Every time you open an app, visit the doctor, or make an online purchase, you're generating data. That data feeds the artificial intelligence (AI) systems that help businesses improve services, doctors detect diseases faster, and governments make informed decisions.

Researchers at the Fraunhofer Institute for Factory Operation and Automation IFF have developed cognitive robot capabilities that can handle complex tasks in manufacturing that were previously impossible to automate. In addition, they are also unveiling PARU and computer-aided safety (CAS), the first safety technologies and planning tools for close human-machine collaboration that can also ensure safety in AI-generated robot movements.

Imagine asking a conversational bot like Claude or ChatGPT a legal question in Greek about local traffic regulations. Within seconds, it replies in fluent Greek with an answer based on UK law. The model understood the language, but not the jurisdiction. This kind of failure illustrates the inability of large language models (LLMs) to understand regional, cultural and, in this case, legal knowledge, while at the same time being proficient in many of the world's languages.

Plastics are lightweight, durable and easy to process, making them truly versatile materials for almost any application. This makes it no surprise that more than 60% of packaged goods in Germany are wrapped, bagged or boxed in plastic.

The ability to communicate effectively in spoken English is a key determinant of both academic and professional success. Traditionally, the degree of mastery over English grammar, vocabulary, pronunciation, and communication skills has been assessed through tedious and expensive human-administered tests.

Artificial Intelligence began as a quest to simulate the human brain.

The main problem with big tech's experiment with artificial intelligence (AI) is not that it could take over humanity. It's that large language models (LLMs) like OpenAI's ChatGPT, Google's Gemini and Meta's Llama continue to get things wrong, and the problem is intractable.

The Little Engine That Could wasn't the most powerful train, but she believed in herself. The story goes that, as she set off to climb a steep mountain, she repeated: "I think I can, I think I can."

Artificial intelligence is everywhere—writing emails, recommending movies and even driving cars—but what about the AI you don't see? Who (or what) is behind the scenes developing the algorithms that go unnoticed? And can it be trusted?

As misinformation exploded during India's four-day conflict with Pakistan, social media users turned to an AI chatbot for verification—only to encounter more falsehoods, underscoring its unreliability as a fact-checking tool.

For Silicon Valley venture capitalists, the world has split into two camps: those with deep enough pockets to invest in artificial intelligence behemoths, and everyone else waiting to see where the AI revolution leads.

Google recently unveiled the next phase of its artificial intelligence (AI) journey: "AI mode".

As artificial intelligence takes off, how do we efficiently integrate it into our lives and our work? Bridging the gap between promise and practice, Jann Spiess, an associate professor of operations, information, and technology at Stanford Graduate School of Business, is exploring how algorithms can be designed to most effectively support—rather than replace—human decision-makers.

Teaching AI to explore its surroundings is a bit like teaching a robot to find treasure in a vast maze—it needs to try different paths, but some lead nowhere. In many real-world challenges, like training robots or playing complex games, rewards are few and far between, making it easy for AI to waste time on dead ends.

A team of roboticists and AI specialists at the Robotics & Artificial Intelligence Lab in Korea has designed, built and successfully tested a four-legged robot that is capable of conducting high-speed parkour maneuvers. In their paper published in the journal Science Robotics, the group describes how they gave their robot a controller capable of both planning and tracking its own movements to allow it to freely traverse a range of environments.

Frameworks are a critical, if underappreciated, component of any space exploration mission. They can range from the overall mission architecture, capturing scientific and technical goals, to the structure of messages sent between two internal components of the system.

A new supercomputer named after a winner of the Nobel Prize in chemistry will help power artificial intelligence technology and scientific discoveries from a perch in the hills above the University of California, Berkeley, federal officials said Thursday.

The New York Times has agreed a deal for Amazon to use its content to train artificial intelligence models, the leading U.S. newspaper announced Thursday in its first generative AI licensing deal.

Two and a half years since ChatGPT rocked the world, scientist and writer Gary Marcus still remains generative artificial intelligence's great skeptic, playing a counter-narrative to Silicon Valley's AI true believers.

It is not often that cold, hard facts determine what people care most about and what they believe. Instead, it is the power and familiarity of a well-told story that reigns supreme. Whether it's a heartfelt anecdote, a personal testimony or a meme echoing familiar cultural narratives, stories tend to stick with us, move us and shape our beliefs.

A small team of roboticists at Robotic Systems Lab, ETH Zurich, in Switzerland, has designed, built and tested a four-legged robot capable of playing badminton with human players.

An AI system that learns language autonomously develops a language structured in the same way as human language. And just as we humans learn from previous generations, AI models get better when they take advantage of the knowledge of older relatives.

Artificial intelligence isn't always a reliable source of information: large language models (LLMs) like Llama and ChatGPT can be prone to "hallucinating" and inventing bogus facts. But what if AI could be used to detect mistaken or distorted claims, and help people find their way more confidently through a sea of potential distortions online and elsewhere?

Many policy discussions on AI safety regulation have focused on the need to establish regulatory "guardrails" to protect the public from the risks of AI technology. In a new paper published in the journal Risk Analysis, two experts argue that, instead of imposing guardrails, policymakers should demand "leashes."

The advancement of artificial intelligence (AI) and the study of neurobiological processes are deeply interlinked, as a deeper understanding of the former can yield valuable insight about the other, and vice versa. Recent neuroscience studies have found that mental state transitions, such as the transition from wakefulness to slow-wave sleep and then to rapid eye movement (REM) sleep, modulate temporary interactions in a class of neurons known as layer 5 pyramidal two-point neurons (TPNs), aligning them with a person's mental states.

Internet giants are diving deeper into e-commerce with digital aides that know shoppers' likes, let them virtually try clothes on, hunt for deals and even place orders.

Interactive robots should not just be passive companions, but active partners—like therapy horses who respond to human emotion—say University of Bristol researchers.

Soil liquefaction—the process where saturated soil loses its structure and transforms to a fluid-like state—can have devastating outcomes, as evidenced by the Great East Japan Earthquake in 2011. Large-scale liquefaction during this disaster damaged thousands of houses in the Tokyo Bay area, posing a formidable challenge to infrastructure in Japan.

A leading large language model displays behaviors that resemble a hallmark of human psychology: cognitive dissonance.

The growing proliferation of AI-powered chatbots has led to debates around their social roles as friend, companion or work assistant.