Even with all the recent advances in the ability of large language models (like ChatGPT) to help us think, research, summarize, and learn complex and technical texts, how do they fare in understanding storytelling and literature? These questions around interpretive nuance remain.
In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output. Concept bottleneck modeling is one method that enables artificial intelligence systems to explain their decision-making process. These methods force a deep-learning model to use a set of concepts, which can be understood by humans, to make a prediction. In new research, MIT computer scientists developed a method that coaxes the model to achieve better accuracy and clearer, more concise explanations.
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."
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.
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?
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.
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.
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.
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.
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.
Sheepdogs, bred to control large groups of sheep in open fields, have demonstrated their skills in competitions dating back to the 1870s. In these contests, a handler directs a trained dog with whistle signals to guide a small group of sheep across a field and sometimes split the flock cleanly into two groups. But sheep do not always cooperate.
Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer. But this method measures self-confidence, and even the most impressive LLM might be confidently wrong. Overconfidence can mislead users about the accuracy of a prediction, which might result in devastating consequences in high-stakes settings like health care or finance.
Large language models (LLMs), artificial intelligence systems that can process and generate texts in different languages, are now used daily by many people worldwide. As these models can rapidly source information and create convincing content for specific purposes, they are now also used in some professional settings or for gathering legal, medical, or financial information.
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.
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.
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