As artificial intelligence (AI) continues to advance, researchers at POSTECH (Pohang University of Science and Technology) have identified a breakthrough that could make AI technologies faster and more efficient.

Artificial intelligence (AI) is increasing productivity and pushing the boundaries of what's possible. It powers self-driving cars, social media feeds, fraud detection and medical diagnoses. Touted as a game changer, it is projected to add nearly US$15.7 trillion to the global economy by the end of the decade.

Coordinating complicated interactive systems, whether it's the different modes of transportation in a city or the various components that must work together to make an effective and efficient robot, is an increasingly important subject for software designers to tackle. Now, researchers at MIT have developed an entirely new way of approaching these complex problems, using simple diagrams as a tool to reveal better approaches to software optimization in deep-learning models.

An unrelenting, ravenous appetite for more and more data may be artificial intelligence's fatal flaw; or at least the fastest way for "poison" to seep in.

In the heart of New York City's Times Square, there are signs of an artificial intelligence (AI) revolution in marketing. In a health supplement store, a popup by British tech startup, Hypervsn, showcases life-size holograms. From behind the glass, a virtual humanoid avatar with spunky pink hair waves to passersby.

To build the experimental stations of the future, scientists at the National Synchrotron Light Source II (NSLS-II), a U.S. Department of Energy (DOE) Office of Science user facility at DOE's Brookhaven National Laboratory, are learning from some of the challenges that face them today. As light source technologies and capabilities continue to advance, researchers must navigate increasingly complex workflows and swiftly evolving experimental demands.

It's obvious when a dog has been poorly trained. It doesn't respond properly to commands. It pushes boundaries and behaves unpredictably. The same is true with a poorly trained artificial intelligence (AI) model. Only with AI, it's not always easy to identify what went wrong with the training.

When ChatGPT entered the public imagination in 2022, Canadians were curious, hopeful, anxious and had plenty of questions. Just three years later, our new report, The State of Generative AI Use in Canada 2025, finds that two-thirds of Canadians have already experimented with generative AI (GenAI) tools.

Artificial intelligence (AI) is often hailed as the defining technology of the 21st century, shaping everything from economic growth to national security. But as global investment in AI accelerates, many experts are beginning to ask whether the world has embarked on an AI "arms race."

Chinese artificial intelligence app DeepSeek was transferring personal data to a cloud services platform without users' consent while it was still available for download, South Korea's data protection authority said on Thursday.

Neurons—the nerve cells responsible for transmitting electrical and chemical signals throughout the body—are organized in tissue, tending to cluster together in groups according to how they function. For example, language studies have found that there are clusters of neurons that seem to specialize in verbs and others that focus specifically on nouns, however researchers aren't sure just how these functional groups form.

For a robot, the real world is a lot to take in. Making sense of every data point in a scene can take a huge amount of computational effort and time. Using that information to then decide how to best help a human is an even thornier exercise.

Large language models (LLMs) are at the forefront of artificial intelligence (AI) and have been widely used for conversational interactions. However, assessing the personality of a given LLM remains a significant challenge.

ChatGPT and alike often amaze us with the accuracy of their answers, but unfortunately, they also repeatedly give us cause for doubt. The main issue with powerful AI response engines (artificial intelligence) is that they provide us with perfect answers and obvious nonsense with the same ease. One of the major challenges lies in how the large language models (LLMs) underlying AI deal with uncertainty.

Humans, it turns out, are better than current AI models at describing and interpreting social interactions in a moving scene—a skill necessary for self-driving cars, assistive robots, and other technologies that rely on AI systems to navigate the real world.

The State Bar of California has disclosed that some multiple-choice questions in a problem-plagued bar exam were developed with the aid of artificial intelligence.

A research team has introduced a new out-of-core mechanism, Capsule, for large-scale GNN training, which can achieve up to a 12.02× improvement in runtime efficiency, while using only 22.24% of the main memory, compared to SOTA out-of-core GNN systems. This work was published in the Proceedings of the ACM on Management of Data .The team included the Data Darkness Lab (DDL) at the Medical Imaging Intelligence and Robotics Research Center of the University of Science and Technology of China (USTC) Suzhou Institute.

Essential for many industries ranging from Hollywood computer-generated imagery to product design, 3D modeling tools often use text or image prompts to dictate different aspects of visual appearance, like color and form. As much as this makes sense as a first point of contact, these systems are still limited in their realism due to their neglect of something central to the human experience: touch.

Addressing the challenges of fragrance design, researchers at the Institute of Science Tokyo (Science Tokyo) have developed an AI model that can automate the creation of new fragrances based on user-defined scent descriptors. The model uses mass spectrometry profiles of essential oils and corresponding odor descriptors to generate essential oil blends for new scents.

Former employees of OpenAI are asking the top law enforcement officers in California and Delaware to stop the company from shifting control of its artificial intelligence technology from a nonprofit charity to a for-profit business.

MIT researchers have created a periodic table that shows how more than 20 classical machine-learning algorithms are connected. The new framework sheds light on how scientists could fuse strategies from different methods to improve existing AI models or come up with new ones.

A whistleblower at the National Labor Relations Board reported an unusual spike in potentially sensitive data flowing out of the agency's network in early March 2025 when staffers from the Department of Government Efficiency, which goes by DOGE, were granted access to the agency's databases. On April 7, the Department of Homeland Security gained access to Internal Revenue Service tax data.

University of Waterloo researchers have developed new artificial intelligence (AI) technology that can accurately analyze pitcher performance and mechanics using low-resolution video of baseball games.

Cornell University researchers have developed a new robotic framework powered by artificial intelligence—called RHyME (Retrieval for Hybrid Imitation under Mismatched Execution)—that allows robots to learn tasks by watching a single how-to video.

A team of researchers from the Institute for Basic Science, Yonsei University, and the Max Planck Institute have developed a new artificial intelligence (AI) technique that brings machine vision closer to how the human brain processes images. Called Lp-Convolution, this method improves the accuracy and efficiency of image recognition systems while reducing the computational burden of existing AI models.

A large-scale study led by Bilal Farooq, civil engineering professor at Toronto Metropolitan University (TMU), is the first of its kind to use virtual reality (VR) to study pedestrian interactions and street crossing behaviors. The research pioneers the use of neurophysiological sensors to better understand pedestrian experiences in urban environments. The work is published in the journal Transportation Research Part C: Emerging Technologies.

The world is facing a "silver tsunami"—an unprecedented aging of the global workforce. By 2030, more than half of the labor force in many EU countries will be aged 50 or above. Similar trends are emerging across Australia, the US and other developed and developing economies.

A group of computer scientists at Microsoft Research, working with a colleague from the University of Chinese Academy of Sciences, has introduced Microsoft's new AI model that runs on a regular CPU instead of a GPU. The researchers have posted a paper on the arXiv preprint server outlining how the new model was built, its characteristics and how well it has done thus far during testing.

Step by mechanical step, dozens of humanoid robots took to the streets of Beijing early Saturday, joining thousands of their flesh-and-blood counterparts in a world-first half marathon showcasing China's drive to lead the global race in cutting-edge technology.

AI models often rely on "spurious correlations," making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem. The work has been published on the arXiv preprint server.