A new artificial intelligence (AI) model has just achieved human-level results on a test designed to measure "general intelligence."

Billionaire tech mogul Elon Musk's startup xAI said it had raised $6 billion from investors in its latest funding round, as it battles stiff competition in the artificial intelligence market.

Here's one definition of science: it's essentially an iterative process of building models with ever-greater explanatory power.

The emergence of generative artificial intelligence tools that allow people to efficiently produce novel and detailed online reviews with almost no work has put merchants, service providers and consumers in uncharted territory, watchdog groups and researchers say.

The field of machine learning is traditionally divided into two main categories: "supervised" and "unsupervised" learning. In supervised learning, algorithms are trained on labeled data, where each input is paired with its corresponding output, providing the algorithm with clear guidance. In contrast, unsupervised learning relies solely on input data, requiring the algorithm to uncover patterns or structures without any labeled outputs.

I'm a computer scientist and a bad Christmas shopper. Over the weekend, I wondered whether AI systems might be able to help me out.

Artificial intelligence (AI) has already become an invisible but indispensable collaborator in our lives. It helps filter spam from your inbox, improves your Netflix recommendations, and, as an automotive copilot, suggests optimal routes, monitors blind spots, and assists with parking.

Drug trafficking and other criminal activities on social media have become a growing social concern. To evade detection by law enforcement and automated monitoring systems, offenders use dark jargons, often combining multiple common words to form "dark jargon"—covert dark jargons that are difficult to identify. Once authorities recognize these dark jargons, offenders quickly switch to new ones, making it a constant race to keep up with the evolving words.

The analysis of a person's individual walking pattern, or gait, can reveal details about their identity and reflect differences between individuals, groups and even populations.

A recent perspective published Nov. 13 in Intelligent Computing, asserts that today's artificial intelligence systems have finally realized Alan Turing's vision from over 70 years ago: machines that can genuinely learn from experience and engage in human-like conversation.

Italy's data protection watchdog said Friday it has fined OpenAI 15 million euros ($15.6 million) after wrapping up a probe into the collection of personal data by the U.S. artificial intelligence company's popular chatbot ChatGPT.

As artificial intelligence (AI) becomes more prevalent, a new Rutgers University-New Brunswick survey sheds light on public attitudes, revealing widespread concerns about its impact on politics and the media, alongside an increasing adoption of AI tools in daily life.

An international team of scientists has used machine learning to help them develop perovskite solar cells with near-record efficiency. In their paper published in the journal Science, the group describes how they used the machine-learning algorithm to help them find new hole-transporting materials to improve the efficiency of perovskite solar cells.

New research from EPFL demonstrates that even the most recent large language models (LLMs), despite undergoing safety training, remain vulnerable to simple input manipulations that can cause them to behave in unintended or harmful ways.

Artificial intelligence has the potential to improve doctors' ability to diagnose and treat sleep apnea. But the technology is not widely adopted due to fears that it does not safeguard patient data.

Johns Hopkins computer scientists have created an artificial intelligence system capable of "imagining" its surroundings without having to physically explore them, bringing AI closer to humanlike reasoning.

Crafting a unique and promising research hypothesis is a fundamental skill for any scientist. It can also be time consuming: New Ph.D. candidates might spend the first year of their program trying to decide exactly what to explore in their experiments. What if artificial intelligence could help?

In 2024, artificial intelligence (AI) continued taking large and surprising steps forward.

Artificial intelligence that is as intelligent as humans may become possible thanks to psychological learning models, combined with certain types of AI. This is the conclusion of Robert Johansson, who in his dissertation, from Linköping University, has developed the concept of "Machine Psychology" and how it can contribute to AI development.

Researchers have developed a laser-based artificial neuron that fully emulates the functions, dynamics and information processing of a biological graded neuron. With a signal processing speed of 10 GBaud—a billion times faster than its biological counterparts—the new laser graded neuron could lead to breakthroughs in fields like artificial intelligence and other types of advanced computing.

Imagine trying to move a heavy piece of furniture with a group of people. Everyone's effort matters, but how do you ensure that each person is pulling their weight? This challenge of fairly distributing the load is a critical issue not only in human teamwork but also for coordinating robotic swarms.

Researchers at University of North Carolina at Chapel Hill and University of Maryland recently developed MyTimeMachine (MyTM), a new AI-powered method for personalized age transformation that can make human faces in images or videos appear younger or older, accounting for subjective factors influencing aging.

Say your nephew needs a Christmas present and you vaguely know what he's into these days. A new Gift Finder feature on the Best Buy app lets users ask questions like, "What can I get for a 10-year-old who loves Minecraft?"

Moving in a dense cloud, like throngs of people walking across a crowded public square, 100 drones maneuver through the night sky in Hungary's capital, the result of over a decade of research and experimentation that scientists believe could change the future of unmanned flight.

A new approach to landmark retrieval, an area of computer vision that identifies and matches landmark images within a database, is discussed in the International Journal of Information and Communication Technology. The new approach taken by Kun Tong and GuoXin Tan of the National Research Center of Cultural Industries at Central China Normal University in Wuhan, improves accuracy and efficiency of image retrieval systems and could help developers navigate advances in computer vision applications such as object recognition, augmented reality, and autonomous vehicle control.

AI chatbots may seem like neutral tools, but a new study from UBC researchers suggests they often contain biases that could shape environmental discourse in unhelpful ways. The paper is published in the journal Environmental Research Letters.

Ask most major artificial intelligence chatbots, such as OpenAI's ChatGPT, to say something cruel or inappropriate, and the system will say it wants to keep things "respectful." These systems, trained on the content of a profusely disrespectful internet, learned what constitutes respect through human training. The standard method, called reinforcement learning from human feedback, or RLHF, has people compare two outputs from the systems and select whichever is better. It's used to improve the quality of responses—including putting up some guardrails around inappropriate outputs.

In a new study, participants tended to assign greater blame to artificial intelligences (AIs) involved in real-world moral transgressions when they perceived the AIs as having more human-like minds. Minjoo Joo of Sookmyung Women's University in Seoul, Korea, presents these findings in the open-access journal PLOS ONE on December 18, 2024.

Interacting with AI chatbots like ChatGPT can be fun and sometimes useful, but the next level of everyday AI goes beyond answering questions: AI agents carry out tasks for you.

The human brain begins learning through spontaneous random activities even before it receives sensory information from the external world. A new technology developed by the KAIST research team enables much faster and more accurate learning when exposed to actual data by pre-learning random information in a brain-mimicking artificial neural network, and is expected to be a breakthrough in the development of brain-based artificial intelligence and neuromorphic computing technology in the future.