Researchers have created an innovative system for generating personalized storybooks. This system utilizes generative artificial intelligence and home IoT technology to assist children in language learning. The findings are published in the journal Proceedings of the CHI Conference on Human Factors in Computing Systems.

An algorithm developed by Washington State University researchers can better find data anomalies than current anomaly-detection software, including in streaming data.

Deep learning models, such as those used in medical imaging to help detect disease or abnormalities, must be trained with a lot of data. However, often there isn't enough data available to train these models, or the data is too diverse.

Engineering researchers at the University of Minnesota Twin Cities have demonstrated a state-of-the-art hardware device that could reduce energy consumption for artificial intelligent (AI) computing applications by a factor of at least 1,000.

A team of computer scientists and roboticists with members from Texas A&M University in the U.S., and the Mohamed Bin Zayed University of Artificial Intelligence in Abu Dhabi, working with a colleague from Boston Dynamics, has configured a robot made by Boston Dynamics to seek out and stun weeds using a small blowtorch. The team has posted a paper describing their efforts to the arXiv preprint server.

OpenAI on Thursday said it was putting its artificial intelligence engine to work in a challenge to Google's market-dominating search engine.

Designing the spatial arrangement of underground powerhouses involves numerous complex parameters and boundaries, requiring frequent reference to various cases and specifications. Traditional methods struggle to efficiently retrieve this information, leading to suboptimal designs and extended project timelines. Due to these challenges, there is a pressing need for a more intelligent and efficient approach to streamline the design process, enhance accuracy, and improve project management in hydropower engineering.

Using AI-generated datasets to train future generations of machine learning models may pollute their output, a concept known as model collapse, according to a new paper published in Nature. The research shows that within a few generations, original content is replaced by unrelated nonsense, demonstrating the importance of using reliable data to train AI models.

Self-driving cars occasionally crash because their visual systems can't always process static or slow-moving objects in 3D space. In that regard, they're like the monocular vision of many insects, whose compound eyes provide great motion-tracking and a wide field of view but poor depth perception.

Many decisions that were previously made by humans will be left to machines in the future. But can we really rely on the decisions made by artificial intelligence? In sensitive areas, people would like a guarantee that the decision is actually sensible, or at least that certain serious errors have been ruled out.

The internet, a vast and indispensable resource for modern society, has a darker side where malicious activities thrive.

Humans are interacting more than ever with artificial intelligence (AI)—from the development of the first "social robots" (a robot with a physical body programmed to interact and engage with humans) like Kismet in the 1990s to smart speakers such as Amazon's Alexa.

Organizations are increasingly utilizing machine-learning models to allocate scarce resources or opportunities. For instance, such models can help companies screen resumes to choose job interview candidates or aid hospitals in ranking kidney transplant patients based on their likelihood of survival.

Most robotic systems developed to date can either tackle a specific task with high precision or complete a range of simpler tasks with low precision. For instance, some industrial robots can complete specific manufacturing tasks very well but cannot easily adapt to new tasks. On the other hand, flexible robots designed to handle a variety of objects often lack the accuracy necessary to be deployed in practical settings.

Los Alamos National Laboratory scientists are developing powerful machine learning models—an application of artificial intelligence—to simulate underground hydrogen storage operations under various cushion gas scenarios. This will play a vital role in the low-carbon economy of the future.

As artificial intelligence models become increasingly prevalent and are integrated into diverse sectors like health care, finance, education, transportation, and entertainment, understanding how they work under the hood is critical. Interpreting the mechanisms underlying AI models enables us to audit them for safety and biases, with the potential to deepen our understanding of the science behind intelligence itself.

In May, a multibillion-dollar UK-based multinational called Informa announced in a trading update that it had signed a deal with Microsoft involving "access to advanced learning content and data, and a partnership to explore AI expert applications." Informa is the parent company of Taylor & Francis, which publishes a wide range of academic and technical books and journals, so the data in question may include the content of these books and journals.

IMDEA Software researchers Facundo Molina, Juan Manuel Copia and Alessandra Gorla present FIXCHECK, a novel approach to improve patch fix analysis that combines static analysis, randomized testing and large language models.

Even the best AI large language models (LLMs) fail dramatically when it comes to simple logical questions. This is the conclusion of researchers from the Jülich Supercomputing Center (JSC), the School of Electrical and Electronic Engineering at the University of Bristol and the LAION AI laboratory.

Recent advances in the field of artificial intelligence (AI) and computing have enabled the development of new tools for creating highly realistic media, virtual reality (VR) environments and video games. Many of these tools are now widely used by graphics designers, animated film creators and videogame developers worldwide.

One thing that makes large language models (LLMs) so powerful is the diversity of tasks to which they can be applied. The same machine-learning model that can help a graduate student draft an email could also aid a clinician in diagnosing cancer.

"Scaling up" is a catchphrase in the artificial intelligence industry as tech companies rush to improve their AI systems with ever-bigger sets of internet data.

Even the coronavirus pandemic could not stop this ambitious project: What began as AVENUE (Autonomous Vehicles to Evolve to a New Urban Experience) has developed into a milestone for the future of European mobility.

Researchers at Carnegie Mellon University and UC Berkeley have developed a new method to improve how computers organize and analyze large datasets. This advancement improves the ability to extract information from knowledge graphs, impacting the ability to analyze social networks and customer behavior.

Researchers at USC have developed a new method to accurately predict wildfire spread. By combining satellite imagery and artificial intelligence, their model offers a potential breakthrough in wildfire management and emergency response.

The 2024 Summer Olympics, kicking off in Paris on July 26, will be novel for more than just the first inclusion of breakdancing. The event will also be the first installment of the quadrennial sportsfest since the International Olympic Committee (IOC) unveiled its Olympic AI Agenda.

Artificial intelligence (AI) has grown rapidly in the last few years, and with that increase, industries have been able to automate and improve their efficiency in operations.

Would it be desirable for artificial intelligence to develop consciousness? Not really, for a variety of reasons, according to Dr. Wanja Wiese from the Institute of Philosophy II at Ruhr University Bochum, Germany.

The world around us is constantly being flash photographed by adaptive radar systems. From salt flats to mountains and everything in between, adaptive radar is used to detect, locate and track moving objects. Just because human eyes can't see these ultra-high frequency (UHF) ranges doesn't mean they're not taking pictures.

Two Master of Accounting students in the UO's Lundquist College of Business are shaking up the academic publishing world with a new paper on a timely topic: artificial intelligence and blockchain.