Computer scientists at UC Riverside have identified troubling flaws in a new generation of artificial intelligence (AI) agents designed to take over routine computer chores while users are away—sorting emails, organizing files, analyzing data, and handling other everyday digital tasks that might otherwise consume hours.Computer scientists at UC Riverside have identified troubling flaws in a new generation of artificial intelligence (AI) agents designed to take over routine computer chores while users are away—sorting emails, organizing files, analyzing data, and handling other everyday digital tasks that might otherwise consume hours.[#item_full_content]

New work explaining the inner workings of artificial intelligence could provide a way around the threat of AI “model collapse,” potentially averting growing numbers of AI hallucinations in the future.New work explaining the inner workings of artificial intelligence could provide a way around the threat of AI “model collapse,” potentially averting growing numbers of AI hallucinations in the future.[#item_full_content]

Researchers from The University of Osaka, Kyushu University, and the University of Victoria have developed a new method called Majority Voting SZZ (MV-SZZ) that accurately identifies defect-inducing software commits. By combining detailed code tracking with a majority voting system, the approach reduces false positives and outperforms existing techniques. This improvement could help developers debug software more efficiently and build more reliable systems. The work is published in the journal IEEE Transactions on Software Engineering.Researchers from The University of Osaka, Kyushu University, and the University of Victoria have developed a new method called Majority Voting SZZ (MV-SZZ) that accurately identifies defect-inducing software commits. By combining detailed code tracking with a majority voting system, the approach reduces false positives and outperforms existing techniques. This improvement could help developers debug software more efficiently and build more reliable systems. The work is published in the journal IEEE Transactions on Software Engineering.[#item_full_content]

Over the past few decades, computer scientists have developed increasingly advanced artificial intelligence (AI) systems that can tackle some tasks exceedingly well. These include computer vision models, systems that can rapidly analyze images and categorize them, recognize objects and faces, or make other accurate predictions.Over the past few decades, computer scientists have developed increasingly advanced artificial intelligence (AI) systems that can tackle some tasks exceedingly well. These include computer vision models, systems that can rapidly analyze images and categorize them, recognize objects and faces, or make other accurate predictions.[#item_full_content]

Researchers from MIT and elsewhere have developed a more user-friendly and efficient method to help networking engineers identify potential system failures before they cause major problems, like a cloud service outage that leaves millions of users unable to access applications.Researchers from MIT and elsewhere have developed a more user-friendly and efficient method to help networking engineers identify potential system failures before they cause major problems, like a cloud service outage that leaves millions of users unable to access applications.[#item_full_content]

Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily powerful, yet their internal workings remain largely a “black box.” To better understand how these systems produce their responses, a group of physicists at Harvard University has developed a simplified mathematical model of learning in neural networks that can be analyzed mathematically using the tools of statistical physics.Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily powerful, yet their internal workings remain largely a “black box.” To better understand how these systems produce their responses, a group of physicists at Harvard University has developed a simplified mathematical model of learning in neural networks that can be analyzed mathematically using the tools of statistical physics.[#item_full_content]

Today, artificial intelligence can describe images, recognize objects, and explain complex relationships. The pace of development is remarkable: So-called vision-language models (VLMs) combine text and image understanding in impressive ways. Yet, of all things, they struggle with a seemingly simple task—counting. Researchers at the Institute for Information Systems (iisys) at Hof University of Applied Sciences are now working to address this issue, with a paper posted to the arXiv preprint server.Today, artificial intelligence can describe images, recognize objects, and explain complex relationships. The pace of development is remarkable: So-called vision-language models (VLMs) combine text and image understanding in impressive ways. Yet, of all things, they struggle with a seemingly simple task—counting. Researchers at the Institute for Information Systems (iisys) at Hof University of Applied Sciences are now working to address this issue, with a paper posted to the arXiv preprint server.[#item_full_content]

Paintings are often made up of thousands of tiny brushstrokes, each going in a certain direction, that are not easily observed by the viewer. A cross-disciplinary research team from the Penn State College of Information Sciences and Technology (IST) and Loughborough University in England has developed an image analysis method that helps to make the underlying brushstroke structure of paintings visible, giving new insight into how artists physically created their works.Paintings are often made up of thousands of tiny brushstrokes, each going in a certain direction, that are not easily observed by the viewer. A cross-disciplinary research team from the Penn State College of Information Sciences and Technology (IST) and Loughborough University in England has developed an image analysis method that helps to make the underlying brushstroke structure of paintings visible, giving new insight into how artists physically created their works.[#item_full_content]

Research conducted by Dr. Sanaz Taheri Boshrooyeh, a Ph.D. graduate of Koç University, Computer Science and Engineering Program, together with Prof. Dr. Alptekin Küpçü and Prof. Dr. Öznur Özkasap, has led to the development of a new scalable method designed to protect user privacy on online platforms that rely on invitation-based registration. The system, called “Anonyma,” prevents even system administrators from identifying who invited a particular user to join the platform, addressing a significant privacy concern in such systems. The research and analysis results were published in Journal of Network and Computer Applications.Research conducted by Dr. Sanaz Taheri Boshrooyeh, a Ph.D. graduate of Koç University, Computer Science and Engineering Program, together with Prof. Dr. Alptekin Küpçü and Prof. Dr. Öznur Özkasap, has led to the development of a new scalable method designed to protect user privacy on online platforms that rely on invitation-based registration. The system, called “Anonyma,” prevents even system administrators from identifying who invited a particular user to join the platform, addressing a significant privacy concern in such systems. The research and analysis results were published in Journal of Network and Computer Applications.[#item_full_content]

Artificial intelligence that cannot explain how it makes decisions—often called “black box” AI—could soon be replaced by more transparent systems, research suggests. A study by Loughborough University, published in Physica D: Nonlinear Phenomena, outlines a new mathematical blueprint for building AI that can reveal how it learns, remembers, and makes decisions.Artificial intelligence that cannot explain how it makes decisions—often called “black box” AI—could soon be replaced by more transparent systems, research suggests. A study by Loughborough University, published in Physica D: Nonlinear Phenomena, outlines a new mathematical blueprint for building AI that can reveal how it learns, remembers, and makes decisions.[#item_full_content]