Articles tagged with Artificial Intelligence
Massimiliano Albanese and Songqing Chen will host a two-day conference to strengthen secure and viable open-source ecosystems amidst the rise of artificial intelligence. The event aims to identify shared priorities and practical opportunities for improving long-term stewardship.
PaveX will develop an AI-sensor-based system to collect street-level data and produce practical road condition ratings, helping agencies plan repairs sooner and more efficiently. The project aims to improve distress detection reliability and reduce route planning overhead.
Professor Hengshuang Zhao has established a general framework for Physical Intelligence, spanning perception to understanding and virtual environments to physical reality. His work cites over 20,000 times and propels AI from perceiving to transforming the world.
Researchers from Stanford University found that using AI companions can exacerbate feelings of loneliness and isolation in vulnerable individuals. Participants who engaged with chatbots for emotional support reported lower well-being despite initial perceptions of improvement.
A research team from HKU School of Computing and Data Science has developed ClairS, a novel deep-learning algorithm that improves the detection of cancer mutations. Tested on breast, lung, and melanoma cell-line datasets, ClairS demonstrates exceptional accuracy across various cancer types and sequencing conditions.
A team from Tsinghua University developed the 'calculus of intelligence' framework to break down complex problems in agentic AI. This framework provides a mathematical approach to decomposing tasks into smaller, manageable subtasks that can be solved independently and then combined for a coherent whole.
The Center for Large Aperture Secure Sensing, Imaging and Communications aims to develop advanced antenna technologies for future wireless systems. Led by Edward Knightly, the research team will investigate how extremely large-scale antenna arrays can expand wireless system capabilities in challenging environments.
A team of researchers at UMass Amherst has developed an AI model, DiffuDose, that generates a patient's radiation dose map with gold-standard accuracy in under 23 seconds. This technology has the potential to unlock the full potential of radiopharmaceutical therapy for prostate cancer treatment.
NC State is leading three Genesis Mission projects using AI to accelerate discovery in high-performance computing, cybersecurity, and astrophysics. The projects focus on developing AI-powered tools for scientific research, defending agentic AI against adversarial attacks, and embedding AI techniques in large-scale simulations of neutro...
A team at Pusan National University created a stretchable organic electrochemical transistor that can be easily reprogrammed to perform different functions. The device combines logic, memory, and a visible color readout without complex circuitry.
Artificial intelligence can exacerbate or mitigate inequities in correctional health systems. Designing AI for access, inclusion, and trust is crucial for ensuring health equity among incarcerated populations. The study outlines policy and implementation considerations necessary to address these concerns.
The $24.9M grant will establish a national user facility for alloy-discovery campaigns using robotic systems and AI, freeing scientists to focus on discovery itself. ARM-MIP will democratize access to state-of-the-art research facilities and accelerate alloy development.
The University of Tennessee is expanding its research excellence with the addition of eight exceptional researchers, tackling pressing challenges in fields like precision health, advanced computing, and sustainable materials. The new faculty members bring expertise that builds on the university's strengths and solidifies its standing a...
A novel AI model can use information collected during routine sleep studies to identify patients' long-term health risks, including heart disease and cognitive decline. The findings also suggest that routine medical tests may contain substantially more physiologic information than current clinical practice extracts.
Researchers nationwide will have access to Georgia Tech's Advanced Manufacturing Pilot Facility remotely, leveraging AI, simulation, and autonomous experimentation to accelerate materials discovery. The cloud lab will expand who can take advantage of AMPF's capabilities, lowering costs and barriers to conducting research.
A new USC study uses AI to generate detailed maps of brain aging, revealing distinct patterns of neurodegeneration in specific regions. The approach sheds light on how local brain age correlates with changes in cognitive function across the lifespan.
A research team at KAIST has developed RL-SPH, a reinforcement-learning-based method that can independently generate feasible solutions satisfying all constraints. The technique achieved a 100% feasibility rate across five benchmarks, reducing the primal gap by an average of 28.6 times and improving search efficiency.
Recent studies explore the impact of digital technologies on health outcomes, highlighting innovations in maternal care and wildfire preparedness. Digital tools are being used to improve access to healthcare, particularly in rural areas, and mitigate adverse effects of wildfires on air quality.
The article highlights the need for AI tools to provide interpretable and reportable results, with a focus on improving turnaround time, diagnostic consistency, and clinical decision-making. While AI is unlikely to replace pathologists, it can help convert tissue morphology into actionable evidence for better patient care
Researchers at Seoul National University have developed a novel approach using porous triply periodic minimal surface (TPMS) feet and deep reinforcement learning controller, which significantly reduces battery power consumption in quadruped robots. The solution reduces energy consumption by up to 6.2% while maintaining stable locomotion.
Researchers introduce a new metric W² to complement traditional measures of predictive accuracy, revealing hidden patterns of systematic bias in AI models. By combining Q² with a penalty for angular difference from the ideal line, W² provides a more comprehensive evaluation of model reliability.
A new study by researchers at Johns Hopkins University highlights a significant disconnect between those who use AI health tools and those who create and fund them. The study reveals that key stakeholders have fundamentally different definitions of value, usability, and cost, creating systemic barriers to technology adoption.
A study by Bar-Ilan University researchers found that learning is driven primarily by changes in the strength of existing neural connections. The models became significantly better at learning as the amount of training data increased, but the proportion of lost connections remained roughly the same.
The collaboration enables researchers and innovators to develop, validate, and scale AI-enabled solutions across a broader representation of patient populations. This global health data network, called Mayo Clinic Platform_Connect, includes eight members and spans seven countries across three continents.
A KAIST research team developed two core technologies to correct AI hallucinations caused by sensory misinterpretation. The first technology uses the Diverse Negative Attributes method to accurately understand special camera sensors, while the second technology Modality-Adaptive Decoding blocks cross-modal hallucinations at the source.
Researchers developed a novel AI framework that optimizes investment decisions directly while accounting for risk. The study found that conventional forecasting-based approaches were outperformed by the decision-focused model in terms of risk-adjusted performance and wealth accumulation.
Scientists from the University of Osaka created an autonomous solid-state nanopore that can sense molecules, generate electrical signals, and retain memories of recent events. The device continuously changes its structure through chemical reactions, creating a dynamic sensing environment that responds to molecules passing through it.
A project led by Michela Taufer aims to accelerate AI-driven scientific discovery by enabling secure data sharing across the nation's research infrastructure. The National Science Data Fabric (NSDF) will connect researchers, computing resources, and data repositories, making advanced AI-driven science accessible to all.
Researchers at Duke University developed an AI framework called Raygun that can create modified versions of proteins while preserving structure and function. The tool uses protein language models to make extensive changes to existing proteins, opening up new avenues for engineering customized-sized proteins.
Researchers have created an AI model that accurately predicts cardiac index, a metric used to evaluate heart function, using non-invasive sensors on patient skin. The system demonstrates potential for accessible cardiovascular assessment beyond major hospitals and specialized clinics.
A new mathematical approach using optics helps computers solve larger, more complex optimization problems by reducing computational demands. The framework can be applied to various real-world challenges, including facility placement and data clustering, with potential benefits for a carbon-neutral future.
Researchers at Emory University developed an AI system, CapuchinAI, to automate cognitive studies of capuchin monkeys in the wild using facial recognition and real-time testing. The system identifies individual capuchins with 97% accuracy and provides a scalable field method for cognitive testing.
The University of Phoenix survey found that 60% of working learners who use AI in the workplace report improved knowledge of accessibility standards, with nearly 1 in 5 experiencing significant improvement. The survey also highlights the need for clearer organizational guidance on accessibility, with 45% of respondents unsure if it is ...
The brain-inspired AI model employs human problem-solving strategies to solve complex problems, consuming significantly less energy than traditional large language models. The system utilizes cognitive maps to guide its approach, allowing it to adapt flexibly to changing situations without requiring retraining.
Researchers at Oregon State University developed a low-cost, semi-automated camera system using AI to monitor bumblebee populations and other insects. The system recorded six species of bumblebees, closely matching those documented through traditional methods, and identified them automatically using custom-built deep learning models.
The review explores how integrating Federated Learning (FL), Reinforcement Learning (RL), and Natural Language Processing (NLP) can overcome modern NLP system limitations, such as protecting user privacy and adapting to changing environments. The study presents a unified framework that combines FL, RL, and NLP as three co-equal pillars.
Researchers developed an inverse-design framework to optimize magnonic crystal design, identifying unconventional lattice structures with large band gaps. The approach enables the exploration of previously unexplored material systems and device dimensions, paving the way for high-speed spin-wave computing and energy-efficient devices
The SNU team developed a new nanostructured catalyst, termed 'nanomace,' by chemically bonding ceria nanocubes and nanorods. The interface where the two crystal structures meet serves as a key active site, enhancing lattice oxygen activation and catalytic reactions.
The National Science Foundation has awarded Boston University $20 million to enhance its cloud lab, allowing researchers to request complex experiments remotely. The BU-based lab will focus on biotechnology and engineering biological systems, with the capacity to host studies on new vaccines, DNA sequencing, and genetic engineering.
Researchers at KAIST have developed Stable-GFlowNet, a new AI safety verification framework that uncovers seven times more hidden vulnerabilities in AI than existing methods. The technology is expected to serve as a foundation for developing safer and more trustworthy generative AI models.
Researchers at MIT developed a new technique called VLASH that allows robots to predict their future position, enabling smoother motions and quicker reactions. This breakthrough doubles the speed of robots performing tasks like pick-and-place and boosts performance in dynamic activities.
Researchers developed a physics-based framework to predict temperature-driven VOC emissions from automotive paint sludge. Higher temperatures increase the release rate of VOCs, with moderate changes leading to substantial increases in quantity and speed of diffusion.
Researchers propose using AI to track nitrogen flows, improve scientific models, and provide practical recommendations for farmers. This approach aims to rebuild the disrupted nutrient cycle and reduce nitrogen losses while increasing food productivity.
Researchers at the University of Minnesota have developed an AI system that tracks a diver's health in real-time using robotic vision and exhaled bubble analysis. The system can detect signs of stress, hyperventilation, or exhaustion, providing a non-contact approach to monitor divers' physiological stress underwater.
Researchers at Duke University have developed a method to systematically develop novel probiotic and prebiotic combinations to maintain gut health and treat gastrointestinal diseases. The approach uses machine learning and automation to explore complex interactions between microbes, nutritional sources, and the environment.
Astronomers at UNC-Chapel Hill have observed an extremely rare 'wandering' black hole located tens of thousands of light-years from the center of its galaxy. This discovery could reshape our understanding of how massive black holes move through the universe after galaxies collide.
The Genesis Mission aims to accelerate breakthroughs in energy, scientific discovery and national security through AI-powered research. Texas A&M University has joined the initiative, contributing to a unified discovery platform connecting government, industry, academia and philanthropy.
Researchers have developed an AI tool that can detect online propaganda in Kinyarwanda, a Bantu language spoken by 350 million Africans. The dataset, called KinyaProp, provides examples of misinformation in Kinyarwanda for large language models to learn from and recognize.
USC researchers have been selected for the U.S. Department of Energy's Genesis Mission to harness artificial intelligence for scientific discovery and innovation. Two projects led by USC will explore ways to develop faster and more energy-efficient computing hardware and better understand the natural concentration of critical minerals.
The VERITAS project establishes AI Assurance as a core function of scientific research infrastructure to document, review, and stress-test AI systems. It aims to develop practical methods for detecting vulnerabilities before they compromise scientific results.
A new AI model called Biogeochemistry-Informed Neural Network (BINN) has been developed to advance scientific discovery in agriculture and biogeochemistry. It is 50 times more efficient than its predecessors and can predict biological processes not yet well understood.
A growing trend of patients turning to AI chatbots for health advice reflects broader healthcare system strain and patient dissatisfaction with traditional care options. Critical care doctor Robert B. Shpiner argues that addressing underlying structural issues is essential to mitigate AI risks.
A new study published in eBioMedicine shows that combining irinotecan with standard chemoradiotherapy improves survival against advanced rectal cancer, particularly in those with high concentrations of cancerous cells. The AI-powered analysis revealed a 43% reduction in cancer recurrence and a 50% reduction in death risk.
A KAIST research team developed a next-generation world model that learns executable theories from observation alone. The Neural Theorizer (NEO) model discovers reusable primitives and composes them into executable programs to explain new situations.
A new study found that four AI-powered photo-based calorie-tracking apps underestimated the calories and fat content of meals by about a third. The researchers used standardized photos of 102 meals prepared in a controlled metabolic kitchen to evaluate the apps' accuracy.
Researchers at Harvard John A. Paulson School of Engineering and Applied Sciences have developed a new AI framework called Orla that streamlines building and running AI workflows. In tests, Orla reduced computing costs and response times without sacrificing quality.
The health tech industry is evolving with AI-powered wearables that enable real-time data interpretation, reducing centralized infrastructure demands. Pharmaceutical AI tools like NoHarm automate reviews, freeing up resources for medication errors. These innovations transform healthcare, improving care and patient outcomes.
A team of researchers at Harvard and Max Planck Institute have developed three new functional components for photonic microchips using an inverse design algorithm. The compact designs are about 500 times smaller than conventional designs and offer a path toward higher-performance integrated light technologies.
The City University of New York has received an $18.1 million NSF award to create a cloud-programmable national laboratory that uses artificial intelligence and robotics to speed the discovery, design, and production of advanced bio-inspired materials. Researchers nationwide will have remote access to automated tools for developing sus...
Twenty NII papers were accepted at ACL 2026, including two that won the Best Theme Paper and Outstanding Paper awards. These achievements lay the groundwork for developing trustworthy AI and advancing research in explainability and transparent AI.