Consider the following scenario. There's a ransomware attack, enhanced by AI, which paralyzes NHS systems—delaying medical care across the country.

New York City's thousands of traffic cameras capture endless hours of footage each day, but analyzing that video to identify safety problems and implement improvements typically requires resources that most transportation agencies don't have.

The ability to analyze the brain's neural connectivity is emerging as a key foundation for brain-computer interface (BCI) technologies, such as controlling artificial limbs and enhancing human intelligence. To make these analyses more precise, it is critical to quickly and accurately interpret the complex signals from many neurons in the brain.

The rapid expansion of large language models' (LLMs) capabilities—including web search, code execution, data analysis and even hypothesis generation and experimental design—is outpacing critical reflection of how the technology fits into academic research.

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.

When outraged Filipinos turned to an AI-powered chatbot to verify a viral photograph of a lawmaker embroiled in a corruption scandal, the tool failed to detect it was fabricated—even though it had generated the image itself.

OpenAI and Taiwan electronics giant Foxconn have agreed to a partnership to design and manufacture key equipment for artificial intelligence data centers in the U.S. as part of ambitious plans to fortify American AI infrastructure.

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?

When the first large language models were unleashed, it triggered a headache for authorities around the world as they tried to figure out how to satisfy data centers' endless demand for electricity.

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.