t the easiest or most desirable to recreate.

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When the MIT Lincoln Laboratory Supercomputing Center (LLSC) unveiled its TX-GAIA supercomputer in 2019, it provided the MIT community a powerful new resource for applying artificial intelligence to their research. Anyone at MIT can submit a job to the system, which churns through trillions of operations per second to train models for diverse applications, such as spotting tumors in medical images, discovering new drugs, or modeling climate effects. But with this great power comes the great responsibility of managing and operating it in a sustainable manner—and the team is looking for ways to improve.

“We have these powerful computational tools that let researchers build intricate models to solve problems, but they can essentially be used as black boxes. What gets lost in there is whether we are actually using the hardware as effectively as we can,” says Siddharth Samsi, a research scientist in the LLSC.

To gain insight into this challenge, the LLSC has been collecting detailed data on TX-GAIA usage over the past year. More than a million user jobs later, the team has released the dataset open source to the computing community.

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