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Category: robotics/AI – Page 213

New AI enhances the view inside fusion energy systems

Imagine watching a favorite movie when suddenly the sound stops. The data representing the audio is missing. All that’s left are images. What if artificial intelligence (AI) could analyze each frame of the video and provide the audio automatically based on the pictures, reading lips and noting each time a foot hits the ground?

That’s the general concept behind a new AI that fills in missing data about plasma, the fuel of fusion, according to Azarakhsh Jalalvand of Princeton University. Jalalvand is the lead author on a paper about the AI, known as Diag2Diag, that was recently published in Nature Communications.

“We have found a way to take the data from a bunch of sensors in a system and generate a synthetic version of the data for a different kind of sensor in that system,” he said. The synthetic data aligns with real-world data and is more detailed than what an actual sensor could provide. This could increase the robustness of control while reducing the complexity and cost of future fusion systems. “Diag2Diag could also have applications in other systems such as spacecraft and robotic surgery by enhancing detail and recovering data from failing or degraded sensors, ensuring reliability in critical environments.”

Cyborgs: We examine the concepts of cyborgs, clarify what they are and how they differ from bionics, androids, and similar concepts

We also discuss some of the lesser known options for augmentation and explore the notion of man-machine integration.

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Cover Art by Jakub Grygier: https://www.artstation.com/artist/jak… by: Dexter Britain “Seeing the Future” Lombus “Hydrogen Sonata” Sergey Cheremisinov “Labyrinth” Kai Engel “Endless Story about Sun and Moon” Frank Dorittke “Morninglight” Koalips “Kvazar” Kevin MacLeod “Spacial Winds” Lombus “Amino” Brandow Liew “Into the Storm”

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Dexter Britain.
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‘Embodied’ AI in virtual reality improves programming student confidence

Researchers have found that giving AI “peers” in virtual reality (VR) a body that can interact with the virtual environment can help students learn programming. Specifically, the researchers found students were more willing to accept these “embodied” AI peers as partners, compared to voice-only AI, helping the students better engage with the learning experience.

“Using AI agents in a VR setting for teaching students programming is a relatively recent development, and this proof-of-concept study was meant to see what kinds of AI agents can help students learn better and work more effectively,” says Qiao Jin, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University.

“Peer learning is widespread in the programming field, as it helps students engage in the . For this work, we focused on ‘pAIr’ learning, where the programming peer is actually an AI agent. And the results suggest that embodying AI in the VR environment makes a real difference for pAIr learning.”

Photodiode design using germanium solves key challenge in on-chip light monitoring

Programmable photonics devices, which use light to perform complex computations, are emerging as a key area in integrated photonics research. Unlike conventional electronics that transmit signals with electrons, these systems use photons, offering faster processing speeds, higher bandwidths, and greater energy efficiency. These advantages make programmable photonics well-suited for demanding tasks like real-time deep learning and data-intensive computing.

A major challenge, however, lies in the use of power monitors. These sensors must constantly track the optical signal’s strength and provide the necessary feedback for tuning the chip’s components as required. However, existing on-chip photodetectors designed for this purpose face a fundamental tradeoff. They either have to absorb a significant amount of the optical signal to achieve a strong reading, which degrades the signal’s quality, or they lack the sensitivity to operate at the low power levels required without needing additional amplifiers.

As reported in Advanced Photonics, Yue Niu and Andrew W. Poon from The Hong Kong University of Science and Technology have addressed this challenge by developing a germanium-implanted silicon waveguide photodiode. Their approach overcomes the tradeoffs that have hindered existing on-chip power monitoring technologies.

Security researchers say G1 humanoid robots are secretly sending information to China and can easily be hacked

Researchers have uncovered serious security flaws with the Unitree G1 humanoid robot, a machine that is already being used in laboratories and some police departments. They discovered that G1 can be used for covert surveillance and could potentially launch a full-scale cyberattack on networks.

It sounds like the stuff of science fiction nightmares, robots that are secretly spying on you and could be controlled by remote hackers. However, the concern is real, as these types of robots are becoming increasingly common in homes, businesses, and .

Simulations show Saturn’s moon Enceladus shoots less ice into space than previous estimates

In the 17th century, astronomers Christiaan Huygens and Giovanni Cassini trained their telescopes on Saturn and uncovered a startling truth: the planet’s luminous bands were not solid appendages, but vast, separate rings composed of countless nested arcs.

Centuries later, NASA’s Cassini–Huygens (Cassini) probe carried the exploration of Saturn even further. Beginning in 2005, it sent back a stream of spectacular images that transformed scientists’ understanding of the system. Among its most dramatic revelations were the towering geysers on Saturn’s icy moon Enceladus, which blasted debris into space and left behind a faint sub-ring encircling the planet.

New supercomputer simulations from the Texas Advanced Computing Center (TACC) based on the Cassini space probe’s data have found improved estimates of ice mass Enceladus is losing to space. These findings help with understanding and future robotic exploration of what’s below the surface of the icy moon, which might harbor life.

AI tensor network-based computational framework cracks a 100-year-old physics challenge

Researchers from The University of New Mexico and Los Alamos National Laboratory have developed a novel computational framework that addresses a longstanding challenge in statistical physics.

The Tensors for High-dimensional Object Representation (THOR) AI framework employs tensor network algorithms to efficiently compress and evaluate the extremely large configurational integrals and central to determining the thermodynamic and mechanical properties of materials.

The framework was integrated with machine learning potentials, which encode interatomic interactions and dynamical behavior, enabling accurate and scalable modeling of materials across diverse physical conditions.

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