More than 1.5 billion people worldwide are expected to watch the 2026 World Cup finals. With that many fans scrutinizing every pass, touch and goal, FIFA is leaning on advanced computer vision technology to help referees make faster, more accurate calls on the way to crowning this year's victors.

Clarkson University researchers have developed a new mathematical tool that could make artificial intelligence systems more accurate, controllable and useful across applications ranging from image editing to drug discovery.

Combining ideas inspired by ant colonies and flocks of birds may hold the key to unlocking more effective artificial intelligence, according to a researcher at Missouri S&T. "With the way AI algorithms are currently structured, they sometimes settle on an answer that seems good enough and stop searching before finding one that may be much better," says Dr. Donald Wunsch, director of Missouri S&T's Kummer Institute Center for AI and Autonomous Systems. "It's important that we find ways to help these algorithms keep searching instead of stopping too soon. When AI is used in areas that affect people's health, safety or cost of living, the difference between good enough and great can have significant implications."

In his 1927 paper, "A law of comparative judgment," the American psychologist L. L. Thurstone proposed that when people select one option among multiple alternatives, they are picking the one that has the highest value to them, even though they cannot assign a particular number to that choice.

A single photograph contains a wealth of information, but determining 3D spatial relationships from a 2D scene is no simple task. Many attempts have been made to develop a method to reconstruct both depth and sharp color images from a single snapshot, but many struggle to deliver accurate and reliable output.

Researchers in Japan have developed quantum multi-programming auto mode, a function that automatically runs quantum programs from different users in parallel. Launched on the Center for Quantum Information and Quantum Biology (QIQB) quantum computer cloud service at the University of Osaka, the system reduces idle qubit resources, improves throughput and may help ease congestion in quantum cloud computing.

Research published in the International Journal of Information and Communication Technology has taken inspiration from the hunting behavior of the fish-eating bird of prey, the osprey, and combined this with inspiration from quantum computing to improve machine translation, particularly for long sentences and technical texts between Chinese and English.

Workshopping, an iterative process in which creators share ideas, test what works and refine what doesn't through collective feedback, is at the heart of any writers group. This collaborative dynamic inspired George Mason University Ph.D. student Shiwei Hong to explore whether artificial intelligence (AI) could benefit from a similar approach.