When the FORTRAN programming language debuted in 1957, it transformed how scientists and engineers programmed computers. Complex calculations could suddenly be expressed in concise, math-like notation using arrays—collections of values that make it easier to describe operations on data. That simple idea evolved into today’s “tensors,” which power many of the world’s most advanced AI and scientific computing systems through modern frameworks like NumPy and PyTorch.When the FORTRAN programming language debuted in 1957, it transformed how scientists and engineers programmed computers. Complex calculations could suddenly be expressed in concise, math-like notation using arrays—collections of values that make it easier to describe operations on data. That simple idea evolved into today’s “tensors,” which power many of the world’s most advanced AI and scientific computing systems through modern frameworks like NumPy and PyTorch.[#item_full_content]

Researchers from Saarland University and the Max Planck Institute for Software Systems have, for the first time, shown that the reactions of humans and large language models (LLMs) to complex or misleading program code significantly align, by comparing brain activity of study participants with model uncertainty.Researchers from Saarland University and the Max Planck Institute for Software Systems have, for the first time, shown that the reactions of humans and large language models (LLMs) to complex or misleading program code significantly align, by comparing brain activity of study participants with model uncertainty.[#item_full_content]

Although deep learning–based image recognition technology is rapidly advancing, it still remains difficult to clearly explain the criteria AI uses internally to observe and judge images. In particular, technologies that analyze how large-scale models combine various concepts (e.g., cat ears, car wheels) to reach a conclusion have long been recognized as a major unsolved challenge.Although deep learning–based image recognition technology is rapidly advancing, it still remains difficult to clearly explain the criteria AI uses internally to observe and judge images. In particular, technologies that analyze how large-scale models combine various concepts (e.g., cat ears, car wheels) to reach a conclusion have long been recognized as a major unsolved challenge.[#item_full_content]

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]