Research applying artificial intelligence across science and engineering. Covers machine learning, sensing and quality control, immune and health prediction, and 5G networks.
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.
Rice University has received a nearly $20 million NSF award to lead an AI-powered materials laboratory that aims to accelerate the manufacturing of electronic and quantum materials. The project, READINESS, will integrate automated synthesis equipment, robotic systems, and digital twins to minimize trial-and-error experimentation and en...
The study of Alan Turing's unpublished short story 'Pryce's Buoy' reveals a more nuanced understanding of the computer scientist. The six-page fragment challenges the stereotype of Turing as socially unaware and isolated, instead portraying him as playful, funny, and emotionally expressive.
The University of Tennessee has received a $20 million NSF grant to establish ATHENA, a national network of AI-powered laboratories for accelerating scientific discovery. The initiative aims to revolutionize materials discovery by accelerating design, synthesis, characterization and autonomous optimization of advanced materials.
Researchers have developed an AI-powered framework that combines multiple AI technologies with automated experiments to accelerate the discovery of advanced energy materials. The '4th+ paradigm' approach enables near-atomic-level accuracy in predicting material properties and rapidly analyzing experimental data.
iFuture, a new academic journal, debuts at WAIC 2026, focusing on foundational AI theories and cutting-edge interdisciplinary research. The journal's supporting platform Oscholar uses AI to support scholarly communication and collaboration, aiming to become a globally recognized high-impact platform for scholarly innovation.
Researchers used AI and single-cell technology to study the 3D genome in brain cells from individuals with Alzheimer's disease. They found increased compartment mingling, reduced gene activity, and altered brain cell organization. The study identifies 3D genome organization as a key layer of Alzheimer's biology.
A UMaine-led research team has been awarded the inaugural DOE Genesis Mission to develop AI-powered models that simulate the impact of microbes on underground chemical reactions. The project aims to improve predictions in groundwater management, environmental remediation, and energy infrastructure planning.
A new study using remote sensing and machine learning found that mining for green technology minerals has unexpected consequences on biodiversity. The research revealed that commodities such as lithium and cobalt have the highest biodiversity risks despite lower levels of forest loss.
Researchers developed an AI that navigates ships using human-like decision-making, handling complex situations such as narrow channels and multiple vessels. The AI achieved compliance with maritime traffic rules and demonstrated unexpected behavior like local navigation customs.
Researchers at Japan Advanced Institute of Science and Technology developed a new computational method combining neural networks with Bayesian localization, achieving accurate predictions while reducing computational cost. This breakthrough enables the high-precision analysis of large-scale materials and complex chemical reaction systems.
A novel cross-modal fusion framework integrates low-altitude drone RSI with ground robot LiDAR-inertial measurement unit (IMU) odometry to create accurate digital models of orchards. The system achieved localization accuracy on the order of a few centimeters, demonstrating robustness to seasonal variations and long-term drift.
The paper proposes 'transcending natural evolution' by using technology to augment human abilities, bridging the gap between biological and algorithmic iteration. Recent advances in AI, robotics, and bioelectronics are blurring the lines between digital and embodied intelligence.
Researchers designed an end-to-end workflow to identify new blue OLED materials using AI and quantum chemistry. They developed a virtual library of over 19,000 molecules and used machine learning to select promising candidates, which were then experimentally evaluated and found to have high color purity and efficiency.
Researchers at Worcester Polytechnic Institute are launching a three-year project to train high school teachers in quantum information science and cybersecurity. The program aims to inspire young people to pursue education and careers in emerging science and technology fields.
Two Rice University teams will use AI to advance quantum computing and sustainable biomanufacturing. One project aims to overcome computational bottlenecks in quantum chemistry, while another develops an AI engine for microbial production of isoprenoids.
The PPPL-led project will use AI to autonomously operate high-power gyrotrons, a crucial method for heating plasma in fusion systems. Researchers from multiple institutions will collaborate on this project.
Two Lehigh University AI projects have been selected for funding from the Department of Energy's Genesis Mission. The RIVER-AI project will improve flood- and water-level prediction, while the REACT project aims to accelerate reactor-scale fusion energy by developing an AI-enabled digital twin. These awards strengthen Lehigh University...
A recent study from Duke University and industry collaborators found that at least 1% of resumes submitted to a popular hiring platform contained hidden instructions designed to trick the AI system. The trend is accelerating quickly, with the rate increasing sevenfold between July 2024 and November 2025.
A Tulane University team is using AI to discover new superconductors, which could improve the nation's electrical grid, medical imaging, and quantum computing. The project combines high-fidelity calculations, physics-aware AI, and experimental measurements to accelerate discovery.
Researchers developed a framework to integrate AI into hospitals, emphasizing patient care, staff experience, and economic sustainability. The Total Mission Value framework aims to ensure high-quality patient care remains the top priority amidst AI's transformative potential.
Researchers developed an AI platform, PeptiVerse, to predict key properties of peptides, enabling early assessment of drug potential. The open-source platform allows users to evaluate ordinary and chemically modified peptides, streamlining the discovery process.
A new study reveals that AI chatbots' popularity stems from their interactive, personalized, and imaginative nature, allowing users to engage with them as trusted companions. The study warns of the potential dangers of these chatbots, including their ability to persuade users to believe false or unethical information.
Researchers developed an AI framework for detecting and managing microplastics in wastewater treatment systems. The system uses computer vision and machine learning to predict removal efficiency and identify pollution sources. While AI can complement chemical analysis, major challenges remain before these tools can be widely deployed.