Articles tagged with Artificial Intelligence
A recent study found that 45.9% of ChatGPT-generated feedback was rejected by second-language learners, with content-focused feedback facing higher rejection rates. The researchers identified four key factors driving rejection: misalignment with goals or external standards, and the subjective nature of content-related suggestions.
Researchers propose future data collection and processing principles to address issues in data accumulation, modeling, and application. A global standard protocol framework is needed for a high-quality data ecosystem and healthy AI development.
Researchers have developed a novel technique to overcome the spurious correlations problem in AI by eliminating a small portion of the training data that contains hard-to-understand features. This approach improves performance even when conventional techniques are ineffective.
A new framework allows users to correct a robot's actions in real-time using intuitive interactions, such as pointing or nudging the arm. The method achieves a success rate of 21% higher than an alternative approach, enabling more efficient and accurate task completion.
AutoCodeRover, a cutting-edge AI agent platform for software development, has been acquired by Sonar. The technology boosts software engineering and agentic AI innovation, creating new R&D jobs in Singapore.
Researchers used deep learning to predict perovskite solar cell material characteristics and efficiency levels. Machine learning helped identify process errors before finishing, improving data analysis speed and effectiveness.
Researchers explore three key directions to address LLM limitations: knowledge empowerment, model collaboration, and co-evolution. Techniques like retrieval-augmented inference and knowledge prompting enhance factual accuracy and interpretability.
A new method of biometric authentication has been developed using hyperspectral imaging and AI to identify individuals through the unique patterns in their blood vessels on the palm of their hand. The technology shows great promise for secure personal identification and could potentially be used as a key to unlock homes.
Researchers have employed artificial intelligence and data analysis to improve forest protection, enabling real-time monitoring and predictive analysis. The innovative forest regeneration model forecasts how forests will grow and change over time, identifying best-suited tree species for different environments.
A new study suggests that artificial intelligence can effectively detect wildfires in the Amazon rainforest, using satellite imaging and deep learning. The technology achieved a 93% success rate in training models via datasets of images with and without wildfires.
The Global Physics Summit will feature nearly 1,200 sessions and 14,000 presentations on various topics, including astrophysics, climate science, medicine, and quantum information. Registered journalists and public information officers will receive daily emails with meeting information.
Researchers at UC San Francisco have enabled a paralyzed man to control a robotic arm through a device that relays signals from his brain to a computer. The device, known as a brain-computer interface (BCI), worked for a record 7 months without needing to be adjusted.
Researchers at Technical University of Munich developed a new AI training method that significantly reduces energy consumption. The approach uses probabilities to determine parameters, making the training process 100 times faster while maintaining accuracy comparable to existing procedures.
Research reveals that AI-generated voices are perceived as more trustworthy and likable, with potential applications in deep-fakes and manipulation. The study found a positive correlation between the similarity of an AI voice to a human voice and its perceived trustworthiness.
Researchers at the University of Kansas are partnering with regional high schools to train about 500 students in AI coding and microelectronics. The program aims to develop a workforce that can specialize in AI and microelectronics, with a focus on community-centered projects and altruistic goals.
A symposium at Lehigh University aims to explore challenges and solutions for improving AI's reliability, inclusivity and ethical impact in healthcare. The event will bring together researchers, clinicians and industry experts to foster cross-disciplinary collaboration and promote people-centered AI-enabled healthcare systems.
A new machine learning algorithm can fully characterize systems of merging neutron stars in under a second, compared to traditional methods which take around an hour. This allows for rapid localization of the source and pointing of telescopes towards the merging neutron stars.
Researchers developed an AI model that predicts ICU length of stay with high accuracy, while providing comprehensive explanations for patient outcomes. The model's predictions were found to be just as accurate as other XAI models, but its explanations were more detailed and useful for hospital staff.
Researchers have developed a high-speed, energy-efficient electro-optic switch with low crosstalk and broad bandwidth. The switch uses a scalable process and consists of four Mach-Zehnder interferometer structures formed by silicon nitride waveguides.
A Texas A&M research team led by Dr. Robert Valeris-Chacin is investigating the use of artificial intelligence to detect lesions in pig lungs, a sign of pneumonia-causing bacteria. The study found that while AI is not yet as accurate as human evaluators, it exhibits similar behaviors and has perfect consistency in its scoring.
Researchers found that GenAI feedback offers timely and personalized feedback, educational benefits as an essay editor and tutor, and social benefits as a supportive environment. However, challenges include limited AI skills, dependency risk, and emotional understanding limitations.
Researchers have developed a new AI tool that uses sensors and real-time data to predict water quality across the US. This tool can be applied nationwide, benefiting communities by providing water quality forecasts, streamlining operations, and informing strategies for managing turbidity in basins worldwide.
Dr. Sarah Du, an associate professor at Florida Atlantic University's College of Engineering and Computer Science, has been selected as a Senior Member of the National Academy of Inventors for her significant contributions to advancing medical technology. Her research focuses on developing point-of-care diagnostic tools and monitoring ...
Researchers have developed an AI method to illuminate nanoparticle behavior, revealing key dynamics and instabilities. The technique combines electron microscopy with AI to capture atomic-level structural changes at unprecedented time resolution.
A Northwestern University study discovered a region of the brain processes subtle changes in voice pitch, transforming them into meaningful linguistic information that guides human understanding. The findings challenge long-held assumptions about speech perception and have implications for speech rehabilitation, AI-powered voice assist...
The UK's labour market is shifting towards valuing practical skills and industry-specific know-how over traditional university education for AI professionals. According to the new Oxford study, skills proficiency commands a significant wage premium compared to higher education degrees.
Scientists developed a method that harnesses chromatic aberration to produce high-quality images using a single exposure. The AI approach uses generative models to retrieve phase information from limited data input.
The AI-GS network achieves high accuracy with 93.52% sensitivity at 95% specificity, excelling in detecting early-stage glaucoma. The system is portable, requiring minimal computational power and delivering results in under a second.
The Rice team created a low-cost, pump-free flow cytometer that uses gravity-driven slug flow to analyze cells with similar accuracy as conventional devices. The device is powered by AI and can count specific immune cells from unpurified blood samples within minutes.
A study developed by the US Department of Energy's Thomas Jefferson National Accelerator Facility aims to lower data center costs using machine learning. The Digital Data Center Twin (DIDACT) system detects anomalies and diagnoses their source using AI continual learning, reducing downtime for scientists processing data from experiments.
A new study from the University of Texas at Austin finds that AI can create high-quality business plans and even critique existing strategies. The research suggests that AI can enhance the speed, quality, and scale of strategic decision-making, potentially giving companies a competitive edge and enhancing performance.
A recent study by the American Psychological Association suggests that professional artists are more creative than AI-generated artwork. The research found that artwork created with prompts by experienced artists was rated as significantly more creative than AI-generated work. Additionally, analysis revealed that professional artists u...
Symplectic Elements now offers AI-generated summaries for publication abstracts, enhancing public profiles and increasing research visibility. The new functionality supports researchers and institutions in showcasing their research outputs and discoverability.
A new AI-powered tool helps predict traumatic brain injury outcomes based on documented assault scenarios, providing an evidence-based approach to improve investigation accuracy. The model achieved remarkable prediction accuracy for TBI-related injuries, integrating mechanical biophysical data with forensic details.
Researchers developed an AI approach to identify genes contributing to neurodevelopmental disorders like autism spectrum disorder, epilepsy, and developmental delay. The tool enhances gene discovery by predicting additional genes involved in these conditions.
Colorectal cancer is a leading cause of mortality in China due to late-stage diagnosis. Emerging non-invasive diagnostic tests, innovative imaging techniques, and AI applications enhance CRC detection and diagnosis. However, expanding accessibility and promoting public awareness remain key challenges.
A team from the University of Trento combined AI techniques with neuroimaging measurements to analyze how emotions and language structure influence brain activity during interactions. The results show that neural synchrony is associated with emotional content and semantic structure, with implications for understanding human relationships.
PRISm is a form of Pre-Chronic Obstructive Pulmonary Disease (Pre-COPD) characterized by respiratory symptoms and reduced lung function. Imaging assessments, such as computed tomography scans and magnetic resonance imaging, play a crucial role in diagnosing and predicting the prognosis of PRISm.
Researchers at Mizzou have developed a proactive approach using artificial intelligence to address evolving threats against smart grids. The CIBR-Fort system can predict cyberattacks with 91.88% accuracy and defend against them in real-time, enabling scalable security for power grids of the future.
Researchers developed ItpCtrl-AI, a transparent AI framework that reads chest X-rays like a radiologist, providing accurate diagnoses and increasing trust in medical technology. The framework uses a gaze heat map to show the computer where to search for abnormalities and what section of the image requires less attention.
Researchers leveraged a large language model to classify artwork types without pre-prepared training data, achieving high accuracy. This approach enables performance comparable to conventional machine learning methods while reducing human effort and time required for data organization.
Film workers express concerns about AI's impact on wages, jobs, and the artistic process. They argue that AI-generated art lacks authenticity, while some see potential benefits when humans maintain control over creative decisions.
An international research team developed a user-friendly software method called Segment Anything for Microscopy, which can precisely segment images of tissues, cells, and similar structures. The new model improved performance for cell segmentation, enabling researchers to automate tasks that previously took weeks of manual effort.
A new AI model uses electrocardiogram (ECG) analysis to identify female patients at higher risk of heart disease, including cardiovascular disease, future heart failure, and heart attacks. The model shows that women whose ECGs more closely match the typical 'male' pattern are at a significantly higher risk of these conditions.
The book, RE-HUMANIZE, offers actionable frameworks to help business leaders navigate the evolving landscape of digital work. It emphasizes the importance of preserving humanity in organizations, highlighting strategies for a digital division of labor that respects human dignity.
A new AI-powered tool, MELD Graph, has been developed to detect subtle brain abnormalities in children with epilepsy. The tool improves detection rates by 64% compared to human radiologists, reducing delays in diagnosis and treatment. This could lead to faster surgical interventions, improved outcomes, and reduced costs for the NHS.
Ebru Demir aims to study how groups of AI-driven microswimmers move in biological fluids for potential applications in drug delivery, fertility treatments, and other medical fields. Her research combines artificial microswimmers with machine learning to uncover the underlying physics governing their movement.
A new AI model measures how fast the brain ages by analyzing MRI scans, providing a more accurate picture of brain health. The tool closely correlates faster brain aging with increased cognitive decline and dementia risk, offering potential for early biomarkers and personalized treatment.
The ESMO Targeted Anticancer Therapies Congress 2025 features state-of-the-art presentations on new targets, tumour-agnostic drug development, and the role of artificial intelligence in cancer treatment. The congress also highlights recent study results and their potential impact on precision medicine.
Research team develops novel method to exploit frictionless sliding for improved memory performance and energy efficiency. The new technology enables unprecedentedly efficient data read/write operations while consuming significantly less energy.
A new review reveals AI's potential to predict diabetes subtypes, identify high-risk patients, and tailor solutions to individual needs. AI tools can detect early signs of eye damage from diabetes as accurately as specialists, improving accuracy and reducing healthcare costs.
Researchers developed a new zero-shot AI pipeline that leverages open-source foundation models like Segment Anything Model (SAM) and Grounding DINO to improve accuracy in automated image segmentation for remote sensing. The pipeline achieves outstanding segmentation results, reaching up to 99% accuracy, and operates in a zero-shot manner.
Researchers developed a frailty index using electronic health record data from over 500,000 patients, identifying those at high risk of adverse health outcomes. The tool classifies patients as robust, pre-frail, frail or very frail based on age-related health deficits.
Researchers identified recent advancements in bioinformatics foundation models, enhancing understanding of molecular landscapes and providing practical foundations for innovation in molecular biology. The models are versatile and essential tools for various downstream tasks, including genomics and drug discovery.
A recent study reveals substantial differences in fear levels towards AI replacement of jobs across cultures, with India, Saudi Arabia, and the US reporting highest fear levels. The study highlights occupation-specific fears, with judges consistently ranked as most feared and journalists as least feared.
Text-to-image technology demonstrates great potential in urban and rural planning design, offering fresh perspectives and tools for innovation. Experimental results show advantages in design process optimization, image generation efficiency, and complex scene modeling.
A new framework for understanding complex systems has been introduced by a groundbreaking study led by Professor Ginestra Bianconi from Queen Mary University of London. The study establishes the field of higher-order topological dynamics and reveals how hidden geometry shapes brain activity, climate patterns, and artificial intelligence.
An international team of researchers has created a detailed map of individual cells in the adult lung, including those affected by chronic lung disease. The AI-driven finding shows that some lung tissue in patients with idiopathic pulmonary fibrosis (IPF) exhibits signs of the disease before significant structural changes occur.
Researchers found that large language models use a semantic hub mechanism to process diverse data, integrating information from various modalities. They demonstrated that models can change their outputs by intervening with text in the dominant language, even when processing other languages.
Researchers developed a machine learning model that predicts the presence of animal feeding operations with high accuracy, filling a data gap crucial for managing their environmental impacts. The model uses predictors such as surface temperature and phosphorus levels to identify locations without relying on aerial images.