Research applying artificial intelligence across science and engineering. Covers machine learning, sensing and quality control, immune and health prediction, and 5G networks.
Researchers used CRISPR and AI to identify two novel drug targets for psoriasis: the oxytocin receptor and ALOX5 enzyme. Topical gels containing these compounds reduced inflammation in mice as effectively as widely used injected therapies, offering a promising new approach to treating moderate-to-severe disease.
A new study finds that human cortical neurons have remarkable computational capabilities, surpassing those of other mammals. The researchers developed a new method to measure the complexity of individual neurons, revealing their sophisticated computing power.
Researchers found that variable-rate seeding (VRS) can help farmers strike a better balance in corn and soybean fields. However, soybeans proved to be more complicated due to their adaptability to weather conditions. The study aims to make farming more accessible and efficient for small land holders using digital tools and data-driven ...
A team from Singapore University of Technology and Design has developed an AI agent that uses artificial intelligence to help patients make life-or-death decisions. The system, called ACPAgent, was tested with 15 participants who agreed with its recommendations in 86.7% of cases, but also showed limitations in handling high-subjectivit...
A new study found that clinician support significantly increases patient adoption of virtual reality therapies, leading to better time-based adherence and technology acceptance. The research team evaluated three implementation environments, with provider-led support demonstrating the most favorable usability pattern.
Researchers developed EleTac, a soft robotic gripper with high-resolution tactile sensing, to handle delicate objects. The gripper's innovative design enables it to adapt to various shapes and provide gentle forces, making it suitable for applications such as handling fruit, lab samples, and medical supplies.
Philip S. Yu and Osmar R. Zaïane succeed at a pivotal stage of Intelligent Computing's growth as an international open-access journal. The journal publishes innovative research on artificial intelligence, machine learning, and emerging interdisciplinary areas.
Researchers warn that AI faces are becoming increasingly trustworthy, with the latest diffusion model outperforming earlier models. This poses significant risks of online fraud and erosion of trust in society, as people become more susceptible to fake faces used for nefarious purposes.
Nora McDonald received a $748,258 NSF CAREER award to investigate how AI-driven personalized technologies shape young people's decision-making and sense of self. Her project aims to develop practical tools and educational resources to help adolescents and adults respond to algorithmic influence.
Researchers have developed a new kind of point-of-load converter to step down power from 48 volts to 1 volt, achieving higher efficiency and faster power delivery. This technology could significantly reduce energy consumption and thermal stress in AI data centers.
A study found that forecast error types influence public emotion during disasters, with anxiety and worry being the most common emotions. The researchers suggest that communicating forecast uncertainty effectively could improve public trust and reduce emotional distress during future extreme weather events.
Researchers developed a technology to stack ultrathin semiconductor chips with improved integration density, overcoming challenges of chip thickness and warpage. The process enables the reliable stacking of over ten chips, potentially leading to significant improvements in AI semiconductor performance.
Dimensions Research Strategy is an AI analytics platform that provides evidence-backed strategic briefs based on the world's most interconnected research data. The platform aims to support university leadership in benchmarking and expertise, offering verifiable intelligence for informed decision-making.
A large-scale study of an online patient portal shows that AI-generated responses can introduce errors and extraneous details, leading to increased editing time for physicians. Adapting AI to individual physician communication styles can improve accuracy by 33% and reduce editing by 26%.
The new institute aims to accelerate data-driven health care with partnerships between six schools, UC San Diego Health, and specialized centers. By integrating digital technology and rigorous research, the institute seeks to achieve scalable, reliable health outcomes for all.
A new learning-based adaptive tuning method integrates chaotic search with particle swarm optimization to improve stability and solution quality in chaotic search algorithms. The approach consistently achieves better results than conventional methods, providing a practical means of enhancing the performance of chaotic search.
A new study found that AI-generated lunar crater catalogs often perform poorly when evaluated using human-defined scientific standards. The research highlights the need for standardizing benchmarks and transparent reporting to ensure the accuracy of AI-generated data.
BetaDescribe, an AI system, converts protein sequences into detailed textual descriptions of their functions and characteristics. The technology helps bridge the gap between characterized and existing proteins in nature, enabling researchers to rapidly generate evidence-based hypotheses regarding unknown proteins.
Researchers developed a physics-informed neural network approach to predict material properties and optimize controlled-release systems. The new method requires significantly less data than traditional AI models, slashing development time for patches, bandages, and implants.
The collaboration aims to develop AI-powered ground systems that can assist operators with routine satellite operations, mission scheduling, and data analysis. The partnership seeks to automate routine tasks with human oversight, enabling more efficient management of larger satellite fleets.
Researchers create tiny swimmers to deliver drugs through the human body, finding they reverse direction in non-Newtonian fluids like mucus and blood. This discovery enhances understanding of fluid behavior and could lead to targeted drug delivery.
A team of researchers has developed a new method for finding effective tuberculosis drugs by leveraging the PAC-MAN technique and artificial intelligence. The approach uses machine learning models to predict which chemical compounds can penetrate the outer membrane of the bacteria, paving the way for more efficient drug discovery.
A new editorial highlights AI's role in scientific discovery, proposing a framework to evaluate AI-enabled scholarship. The authors emphasize the need for transparency, reproducibility, and real-world value in AI-driven research.
The study reviews SLM optimization strategies and evaluates a Greek labor-law assistant that combines fine-tuning, Retrieval-Augmented Generation and quantization for practical local deployment. The evaluation used standard language-generation and retrieval-oriented metrics, indicating that larger models achieved stronger scores while ...
Researchers emphasize the importance of enhancing visual intelligence in autonomous robots to achieve complex tasks. Visual perception, decision-making, path planning, and control must be integrated for robots to perform reliably. The ultimate goal is to make robots have human-like intelligence and enable collaboration between robots.
A new study reveals that large language models can systematically alter the direction of users' messages on contested topics, even when instructed to preserve the original meaning. The researchers show how these small changes can accumulate and gradually influence broader public opinion over time.
Researchers developed an on-chip all-optical supernode for ultra-low-latency deep neural network inference, achieving a 100-fold increase in inference speed while using only one-ninth of computing resources. The system supports high-speed data routing and switching with low loss and flat response over a spectral range exceeding 100 nm.
Researchers propose a new framework, BOC, inspired by biological nervous systems to build more adaptive intelligent systems. The framework integrates sensing, memory, and computation directly at the hardware level, reducing data movement and latency.
NII and Indian Institute of Technology Bombay form a collaboration to advance the research and development of transparent and reliable large language models. BharatGen, an India-based AI initiative, contributes to building an open and inclusive AI ecosystem.
A recent study found that companies relying heavily on AI-powered recruitment may be losing out to competitors due to a lack of human connections. Employers should prioritize transparency, bias auditing, and human oversight in their recruitment processes.
Researchers developed a low-cost AI tool to forecast demand and optimize medicine allocation in Sierra Leone. The tool increased consumption of allocated medical products by 19% and improved access to essential medicines for over two million women and children under five, with no additional workforce costs.
A new study by Rice University researcher Conner Joyce examines the evolution of cybercriminal communities into politically motivated networks. The research found that online interactions can create strong social bonds that facilitate the spread of political and extremist beliefs.
Researchers developed an ensemble deep learning approach to detect cracks and cold flows on aluminum gas meter lids with over 97% accuracy. The system combines three AI models, significantly outperforming traditional manual checks.
Researchers developed an AI-guided strategy to identify bioactive nucleoside hydrogels for periodontal therapy, using machine learning-based predictive models and experimental validation. The study identified two promising candidates, guanosine monophosphate and deoxyguanosine monophosphate, which successfully formed stable hydrogels w...
Researchers used LLMs to analyze verbal reports of decision-making processes and found that people's own insights are a valuable source of data. The framework also showed that reasons people rely on shift systematically with the structure of the decision problem.
Researchers create custom-fit prosthetic hands with soft magnetic sensors that capture subtle changes in muscle shape and pressure. The system performs consistently and reliably, translating intent into control of a dexterous robotic hand with up to 90% accuracy.
A recent study from University of North Carolina at Chapel Hill found that large language models mimic social behaviors humans display when navigating differences in status and authority. The findings suggest that AI systems can become more accommodating and even comply with unsafe instructions when positioned as subordinates.
Researchers analyzed eight aspects of character portrayal in AI-generated stories, finding that AI models tend to 'play it safe' with their characters, leaving them mysterious or fully understood by the end. Human writers, on the other hand, are more willing to leave questions unanswered and let characters remain open to interpretation.
Researchers built a simple neural network that mimics the brain's processing of visual information, revealing a key role for inhibitory neurons in fine-tuned control. The findings suggest a revised understanding of how the brain processes and represents information.
The article explores AI-driven transformations in digital health, including a novel malaria intelligence platform and the Centers for Medicare & Medicaid Services' Landmark ACCESS program. Genuine institutional change is crucial for safe clinical AI adoption.
A team of physicists, including Giorgio Parisi and Francesco Zamponi, collaborated with AI system Claude to solve a decade-old mathematical problem in physics. They found that two different theoretical approaches to jamming lead to the same physical laws, confirming a surprising relationship.
Prof. Orly Lewis is developing a flexible publishing platform for interactive knowledge environments, while Prof. Nir Friedman is creating an epigenomic liquid biopsy for early detection and monitoring of Metabolic Dysfunction-Associated Steatohepatitis.
A new AI model, SpliceSelectNet, accurately predicts RNA splicing by capturing long-range DNA signals. The model's hierarchical Transformer architecture preserves high computational efficiency while maintaining single-nucleotide resolution, enabling accurate analysis of genomic regions.
A new method uses machine-learning algorithms to detect scatterable PFM-1 landmines over wide areas, providing a first-pass analysis of suspected hazardous areas. The technique requires only a lightweight laptop, drone, and camera, operating without an internet connection.
A chatbot using GPT-4o technology delivers personalized messages to American adults, increasing support for transgender rights and improving attitudes, but effects are short-term. The intervention demonstrates AI's potential as a scalable complement to community-based approaches aimed at fostering inclusion.
Researchers at ISTA have developed a way to guide AlphaFold with experimental data, enabling the model to better reflect physical and biological reality. This approach aims to improve future predictive models by accounting for structural heterogeneity and dynamism in proteins.
Physicists have identified two new superconductors, YRu3B2 and LuRu3B2, using machine-learning to filter material combinations. This breakthrough aims to find a room-temperature superconductor, which could slash global energy consumption and reduce the heat footprint of ICT sector.
University of Jaume I students Pau Montagut and Mario García won first place at the ICRA 2026 robotics conference with an AI model that taught a Toyota HSR robot to perform household tasks. The team's achievement is notable given their undergraduate status, competing against teams of research personnel.
The new photonic architecture harnesses three fundamental degrees of freedom: wavelength, mode, and polarization, achieving 192 parallel computing channels. The chip supports large, reconfigurable convolution kernels up to 13x13, capturing global structural contours while preserving fine details.
Researchers warn that teens' reliance on AI chatbots may bypass opportunities for developing essential relationship skills. The technology offers immediate, nonjudgmental guidance but lacks needed safeguards, potentially reinforcing unhealthy relationship patterns and increasing vulnerability to mental health problems.
The Hebrew University of Jerusalem and Edut 710 have partnered to create a groundbreaking AI-powered living archive, featuring nearly 2,000 survivor, witness, and first responder testimonies. The archive will enable users to search, translate, and explore the accounts through natural language, preserving authenticity and integrity.
A large clinical trial found that an AI support tool improved the quality of clinical documentation and treatment planning, but did not significantly change short-term patient outcomes. The study involved over 9,600 patients in Kenya and used a randomized controlled design to test the effectiveness of the AI tool.
Stanford researcher Ellen Kuhl's AI tool, BurgerAI, creates novel burger recipes optimized for deliciousness, sustainability and nutrition. The burgers were tested in a blinded taste test and found to be comparable to popular fast-food options.
Digital twins are expanding rapidly, using real-time data to simulate and analyze systems before applying them in the real world. The study emphasizes the need for interoperable architectures, machine-readable metadata, and standardized trust frameworks to address challenges such as privacy, cybersecurity, and uncertainty.
The rise of AI-generated content threatens authenticity in the clinical landscape, from AI chatbot impersonations to deepfakes of real physicians. Existing laws and regulations are struggling to keep pace with these emerging challenges.
The review discusses key AI concepts, including algorithms, models, architectures, machine learning, deep learning, and multimodal models. It highlights their clinical applications, such as detection of lymph node metastases, Nottingham grading, biomarker quantification, risk stratification, and prognostic prediction.
The University of Phoenix College of Doctoral Studies has published a new white paper examining the growing AI fluency gap in the workplace. The paper argues that AI fluency is no longer only a productivity issue but a retention issue, as employees are rapidly building AI skills while organizations struggle to keep pace.
Artificial intelligence is being harnessed to integrate multi-omics data from large-scale repositories such as TCGA and GDSC to decode tumour drug resistance. The review advocates for explainable AI frameworks, multimodal fusion strategies, and dynamic liquid-biopsy monitoring to address barriers to clinical adoption.
Researchers developed an AI framework to identify viable target antigens for CAR T cell therapy, using publicly available single-cell RNA sequencing data. The framework showed robust tumor-killing activity in mouse models of multiple cancer types, including melanoma, leukemia, and colorectal cancer.
Researchers developed a new system called Murakkab to optimize agentic workflows for AI applications. It enables developers to describe their intent in high-level terms, automating the selection of models and tools to use, and optimizing hardware configurations in real-time.