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.

Programmers can now use large language models (LLMs) to generate computer code more quickly. However, this only makes programmers' lives easier if that code follows the rules of the programming language and doesn't cause a computer to crash.

Creativity is no longer exclusive to humans. Some forms of artificial intelligence are capable of producing poetry, entrepreneurial concepts, even visual art. Many people use large language models (LLMs) such as ChatGPT, which are trained on vast amounts of text, for co-creation: The artificial intelligence offers ideas and suggestions, while the human provides guidance, context, and direction.

As television grew rapidly in popularity in the second half of the 20th century, many people assumed it would cause a knock-on crisis for the film industry. After all, it meant that viewers no longer had to leave their sofas to enjoy onscreen entertainment.

ChatGPT and other AI chatbots based on large language models are known to occasionally make things up, including scientific and legal citations. It turns out that measuring how accurate an AI model's citations are is a good way of assessing the model's reasoning abilities.

As Internet of Things (IoT) devices proliferate in sectors like smart cities, health care, and industrial systems, they have become prime targets for cyberattacks such as Distributed Denial of Service (DDoS), ransomware, and botnets. However, traditional security methods struggle to cope with these attacks due to the limited computational power of IoT devices and the dynamic nature of cyber threats.

When some commuter trains arrive at the end of the line, they must travel to a switching platform to be turned around so they can depart the station later, often from a different platform than the one at which they arrived.

A team of Lehigh University researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time—a development that could lead to the creation of stronger, more reliable materials for high-stress environments, such as combustion engines. A paper describing their novel machine learning method was recently published in Nature Computational Materials.

South Korean actor Simon Lee was stunned when he saw his likeness—at times as a gynecologist or a surgeon—being used to promote questionable health cures on TikTok and Instagram.

For the first time, a drone has beaten human pilots in an international drone racing competition, marking a new milestone in the development of artificial intelligence. On Saturday April 14, 2025, two drone racing events took place simultaneously: The Falcon Cup Finals for human pilots and the A2RL Drone Championship for AI-powered, autonomous drones.

Penn Engineers have developed the first programmable chip that can train nonlinear neural networks using light—a breakthrough that could dramatically speed up AI training, reduce energy use and even pave the way for fully light-powered computers.

It seems that no matter the topic of conversation, online opinion around it will be split into two seemingly irreconcilable camps.

AI models like ChatGPT have amazed the world with their ability to write poetry, solve equations and even pass medical exams. But they can also churn out harmful content, or promote disinformation.

Earlier this year, scientists discovered a peculiar term appearing in published papers: "vegetative electron microscopy."

Fraud is widespread in the United States and increasingly driven by technology. For example, 93% of credit card fraud now involves remote account access, not physical theft. In 2023, fraud losses surpassed $10 billion for the first time.

As we mature from childhood, our vocabulary—as well as the ways we use it—grows, and our experiences become richer, allowing us to think, reason, and interact with others with specificity and intention. Accordingly, our word choices evolve to align with our personal values, ethics, cultural norms, and views.

The Titanic sank 113 years ago on April 14–15, after hitting an iceberg, with human error likely causing the ship to stray into those dangerous waters. Today, autonomous systems built on artificial intelligence can help ships avoid such accidents, but could such a system explain to the captain why it was maneuvering a certain way?

They have names, faces, and email addresses, but the five new colleagues at Denmark's Royal Unibrew only exist in the virtual realm, which the brewer hails as a milestone to unleash the full potential of its staff.

A new ultra-lightweight artificial intelligence (AI) model has been developed that assists in generating high-quality images without directly sending sensitive data to servers. This technological advancement paves the way for the safe utilization of high-performance generative AI in environments where privacy is paramount, such as in the analysis of patient MRI and CT scans.

Neural networks are one typical structure on which artificial intelligence can be based. The term "neural" describes their learning ability, which to some extent mimics the functioning of neurons in our brains. To be able to work, several key ingredients are required: one of them is an activation function which introduces nonlinearity into the structure.

Dreaming of going on vacation but not sure where to go? Increasingly the answer—and how to get there, what to eat and where to stay—will come from advanced AI, rather than your old-school travel agent.

A small team of AI researchers from Carnegie Mellon University, Stanford University, Harvard University and Princeton University, all in the U.S., has found that if large language models are over-trained, it might make them harder to fine-tune. In their paper posted on the arXiv preprint server, the group compared the impact of different amounts of training on a single LLM.

We are constantly fed a version of AI that looks, sounds and acts suspiciously like us. It speaks in polished sentences, mimics emotions, expresses curiosity, claims to feel compassion, even dabbles in what it calls creativity.

Ideally, artificial intelligence agents aim to help humans, but what does that mean when humans want conflicting things? My colleagues and I have come up with a way to measure the alignment of the goals of a group of humans and AI agents.