Lifeboat Foundation

Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a new approach that combines generative artificial intelligence (AI) and first-principles simulations to predict three-dimensional atomic structures of highly complex materials.

This research highlights LLNL’s efforts in advancing machine learning for materials science research and supporting the Lab’s mission to develop innovative technological solutions for energy and sustainability.

The study, recently published in Machine Learning: Science and Technology, represents a potential leap forward in the application of AI for materials characterization and inverse design.

Yongcui Mi has developed a new technology that enables real-time shaping and control of laser beams for laser welding and directed energy deposition using laser and wire. The innovation is based on the same mirror technology used in advanced telescopes for astronomy.

In a few years, this new technology could lead to more efficient and reliable ways of using high-power lasers for welding and directed energy deposition with laser and wire. The manufacturing industry could benefit from new opportunities to build more robust processes that meet stringent quality standards.

“We are the first to use deformable mirror technology for this application. The mirror optics can handle multi-kilowatt laser power, and with the help of computer vision and AI, the laser beam can be shaped in real time to adapt to variations in joint gaps,” explains Yongcui, a newly minted Ph.D. in Production technology from University West.

Researchers have developed XLuminA, an AI framework for the automated discovery of super-resolution microscopy techniques. With 10,000x faster optimization than traditional methods, it discovers unexplored designs breaking the diffraction limit.

Updated Antidot banking trojan targets Android users via fake job offers, stealing credentials and taking remote control.

Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a new approach that combines generative artificial intelligence (AI) and first-principles simulations to predict three-dimensional (3D) atomic structures of highly complex materials.

This research highlights LLNL’s efforts in advancing machine learning for materials science research and supporting the Lab’s mission to develop innovative technological solutions for energy and sustainability.

The study, recently published in Machine Learning: Science and Technology, represents a potential leap forward in the application of AI for materials characterization and inverse design.

Scientists have developed the first electrically pumped continuous-wave semiconductor laser composed exclusively of elements from the fourth group of the periodic table—the “silicon group.”

Built from stacked ultrathin layers of silicon germanium-tin and germanium-tin, this new laser is the first of its kind directly grown on a silicon wafer, opening up new possibilities for on-chip integrated photonics. The findings have been published in Nature Communications. The team includes researchers from Forschungszentrum Jülich, FZJ, the University of Stuttgart, and the Leibniz Institute for High Performance Microelectronics (IHP), together with their French partner CEA-Leti.

The rapid growth of artificial intelligence and the Internet of Things are driving the demand for increasingly powerful, energy-efficient hardware. Optical data transmission, with its ability to transfer vast amounts of data while minimizing energy loss, is already the preferred method for distances above 1 meter and is proving advantageous even for shorter distances. This development points towards future microchips featuring low-cost photonic integrated circuits (PICs), offering significant cost savings and improved performance.

There are contexts where human cognitive and emotional intelligence takes precedence over AI, which serves a supporting role in decision-making without overriding human judgment. Here, AI “protects” human cognitive processes from things like bias, heuristic thinking, or decision-making that activates the brain’s reward system and leads to incoherent or skewed results. In the human-first mode, artificial integrity can assist judicial processes by analyzing previous law cases and outcomes, for instance, without substituting a judge’s moral and ethical reasoning. For this to work well, the AI system would also have to show how it arrives at different conclusions and recommendations, considering any cultural context or values that apply differently across different regions or legal systems.

4 – Fusion Mode:

Artificial integrity in this mode is a synergy between human intelligence and AI capabilities combining the best of both worlds. Autonomous vehicles operating in Fusion Mode would have AI managing the vehicle’s operations, such as speed, navigation, and obstacle avoidance, while human oversight, potentially through emerging technologies like Brain-Computer Interfaces (BCIs), would offer real-time input on complex ethical dilemmas. For instance, in unavoidable crash situations, a BCI could enable direct communication between the human brain and AI, allowing ethical decision-making to occur in real-time, and blending AI’s precision with human moral reasoning. These kinds of advanced integrations between humans and machines will require artificial integrity at the highest level of maturity: artificial integrity would ensure not only technical excellence but ethical robustness, to guard against any exploitation or manipulation of neural data as it prioritizes human safety and autonomy.

What processes are responsible for dust storms on Mars? This is what a study presented today at the American Geophysical Union 2024 Fall Meeting hopes to address as a pair of researchers from the University of Colorado Boulder (CU Boulder) investigated the causes behind the massive dust storms on Mars, which periodically grow large enough to engulf the entire planet. This study holds the potential to help researchers predict dust storms on Mars, which could help current and future robotic missions survive these calamities, along with future human crews to the Red Planet.

“Dust storms have a significant effect on rovers and landers on Mars, not to mention what will happen during future crewed missions to Mars,” said Heshani Pieris, who is a PhD Candidate in planetary science at CU Boulder and lead author of the study. “This dust is very light and sticks to everything.”

For the study, the researchers examined 15 (Earth) years of data obtained from NASA’s Mars Reconnaissance Orbiter (MRO) to ascertain the processes responsible for kickstarting dust storms. After analyzing countless datasets of Martian surface temperatures, the researchers found that 68 percent of large dust storms on Mars resulted from spikes in surface temperatures during periods of increased sunlight through Mars’ thin atmosphere.

A year later, he got a myoelectric arm, a type of prosthetic powered by the electrical signals in his residual limb’s muscles. But Smith hardly used it because it was “very, very slow” and had a limited range of movements. He could open and close the hand, but not do much else. He tried other robotic arms over the years, but they had similar problems.

“They’re just not super functional,” he says. “There’s a massive delay between executing a function and then having the prosthetic actually do it. In my day-to-day life, it just became faster to figure out other ways to do things.”

Recently, he’s been trying out a new system by Austin-based startup Phantom Neuro that has the potential to provide more lifelike control of prosthetic limbs. The company is building a thin, flexible muscle implant to allow amputees a wider, more natural range of movement just by thinking about the gestures they want to make.