Technology
Here we bring you all the latest technological news both here on Earth and in space.
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NASA’s New AI Processor Is 500x Faster Than Current Space Computers
By Jet Propulsion Laboratory – SciTechDaily
NASA’s new AI-ready space chip could give future spacecraft a brain of their own.
NASA is developing a powerful new computer chip that could dramatically change how future spacecraft operate in deep space. Created through a commercial partnership, the advanced processor is designed to give spacecraft the ability to process information far more quickly and even make certain decisions independently during missions far from Earth.
NASA’s Push for Smarter Spacecraft: The agency’s High Performance Spaceflight Computing project is focused on increasing the computing capabilities of spacecraft used for exploration missions. Current spacecraft rely on older processors because they are reliable and durable enough to survive the harsh conditions of space. However, those chips lack the performance needed for the next generation of missions.
NASA says more advanced processors are essential for developing autonomous…Read more here.
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Scientists Use AI To Supercharge Ultrafast Laser Simulations by More Than 250x
SPIE–International Society for Optics and Photonics SciTechDaily
Researchers have developed a deep-learning-based surrogate model that dramatically speeds up simulations of nonlinear optical processes used in advanced laser systems.
Simulating the complex optical behavior behind ultrafast laser systems requires enormous computing power, creating a major challenge for experiments that depend on rapid feedback.
Researchers from Stanford University, the University of California, Los Angeles (UCLA), and SLAC National Accelerator Laboratory have now developed a deep learning surrogate model that dramatically speeds up these simulations while still maintaining high accuracy across a wide variety of laser pulse shapes.
Nonlinear Optics and X-Ray Production… The research focuses on second-order nonlinear optics, also known as χ² processes. In these interactions, light waves exchange energy inside specially designed crystals, producing new frequencies and customized pulse shapes. Read more here.
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Scientists Build a Living AI Device Using Real Brain Cells
By Princeton University – SciTechDaily
A 3D network of living neurons and electronics can recognize electrical patterns and may help researchers study both brain function and low-energy computing.
Princeton researchers have built a 3D device that brings living brain cells and advanced electronics together in one system. The device can be programmed with computational methods to recognize patterns.
Earlier efforts to use brain cells for computation have typically depended on flat 2D cell cultures grown in petri dishes or 3D cell clusters that are monitored and stimulated from the outside. The Princeton system is different because it is designed to interact with the cells from within the network.
The team used advanced fabrication methods to build a 3D mesh of microscopic metal wires and electrodes, held together by a very thin epoxy coating. That coating is flexible enough to work with the soft neurons that grow around it. The researchers used the mesh as a scaffold, allowing…Read more here.
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AI Learns To Work Backward and Reveal Hidden Forces in Nature
University of Pennsylvania School of Engineering and Applied Science / SciTechDaily
A new AI breakthrough helps scientists uncover the hidden forces shaping the world around us.
Engineers at the University of Pennsylvania have developed a new AI-based technique that could help scientists solve some of the most difficult mathematical problems used to study the natural world.
The approach, called “Mollifier Layers,” is designed to handle inverse partial differential equations (PDEs), a class of equations that allows researchers to work backward from visible patterns to uncover the hidden processes that created them. These problems appear in fields ranging from genetics and materials science to weather forecasting.
“Solving an inverse problem is like looking at ripples in a pond and working backward to figure out where the pebble fell,” says Vivek Shenoy, Eduardo D. Glandt President’s Distinguished Professor in Materials Science and Engineering (MSE) and senior author of a study published in Transactions on Machine Learning Research (TMLR), which will be presented at the Conference on Neural Information Processing Systems (NeurIPS 2026). “You can see the effects clearly, but the real challenge is inferring the hidden cause.”…Read more here.
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