Articles tagged with Artificial Intelligence
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
Researchers created a robot inspired by pangolins' ability to curl up into a ball, with a soft layer and hard metal components. The robot can emit heat when needed and transport particles like medicines, making it promising for minimally invasive medical procedures.
An AI model called SCORE-AI achieved human expert level performance in fully automated electroencephalogram interpretation. This technology has the potential to improve diagnosis and patient care in underserved areas and increase efficiency in specialized epilepsy centers.
Researchers applied machine learning to brain responses and achieved near-perfect classification accuracy for songs that may become hits. The approach, called 'neuroforecasting,' uses data from a small group of people to predict population-level effects without needing to measure the brain activity of hundreds of people.
A communication robot is being developed to prevent elderly isolation by facilitating self-disclosure through different communication options. The study found that the most appropriate option depends on the recipient, message content, and sender's gender and personality traits, reducing participants' anxiety in self-disclosure.
Researchers have developed DyLiN and CoDyLiN, methods that handle non-rigid deformations and topological changes in 3D structure representation. These advancements enable real-time volumetric rendering and animation with improved visual fidelity and speed.
Generative AI raises fundamental questions about the creative process and human's role in it. Researchers highlight gaps in understanding perceptions of AI-generated content, ownership, credit, labor economics, and media ecosystem impact.
Researchers at Beth Israel Deaconess Medical Center used a generative AI chatbot to solve complex diagnostic cases, finding it accurate in nearly 40% of cases and providing correct diagnoses in two-thirds of challenging scenarios. The study suggests generative AI has the potential to assist medical professionals in diagnosis.
A new research project at University of Leicester will develop a digital 'twin' of the UK using artificial intelligence and big data to inform environmentally-friendly land use. The project aims to reduce emissions from cattle and sheep farming, which contribute around 10% of the UK's emissions.
Research by Francis de Véricourt and Huseyin Gurkan found that trust in machines' decision-making ability is key to effective learning. They discovered that biased learning occurs when humans override algorithmic decisions without observing the machine's correctness, leading to incorrect usage of machines in decision making.
A new AI-based technique measures brain fluid flow with unprecedented accuracy, revealing pressures and three-dimensional flow rates. This breakthrough could lead to the development of new treatments for Alzheimer's, small vessel disease, strokes, and traumatic brain injuries.
Lero is recruiting 16 world-class researchers to work on privacy, trust and inclusion in software development through the €2.9 million SyMeCo programme. The programme will attract outstanding international candidates from computer science and related fields.
A machine-learning study has found that individual characteristics, including age and weight, determine which drug combinations most effectively reduce COVID-19 recurrence rates. The study used real-world data from a hospital in China and identified unique treatment combinations for different demographic groups.
Research by the American Psychological Association found that employees who frequently interact with AI systems are more likely to experience loneliness, insomnia, and increased after-work alcohol consumption. However, these effects may be mitigated by equipping AI systems with social features or limiting their use.
Researchers developed MOVA to evaluate the pathogenicity of missense variants based on their structural position. The new method showed superior performance in predicting pathogenicity, especially for hotspot mutations in ALS-causing genes like TARDBP and FUS.
A new study published in JMIR Cardio reveals the potential of AI-QCA in analyzing coronary angiography, providing accurate measurements of coronary stenotic lesions similar to IVUS. This technology has the potential to improve patient outcomes and support clinical decision-making.
The Mori3 robot is a polygon shape-shifting robot designed for space travel, capable of morphing into any 3D object and adapting to various environments. Its versatility makes it an ideal candidate for communication and external repairs in spacecraft.
Researchers have developed a new AI model that can quickly screen large libraries of potential drug compounds against target proteins. The ConPLex model uses language analysis to match potential drugs with proteins without needing to calculate molecular structures, enabling fast screening of over 100 million compounds per day.
Researchers developed Eye2Gene, an AI system that accurately identifies genetic causes of inherited retinal diseases from retinal scans. The system achieved higher accuracy than human experts in identifying causative genes, with a ranking score above 70% in over 70% of cases.
Researchers developed an AI model to estimate best-corrected visual acuity from fundus photographs in patients with diabetic macular edema, achieving accuracy within one to two lines on a standard chart. The study's findings support the potential of AI in clinical settings, with opportunities for further improvement.
MethaneMapper is an artificial intelligence-powered hyperspectral imaging tool that can detect real-time methane emissions and trace them to their sources. With a performance accuracy of 91%, it has the potential to revolutionize the way we monitor oil and gas operations and curb climate change.
Researchers at TU Delft and EPFL collaborated with ChatGPT to design a tomato-harvesting robot, showcasing the potential of AI in robotics. The study highlights the need for verification and validation of LLM output to prevent misinformation and bias.
Researchers found that manual labor's perceived meaning can predict the adoption of autonomous products. Consumers tend to reject these products due to a loss of meaning in life. Companies can highlight alternative sources of meaning to reduce negative effects on adoption.
Researchers at NYU Grossman School of Medicine have developed an AI tool called NYUTron that can accurately estimate patients' risk of death, length of hospital stay, and other factors important to care. The tool achieved impressive results in predicting readmission rates, improving upon standard methods by up to 7%.
ChatGPT achieved high scores in clinical reasoning and demonstrated rapid response times to evolving questions. However, its lack of local ethnic knowledge and limited medical references hindered its performance in highly contextual situations.
A study by the University of California - San Diego found that ChatGPT provided evidence-based responses to 91% of public health questions, but only referred users to resources in 22% of cases. The researchers emphasize the importance of promoting human-powered critical resources like life-saving hotlines.
ChatGPT consistently provided accurate, evidence-based answers in public health questions, beating rival AI assistants. Meanwhile, Amazon Alexa and Google Assistant struggled to recognize or refer users for help.
Researchers developed a new AI framework that significantly improves the ability to analyze team communication, enabling adaptive training technologies to facilitate effective team collaboration. The framework performed substantially better than previous AI technologies in classifying dialogue and following information flow.
A University of Minnesota team developed a new superconducting diode that is more energy efficient and versatile than past models. The device can process multiple electrical signals at once and has gates to control the flow of energy, which could enable faster quantum computers for industry use and enhance AI performance.
A recent study published in Radiology found that AI algorithms performed better than the standard Breast Cancer Surveillance Consortium (BCSC) risk model for predicting five-year breast cancer risk. The AI models extracted hundreds of additional mammographic features, leading to improved predictive performance.
Researchers at KAUST developed smart digital image sensors that can recognize images with high accuracy, using a charge-trapping 'in-memory' sensor sensitive to visible light. The devices have an extremely long-lived retention time of up to 10 years and can perform optical sensing, storage, and computation.
A team of University of Colorado School of Medicine researchers explores the impact of chatbot avatars on patient experience, trust, and compassion in healthcare. They found that chatbots may reinforce social stereotypes and biases, but also have potential to increase trust among underserved patient groups if designed with respect for ...
Scientists using popular computational tools to interpret AI predictions are picking up too much 'noise' when analyzing DNA. Researchers have found a way to fix this by applying a new line of code, leading to more reliable explanations and potentially unlocking the next breakthrough in health and medicine.
Researchers develop a novel voice spoofing detection method combining physiological and physical features, achieving improved performance over existing methods. The proposed method outperforms single systems in terms of EER and t-DCF scores.
Researchers updated their protein localization prediction model, MULocDeep, to provide more targeted predictions for biological discoveries. The tool helps researchers design more effective experiments and advance scientific discoveries related to drug development and treating diseases like epilepsy.
Researchers developed an AI-powered mass spectrometric technique to determine a polymer's monomeric sequence. This breakthrough may lead to improved materials performance and tackle plastic recycling issues.
Researchers at UCLA Health highlight the impact of test results on lung cancer screening adherence and the potential unnecessary use of axillary ultrasound in breast cancer diagnostics. Meanwhile, a new AI system helps check endotracheal tube placement with high accuracy and user satisfaction.
Researchers at North Carolina State University have developed a new methodology called Patch-to-Cluster attention (PaCa) that addresses the challenges of vision transformers. PaCa improves ViT's ability to identify, classify, and segment objects in images while reducing computational demands and enhancing model interpretability.
A new Danish research project, CP-SENS, aims to develop a digital twin platform for the mechanical and construction industries. The project will provide companies with access to intelligent IT systems tailored to their needs, enabling them to adopt digital twins without significant financial investment.
FES2023 brings together leading researchers and industry experts to share groundbreaking research on AI-based energy solutions, smart grids, and renewable energy systems. The conference covers key themes including planning and operation of energy systems, electricity markets, and demand response.
Researchers develop AI-based method to quantify cracking patterns in reinforced concrete structures, enabling more accurate and efficient assessments of structural damage. The approach uses graph theory and machine learning algorithms to create a unique 'fingerprint' for each set of cracks, allowing for quick and consistent evaluations.
A WVU researcher is developing a data-driven approach to enhance roadway maintenance operations, incorporating AI analysis of real-time data from sensors on consumer cars and roadside observation stations. The goal is to minimize time and resources spent on less affected roadways, reducing winter maintenance costs and time.
Researchers developed an AI system, Geneformer, to predict how disruptions in human gene connections cause disease. The model, trained on data from thousands of genes, can identify potential drug targets for diseases like heart disease and cancer.
A new study published in The Neuroradiology Journal introduces an artificial intelligence computer program that can accurately identify changes in brain structure resulting from repeated head injury. This AI tool uses machine learning to process magnetic resonance imaging (MRI) scans and distinguish between the brains of male athletes ...
Researchers at Ohio State University developed an AI framework called G2 Retro to automatically generate chemical reactions for molecules. The framework was shown to cover a vast range of possible reactions and accurately predict the best synthesis routes, offering more efficient drug design options.
A nationwide VR research project is working to create diverse groups of participants to reflect real-world social dynamics. The Virtual Experience Research Accelerator (VERA) aims to provide researchers with access to large, reliable, and diverse groups for various VR research projects.
Researchers at the Beckman Institute for Advanced Science and Technology have developed a new framework for super-resolution ultrasound using deep learning, reducing processing speeds from minutes to seconds. The new technology enables real-time blood flow visualization, overcoming challenges faced by conventional methods.
Researchers used AI to identify a compound that kills Acinetobacter baumannii, a bacterium responsible for many drug-resistant infections. The new antibiotic shows promise in combating this growing public health threat.
A deep learning-based framework called EMGSense enables accurate wearable EMG device usage through AI self-training techniques. It achieves average accuracy of 91.9% in gesture recognition and 81.2% in activity recognition, outperforming state-of-the-art approaches.
A deep learning system accurately classifies autism spectrum disorder and predicts symptom severity based on behavioral responses to social cues. The method offers a digital measurement of joint attention, but further validation is needed.
Researchers at MIT have developed a new approach to match 3D shapes by mapping volumes to volumes, resulting in more accurate animations and CAD designs. This method represents shapes as tetrahedral meshes that include the mass inside a 3D object, allowing for better modeling of fine parts and avoiding common artifacts.
Researchers investigate the potential of ChatGPT in clinical radiology, highlighting its positive considerations, such as enhancing patient care and radiologist education, as well as negative concerns, like accuracy and transparency issues. The paper aims to initiate an engaging discussion on the use of AI-powered chatbots in this field.
A new AI framework developed by Danish researchers significantly improves the accuracy of early stroke identification by human emergency call handlers. The study found that the AI framework achieved a recall of 63.0% and precision of 24.9%, compared to 52.7% and 17.1% for human handlers, respectively.
A new camera technology developed by Aarhus University and Newtec Engineering A/S aims to make it easier to recycle plastic materials. The technology uses hyperspectral imaging to analyze the chemical composition of plastic waste, allowing for the removal of unwanted additives that may be banned or harmful.
Researchers at Rensselaer Polytechnic Institute and Albany Medical College will use AI to develop a novel mesoscopic, multimodal preclinical imaging approach to test the hypothesis that drug distribution mediates tumor resistance. The goal is to improve targeted therapy for HER2-positive breast cancer.
AI enhances drug discovery by analyzing abundant data, identifying new targets and designing effective treatments, significantly reducing cycle time and costs. AI also improves clinical trial design and post-market surveillance, leading to safer and more personalized medications.
Researchers used deep learning to analyze drone images of pine trees, detecting pine processionary moth larvae nests with high accuracy. The method can be applied in various settings to inform tree health managers about potential threats.
A new CAR T cell design approach using machine learning and artificial intelligence is being developed to improve cancer treatment. The project aims to create a hybrid knowledge- and data-driven approach to guide the design of immunotherapeutic cells.
A deep learning-based Transformer model, Bloomformer-1, has been developed to identify the driving factors of algal growth in the Middle Route of the South-to-North Water Diversion Project. The results reveal that total phosphorus is the most significant factor affecting algal growth, especially in the Henan section, while total nitrog...
A new AI-enabled forecasting model can predict ENSO events for up to 22 months, overcoming the limitations of previous models. The Spatio-Temporal Information Extraction and Fusion (STIEF) model uses deep learning to extract space and time features and fuse them together, providing a more accurate prediction length.
Researchers used machine learning to identify 'synthetic extreme' DNA sequences that are active in humans but not fruit flies. These rare sequences have potential practical applications in biotechnology and biomedical research.