Generative AI models like ChatGPT are trained using vast amounts of data obtained from websites, forums, social media and other online sources; as a result, their responses can contain harmful or discriminatory biases.

As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height on a global level.

Machine-learning models can fail when they try to make predictions for individuals who were underrepresented in the datasets they were trained on.

Tech giant Apple on Wednesday rolled out major updates to its artificial intelligence offerings that integrate OpenAI's ChatGPT into its native applications, including Siri, on the iPhone.

Google on Wednesday announced the launch of Gemini 2.0, its most advanced artificial intelligence model to date, as the world's tech giants race to take the lead in the fast developing technology.

In 2017, the city of Rotterdam in the Netherlands deployed an artificial intelligence (AI) system to determine how likely welfare recipients were to commit fraud. After analyzing the data, the system developed biases: it flagged as "high risk" people who identified as female, young, with kids, and of low proficiency in the Dutch language.

Developing vehicles that can operate safely without a human driver has been a key goal of many teams in the AI research community. As only testing autonomous vehicles on real streets would be both unsafe and unfeasible, their underlying algorithms are first trained and tested extensively in simulations.

Large language models (LLMs) that drive generative artificial intelligence apps, such as ChatGPT, have been proliferating at lightning speed and have improved to the point that it is often impossible to distinguish between something written through generative AI and human-composed text. However, these models can also sometimes generate false statements or display a political bias.

An AI model that creates images as the user types, using only modest and affordable hardware, has been announced by the Surrey Institute for People-Centred Artificial Intelligence (PAI) at the University of Surrey.

Online shopping often involves endless options and fleeting discounts. A single search for running shoes can yield hundreds of results across multiple platforms, each promising the "best deal." The holiday season brings excitement, but it also brings a blend of decision fatigue and logistical nightmares.

A recent study published in the California Management Review by innovation scholars, including Linus Dahlander, professor of strategy and Lufthansa Group Chair in Innovation at ESMT Berlin, presents a comprehensive framework detailing how artificial intelligence (AI) is transforming open innovation practices.

To efficiently compute where waves of light, sound, or earthquakes will go when scattered by irregular obstacles is useful in various fields but difficult and expensive to do, even using recent machine learning techniques. To improve the scalability and practicality of such computations, Laurynas Valantinas and Tom Vettenburg, researchers at the University of Dundee in the UK, mapped the wave equations onto the structure of a recurrent neural network. Its minimal memory requirements allowed them to scale up wave scattering calculations by two orders of magnitude or more.

The arrival of AI systems called large language models (LLMs), like OpenAI's ChatGPT chatbot, has been heralded as the start of a new technological era. And they may indeed have significant impacts on how we live and work in the future.

Image classification is one of AI's most common tasks, where a system is required to recognize an object from a given image. Yet real life requires us to recognize not a single standalone object but rather multiple objects appearing together in a given image.

Quantum computers differ fundamentally from classical ones. Instead of using bits (0s and 1s), they employ "qubits," which can exist in multiple states simultaneously due to quantum phenomena like superposition and entanglement.

Self-driving cars rely on artificial intelligence to predict where nearby cars will go. But when those predictions don't match reality, that discrepancy can potentially lead to crashes and less safe roadways.

On September 11, 2001, during the terrorist attacks in New York, the commander of one of the firefighting teams had an idea that he had to get his team out of the skyscraper they were in. He could not explain why he suddenly ordered everyone out.

Machine-learning models can make mistakes and be difficult to use, so scientists have developed explanation methods to help users understand when and how they should trust a model's predictions.

A team of mechanical engineers at Nanyang Technological University, in Singapore, has developed a way to automate the process of merging live cockroaches and electronics to create cyborg cockroaches, greatly speeding up the process. In their study, available on the arXiv preprint server, the group taught a robot arm to connect electronics to living insects.

OpenAI on Monday released the latest version of its highly anticipated Sora video generator to the public, stepping into an increasingly crowded field of AI tools that has raised concerns about disruption to creative industries.

The capabilities of large-scale pre-trained AI models have recently skyrocketed, as demonstrated by large-scale vision-language models like CLIP or ChatGPT. These typical generalist models can perform reasonably well in tasks covering a large variety of fields, which has paved the way for their widespread adoption by the public. However, such versatility no doubt comes at a cost.

Imagine an artificial intelligence (AI) model that can watch and understand moving images with the subtlety of a human brain. Now, scientists at Scripps Research have made this a reality by creating MovieNet: an innovative AI that processes videos much like how our brains interpret real-life scenes as they unfold over time.

Chatbots can wear a lot of proverbial hats: dictionary, therapist, poet, all-knowing friend. The artificial intelligence models that power these systems appear exceptionally skilled and efficient at providing answers, clarifying concepts, and distilling information. But to establish trustworthiness of content generated by such models, how can we really know if a particular statement is factual, a hallucination, or just a plain misunderstanding?

To help humans to complete everyday manual tasks, robots should be able to reliably manipulate everyday objects that vary in shape, texture and size. Many conventional approaches to enable robotic manipulation of various objects rely on extensive training and precise programming, also delineating the properties of objects that the robots will be manipulating.

Businesses are already being radically transformed by artificial intelligence (AI). Tools now exist that offer instantaneous, high-quality results in improving certain operations without the burden of high costs or delays. In fact, generative AI could completely upend the traditional ways that we measure success in business.

A team led by Prof. Dong Erbao from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences (CAS), collaborating with Prof. Yu Xinge from the City University of Hong Kong, developed a novel tactile perception method based on flexible grating structural color. The work was published in National Science Review.

Modeling how cars deform in a crash, how spacecraft respond to extreme environments, or how bridges resist stress could be made thousands of times faster thanks to new artificial intelligence that enables personal computers to solve massive math problems that generally require supercomputers.

In recent years, engineers have been trying to create hardware systems that better support the high computational demands of machine learning algorithms. These include systems that can perform multiple functions, acting as sensors, memories and computer processors all at once.

Researchers at Stanford Engineering have developed an AI-trained model to accurately recreate the hand movements of elite-level pianists and the physical stresses they endure while playing.

A team of roboticists at the Beijing Institute of Technology, working with a pair of colleagues from the Technical University of Munich, has created a new kind of rat robot—one that was designed to interact in social ways with real rats.