Articles tagged with Artificial Intelligence
Researchers developed Localizatome to study oxidative stress-related changes in protein localization. The database provides comprehensive information on subcellular protein localization and dynamic localization changes under stress.
The EVOaware project aims to develop an innovative platform that addresses tumour resistance to therapies by using advanced tissue imaging technologies and integrating genetic screening, lineage tracing, and spatial omics techniques. This platform has the potential to accelerate the discovery and development of new cancer therapies.
A recent survey found that nearly two-thirds of U.S. adults consider AI-generated summaries to be somewhat or very reliable, while another 45% say they sometimes give them the answer they need. However, half of respondents are not comfortable with healthcare providers using AI tools.
A new method using label-free optical microscopy and artificial intelligence effectively identifies disease phenotypes in pancreatic cancer. The approach achieved nearly 90% accuracy in predicting tissue phenotypes, demonstrating the promise of combining light-based imaging with AI for precision medicine.
Researchers developed a self-driving lab that collects at least 10 times more data than previous techniques, dramatically expediting materials discovery research while slashing costs and environmental impact. The system uses dynamic flow experiments to continuously characterize samples, capturing data every half second.
A new study by Cornell University reveals Amazon's AI shopping assistant Rufus gives vague or incorrect responses to users writing in some English dialects like AAE. The researchers propose a framework for evaluating chatbots that can better serve users from diverse linguistic backgrounds.
Researchers at The University of Osaka develop an AI-powered framework to measure micro-scale occupancy in open-plan offices, providing detailed data for evidence-based design decisions. The system uses computer vision and 3D pose estimation to analyze video footage and track individuals within predefined zones.
Researchers developed an AI system that enables a four-legged robot to adapt its gait to different terrain, just like animals. The robot learned to switch gaits on the fly and navigate uneven surfaces without any alterations to the system itself, overcoming previous limitations around adaptability.
Researchers from Université Laval designed an ultra-fast and greener optical chip that can transfer massive amounts of data at speeds of 1,000 gigabits per second while reducing energy consumption. This innovation uses the phase of light to add a new dimension to the signal, reaching unprecedented performance levels.
Researchers demonstrated R-STDP synaptic plasticity in a single ferroelectric floating-gate device, enabling SNN to simulate robotic capture tasks with excellent performance. The network achieved a success rate up to 85.5% in dynamic capturing tasks.
Pusan National University researchers developed a comprehensive issue set for MDA implementation, covering technical, organizational, and environmental challenges. The framework helps manufacturers prioritize issues and address them proactively, enabling efficient and effective data-driven smart manufacturing.
The Chan Zuckerberg Initiative's latest AI model, GREmLN, is designed to predict and understand how genes interact within cells. It focuses on the molecular logic that defines gene behavior, allowing researchers to track critical changes pinpointing the earliest signs of disease.
A new report by Digital Science reveals China is outstripping the rest of the world in AI research, with a nationwide innovation ecosystem and a young, growing workforce. The UK remains small but globally impactful, while the EU risks falling behind due to weaker international collaboration.
A new AI tool called Ark+ has demonstrated exceptional capability in diagnosis of lung diseases and rare conditions like COVID-19. The tool outperformed proprietary software developed by Google and Microsoft, making it a potential game-changer for healthcare.
A 70-billion-parameter large language model called ChemELLM has been developed for chemical engineering applications. It was trained on a specialized dataset and demonstrated superior performance in various tasks compared to mainstream models.
A study using AI language models analyzed sentiments and emotions expressed by almost 400 pediatric patients on social media, finding that 94% of comments were negative, with sadness and fear prevalent. The study highlights the need for integrated care approaches to support vulnerable young patients managing complex medical conditions.
Researchers found that LLMs are better at representing non-sensorimotor concepts but struggle with sensory and motor concepts. Incorporating sensory input improves LLM performance and leads to more human-like representation, consistent with previous human studies on multimodal learning.
Yen-Ling Kuo, a UVA computer scientist, has been awarded the NSF CAREER Award to help robots understand and respond to human behavior. Her research focuses on building AI systems that can reason, communicate, and collaborate like humans, enabling robots to support people in various settings.
Researchers at KIT have developed a new AI method that generates high-resolution precipitation maps from low-resolution data, offering improved assessments of regional climate risks and disaster control. The model provides physically plausible precipitation maps with statistical uncertainty, applicable to diverse climatic conditions.
Researchers at Charité analyzed the laws of enzyme evolution on a large scale, revealing that certain areas change faster than others. The findings have implications for the development of new antibiotics and other medicines.
A new AI model developed by Mayo Clinic and Ultromics can detect cardiac amyloidosis, a rare and progressive type of heart failure, with high accuracy. The model was validated on a large patient population and outperformed traditional screening methods in identifying the disease.
The new journal aims to accelerate knowledge dissemination in AI by prioritizing theoretical breakthroughs, practical applications, and societal implications. It features short peer-reviewed contributions and opinion pieces to facilitate swift engagement with contemporary AI issues.
Artificial intelligence (AI) is revolutionizing medical needle procedures by providing unprecedented levels of accuracy and precision. AI-powered robots can assist physicians in complex tasks, enabling safer and more effective procedures. The four components of AI guidance are formalized, categorizing current systems into Eyes-on/Hands...
Researchers developed a way to boost LLMs' performance on challenging tasks using test-time training. This method involves updating some model parameters using new data, leading to significant improvements in accuracy, especially for tasks requiring logic and reasoning.
The journal is seeking original research, viewpoints, and tutorials exploring the use of AI-driven documentation in clinical settings. Submissions should address practical applications, challenges, and future directions of AI-facilitated clinical documentation.
A new report from the University of Cambridge's Minderoo Centre for Technology and Democracy highlights the alarming trajectory of energy use by big tech companies, driven by AI growth. The report predicts that data centres could consume up to 8% of global greenhouse gas emissions by 2040, far exceeding current emissions from air travel.
Researchers developed an AI model that screens for cardiac amyloidosis with high accuracy, outperforming existing methods. The tool analyzes routine heart ultrasound images to quickly detect the condition and differentiate it from other heart issues.
The collaboration aims to develop a predictive, high-fidelity AI model that identifies disease mechanisms and predicts patient outcomes. This initiative will focus on cancer but may expand to other complex medical challenges, such as transplant rejection and immunology.
The Buck Institute is pioneering a cloud-based platform to simulate the dynamic behavior of microbial cells, overcoming current whole-cell models' limitations. The SIMBA project aims to advance our understanding of bacterial behavior and address critical challenges in biomanufacturing and national security.
Researchers have developed an AI-assisted diagnostic system that can estimate bone mineral density in the lumbar spine and femur with high sensitivity and specificity. The system has the potential to transform routine clinical X-rays into a powerful tool for opportunistic screening, enabling earlier detection of osteoporosis.
A study published in Radiology found that AI decision support systems improve breast cancer detection accuracy and visual search patterns among radiologists. With AI assistance, radiologists spend more time examining regions with actual lesions, leading to better performance and efficiency.
A recent study published in the Association for Computing Machinery Conference on Fairness, Accountability, and Transparency found that AI chatbots are not safe replacements for therapists. The researchers evaluated these chatbots against clinical standards for therapists and concluded that they don
A recent study using machine learning and computational modeling reveals that astrocytes play a more active role in brain function than previously thought. Astrocytes subtly modulate communication between neurons during synchronous brain activity, influencing network coordination and stability.
Researchers highlight the benefits of artificial intelligence in glioblastoma management, including improved diagnosis, treatment planning, and personalized therapy. AI-based models can analyze radiological images, predict tumor characteristics, and suggest effective treatments, promising to improve patient outcomes and survival rates.
A team of researchers from UC3M, MIT and Adobe have developed Imprinto, a system for embedding invisible digital information in printed documents. This technology uses infrared ink and a special camera to enable advanced interaction with physical documents without altering their visual appearance.
Researchers at Boston University have created a new language model called PodGPT that combines expert knowledge from podcasts with the latest research articles. The model has been shown to better understand and answer scientific questions, particularly those related to biology, math, and medicine.
A Mayo Clinic team developed an AI system that can automatically identify surgical incisions and flag signs of infection in patient-submitted photos. The model achieved high accuracy in detecting incisions and identifying infections, with potential to streamline communication between patients and their care teams.
A new study reveals that AI systems transition from relying on word positions to meaning-based understanding as they receive enough data for training. The transition occurs abruptly, similar to a phase transition in physical systems, and is driven by the amount of data available.
Frontiers has integrated Cactus' Paperpal Preflight and Clear Skies' Papermill Alarm and Oversight into its AIRA submission-screening system to boost fraud-detection capabilities. This integration strengthens the reliability and integrity of every manuscript submitted to Frontiers.
A new method for rapid generation of aircraft concept solutions based on generative artificial intelligence has been proposed, demonstrating the application potential of large language models in aircraft conceptual design. The method improved efficiency and innovation in the conceptual stage.
Researchers developed an AI-informed method for rapid protein evolution, integrating structural and evolutionary constraints. The approach, AiCE, outperforms traditional methods in predicting high-fitness mutations, enabling efficient protein redesign and applications in precision medicine.
A new study demonstrates that retrieval-augmented generation can eliminate hallucinations in clinical LLMs while protecting patient privacy. The RAG-enhanced model responds significantly faster than cloud-based systems, delivering safer results without compromising data security.
A new technique called WeGeFT improves large language model performance in tasks such as commonsense reasoning and code generation. By fine-tuning key parameters, researchers reduce the need for significant computational power, advancing the field of artificial intelligence.
Researchers developed an AI-powered microscope system to measure soil fungi presence and quantity, providing insights into soil health and fertility. The low-cost optical microscopy with machine learning technology can be used by farmers and land managers worldwide.
Deep learning models such as CNNs and RNNs have been applied to analyze aquatic species imagery and acoustic data, enhancing identification accuracy. AI-driven automation supports the delineation of protected areas and optimal allocation of conservation resources by leveraging remote sensing technologies and AI algorithms.
A fully autonomous robotic system developed by MIT researchers can measure important material properties like photoconductivity, increasing the speed and precision of research. The system uses machine learning and robotics to analyze new semiconductors and optimize the development of more powerful solar panels.
A study by Karolinska Institutet found that an AI tool improved pathologists' assessments of tissue samples from skin cancer tumours, leading to more consistent results and better prognoses. The tool helped quantify tumor-infiltrating lymphocytes, a biomarker in several cancers, including malignant melanoma.
The College of Engineering at Texas A&M is developing a suite of university-wide resources to integrate generative AI into course material, research, and outreach. The initiative aims to make generative AI a core part of the academic toolkit accessible to faculty across disciplines.
Researchers developed an AI model that converts fragmented electronic health records into readable narratives, enabling artificial intelligence to make sense of complex patient histories. The Multimodal Embedding Model for EHR (MEME) transformed tabular health data into 'pseudonotes' that mirror clinical documentation, achieving superi...
Researchers analyzed viral loads during early infection to predict disease progression and developed a method to identify patients at risk of severe symptoms. The study suggests that patients with high viral loads are more likely to experience long-lasting skin lesions and severe symptoms.
Researchers at the Wyss Institute have identified vorinostat as a promising treatment for Rett Syndrome using an AI-driven drug discovery process and innovative disease modeling. The findings demonstrate disease-modifying abilities across multiple tissues, offering hope for a potentially curative treatment.
A groundbreaking study reveals that biological brains and AI systems develop similar neural patterns during social interaction, with synchronized patterns driving social behaviors. The research has significant implications for understanding human social disorders and developing socially-aware AI systems.
A new study in Nature Communications found that AI models exhibit a geometric property called convexity, which helps humans form and share concepts. Convexity is also linked to the performance of AI models on specific tasks.
The Hebrew University of Jerusalem is part of a €9.3 million, 36-month Horizon Europe project to develop trustworthy GenAI systems. The consortium aims to integrate neural and symbolic methods for human-centric design in complex systems.
Researchers developed AI-created thermal meta-emitters that can selectively emit heat at various levels, making them ideal for energy efficiency. These materials were found to be up to 20 degrees Celsius cooler than commercial paints in a four-hour exposure to direct sunlight.
Researchers used machine learning to simulate galaxy evolution and supernova explosions, achieving speeds four times faster than supercomputers. This breakthrough enables the study of galaxy origins, including the creation of the Milky Way's elements essential for life.
Researchers propose a paradigm shift toward an educational model built on human interdependence, urging a re-examination of success and purpose. The new approach emphasizes uniquely human capacities, creativity, emotional intelligence, and collaboration with AI.
Researchers have demonstrated a new technique, RisingAttacK, to manipulate all widely used AI computer vision systems, allowing them to control what the AI 'sees'. The attack is effective at influencing the AI's ability to detect top targets, such as cars, pedestrians, or stop signs.
The University of Illinois team created a user-friendly process to improve enzyme performance using AI and automated robotics. By predicting sequence changes and testing variants, they increased the activity of two key industrial enzymes by up to 26 times and 90 times.
The new AIFS ENS model outperforms state-of-the-art physics-based models, with gains of up to 20%, and generates forecasts over 10 times faster while reducing energy consumption by approximately 1,000 times. This complements ECMWF's portfolio by leveraging machine learning and artificial intelligence.