Over the past couple of decades, computer scientists have developed a wide range of deep neural networks (DNNs) designed to tackle various real-world tasks. While some of these models have proved to be highly effective, some studies found that they can be unfair, meaning that their performance may vary based on the data they were trained on and even the hardware platforms they were deployed on.

The rapid development of artificial intelligence (AI) in the past decade has greatly enhanced the ability of wearable devices to process complex data. Among them, machine learning—a major category of AI algorithms—and its important branch, deep learning, are the main thrust of this wave of AI.

Training deep learning models on large datasets is essential for their success; however, these datasets often contain label noise, which can significantly decrease the classification performance on test datasets.

Characterized by their intricate patterns and hierarchical designs, lattice structures hold immense potential for revolutionizing industries ranging from aerospace to biomedical engineering, due to their versatility and customizability. However, the complexity of these structures and the vast design space they encompass have posed significant hurdles for engineers and scientists, and traditional methods of design exploration and optimization often fall short when faced with the sheer magnitude of possibilities within the lattice-design landscape.

The narrative around the infiltration of artificial intelligence (AI) into our professional lives is often tinged with fear. Can a computer do what I do faster and more cheaply? Will my skills become obsolete? What if a robot takes my job?

A research team has developed a computer vision technique that can perform dichotomous image segmentation, high-resolution salient object detection, and concealed object detection in the same framework. Their novel bilateral reference framework (BiRefNet) is able to capture tiny-pixel features and holds potential for a wide range of practical computer vision applications.

In the competitive arena of global technology, China's ambitions in artificial intelligence stand out—not just for their scale but for their distinct strategic approach.

A team of AI researchers and computer scientists at the University of Alberta has found that current artificial networks used with deep-learning systems lose their ability to learn during extended training on new data. In their study, reported in the journal Nature, the group found a way to overcome these problems with plasticity in both supervised and reinforcement learning AI systems, allowing them to continue to learn.

The office of the Australian Information Commissioner announced this week it would be taking no further action against facial recognition company Clearview AI. This marked a significant victory for one of the most controversial technology companies in the world.

A new study from the Oxford Internet Institute (part of the University of Oxford) and the London School of Economics examines how governments have gradually adopted Silicon Valley's way of working.

An artificial intelligence algorithm can allow researchers to more efficiently use 3D printing to manufacture intricate structures.

When five-year-old goldendoodle Mozzi started walking "unnaturally," his concerned owner rushed him to the vet in South Korea, where the problem was diagnosed not by humans but by AI.

California will be the first U.S. state to direct millions of dollars from taxpayer money and tech companies to help pay for journalism and AI research under a new deal announced Wednesday.

A new national survey has found that most adult Australians use different types of digital media regularly, yet their overall confidence in their digital media abilities remains quite low, showing very little change since 2021.

A multidisciplinary research team based across China and Brazil has used a dog-like robot and AI to create a new way to find fire ant nests. Published in the journal Pest Management Science, the study highlights how a "CyberDog" robot integrated with an AI model can automate the identification and control of Red Imported Fire Ants (RIFA), a globally destructive pest.

Scientific discovery is one of the most sophisticated human activities. First, scientists must understand the existing knowledge and identify a significant gap. Next, they must formulate a research question and design and conduct an experiment in pursuit of an answer. Then, they must analyze and interpret the results of the experiment, which may raise yet another research question.

We are excited to present an advancement in the management of lithium-ion battery performance, a critical component in the transition towards sustainable energy. Our team from the School of Engineering, Technology, and Design at Canterbury Christ Church University, U.K., has focused on utilizing machine/deep learning to enhance the State of Charge (SOC) estimation for lithium-ion batteries, particularly those being repurposed for second-life applications.

A team of Chinese scientists has established a novel brain-inspired network model based on internal complexity to address challenges faced by traditional models, such as high consumption of computing resources, according to the Institute of Automation under the Chinese Academy of Sciences.

A team of AI researchers and computer scientists from Cornell University, the University of Washington and the Allen Institute for Artificial Intelligence has developed a benchmarking tool called WILDHALLUCINATIONS to evaluate the factuality of multiple large language models (LLMs). The group has published a paper describing the factors that went into creating their tool on the arXiv preprint server.

Artificial intelligence (AI) and machine learning tools have proved to be highly effective in tackling various tasks that entail analyzing data and making accurate predictions. Despite their advantages, these tools have significant computational demands, and when running on existing processing units, they can consume a lot of energy.

Artificial intelligence can completely transform current driver training. This is also the aim of a collaboration between NTNU, the IT company Virtur, and WAY AS in Trondheim, which is Norway's only simulator-based driving school.

When we flip the light switch in our homes, we have come to expect instant access to electricity. Behind the scenes, that reliability depends on utility operators who have developed control systems and fail-safes to keep the power flowing.

ChatGPT and other large language models (LLMs) cannot learn independently or acquire new skills, meaning they pose no existential threat to humanity, according to new research from the University of Bath and the Technical University of Darmstadt in Germany.

On a research cruise around Hawaii in 2018, Yuening Zhang SM '19, Ph.D. '24 saw how difficult it was to keep a tight ship. The careful coordination required to map underwater terrain could sometimes lead to a stressful environment for team members, who might have different understandings of which tasks must be completed in spontaneously changing conditions.

Porous media play a critical role in various industrial fields due to their complex pore networks and considerable specific surface areas. The transport and reaction phenomena within porous media are key factors influencing fundamental parameters such as energy storage efficiency, catalytic performance, and adsorption rates.

The increase in high-rise and densely populated urban development has heightened the demand for safety and resilience solutions against emergencies, such as fires. The Hong Kong Polytechnic University (PolyU) has created advanced technological solutions to enhance firefighting and urban resilience.

Less than two years ago, the launch of ChatGPT started a generative AI frenzy. Some said the technology would trigger a fourth industrial revolution, completely reshaping the world as we know it.

After becoming mainstream in 2023, generative artificial intelligence (AI) is now transforming the way we live.

Artificial intelligence (AI) prophets and newsmongers are forecasting the end of the generative AI hype, with talk of an impending catastrophic "model collapse."

Cognitive flexibility, the ability to rapidly switch between different thoughts and mental concepts, is a highly advantageous human capability. This salient capability supports multi-tasking, the rapid acquisition of new skills and the adaptation to new situations.