Articles tagged with Artificial Intelligence
OpenScholar, an open-source AI model, was designed to synthesize current scientific research and cite sources accurately. The model outperformed other state-of-the-art models in accuracy, writing quality, and relevance.
A new AI-powered analysis tool called PROVIZ is being tested in Norwegian hospitals to diagnose prostate cancer. The tool helps doctors determine which patients need a biopsy, reducing the number of unnecessary procedures.
A review of 30 years of AI-driven electrocatalysis research highlights five structural bottlenecks and introduces transformative technologies. Machine-learning interatomic potentials and physics-informed models enable predictive and interpretable simulations, while autonomous 'robotic electrochemists' integrate AI decision-making with ...
The Big Earth Data journal is launching a special issue to reflect on its decade-long impact and showcase cutting-edge advancements in big data research. The journal focuses on Earth-related big data, emerging as a flagship platform at the intersection of Earth science, space science, information science, and sustainability science.
A new study by NYU Langone Health researchers identified the high-level visual cortex as the brain region responsible for one-shot perceptual learning, a type of fast flashes of insight. The team developed an AI model that replicated human-like perception using stored priors and achieved one-shot learning capability.
A new study from George Mason University reveals that AI-driven antidepressant treatment can be less effective for African American patients due to the use of general population data. The study suggests that incorporating additional patient demographics, such as race and ethnicity, can improve the tool's effectiveness.
A new AI tool uses discovery learning to predict battery cycle life with just a few days' data, saving months to years of testing and substantial energy. The tool leverages physics-based features to establish parallels between historical battery designs, allowing for accurate prediction performance.
A nationwide study found that nearly two-thirds of US hospitals using Epic have adopted ambient AI tools to reduce documentation burden and improve patient-provider communication. However, adoption rates vary significantly across hospitals with differing operating margins, size, location, ownership, and staffing levels.
A study examines how human-AI coexistence is represented in fictional works, revealing varying roles and degrees of autonomy. The findings propose a theoretical model conceptualizing AI as an entity forming relationships with humans.
Researchers developed a new AI control system that allows soft robots to learn a broad set of motions once and adapt instantly to changing conditions without retraining. The system combines structural learning with real-time adaptiveness, making it suitable for diverse tasks and environments.
Researchers identify contextual errors as a major contributor to the gap between medical AI model performance on standardized test cases and real-world clinical applications. To address this, experts suggest incorporating contextual information into datasets, computational benchmarks, and model architecture.
The integration of Prophy with ARPHA's editorial platform enables data-driven reviewer recommendations based on researcher expertise and publication history. This supports a more sustainable and balanced approach to peer review, reducing delays and fatigue.
The ESMT Berlin DEEP Institute and Creative Destruction Lab will be launched in Doha to support technology transfer and innovation. The initiative aims to connect the innovation ecosystems of Germany and Qatar, fostering entrepreneurial growth and driving economic growth.
A new Royal Society paper proposes a model of adaptive decision making in naïve animals, showing early biases are surprisingly widespread and interact to support survival. This 'unsupervised strategy' can help inexperienced animals and artificial intelligence reduce the need for training.
Researchers at Tohoku University developed DIVE, an AI multi-agent workflow that extracts information from images to propose new materials within minutes. The system outperforms commercial models, offering 10-15% better accuracy and coverage of data extraction.
Researchers at MIT developed a generative AI model called DiffSyn that suggests promising synthesis routes for complex materials like zeolites. By using this model, scientists can test millions of theoretical materials in under a minute, accelerating the materials discovery process.
A cross-sectional study found that up to half of adolescents in the US were using generative AI apps, while a small subset engaged in heavy use. The study highlights the need for future research on individual differences in GenAI use and its impacts on adolescent development.
Researchers at Queen Mary University of London have developed an AI system called Auto MV that can generate complete music videos directly from full-length songs. The system uses a multi-agent approach to analyze the musical structure and lyrics, plan scenes, and generate images and video clips.
A new AI model can predict moderate heat stress on Florida reefs up to six weeks ahead of time. The model is accurate within one week and provides site-specific predictions, helping coral scientists and restoration practitioners with local reef management and emergency response planning.
Researchers have developed a new AI-powered correction that addresses instability in hybrid climate models, allowing for reliable simulation of months or years-long processes. The CondensNet architecture learns from reference simulations to correct condensation errors, ensuring physically consistent results.
Photonic noses leverage light-matter interactions and machine learning to capture detailed chemical fingerprints and interpret them with high accuracy. AI integration enables fast, label-free, and highly sensitive detection of volatile compounds, paving the way for smarter sensing platforms.
Researchers at Seoul National University have developed an AI-based material redesign technology that transforms hard-to-synthesize materials into synthetically feasible forms. The SynCry model successfully redesigned over 3,400 structures, accelerating the development of next-generation semiconductor and battery materials.
The University of Ottawa has launched the Ottawa Medical Artificial Intelligence Research Institute (OMARI), a center for research, education, and innovation in medical Artificial Intelligence. Led by Dr. Khaled El Emam, OMARI aims to facilitate cross-cutting collaborations and sharpen the university's competitive edge in AI-driven hea...
Researchers developed an AI tool called ONCO-ACS to predict the risk of secondary heart attacks in cancer patients after a heart attack. The tool combines cancer-related factors with standard clinical data to provide reliable information for doctors to balance treatment benefits and harms.
Researchers warn that AI-controlled personas, mimicking humans, can tilt elections through coordinated narratives and persuasive messages. The next election may be the proving ground for this technology, threatening democratic discourse.
A team of researchers used AI to analyze the activity of genetic control elements in the developing mammalian cerebellum, identifying specific elements unique to the human lineage. The study reveals how these elements may have contributed to key evolutionary innovations in the human brain, including the expansion of the cerebellum.
The study found a 12% reduction in interval cancers diagnosed after screening, as well as fewer aggressive sub-type cancers detected. AI-supported mammography also increased cancer detection without an increase in false positives.
Researchers have developed a passive, solar-powered orbital data center that can scale AI computing and reduce environmental impact. The system leverages decades of research on 'tethers' and could host thousands of computing nodes to replicate terrestrial data centers.
A Kennesaw State University researcher is working on a new type of robotic hand that can move more naturally, enabling robots to handle objects with greater precision. The research uses advanced artificial intelligence to replicate human-like behavior, allowing robots to interact with their environment in a more intuitive way.
This review synthesizes current applications of AI and smart devices in MASLD care, discussing their benefits and limitations. AI models leverage EHR, laboratory data, and multi-omics information to predict risk and severity, while also enhancing medical imaging interpretation and liver histopathology assessment.
A new fabrication method, optofluidic assembly, has been developed to create tiny 3D objects from a variety of materials, including metals, semiconductors and polymers. The technique uses light-driven flow to guide the assembly of micro- or nanoparticles within a confined space.
A novel AI framework uses speech patterns to detect cognitive decline years before traditional tools can, offering non-invasive screening for neurodegenerative conditions. The approach analyzes complex dynamics and transitions hidden in speech to reveal subtle patterns and extract objective biomarkers from natural patient behavior.
A new AI-driven model predicts patient risk of suffering a major adverse cardiac event (MACE) with higher accuracy than traditional predictive models. This improvement in prognostic risk assessment could help personalize individual care plans and maintain quality of life for patients.
A new study found that participants felt closer to AI in emotional conversations than humans, especially when the AI provided personal information. However, this closeness decreased significantly when participants knew they were communicating with an AI.
A new study using AI-powered BioBERT model accurately identifies fungal lifestyles, switching between helpful partner for plants to aggressive decomposers. The tool has nearly 90% accuracy and can scan thousands of papers in minutes, flagging species that may switch roles.
An AI tool developed by researchers at NYU Langone Health can accurately predict which patients will require skilled nursing facilities after leaving the hospital, enabling faster and more effective discharge planning. The tool uses short summaries of doctor notes to identify key risk factors and predicts patient needs with 88% accuracy.
Researchers have developed a new AI tool called VASCilia that provides unprecedented 3D views of cochlear hair cells. The tool accelerates the imaging process by 50-fold, allowing scientists to analyze cells with greater precision and accuracy. This advancement offers new insights into hearing loss caused by damaged inner ear hair cells.
Researchers at OIST found that combining inner speech with working memory enhances AI learning, enabling easier adaptation to new situations. This discovery holds promise for developing more human-like AI systems capable of handling complex, dynamic environments.
Researchers at Chungnam National University have developed an AI model that uses deep learning to predict stable defect configurations in materials. The model, trained on data generated by conventional simulations, can generate results in milliseconds rather than hours, accelerating the material design process.
A recent study from Binghamton University School of Management reveals that focusing on human-robot collaboration can generate additional economic value and improve a company's ability to capture a greater share of the competitive market. By leveraging robots in collaborative settings, organizations can foster a positive sense of commi...
A team of researchers led by Academy Professor Otso Ovaskainen has developed a prediction model that utilizes citizen bird observations alongside cutting-edge information technology. The new model provides the most accurate predictions yet of bird movements and singing activity, enabling anyone to participate in research.
The MLEDGE project has successfully combined federated learning with cloud and edge computing to develop more secure, efficient, and personalized AI solutions. The project's applications include real-time COVID risk maps and energy consumption optimization in industry.
Researchers have identified a new vulnerability in AI-powered robots, where malicious text on signs or objects can hijack their decision-making. The study, led by UC Santa Cruz professors Alvaro Cardenas and Cihang Xie, presents the first academic exploration of environmental indirect prompt injection attacks against embodied AI systems.
Researchers are leveraging AI agents to design and evaluate solid electrolytes for safer, more reliable batteries. By integrating data analysis, materials modeling, simulations, and experimental planning, these AI agents enable coordinated research strategies that evolve as new information becomes available.
Researchers adapted an AI model to predict neurological recovery after cardiac arrest in a resource-limited setting, achieving 80% accuracy. AI holds promise across various healthcare applications in low-and middle-income countries, including improved diagnostics and clinical decision-making.
A new study shows that people who perceive artificial intelligence as replacing human labor are more dissatisfied with democracy and less politically engaged. This perception is linked to a causal decline in democratic legitimacy, even if AI has only had a limited impact on the jobs market.
The Association for Computing Machinery has named 72 new Fellows for their technical innovations and service to the field. The honorees represent the top 1% of professionals worldwide, with contributions in AI for healthcare, computer graphics, data management, and more.
The Institute of Science and Technology Austria (ISTA) has received a significant donation to advance trustworthy AI technology. The €5 million gift from Garrett Camp will support fundamental research in artificial intelligence, focusing on interdisciplinary collaboration and long-term impact.
The expert consensus provides a structured framework for assessing large language models (LLMs) before deployment in clinical workflows. The framework includes retrospective evaluation, six key capability domains, and essential safeguards for patient data protection and bias mitigation.
The journal has been recognized for its significant international citations, with over 3 million documented in the past year. Its editorial leadership and publishing model ensure rigorous peer review and a multidisciplinary perspective, supporting high-quality research on AI and clinical applications.
A new special issue explores how artificial intelligence can enhance soil carbon storage and ecosystem health in farmlands, forests, and grasslands. The journal Biochar publishes original studies on biochar production, processing, and applications.
A new study by Penn State researchers found that the level of transparency in artificial intelligence (AI) systems affects users' trust. When systems meet or exceed users' expectations, trust increases without needing detailed explanations. However, when expectations are not met, users require more information and a deeper understandin...
A new study reveals that around one-third of all newly written software functions in the United States are already being created with AI support, with productivity gains mainly driven by experienced developers. The study found wide regional gaps, with the US leading at 29% AI-assisted code, while China and Russia lag behind.
The event highlighted urgent global risks and science-led solutions for antimicrobial resistance, trust in data, and AI-enabled sustainable cities. Experts emphasized the need for immediate actions against AMR, which could kill more people than cancer by 2050.
The Center for AI Responsibility and Research will build upon Binghamton research advancing AI for the public good. The new center is supported by a $30 million commitment from Tom Secunda and $25 million from Gov. Hochul and the NY State Legislature.
Researchers at Tohoku University's WPI-AIMR propose Harvested Reservoir Computing framework that harnesses road traffic dynamics for energy-efficient AI systems. The approach yields better prediction accuracy before congestion begins, reducing computational overhead and energy consumption.
Researchers used CT scans and AI to find links between poorer abdominal muscle measures and higher incidence of falls in middle-aged adults. Maintaining good core strength throughout adulthood may provide benefits that start in midlife and continue into older adulthood.
Researchers created a new frequency-aware approach to crafting adversarial images that better match human visual perception. The method, called Input-Frequency Adaptive Adversarial Perturbation (IFAP), significantly outperformed existing techniques in structural and textural similarity.
The new platform, led by PPPL, aims to speed up simulations needed to advance fusion energy research. STELLAR-AI will integrate CPUs, GPUs, and QPUs to tackle the challenges of private fusion companies, enabling faster design and optimization of stellarator devices.
LeapSpace is a research-grade AI-assisted workspace built on trusted scientific content, providing transparent and explainable results. It helps researchers uncover new insights, save time, and improve collaboration, while supporting critical thinking and rigorous transparency.