Making Artificial Intelligence systems robustly perceive humans remains one of the most intricate challenges in computer vision. Among the most complex problems is reconstructing 3D models of human hands, a task with wide-ranging applications in robotics, animation, human-computer interaction, and augmented and virtual reality. The difficulty lies in the nature of hands themselves, often obscured while holding objects or contorted into challenging orientations during tasks like grasping.

Studies have long shown that men are more likely to interrupt, particularly when speaking with women. New research by Johns Hopkins engineers reveals that this behavior also extends to AI-powered voice assistants like Alexa and Siri, with men interrupting them almost twice as often as women do. The findings are published in Proceedings of the ACM on Human-Computer Interaction.

The search for long-lost shipwrecks, downed aircraft and even rare species of coral and fish could become easier thanks to an image enhancement technology developed by James Cook University researchers.

Apple pushed out a software update on Thursday which disabled news headlines and summaries generated using artificial intelligence that were lambasted for getting facts wrong.

Researchers have developed a fast and accurate flexible optical skin that can be used to read Braille. The advance could not only improve access to information for people who are blind but also help move us closer to a future where accessible and adaptable technology can benefit everyone.

Artificial intelligence (AI) once seemed like a fantastical construct of science fiction, enabling characters to deploy spacecraft to neighboring galaxies with a casual command. Humanoid AIs even served as companions to otherwise lonely characters. Now, in the very real 21st century, AI is becoming part of everyday life, with tools like chatbots available and useful for everyday tasks like answering questions, improving writing, and solving mathematical equations.

The UK government's goal to increase public-controlled artificial intelligence computing power twentyfold by 2030 would significantly raise electricity demand. Can renewable energy supply meet it—and still have enough left over to electrify sectors like heating and transport, which must be fully decarbonized by 2050?

Boxing, the "sweet science," is a theater of skill, power and drama that has captivated audiences for centuries. Yet, behind the allure of championship nights and historic rivalries lies a persistent shadow—controversial decisions that spark cries of "robbery."

Advanced computing and data centers are massive energy consumers. In fact, a recent report from Lawrence Berkeley National Laboratory found that U.S. data centers consumed about 4.4% of total electricity in 2023 and projects them to potentially triple consumption by 2028. Widespread deployment of artificial intelligence (AI) is a driving factor of this rise in energy consumption and is already having an economic impact as electricity costs rise for consumers.

Reservoir computing (RC) is a powerful machine learning module designed to handle tasks involving time-based or sequential data, such as tracking patterns over time or analyzing sequences. It is widely used in areas such as finance, robotics, speech recognition, weather forecasting, natural language processing, and predicting complex nonlinear dynamical systems. What sets RC apart is its efficiency―it delivers powerful results with much lower training costs compared to other methods.

Accurate object pose estimation refers to the ability of a robot to determine both the position and orientation of an object. It is essential for robotics, especially in pick-and-place tasks, which are crucial in industries such as manufacturing and logistics.

Over the past two years, generative artificial intelligence (AI) has captivated public attention. This year signals the beginning of a new phase: the rise of AI agents.

As international trade and global security become more reliant on marine resources, the demand for advanced maritime surveillance and port management has never been greater. One of the big challenges in this area is the detection of ships in complex environments, a task that has traditionally relied on manual techniques. These methods, while functional, are often inadequate in dynamic, cluttered marine conditions, where varying sea states, weather patterns, and ship sizes can easily confound detection efforts.

A new initiative steered by Concordia University researchers is challenging the conversation around the direction of artificial intelligence (AI). It charges that the current trajectory is inherently biased against non-Western modes of thinking about intelligence—especially those originating from Indigenous cultures.

Researchers from Skoltech and the Institute of High Temperature Electrochemistry of UB RAS have developed and tested a model based on machine learning that predicts the properties of molten salts. These compounds are already used in metallurgy and hold promise for resolving the problem of mounting nuclear waste.

Effective urban traffic management remains a cornerstone of smart city development. With the rise of autonomous vehicles and connected transportation systems, dynamic surveillance solutions are critical to ensuring smooth traffic flow, minimizing accidents, and optimizing efficiency. However, traditional static camera setups often fail to adapt to rapid changes in traffic conditions, resulting in inefficient monitoring and resource use.

Mark Zuckerberg's recent decision to remove factcheckers from Meta's platforms—including Facebook, Instagram and Threads—has sparked heated debate. Critics argue it may undermine efforts to combat misinformation and maintain credibility on social media platforms.

A tool developed by Keele University researchers has been shown to help detect fake news with an impressive 99% level of accuracy, offering a vital resource in combating online misinformation.

An AI model that can translate speech and text, including direct speech-to-speech translations, for up to 101 languages is described in Nature. The model, named SEAMLESSM4T, fills gaps in language coverage and outperforms existing systems. The work may pave the way for rapid universal translations, with resources being made publicly available (for non-commercial use) to assist further research on inclusive speech translation technologies.

Sign languages have been developed by nations around the world to fit the local communication style, and each language consists of thousands of signs. This has made sign languages difficult to learn and understand.

Automatic speech recognition (ASR) has made incredible advances in the past few years, especially for widely spoken languages such as English. Prior to 2020, it was typically assumed that human abilities for speech recognition far exceeded automatic systems, yet some current systems have started to match human performance.

A recent study involving Central European University (CEU) is paving the way for a new field of research that merges artificial intelligence and complexity science to understand how ongoing interaction between humans and algorithms can profoundly impact social dynamics.

A team of AI researchers at Mohamed bin Zayed University of AI, in Abu Dhabi, working with a colleague from the University of Central Florida, has developed a curriculum learning–based LLM, called LlamaV-o1, that its makers claim shows the benefits of step-by-step reasoning in AI systems. In their study, published on the arXiv preprint server (and also on GitHub), the group built their LLM with a new level of step-by-step reasoning to understand how it arrives at its answers.

President Joe Biden on Tuesday signed an ambitious executive order on artificial intelligence that seeks to ensure the infrastructure needed for advanced AI operations, such as large-scale data centers and new clean power facilities, can be built quickly and at scale in the United States.

Vijay Gadepally, a senior staff member at MIT Lincoln Laboratory, leads a number of projects at the Lincoln Laboratory Supercomputing Center (LLSC) to make computing platforms, and the artificial intelligence systems that run on them, more efficient.

Years ago, as she sat in waiting rooms, Maytal Saar-Tsechansky began to wonder how people chose a good doctor when they had no way of knowing a doctor's track record on accurate diagnoses. Talking to other patients, she found they sometimes based choices on a physician's personality or even the quality of their office furniture.

A study from Oxford University has uncovered why the deep neural networks (DNNs) that power modern artificial intelligence are so effective at learning from data.

AI applications like ChatGPT are based on artificial neural networks that, in many respects, imitate the nerve cells in our brains. They are trained with vast quantities of data on high-performance computers, gobbling up massive amounts of energy in the process.

Last week the billionaire and owner of X, Elon Musk, claimed the pool of human-generated data that's used to train artificial intelligence (AI) models such as ChatGPT has run out.

Birth asphyxia (BA) is a condition that occurs when newborn babies do not receive enough oxygen during delivery, and it's one of the primary causes of neonatal death. Developing countries, particularly in sub-Saharan Africa, experience the highest under-five mortality rates. Researchers from Carnegie Mellon University are developing a new mobile application, called HumekaFL, to detect BA.