Among the primary concerns surrounding artificial intelligence is its tendency to yield erroneous information when summarizing long documents. These “hallucinations” are problematic not only because they convey falsehoods, but also because they reduce efficiency—sorting through content to search for mistakes of AI outputs is time-consuming.Among the primary concerns surrounding artificial intelligence is its tendency to yield erroneous information when summarizing long documents. These “hallucinations” are problematic not only because they convey falsehoods, but also because they reduce efficiency—sorting through content to search for mistakes of AI outputs is time-consuming.[#item_full_content]

Electrical distribution systems are characterized by dynamic operating conditions and complex network topologies, which pose significant challenges for the effective deployment of protection schemes. While impedance relays are widely used in high-voltage transmission systems, their application in medium-voltage distribution networks has been relatively limited due to operational and structural constraints.Electrical distribution systems are characterized by dynamic operating conditions and complex network topologies, which pose significant challenges for the effective deployment of protection schemes. While impedance relays are widely used in high-voltage transmission systems, their application in medium-voltage distribution networks has been relatively limited due to operational and structural constraints.[#item_full_content]

Again and again, Washington State University professor Mesut Cicek and his colleagues fed hypotheses from scientific papers into ChatGPT and asked it to determine whether the statements had been upheld by research—whether they were true or false. They did this with more than 700 hypotheses, repeating each query 10 times.Again and again, Washington State University professor Mesut Cicek and his colleagues fed hypotheses from scientific papers into ChatGPT and asked it to determine whether the statements had been upheld by research—whether they were true or false. They did this with more than 700 hypotheses, repeating each query 10 times.[#item_full_content]

New research published in Machine Learning shows pattern learning is not enough to train AI to tackle games—and abstract representations or hybrid approaches may help. Many AI researchers describe game-playing as the “Formula 1” of AI: it’s a controlled test environment with clear rules and clear success criteria. This paper uses that idea as a diagnostic, by studying a very simple game Nim, a children’s matchstick game whose optimal strategy is known exactly.New research published in Machine Learning shows pattern learning is not enough to train AI to tackle games—and abstract representations or hybrid approaches may help. Many AI researchers describe game-playing as the “Formula 1” of AI: it’s a controlled test environment with clear rules and clear success criteria. This paper uses that idea as a diagnostic, by studying a very simple game Nim, a children’s matchstick game whose optimal strategy is known exactly.[#item_full_content]

Most of you have used a navigation app like Google Maps for your travels at some point. These apps rely on algorithms that compute shortest paths through vast networks. Now imagine scaling that task to calculate distances between every pair of points in a massive system, for example, a transportation grid, a communication backbone, or even a biological network such as protein or neural interaction networks.Most of you have used a navigation app like Google Maps for your travels at some point. These apps rely on algorithms that compute shortest paths through vast networks. Now imagine scaling that task to calculate distances between every pair of points in a massive system, for example, a transportation grid, a communication backbone, or even a biological network such as protein or neural interaction networks.[#item_full_content]

Over the past decades, computer scientists have developed numerous artificial intelligence (AI) systems that can process human speech in different languages. The extent to which these models replicate the brain processes via which humans understand spoken language, however, has not yet been clearly determined.Over the past decades, computer scientists have developed numerous artificial intelligence (AI) systems that can process human speech in different languages. The extent to which these models replicate the brain processes via which humans understand spoken language, however, has not yet been clearly determined.[#item_full_content]

AI chatbots are standardizing how people speak, write, and think. If this homogenization continues unchecked, it risks reducing humanity’s collective wisdom and ability to adapt, computer scientists and psychologists argue in an opinion paper published in Trends in Cognitive Sciences.AI chatbots are standardizing how people speak, write, and think. If this homogenization continues unchecked, it risks reducing humanity’s collective wisdom and ability to adapt, computer scientists and psychologists argue in an opinion paper published in Trends in Cognitive Sciences.[#item_full_content]

To stay up to date and work forward in their fields, scientists must have at their fingertips and in their minds thousands of published studies. Large language models (LLMs) show promise as a tool for exploring the vast scientific literature, but are they trustworthy when it comes to providing full and scientifically accurate answers to complex questions in specialized fields?To stay up to date and work forward in their fields, scientists must have at their fingertips and in their minds thousands of published studies. Large language models (LLMs) show promise as a tool for exploring the vast scientific literature, but are they trustworthy when it comes to providing full and scientifically accurate answers to complex questions in specialized fields?[#item_full_content]

For years, the guiding assumption of artificial intelligence has been simple: an AI is only as good as the data it has seen. Feed it more, train it longer, and it performs better. Feed it less, and it stumbles. A new study from the USC Viterbi School of Engineering was accepted at the IEEE SoutheastCon 2026, taking place March 12–15. It suggests something far more surprising: with the right method in place, an AI model can dramatically improve its performance in territory it was barely trained on, pushing well past what its training data alone would ever allow.For years, the guiding assumption of artificial intelligence has been simple: an AI is only as good as the data it has seen. Feed it more, train it longer, and it performs better. Feed it less, and it stumbles. A new study from the USC Viterbi School of Engineering was accepted at the IEEE SoutheastCon 2026, taking place March 12–15. It suggests something far more surprising: with the right method in place, an AI model can dramatically improve its performance in territory it was barely trained on, pushing well past what its training data alone would ever allow.[#item_full_content]

Researchers at the University at Albany and Rutgers University have developed an early-warning framework that can predict harmful social media interactions before they erupt, paving the way for interventions that can minimize harm and make platforms safer for users. Using publicly available datasets from Reddit and Instagram, two social media platforms with distinct conversation dynamics, researchers trained models to predict from just the first 10 comments whether a thread would escalate into “concentrated waves of toxic interactions”—or what they have dubbed a “negative storm” or “neg storm.”Researchers at the University at Albany and Rutgers University have developed an early-warning framework that can predict harmful social media interactions before they erupt, paving the way for interventions that can minimize harm and make platforms safer for users. Using publicly available datasets from Reddit and Instagram, two social media platforms with distinct conversation dynamics, researchers trained models to predict from just the first 10 comments whether a thread would escalate into “concentrated waves of toxic interactions”—or what they have dubbed a “negative storm” or “neg storm.”[#item_full_content]