Language models based on artificial intelligence (AI) can answer any question, but not always correctly. It would be helpful for users to know how reliable an AI system is. A team at Ruhr University Bochum and TU Dortmund University suggests six dimensions that determine the trustworthiness of a system, regardless of whether the system is made up of individuals, institutions, conventional machines, or AI.Language models based on artificial intelligence (AI) can answer any question, but not always correctly. It would be helpful for users to know how reliable an AI system is. A team at Ruhr University Bochum and TU Dortmund University suggests six dimensions that determine the trustworthiness of a system, regardless of whether the system is made up of individuals, institutions, conventional machines, or AI.[#item_full_content]

AI and human-movement research intersect in a study that enables precise estimation of hand muscle activity from standard video recordings. Using a deep-learning framework trained on a large, comprehensive multimodal dataset from professional pianists, the researchers introduce a system that accurately reconstructs muscle activation patterns without sensors.AI and human-movement research intersect in a study that enables precise estimation of hand muscle activity from standard video recordings. Using a deep-learning framework trained on a large, comprehensive multimodal dataset from professional pianists, the researchers introduce a system that accurately reconstructs muscle activation patterns without sensors.[#item_full_content]

If you’ve spent any time with ChatGPT or another AI chatbot, you’ve probably noticed they are intensely, almost overbearingly, agreeable. They apologize, flatter and constantly change their “opinions” to fit yours.If you’ve spent any time with ChatGPT or another AI chatbot, you’ve probably noticed they are intensely, almost overbearingly, agreeable. They apologize, flatter and constantly change their “opinions” to fit yours.[#item_full_content]

Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study. Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail unexpectedly when deployed on new tasks.Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study. Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail unexpectedly when deployed on new tasks.[#item_full_content]

Powerful artificial intelligence (AI) systems, like ChatGPT and Gemini, simulate understanding of comedy wordplay, but never really “get the joke,” a new study suggests.Powerful artificial intelligence (AI) systems, like ChatGPT and Gemini, simulate understanding of comedy wordplay, but never really “get the joke,” a new study suggests.[#item_full_content]

A new Australian study has smashed the myth that generative AI systems such as ChatGPT could soon replace society’s most creative playwrights, authors, songwriters, artists and scriptwriters.A new Australian study has smashed the myth that generative AI systems such as ChatGPT could soon replace society’s most creative playwrights, authors, songwriters, artists and scriptwriters.[#item_full_content]

You’ve just put a dollar into a machine to play a song and it stopped playing after a few seconds. You put in another dollar and the tune stops after a minute. You can’t get your dollars back and can’t listen to the song you want. But what if you had known ahead of time what would happen, and could have saved yourself the money and frustration?You’ve just put a dollar into a machine to play a song and it stopped playing after a few seconds. You put in another dollar and the tune stops after a minute. You can’t get your dollars back and can’t listen to the song you want. But what if you had known ahead of time what would happen, and could have saved yourself the money and frustration?[#item_full_content]

Soon, researchers may be able to create movies of their favorite protein or virus better and faster than ever before. Researchers at the Department of Energy’s SLAC National Accelerator Laboratory have pioneered a new machine learning method—called X-RAI (X-Ray single particle imaging with Amortized Inference)—that can “look” at millions of X-ray laser-generated images and create a three-dimensional reconstruction of the target particle. The team recently reported their findings in Nature Communications.Soon, researchers may be able to create movies of their favorite protein or virus better and faster than ever before. Researchers at the Department of Energy’s SLAC National Accelerator Laboratory have pioneered a new machine learning method—called X-RAI (X-Ray single particle imaging with Amortized Inference)—that can “look” at millions of X-ray laser-generated images and create a three-dimensional reconstruction of the target particle. The team recently reported their findings in Nature Communications.[#item_full_content]

By studying the theoretical limits of how light can be used to perform computation, Cornell researchers have uncovered new insights and strategies for designing energy-efficient optical computing systems.By studying the theoretical limits of how light can be used to perform computation, Cornell researchers have uncovered new insights and strategies for designing energy-efficient optical computing systems.[#item_full_content]

Hospitals do not always have the opportunity to collect data in tidy, uniform batches. A clinic may have a handful of carefully labeled images from one scanner while holding thousands of unlabeled scans from other centers, each with different settings, patient mixes and imaging artifacts. That jumble makes a hard task—medical image segmentation—even harder still. Models trained under neat assumptions can stumble when deployed elsewhere, particularly on small, faint or low-contrast targets.Hospitals do not always have the opportunity to collect data in tidy, uniform batches. A clinic may have a handful of carefully labeled images from one scanner while holding thousands of unlabeled scans from other centers, each with different settings, patient mixes and imaging artifacts. That jumble makes a hard task—medical image segmentation—even harder still. Models trained under neat assumptions can stumble when deployed elsewhere, particularly on small, faint or low-contrast targets.[#item_full_content]