Researchers from Simon Fraser University have unveiled an artificial intelligence framework that could transform drug development and accelerate the discovery of new medicines.

A self-driving motor vehicle is cruising along, its numerous sensors and cameras telling it when to brake, change lanes, and make turns. The vehicle approaches a stop sign at a high rate of speed, but instead of stopping, it barrels through, causing an accident. The problem will probably never be found by investigators: Instead of reading the stop sign as a stop sign, the car had been hacked to see it as a speed limit sign.

Imagine a busy train station. Cameras monitor everything, from how clean the platforms are to whether a docking bay is empty or occupied. These cameras feed into an AI system that helps manage station operations and sends signals to incoming trains, letting them know when they can enter the station.

From self-driving cars to facial recognition, modern life is growing more dependent on machine learning, a type of artificial intelligence (AI) that learns from datasets without explicit programming.

Arts minister Tony Burke said last week, "We have copyright laws. We have no plans, no intention, no appetite to be weakening those copyright laws based on this draft report that's floating around."

On a sunny day in San Francisco, along the city's waterfront, families dived into the wacky world of artificial intelligence inside the Exploratorium museum.

To effectively tackle a variety of real-world tasks, robots should be able to reliably grasp objects of different shapes, textures and sizes, without dropping them in undesired locations. Conventional approaches to enhancing the ability of robots to grasp objects work by tightening the grip of a robotic hand to prevent objects from slipping.

Elon Musk has taken his feud against OpenAI to the App Store, accusing Apple of favoring ChatGPT in the digital shop and vowing legal action.

Large language models are built with safety protocols designed to prevent them from answering malicious queries and providing dangerous information. But users can employ techniques known as "jailbreaks" to bypass the safety guardrails and get LLMs to answer a harmful query.

Researchers from the University of Oxford, EleutherAI, and the UK AI Security Institute have reported a major advance in safeguarding open-weight language models. By filtering out potentially harmful knowledge during training, the researchers were able to build models that resist subsequent malicious updates—especially valuable in sensitive domains such as biothreat research.

Accurately estimating human pose was among the first tasks addressed by deep learning. Early models like OpenPose focused on localizing human joints as 2D keypoints in image coordinates. Later, Google came up with Mediapipe, followed by YOLOpose, which gained major attention and is widely adopted due to its efficiency and accuracy.

OpenAI claims that its new flagship model, GPT-5, marks "a significant step along the path to AGI"—that is, the artificial general intelligence that AI bosses and self-proclaimed experts often claim is around the corner.

Researchers have demonstrated that brain cells learn faster and carry out complex networking more effectively than machine learning by comparing how both a Synthetic Biological Intelligence (SBI) system known as "DishBrain" and state-of-the-art RL (reinforcement learning) algorithms react to certain stimuli.

A new technique developed by Australian researchers could stop unauthorized artificial intelligence (AI) systems learning from photos, artwork and other image-based content.

For Hong Kong law student "C," AI-made pornography was a distant danger until a university classmate created graphic images of her and multiple other women.

Researchers from the Faculty of Data and Decision Sciences at the Technion have developed a technology capable of identifying various aspects of a reader's interaction with a text that is based solely on their eye movements.

The University of Cincinnati is working with Ohio's Bureau of Workers' Compensation to use digital-twin technology to make workplaces safer.

"A building is only as strong as its foundation" is a common adage to signify the importance of having a stable and solid base to build upon. The type and design of foundation are important for ensuring the structural safety of a building.

Humanoid robots, robots with a human-like body structure, have so far been primarily tested on manual tasks that entail supporting humans in their daily activities, such as carrying objects, collecting samples in hazardous environments, supporting older adults or acting as physical therapy assistants. In contrast, their potential for completing expressive physical tasks rooted in creative disciplines, such as playing an instrument or participating in performance arts, remains largely unexplored.

OpenAI on Thursday released the fifth generation of the artificial intelligence technology that powers ChatGPT, a product update that's being closely watched as a measure of whether generative AI is advancing rapidly or hitting a plateau.

In an article published in Frontiers in Robotics and AI, Dr. Zhao Zhao and her colleagues have investigated the long-term lifespan of a social robot given to 20 families in 2021 to see whether it could help their children learn to read. Four years after their previous study, the robot was no longer needed for its primary purpose, but that didn't mean it was no longer wanted.

OpenAI released a keenly awaited new generation of its hallmark ChatGPT on Thursday, touting "significant" advancements in artificial intelligence capabilities as a global race over the technology accelerates.

Generative artificial intelligence (AI) is getting better at imitating human beings. It can create things that previously only humans could produce, like music, texts and images. AI is now also being used to imitate God, through chatbots that simulate conversation with human users and can be accessed on websites and apps.

High-quality output at low latency is a critical requirement when using large language models (LLMs), especially in real-world scenarios, such as chatbots interacting with customers, or the AI code assistants used by millions of users daily.

AI seems to be well on its way to becoming pervasive. You hear rumbles of AI being used, somewhere behind the scenes, at your doctor's office. You suspect it may have played a role in hiring decisions during your last job search. Sometimes—maybe even often—you use it yourself.

Any motorist who has ever waited through multiple cycles for a traffic light to turn green knows how annoying signalized intersections can be. But sitting at intersections isn't just a drag on drivers' patience—unproductive vehicle idling could contribute as much as 15% of the carbon dioxide emissions from U.S. land transportation.

When we look at the world, our brain doesn't just recognize objects such as "a dog" or "a car," it also understands the broader meaning, like what's happening, where it's happening, and how everything fits together. But for years, scientists didn't have a good way to measure that rich, complex understanding.

Fuel cells are energy solutions that can convert the chemical energy in fuels into electricity via specific chemical reactions, instead of relying on combustion. Promising types of fuel cells are direct methanol fuel cells (DMFCs), devices specifically designed to convert the energy in methyl alcohol (i.e., methanol) into electrical energy.

A new approach called open ad-hoc categorization (OAK) helps AI systems dynamically reinterpret the same image differently depending on the categorization context, rather than using fixed visual interpretation. A University of Michigan-led study on this topic was presented in June 2025 at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) in Nashville, Tennessee.

Imagine trying to make an accurate three-dimensional model of a building using only pictures taken from different angles—but you're not sure where or how far away all the cameras were. Our big human brains can fill in a lot of those details, but computers have a much harder time doing so.