Articles tagged with Artificial Intelligence
Researchers will gather to discuss how to engineer AI systems that work in the real world, focusing on composition, optimization, verification, and evaluation. The conference aims to establish shared foundations for a new class of software, including methods for evaluating models and ensuring durability, efficiency, and dependability.
Researchers developed an AI-based system to generate high-resolution soybean yield maps across Brazil, leveraging knowledge from U.S.-based models through transfer learning. The approach achieved strong predictive performance without using municipal-level yield data, improving estimates for this key agricultural region.
This journal leverages AI to advance environmental science, offering a platform for global researchers to exchange insights on ecological protection, water management, pollution control, and climate change mitigation. The mission is to empower experts with computational tools and provide algorithms with real-world applications.
A large dataset of infrared images analyzed using AI has established a foundation for objective assessment of meibomian gland morphology in children and adolescents. The study found that major gland parameters remain stable across childhood, but subtle sex-related differences were observed.
Researchers at Nagoya University developed an AI system called YORU that recognizes animal behaviors with over 90% accuracy. The system combines real-time video capture with optogenetics to selectively target brain cells driving specific behaviors, offering a major breakthrough in social behavior studies.
Researchers at the University of Pennsylvania have developed HoloRadar, a system that enables robots to reconstruct hidden 3D spaces beyond their line of sight using radio waves processed by AI. This capability can improve safety and performance in driverless cars and cluttered indoor settings.
Researchers at UC3M develop a new methodology for autonomous arm movement using observational learning and intercommunication between limbs. The ADAM robot can perform daily tasks such as setting and clearing the table, ironing, or tidying up the kitchen with fluid efficiency and natural movement.
A new prototype device accelerates and reduces energy cost of AI computation by encoding data into light patterns, enabling faster and more efficient processing. This innovation aims to ease the energy bottleneck in AI technology, making it more sustainable and accessible for various applications.
A study found that AI-generated messages about public policies are highly persuasive, influencing policy views by an average of 9.74 percentage points. The study's results suggest that authorship labels have no significant impact on the persuasiveness of AI-generated content.
New machine-learning tools significantly improve phishing site detection accuracy, surpassing 95% precision/recall. The study evaluates ten classifiers across three public datasets using URL, domain, and content features.
A new study suggests that AI can help doctors detect serious heart valve disease years earlier, potentially saving thousands of lives. The AI algorithm correctly identified 98% of patients with severe aortic stenosis and 94% of those with severe mitral regurgitation.
Researchers at Oregon State University have developed a deep learning-based model for rapid bacterial contamination detection, eliminating misclassifications of food debris. The enhanced model can reliably detect bacteria in three hours and has the potential to prevent outbreaks and protect consumer health.
Researchers at UC San Diego developed a new training method for AI systems to improve their performance in solving complex problems that require both text and image interpretation. The approach evaluates the quality of training data and grades models based on their logical reasoning, reducing the risk of incorrect interpretations.
Researchers are recruiting adults for a five-week study to accurately track their diets using wearable cameras, blood monitoring devices, and metabolomic analysis. The goal is to find reliable ways to measure diets, paving the way for better public health strategies to tackle diseases linked to poor diets.
Researchers found that chatbot-generated summaries changed sentiments in 26.5% of cases and hallucinated answers 60% of the time. Participants who read biased summaries were 84% likely to buy, compared to 52% for original reviews.
Researchers developed an AI-based method to create binaural audio from monaural recordings, using visual information from the video to guide spatialization. The system accurately preserves a sense of direction and space, even in complex environments.
A new AI-powered model can read a brain MRI and diagnose neurological conditions with up to 97.5% accuracy, predicting treatment urgency and automating alerts for immediate medical attention. The technology has the potential to transform neuroimaging at health systems across the US, reducing workload and improving patient outcomes.
Researchers created a model using centuries of scientific theory and scholarly research to test the accuracy of chatbots in generating images and narratives about Neanderthals. The study found that accuracy rests on AI's ability to access source information, with many generated content referencing outdated research.
Researchers developed BrainIAC, a robust AI foundation model that analyzes brain MRI datasets to identify key neurological health indicators. The tool outperformed conventional AI models in diverse tasks, including predicting dementia risk and detecting brain tumor mutations.
Researchers created a framework allowing AI agents to dynamically interrupt and stay silent based on assigned personality traits and urgency scores. This human-like flexibility led to higher accuracy on complex tasks compared to standard models.
Drexel researchers create machine learning program that integrates qualitative and quantitative data to identify gentrification in Philadelphia neighborhoods. The program, trained with data from thousands of images and focus groups, accurately identifies new-build gentrification with 84% accuracy.
A new study uses AI to predict substance use disorder-defining behaviors with high accuracy, providing a tool for clinicians to assess patients and offer timely treatment. The system utilizes concepts familiar in economics to evaluate human judgments and identify the type of substance used and severity of addiction.
Ryan Milton, a fourth-year doctoral student in nuclear physics, has been awarded a US Department of Energy's Office of Science Graduate Student Research Fellowship to conduct AI-based research at SLAC. He will develop novel AI-based analysis methods to better understand proton and neutron behavior in nuclei.
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.