The automatic detection of surface-level irregularities—defects or anomalies—in 3D data is of significant interest for various real-world purposes, such as industrial quality inspection, infrastructure monitoring, robotics, and autonomous systems. However, collecting annotated defect examples at a large scale is costly, and existing 3D anomaly detection methods either require templates or heavy memory, multiple inference passes, and brittle heuristic clustering. These shortcomings limit real-life deployment.The automatic detection of surface-level irregularities—defects or anomalies—in 3D data is of significant interest for various real-world purposes, such as industrial quality inspection, infrastructure monitoring, robotics, and autonomous systems. However, collecting annotated defect examples at a large scale is costly, and existing 3D anomaly detection methods either require templates or heavy memory, multiple inference passes, and brittle heuristic clustering. These shortcomings limit real-life deployment.[#item_full_content]

When a human says an event is “probable” or “likely,” people generally have a shared, if fuzzy, understanding of what that means. But when an AI chatbot like ChatGPT uses the same word, it’s not assessing the odds the way we do, my colleagues and I found.When a human says an event is “probable” or “likely,” people generally have a shared, if fuzzy, understanding of what that means. But when an AI chatbot like ChatGPT uses the same word, it’s not assessing the odds the way we do, my colleagues and I found.[#item_full_content]

It is not easy to bring new technologies from the laboratory to market. Researchers and companies face very different demands for new developments and do not always find common ground. Scientists at Empa and other institutions have analyzed two emerging solar cell technologies to identify the greatest risks. Their conclusion: Research and industry must start collaborating much earlier.It is not easy to bring new technologies from the laboratory to market. Researchers and companies face very different demands for new developments and do not always find common ground. Scientists at Empa and other institutions have analyzed two emerging solar cell technologies to identify the greatest risks. Their conclusion: Research and industry must start collaborating much earlier.[#item_full_content]

Large language models (LLMs) are dealing with an increasing amount of morally sensitive information as people turn to them for medical advice, companionship and therapy. However, they are not exactly known for possessing a moral compass.Large language models (LLMs) are dealing with an increasing amount of morally sensitive information as people turn to them for medical advice, companionship and therapy. However, they are not exactly known for possessing a moral compass.[#item_full_content]

The use of artificial intelligence (AI) agents, systems that learn to make predictions, generate content or tackle other tasks by analyzing large amounts of data, is becoming increasingly widespread. Some of these systems have become so advanced that they can also be combined in ways that allow them to interact with each other.The use of artificial intelligence (AI) agents, systems that learn to make predictions, generate content or tackle other tasks by analyzing large amounts of data, is becoming increasingly widespread. Some of these systems have become so advanced that they can also be combined in ways that allow them to interact with each other.[#item_full_content]

Just like each person has unique fingerprints, every CMOS chip has a distinctive “fingerprint” caused by tiny, random manufacturing variations. Engineers can leverage this unforgeable ID for authentication, to safeguard a device from attackers trying to steal private data.Just like each person has unique fingerprints, every CMOS chip has a distinctive “fingerprint” caused by tiny, random manufacturing variations. Engineers can leverage this unforgeable ID for authentication, to safeguard a device from attackers trying to steal private data.[#item_full_content]

A University of Hawaiʻi at Mānoa student-led team has developed a new algorithm to help scientists determine direction in complex two-dimensional (2D) data, with potential applications ranging from particle physics to machine learning. The research was published in AIP Advances.A University of Hawaiʻi at Mānoa student-led team has developed a new algorithm to help scientists determine direction in complex two-dimensional (2D) data, with potential applications ranging from particle physics to machine learning. The research was published in AIP Advances.[#item_full_content]

One night in 2010, Mohit Gupta decided to try something before leaving the lab. Then a Ph.D. student at Carnegie Mellon University, Gupta was in the final days of an internship at a manufacturing company in Boston. He’d spent months developing a system that used cameras and light sources to create 3D images of small objects. “I wanted to stress test it, just for fun,” said Gupta, who would begin his postdoctoral research at Columbia Engineering a few months later.One night in 2010, Mohit Gupta decided to try something before leaving the lab. Then a Ph.D. student at Carnegie Mellon University, Gupta was in the final days of an internship at a manufacturing company in Boston. He’d spent months developing a system that used cameras and light sources to create 3D images of small objects. “I wanted to stress test it, just for fun,” said Gupta, who would begin his postdoctoral research at Columbia Engineering a few months later.[#item_full_content]

A team of researchers has found a way to steer the output of large language models by manipulating specific concepts inside these models. The new method could lead to more reliable, more efficient, and less computationally expensive training of LLMs. But it also exposes potential vulnerabilities.A team of researchers has found a way to steer the output of large language models by manipulating specific concepts inside these models. The new method could lead to more reliable, more efficient, and less computationally expensive training of LLMs. But it also exposes potential vulnerabilities.[#item_full_content]

It happens every day—a motorist heading across town checks a navigation app to see how long the trip will take, but they find no parking spots available when they reach their destination. By the time they finally park and walk to their destination, they’re significantly later than they expected to be.It happens every day—a motorist heading across town checks a navigation app to see how long the trip will take, but they find no parking spots available when they reach their destination. By the time they finally park and walk to their destination, they’re significantly later than they expected to be.[#item_full_content]