Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive optical networks, in particular, enable large-scale parallel computation through the use of passive structured phase masks and the propagation of light. However, one major challenge remains: systems trained in model-based simulations often fail to perform optimally in real experimental settings, where misalignments, noise, and model inaccuracies are difficult to capture.Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive optical networks, in particular, enable large-scale parallel computation through the use of passive structured phase masks and the propagation of light. However, one major challenge remains: systems trained in model-based simulations often fail to perform optimally in real experimental settings, where misalignments, noise, and model inaccuracies are difficult to capture.Hardware[#item_full_content]

Since 2021, Korean researchers have been providing a simple software development framework to users with relatively limited AI expertise in industrial fields such as factories, medical, and shipbuilding, providing them with a significant boost.Since 2021, Korean researchers have been providing a simple software development framework to users with relatively limited AI expertise in industrial fields such as factories, medical, and shipbuilding, providing them with a significant boost.Software[#item_full_content]

Until now, AI services based on large language models (LLMs) have mostly relied on expensive data center GPUs. This has resulted in high operational costs and created a significant barrier to entry for utilizing AI technology. A research team at KAIST has developed a technology that reduces reliance on expensive data center GPUs by utilizing affordable, everyday GPUs to provide AI services at a much lower cost.Until now, AI services based on large language models (LLMs) have mostly relied on expensive data center GPUs. This has resulted in high operational costs and created a significant barrier to entry for utilizing AI technology. A research team at KAIST has developed a technology that reduces reliance on expensive data center GPUs by utilizing affordable, everyday GPUs to provide AI services at a much lower cost.Hardware[#item_full_content]

Japanese tech investor SoftBank said Wednesday that its stake in OpenAI is now around 11% after completing the second stage of a $41-billion investment in the maker of ChatGPT.Japanese tech investor SoftBank said Wednesday that its stake in OpenAI is now around 11% after completing the second stage of a $41-billion investment in the maker of ChatGPT.Business[#item_full_content]

Five miles south of downtown Los Angeles, a single industrial block in Vernon is drawing as much electricity as a small town.Five miles south of downtown Los Angeles, a single industrial block in Vernon is drawing as much electricity as a small town.Energy & Green Tech[#item_full_content]

A research team affiliated with UNIST has unveiled a novel AI system capable of grading and providing detailed feedback on even the most untidy handwritten math answers—much like a human instructor.A research team affiliated with UNIST has unveiled a novel AI system capable of grading and providing detailed feedback on even the most untidy handwritten math answers—much like a human instructor.Computer Sciences[#item_full_content]

In artificial intelligence, 2025 marked a decisive shift. Systems once confined to research labs and prototypes began to appear as everyday tools. At the center of this transition was the rise of AI agents—AI systems that can use other software tools and act on their own.In artificial intelligence, 2025 marked a decisive shift. Systems once confined to research labs and prototypes began to appear as everyday tools. At the center of this transition was the rise of AI agents—AI systems that can use other software tools and act on their own.Consumer & Gadgets[#item_full_content]

Researchers at National Taiwan University have developed an AI system that recognizes construction activities at both the individual and crew levels using ordinary site videos. The approach reveals how teamwork shapes productivity and provides a foundation for future human–robot collaboration on construction sites.Researchers at National Taiwan University have developed an AI system that recognizes construction activities at both the individual and crew levels using ordinary site videos. The approach reveals how teamwork shapes productivity and provides a foundation for future human–robot collaboration on construction sites.Robotics[#item_full_content]