Earlier this year, Facebook rolled back rules against some hate speech and abuse. Along with changes at X (formerly Twitter) that followed its purchase by Elon Musk, the shifts make it harder for social media users to avoid encountering toxic speech.

Animators could create more realistic bouncy, stretchy, and squishy characters for movies and video games thanks to a new simulation method developed by researchers at MIT.

Given the recent explosion of large language models (LLMs) that can make convincingly human-like statements, it makes sense that there's been a deepened focus on developing the models to be able to explain how they make decisions. But how can we be sure that what they're saying is the truth?

As meaning-makers, we use spoken or signed language to understand our experiences in the world around us. The emergence of generative artificial intelligence such as ChatGPT (using large language models) calls into question the very notion of how to define "meaning."

A novel vision sensor inspired by the neural transmission mechanisms of the human brain has been developed to efficiently and accurately extract object outlines even under fluctuating lighting environments. This advancement promises to significantly enhance perception capabilities in autonomous vehicles, drones, and robotic systems, enabling faster and more precise recognition of surroundings.

Social media outlet Reddit filed a lawsuit Wednesday against artificial intelligence company Anthropic, accusing the startup of illegally scraping millions of user comments to train its Claude chatbot without permission or compensation.

A trio of AI researchers at KAIST AI, in Korea, has developed what they call a Chain-of-Zoom framework that allows the generation of extreme super-resolution imagery using existing super-resolution models without the need for retraining.

Whether you're streaming a show, paying bills online or sending an email, each of these actions relies on computer programs that run behind the scenes. The process of writing computer programs is known as coding. Until recently, most computer code was written, at least originally, by human beings. But with the advent of generative artificial intelligence, that has begun to change.

Researchers at Edith Cowan University (ECU) are helping machines become more emotionally aware using a new method that allows them to better recognize human facial expressions.

Can AI do humor? A new study suggests artificial intelligence can create internet memes as funny as those made by humans. But when it comes to gags that truly connect with viewers, people still have the edge.

Many bold claims have been made about generative artificial intelligence (GenAI) and its capacity to improve productivity and generate workplace efficiencies.

A new study finds that machine learning can help reduce textile manufacturing waste by more accurately mapping how colors will change during the dyeing process.

Even with all its training and computer power, an artificial intelligence (AI) tool like ChatGPT can't represent the concept of a flower the way a human does, according to a new study.

Concerned about the rapid spread of generative AI, a pioneer researcher is developing software to keep tabs on a technology that is increasingly taking over human tasks.

Generative artificial intelligence is everywhere, but not everyone is ready to embrace it—and it's not just people who fear that AI might replace their jobs or that ChatGPT will become sentient and take over the world.

Artificial intelligence systems like ChatGPT provide plausible-sounding answers to any question you might ask. But they don't always reveal the gaps in their knowledge or areas where they're uncertain. That problem can have huge consequences as AI systems are increasingly used to do things like develop drugs, synthesize information, and drive autonomous cars.

A new AI system that can automatically identify contaminated construction and demolition wood waste has been developed by researchers from Monash University and Charles Darwin University (CDU).

When you're trying to communicate or understand ideas, words don't always do the trick. Sometimes the more efficient approach is to do a simple sketch of that concept—for example, diagramming a circuit might help make sense of how the system works.

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".