Articles tagged with Artificial Intelligence
Researchers at FAU have developed a smarter AI framework that can manage complex systems with unequal levels of authority and adapt to imperfect information. The framework, based on reinforcement learning and game theory, reduces unnecessary computation while maintaining system stability and optimal strategy outcomes.
A large-scale randomized trial will examine whether AI supports radiologists in interpreting mammograms more accurately, reducing unnecessary callbacks and anxiety. The PRISM Trial, led by UCLA, aims to inform future policy decisions and best practices in breast cancer screening.
The USC team is developing a new drug aimed at a previously unexplored biological target in Alzheimer’s disease, focusing on a trigger of brain inflammation. They have identified an enzyme that predisposes the brain to inflammation and are now working to find a drug that can block it without harmful side effects.
Researchers at uOttawa are developing an AI tool to provide personalized dietary strategies for people living with inflammatory bowel disease (IBD). The project aims to match specific types of carbohydrates to individual patients' unique gut microbiomes.
This special issue aims to showcase cutting-edge research on assessing, pricing, and managing cyber risks in AI-driven environments. Scholars worldwide are encouraged to submit and join the conversation on cyber risks in the AI era.
Researchers at MIT developed a technique called SCIGEN that steers generative AI models to create promising quantum materials by following specific design rules. The approach led to the synthesis of two actual materials with exotic magnetic traits.
A research team developed a pipeline using pre-trained large language models to unify radiology and pathology reports, achieving high accuracy in tumor presence and stability. The system also predicted survival outcomes without additional training, distinguishing high-risk from low-risk groups.
A new framework developed by Cranfield University aims to balance AI's benefits with safety and fairness concerns in disaster response. The study shows the framework outperforms human operators and conventional AI systems, providing 39% higher accuracy across various scenarios.
A team of scientists developed a novel model, DASFormer, that learns to monitor earthquakes from continuous DAS data on its own, effectively serving as an 'artificial seismologist'. The model achieved the highest performance across all evaluation metrics, with a peak ROC-AUC of 0.906 and an F1 score of 0.565.
Two projects funded by federal grants use AI to design proteins for industrial applications, such as producing acrylates in paints. UC Davis will also expand its student training program in protein design to bring hands-on research opportunities to thousands of students nationwide.
Researchers developed MoBluRF, a two-stage motion deblurring method for NeRFs, achieving high-quality 3D reconstructions from ordinary blurry videos. The framework outperforms state-of-the-art methods and is robust against varying degrees of blur, enabling smartphones to produce sharper and more immersive content.
The University of Texas at Arlington is hosting its fifth annual Texas Health Informatics Alliance Conference, which will delve into the ethical and effective applications of artificial intelligence in healthcare. Keynote speakers and panel discussions will explore topics such as AI security, cybersecurity, and legislative outcomes.
Researchers developed a robust QA system using Retrieval Augmented Generation (RAG) to answer queries on building codes. They found that Elasticsearch was the most effective retriever and fine-tuning Large Language Models (LLMs) significantly enhanced generational accuracy, achieving a 6.83% relative improvement in BERT F1-score.
Frasky, developed by IIT, combines robotics and AI to address environmental sustainability and labor shortages in agriculture. The robot can navigate autonomously, map its surroundings, and apply selective treatments to grape clusters with precision.
Researchers have identified over 120 genetic signals shaping foveal development, including pathways involved in vitamin A metabolism and retinal cell fate. The study provides the first comprehensive genetic dissection of human foveal pit architecture, revealing new insights into childhood visual disorders.
Researchers at PolyU are pioneering next-generation personal cooling solutions using advanced textiles and intelligent wearables. Their innovations include spectrum-selective textiles, thermal insulation, ventilative and evaporative cooling, and AI-driven frameworks that deliver personalized, energy-efficient cooling.
A deep learning model achieved up to 98% accuracy in distinguishing autistic from neurotypical participants, providing clear insights into brain regions most influential to its decisions. The model could benefit autistic people and clinicians by offering accurate and explainable results to inform assessment and support.
A recent study from Spain aims to develop inclusive medical AI applications by overcoming binary logics and including the needs of trans people. The research team, led by Nataly Buslon Valdez, collaborated with PRISMA association to design AI apps that promote personalized treatments tailored to individual needs.
A new study uses three wearable sensors to track eating behavior and identifies five distinct patterns, including late-night snacking and stress binging. The findings lay the groundwork for personalized interventions to help individuals overcome unconscious habits that drive overeating.
A team of researchers developed a new manufacturing process using bio-based solvents to reduce the production cost of perovskite solar cells by half and decrease climate impact by over 80%. AI-based reverse engineering technology was used to identify optimal conditions for efficiency and sustainability.
Researchers developed a fully automated finite element analysis pipeline to transform spine diagnostics and personalized treatment planning. The new approach enables rapid, patient-specific simulations that support preoperative planning, spinal implant optimization, and early detection of degenerative spine conditions.
A new AI model can estimate the long-term risk of over 1,000 diseases and forecast human health changes over a decade. The model was trained on anonymised patient data from 400,000 participants and tested on data from 1.9 million patients in the Danish National Patient Registry.
A Lund University AI model analyzed mammograms to identify breast cancer patients at high risk of armpit metastasis, suggesting that 41.7% of axillary surgeries could be avoided. The study's findings have the potential to revolutionize breast cancer treatment by enabling more personalized care and reducing unnecessary surgeries.
A study by University of Cambridge researchers found that ChatGPT, an AI chatbot, improvised solutions to a 2,400-year-old mathematical challenge. The bot made mistakes and adapted its approach based on user input, demonstrating a 'learner-like' behavior.
Mass General Brigham has identified key areas of innovation in medicine, including gene editing, immune system modulation, AI-powered care, and organ transplantation. The 'Big Ideas in Medicine' aim to transform diagnosis, treatment, and prevention of disease with next-generation technologies.
The book provides an overview of agent-based modeling and multi-agent systems, highlighting their application in understanding economic crises. It integrates machine learning to enhance adaptation and behavior of agents in dynamic environments.
Researchers developed computational tools to analyze ecological data, identifying functionally equivalent species across different ecosystems. These tools use optimal transport distances to compare network structures, allowing for large-scale monitoring of ecosystem health and guiding conservation efforts.
Researchers developed an AI-driven algorithm that can predict nearly 70% of hot flashes before they're felt. The Embr Wave wearable device will incorporate this technology to mitigate symptoms and provide meaningful relief.
Researchers used artificial intelligence to analyze facial expressions of Japanese undergraduates and found subtle eye and mouth movements linked to depressive symptoms. The study suggests that AI-based facial analysis can detect mental health issues efficiently, enabling early interventions.
Researchers at Stanford Medicine have created an AI-powered CRISPR tool called CRISPR-GPT, which helps scientists design and analyze gene-editing experiments. The technology aims to produce lifesaving drugs faster and expand access to gene editing for a wider range of scientists, including those with limited experience.
Researchers used AlphaFold3 to predict the structure of human bitter taste receptors, achieving higher accuracy than previous models. The study found similarities and differences in the structures of T2Rs, shedding light on their function and potential role in health and pharmaceutical research.
A new study developed a flood-forecasting AI that can be tuned for any country, reducing errors in national flood prediction programming. The hybrid model combining the AI with the National Water Model was four to six times more accurate, improving forecast accuracy and potential economic impacts of floods.
The team will study neurons within a brain organoid, a millimeter-sized, three-dimensional structure grown in the lab from adult stem cells, to design smarter and more sustainable artificial intelligence. They aim to replicate complex computations that occur in the human brain to improve AI efficiency.
A new chip architecture called Double Duty has been developed to make AI-powered computing more efficient and less energy-intensive. This innovation allows for reduced space needed for specific AI tasks by over 20% and improved overall performance on a large suite of circuits by nearly 10%.
Kennesaw State University has received two National Science Foundation grants to unite educators from across the country in defining how students should learn to use artificial intelligence responsibly. The project aims to create a shared resource network, foster collaboration and lay the groundwork for common guidelines in AI education.
Researchers developed a custom speech recognition system trained on Supreme Court hearings, reducing transcription errors by up to 9% compared to leading commercial tools. The AI tool semantically matches paragraphs with timestamps, allowing users to scroll through judgements and instantly watch relevant exchanges from the hearing.
A new open-source AI tool, ProRNA3D-single, has significantly increased accuracy in predicting and visualizing protein-RNA complex structures. This method can generate finely detailed images of molecules in 3D, enabling drug developers to design treatments by analyzing where viruses attach to human proteins.
A new AI-powered deep learning model has been shown to accurately estimate the malignancy risk of lung nodules, achieving high detection rates while reducing false-positive results. The model outperformed existing probability-based tools in a retrospective study, with a relative reduction of 39.4% in false positives.
A new paper highlights the importance of human historians in capturing emotional complexity behind world events as AI struggles to accurately represent Holocaust survivors' experiences. Historians possess skills that AI lacks, including the ability to capture human suffering and preserve fracture and silence.
Researchers developed a novel framework to qualitatively evaluate the quality of synthetic wireless data, improving AI model training with 4.3% performance gain. The framework assesses affinity and diversity in wireless signals, essential qualities for effective training.
A NJIT student-faculty team won a best presentation award for their research on simulating ant swarm aggregations dynamics. Their study showed that ant swarms exhibit fluid and elastic properties, similar to biological systems.
The new lab, led by Dr. Annapoorna Kini, will focus on leveraging AI in interventional cardiology, from procedural to educational aspects. The lab aims to optimize complex treatment decisions and improve patient outcomes through AI-powered insights.
A study found that a ChatGPT-assisted writing system improved emotional engagement and intrinsic motivation for students with dyslexia, but negatively impacted writing performance. The system's effectiveness depends on teacher-guided integration to avoid undermining writing skills.
Researchers from Bielefeld University explore how humans and machines learn differently, highlighting the importance of bridging cognitive science and AI research. The study shows that machines generalize differently than humans, making it crucial for successful human-AI collaboration.
AI TechX, a UT Knoxville initiative, awards $60,000 seed funding to nine projects addressing real-world challenges through AI research and innovation. The funded projects accelerate adoption of AI across various disciplines, enabling high-impact collaborations and job creation.
Researcher Angus Fletcher argues that AI excels at logic but fails to replicate human creativity and problem-solving skills. He advocates for developing 'primal intelligence,' which involves intuition, imagination, emotion, and commonsense, and has developed a program to help people tap into this ability.
Researchers found that more interactive mobile apps and AI chatbots foster a sense of playfulness, lowering privacy concerns. However, this interactivity can also distract users from thinking about the information they share with the system.
The TCT AI Lab equips clinicians with skills to integrate AI into clinical practice, advancing precision, efficiency, and patient outcomes. The initiative is made possible by a generous grant from the Jon DeHaan Foundation.
The new Fir supercomputer at Simon Fraser University is a $80m upgrade to Canada's national computing infrastructure, empowering researchers across Canada. With its clean energy and world-class AI infrastructure, Fir will drive research and innovation in fields like tech, life sciences, and healthcare.
Biochar, a carbon-rich material, is gaining attention for its ability to improve soils, clean water, and capture carbon. Machine learning models can predict biochar yield and pollutant removal efficiency with over 90% accuracy, accelerating its development.
A recent study found dramatic differences in how large language models classify hate speech, with significant variations for specific demographic groups. The analysis of seven models revealed that content moderation systems have inconsistent detection accuracy, which can erode trust and create perceptions of bias.
The University of Texas at San Antonio has launched the College of AI, Cyber and Computing, which will serve over 5,000 students pursuing degrees in rapidly growing career fields. The college is positioned to advance technological education and research, with faculty securing major grants and launching industry partnerships.
A new tool called Flexynesis uses deep neural networks to evaluate multi-modal data, enabling doctors to make better diagnoses and develop more precise treatment strategies for patients. The tool is designed to be flexible and accessible to non-experts, bridging the gap in precision cancer therapy.
Researchers used AI to identify shared biological features between aging and fibrosis, finding new potential targets for therapy. The study found that IPF is not simply accelerated aging, but a unique biological condition shaped by age-related dysfunction.
A systematic review of 53 studies found that machine learning algorithms for predicting suicidal behavior have low accuracy. The algorithms were poor at identifying individuals who would later self-harm or die by suicide, while exceling in detecting those who would not.
Researchers used AI to analyze protein complexes, discovering catch-bonds 'switch on' almost immediately after force is applied. This finding has implications for understanding bacterial attachment, tissue resilience and developing new biomaterials.
Researchers developed AI-assisted conductive hydrogel dressings for intelligent wound monitoring, combining real-time physiological signal detection with artificial intelligence. The smart dressings offer personalized wound care and have the potential to bring hope to millions suffering from chronic wounds.
Scientists have discovered key circuit changes that lead to the maturation of reliable brain activity patterns in response to visual experience. The findings suggest that the brain develops a modular organization, with patches of neurons activating together in response to specific features, allowing for efficient learning and adaptation.
A new University of South Florida study highlights the impact of menopause on women's voices, particularly those who rely on their voices professionally. The research outlines a range of treatment options, including AI-powered voice biomarkers, to detect subtle vocal changes linked to menopause.
Bobin Deng's research focuses on developing AI tools that can run directly on mobile phones, drones, and other smaller systems without relying on costly supercomputers. His technique uses activation sparsity to predict which data will be activated, reducing memory usage and energy demands.