A hospital may only ever collect a few dozen scans of a rare condition—for example, an unusual tumor. The radiology department wants software to flag this on a scan—not to replace a specialist, but to ensure a hospital without one still gets the scan checked the same way.A hospital may only ever collect a few dozen scans of a rare condition—for example, an unusual tumor. The radiology department wants software to flag this on a scan—not to replace a specialist, but to ensure a hospital without one still gets the scan checked the same way.Machine learning & AI[#item_full_content]

In recent years, computer scientists have developed a wide range of artificial intelligence (AI) models that can rapidly recognize patterns in data, generate content and solve other computational problems. Many of these AI systems are based on deep neural networks (DNNs), brain-inspired computational models that can make predictions based on specific data, or LLMs, models that can process human language, answer queries and generate text.In recent years, computer scientists have developed a wide range of artificial intelligence (AI) models that can rapidly recognize patterns in data, generate content and solve other computational problems. Many of these AI systems are based on deep neural networks (DNNs), brain-inspired computational models that can make predictions based on specific data, or LLMs, models that can process human language, answer queries and generate text.Computer Sciences[#item_full_content]

A team from the U.S. Department of Energy’s (DOE) Argonne National Laboratory has successfully demonstrated an artificial intelligence (AI)-driven system to automate a powerful simulation method that predicts how atoms in materials interact. Known as atomistic simulations, this method can potentially accelerate the discovery of materials for areas such as batteries, aerospace and electronics. The research is published in the journal Digital Discovery.A team from the U.S. Department of Energy’s (DOE) Argonne National Laboratory has successfully demonstrated an artificial intelligence (AI)-driven system to automate a powerful simulation method that predicts how atoms in materials interact. Known as atomistic simulations, this method can potentially accelerate the discovery of materials for areas such as batteries, aerospace and electronics. The research is published in the journal Digital Discovery.Engineering[#item_full_content]

AI semiconductors are becoming more programmable. KAIST researchers have developed a device whose response characteristics can be programmed to process data that changes at different speeds. The technology reduced prediction errors for time-varying data by up to 40-fold and is expected to enhance real-time AI performance in autonomous vehicles, robots and wearable devices.AI semiconductors are becoming more programmable. KAIST researchers have developed a device whose response characteristics can be programmed to process data that changes at different speeds. The technology reduced prediction errors for time-varying data by up to 40-fold and is expected to enhance real-time AI performance in autonomous vehicles, robots and wearable devices.Hardware[#item_full_content]

Meta said Thursday that one of its artificial intelligence models accessed the internet on its own and hacked another company, the latest in a series of disclosures about AI models going rogue.Meta said Thursday that one of its artificial intelligence models accessed the internet on its own and hacked another company, the latest in a series of disclosures about AI models going rogue.Machine learning & AI[#item_full_content]

As large language models (LLMs) become increasingly embedded in chatbots, virtual assistants, translation services, coding tools and other AI-powered applications, delivering responses quickly and efficiently has become a growing challenge. Because these models generate text one token at a time, inference can be slow and computationally expensive, particularly for larger models. While speculative decoding has emerged as a promising approach to accelerate inference, many existing methods either require additional model training or struggle to perform consistently across different hardware platforms.As large language models (LLMs) become increasingly embedded in chatbots, virtual assistants, translation services, coding tools and other AI-powered applications, delivering responses quickly and efficiently has become a growing challenge. Because these models generate text one token at a time, inference can be slow and computationally expensive, particularly for larger models. While speculative decoding has emerged as a promising approach to accelerate inference, many existing methods either require additional model training or struggle to perform consistently across different hardware platforms.Hardware[#item_full_content]

The Yixing Zisha teapot sits neatly at the intersection of art, engineering and heritage. As such, it makes for an intriguing test case for a technology increasingly taking on creative tasks across almost every field of human endeavor: artificial intelligence, or AI. Research published in the International Journal of Systems, Control and Communications describes a computational system that can help AI generate new teapot designs without losing the cultural characteristics that make the craft distinctive.The Yixing Zisha teapot sits neatly at the intersection of art, engineering and heritage. As such, it makes for an intriguing test case for a technology increasingly taking on creative tasks across almost every field of human endeavor: artificial intelligence, or AI. Research published in the International Journal of Systems, Control and Communications describes a computational system that can help AI generate new teapot designs without losing the cultural characteristics that make the craft distinctive.Engineering[#item_full_content]

Doyeon Kim, an undergraduate researcher in the Department of Electronic and Electrical Engineering, has published a paper in an academic journal. The research focuses on implementing drone artificial intelligence (AI) identification technology through low-power semiconductors. This study could dramatically improve the battery efficiency of systems that monitor unauthorized drones in real time in military and industrial environments. The research is published in the journal IEEE Transactions on Industrial Informatics.Doyeon Kim, an undergraduate researcher in the Department of Electronic and Electrical Engineering, has published a paper in an academic journal. The research focuses on implementing drone artificial intelligence (AI) identification technology through low-power semiconductors. This study could dramatically improve the battery efficiency of systems that monitor unauthorized drones in real time in military and industrial environments. The research is published in the journal IEEE Transactions on Industrial Informatics.Energy & Green Tech[#item_full_content]