Articles tagged with Artificial Intelligence
Researchers found that social media consumers trust verified accounts less than unverified ones if the content is inconsistent with the influencer's brand. Verified accounts can actually harm trust and may not be more effective at selling products.
A global study found symptoms of COVID-19 differ significantly between countries and individuals with underlying health conditions. Researchers analyzed data from 78,299 individuals in 190 countries, revealing varied symptom profiles that can inform clinical practice, public health messaging, and treatment strategies.
A Bocconi University study used machine learning to analyze data on 2038 couples in Germany, finding that life satisfaction and housework are key predictors of union dissolution. The analysis also revealed complex interactions between variables, including the impact of personal traits like openness and extraversion.
A new study found that music and auditory beat stimulation can significantly reduce state anxiety in people with moderate trait anxiety. The treatment was more effective than music alone or pink noise for reducing somatic anxiety, while music alone was more effective for reducing cognitive state anxiety.
Researchers at the University of Tsukuba created a handheld social robot, OMOY, that can appear to convey emotions by shifting an internal weight while reading out text messages. The robot was tested with 94 people and found to reduce negative emotions such as anger, revenge, and avoidance motivation.
Researchers at Rutgers University have successfully stabilized an enzyme that degrades scar tissue resulting from spinal cord injuries, promoting tissue regeneration. The study used AI-driven liquid handling robotics to synthesize and test copolymers that stabilize the enzyme, offering new hope for patients with spinal cord injuries.
A new study by Anglia Ruskin University found that AI-powered apps reduced business risks during the pandemic by 3.1%. The use of AI apps was also linked to lower profits (2%) and overall business risk (1.2%). Only 26% of small enterprises are currently using these applications.
Researchers at the University of Washington developed an AI-designed protein that can awaken individual dormant genes by disabling chemical 'off switches'. This approach allows for safe upregulation of specific genes to affect cell activity without permanently changing the genome.
Researchers at MIT and Harvard University applied cognitive science theories to human-robot interaction, finding that humans need to see variation in robot behavior to build accurate mental models. Theories suggest that strategic variation can reveal concepts that might be difficult for a person to discern otherwise.
Researchers at Mainz University will examine algorithm decisions on transparency, fairness, and data protection while optimizing resource use. The project aims to create workable trade-offs for applications and integrates young researcher promotion programs.
Biohybrid micro- and nanorobots promise to deliver drugs to body tissues with high precision, enabling tasks such as cancer treatment, cell microsurgery, and tissue engineering. Researchers envision incorporating novel biological components into robots to overcome immune responses and increase efficiency in manufacturing.
Researchers found that ant colonies use an algorithm similar to the internet's data optimization, which senses and stabilizes behavior. This principle is also used in cells and neurons. Nature's algorithms may inspire new cybersecurity strategies or alternative approaches to gene regulation.
Researchers at University of Illinois develop new method to accurately estimate soil organic carbon using airborne and satellite hyperspectral sensing. The study leverages machine learning algorithms with a comprehensive soil spectral library, enabling large-scale monitoring of surface soil organic carbon.
Researchers propose various approaches using AI, deep learning, and machine learning to improve the accuracy and predictive power of biomarkers for cancer and other diseases. The tools have shown promising applications in identifying early-stage cancers, inferring the site of specific cancers, and predicting response to immunotherapy.
Researchers developed a machine-learning technique that can pinpoint anomalies in large datasets, such as power grid failures and traffic bottlenecks. The model uses advanced probability distributions to identify low-density values, allowing for faster and more accurate anomaly detection.
GIST researchers propose a new strategy for crime prevention using artificial intelligence, trained on a large-scale dataset of deviant incident reports and corresponding images. The model, called DevianceNet, can accurately classify and detect deviant places, making it a useful tool in urban safety development.
Researchers warn of substantial risks associated with AI in agriculture, including cyber-attacks and environmental degradation. They suggest involving ecologists in technology design to avoid scenarios like overuse of fertilisers and soil erosion.
A new AI algorithm developed by physician-scientists can effectively identify and distinguish between two life-threatening heart conditions: hypertrophic cardiomyopathy and cardiac amyloidosis. The algorithm uses specific features from cardiac ultrasound videos to flag high-risk patients, enabling earlier diagnosis and treatment.
Pharmaceutical firms are working towards using machine learning to analyze vast stores of data, developing models that evolve and improve as the data are processed. However, experts agree that a fully functional end-to-end approach is still a ways off due to biology's complexity.
Researchers studied how diverse neural network training datasets impact generalization. They found that data diversity is key to overcoming bias, but also degrade performance when neural networks are trained for multiple tasks simultaneously. The study highlights the importance of designing diverse and controlled datasets in machine le...
A three-year project funded by the National Science Foundation will use artificial intelligence (AI) to identify fossil shark teeth, including those of the extinct megalodon. The program aims to increase interest in STEM careers among middle schoolers.
Researchers at the University of Copenhagen have developed an AI method to recognize and detect insect species based on their wingbeats, enabling easier monitoring of biodiversity. The method uses infrared sensors to measure wingbeats and group insects into different species without human input.
Researchers created an artificial sensory receptor that generates spike signals on its own, enabling the e-skin to analyze spatial information and react to external stimuli in real-time. The e-skin's functionality overcomes limitations of conventional electronic skins, which can only process tactile information sequentially.
Researchers analyzed 60 billion tweets to understand vaccine hesitancy on social media. They found that anti-vaxxer profiles often share links to YouTube videos and commercial sites selling alternative health products, highlighting the spread of misinformation in echo chambers.
Developed by University of Seville researchers, the new methodology has a sensitivity of 100% and specificity of 87.5%. It can detect SARS-CoV-2 in saliva and synthetic viruses with minimal equipment and training.
Researchers used AI to identify detrimental dams and reveal lost environmental benefits from existing 158 hydropower dams. The study considers six socio-environmental criteria, including river flow and energy production, to optimize dam selection across the entire Amazon basin.
Researchers analyzed new kinds of atomic-scale microscopic images using artificial intelligence to understand why rechargeable batteries wear out. They discovered nanofractures caused by mechanical strain on materials, which could lead to the development of more indestructible batteries.
The American Roentgen Ray Society recognizes Drs. Nguyen, Beheshtian, Harfouch, and Hashiba for their outstanding research and education contributions to the field of radiology. The awards honor the candidates' scientific merit and potential impact on imaging and allied sciences research.
A machine-learning algorithm named MAD3 can predict mechanical properties of metals without performing physical tests, cutting testing time by 1,000 times. The algorithm replaces traditional simulation software, enabling faster research and development with minimal resources.
Researchers have developed an AI-based method to analyze cryo-electron microscopy data, enabling the simultaneous examination of multiple protein complexes in cells. This breakthrough can lead to a better understanding of protein functions and potentially create new treatments for diseases like Alzheimer's and cancer.
Researchers found AI-synthesized faces to be nearly indistinguishable from real faces and rated as more trustworthy. The study's results have significant implications for the spread of manipulated images, including potential use in revenge porn and propaganda.
A study published in The Journal of Finance and Data Science found that using machine learning to estimate reward and risk improves portfolio performance. Machine learning helps provide accurate estimates by taking advantage of complex non-linear relationships between market variables, outperforming linear methods.
Researchers from Argonne National Laboratory have created a set of new practices to guide the curation of high energy physics datasets, making them more FAIR and reusable. The goal is to automate the finding and use of data for humans and streamline the development of AI tools for scientific discovery.
Researchers developed a new method using smartphone vibration motor and camera to monitor blood clots, with accuracy similar to commercially available techniques. The device can be used at home, making it easier for patients to test their PT/INR levels, potentially reducing the need for frequent clinical visits.
Researchers developed an AI system called GestaltMatcher that uses facial characteristics to detect rare diseases with high accuracy. The system considers similarities between patients and can even suggest diagnoses for previously unknown diseases.
Researchers found that consumers respond better to AI agents when a product offer is worse than expected, but more favorably to human agents when an offer is better than expected. Designing AI agents to appear more humanlike can change consumer response.
A team of scientists has developed a pioneering approach to combine advances in computer vision with ecological expertise to analyze wildlife populations. By leveraging AI and machine learning algorithms, researchers can extract key features from images and videos to quickly classify species, count individuals, and track behavior.
The grant aims to create toolkits and training for AI developers to prevent existing structural inequalities from becoming embedded into emerging technology. Researchers will investigate the impact of AI on core human values, including democratic rights and minority rights.
Researchers at Purdue University have created a device that can dynamically rewire itself to adapt to new data, enabling artificial intelligence to learn and remember information like the human brain. This breakthrough could lead to more efficient AI systems for tasks such as image recognition and decision-making.
Researchers developed an algorithm that incorporates customer behavior into recommendation systems, making more accurate and personalized suggestions. The technique, known as tensor decomposition, analyzes data in multiple dimensions to capture complex patterns and relationships.
Researchers developed a machine learning algorithm to automate propofol dosing for unconscious patients, matching human performance in sophisticated simulations. The 'dose penalty' model improved upon traditional software, but limitations remain, highlighting challenges in AI system accuracy and real-world application.
A new technique uses compression to reduce data transmission size, allowing for efficient federated learning on wireless devices. The approach has been shown to condense data packets by up to 99%, making it suitable for areas with limited bandwidth.
A team of researchers from the Institute of Industrial Science, The University of Tokyo, used a mathematical model to examine the implications of intergenerational learning. They found that learning accelerated the evolutionary process, which may assist in designing more efficient hybrid algorithms.
Researchers from UOC-led OptimalSharing@SmartCities project will analyze inhabitants' mobility patterns and demands to design more efficient shared transport practices. The project aims to develop agile optimization algorithms capable of processing large volumes of data in real-time for dynamic system coordination.
A new AI-powered system tracks food intake to ensure residents meet their nutritional needs, providing accurate data and reducing errors. The system analyzes photos of plates after meals, estimating food consumption and calculating its nutritional value.
Researchers at OHSU are developing a new approach to scientific imaging to study the dynamic organization of cells by examining how tiny molecules make cells work. The W. M. Keck Foundation has awarded $1 million to develop a one-of-a-kind imaging and computational system.
Researchers developed MonoCon, a new AI technique that enables accurate identification of 3D objects in 2D images. By incorporating auxiliary context, the method improves object detection and estimation accuracy, paving the way for safer and more robust autonomous vehicles.
Researchers at the University of Groningen have developed an AI system that can recognize indoor spaces with high accuracy by combining image and audio data. The system achieved a 70% accuracy rate in recognizing nine different types of indoor spaces, surpassing previous results.
A robot performed laparoscopic surgery on a pig without human guidance, demonstrating improved results compared to human surgeons. The Smart Tissue Autonomous Robot (STAR) excelled at intestinal anastomosis, a procedure requiring high precision and accuracy.
Researchers from KTU proposed a deep-learning-based method for 3D human shape reconstruction using limited-angle depth data. The method can be integrated with existing virtual reality tools and has potential applications in telemedicine and remote diagnostics.
A team of scientists developed an AI-based model to predict personal thermal comfort based on spatial parameters, achieving exceptional accuracy. The study highlights the importance of incorporating architectural features in models to reduce energy consumption.
A new software uses pose estimation to track human motion with high accuracy, providing an objective assessment of motor function. The technology has the potential to revolutionize neurological care by enabling patients to record video that can be analyzed by their physicians remotely.
Physicists have detected X particles in quark-gluon plasma produced in the Large Hadron Collider, a phenomenon that could reveal the particles' unknown structure. The discovery uses machine-learning techniques to sift through massive datasets and identify decay patterns characteristic of X particles.
A team of researchers has successfully developed a material-based reservoir computing device using single-walled carbon nanotubes and porphyrin-polyoxometalate composites. This innovation enables AI robots to classify grasped objects based on tactile signals, demonstrating the potential for energy-saving AI systems and situational awar...
A joint research team at KAIST developed a technique for facial expression detection using near-infrared light-field cameras and AI technology. The resulting NIR-based light-field camera (NIR-LFC) acquired high-quality 3D reconstruction images of facial expressions regardless of lighting conditions.
MIT researchers develop teaching phase that guides humans in understanding AI strengths and weaknesses, enabling more accurate decisions and faster conclusions. The technique helps humans build a mental model of the AI agent, reducing reliance on biased assumptions.
Researchers at Brigham and Women's Hospital conducted a scoping review of 78 articles to identify key use cases for AI in preventing or mitigating adverse drug events (ADEs). Genetic information is thought to be critical in improving AI algorithm performance, but systematic evaluations are necessary to generate evidence for this field.
MIT researchers develop a method to test feature-attribution methods for machine-learning models. They find that even the most popular methods often miss important features in an image and some perform as poorly as a random baseline. This has major implications for high-stakes situations like medical diagnoses.
A study by the University of Texas at Austin found that software development teams given greater autonomy are more productive and have higher customer satisfaction rates. The researchers tested 461 projects over 50 months and found a 39% increase in value added for autonomous teams compared to traditional teams.
A team of scientists from Gwangju Institute of Science and Technology developed a deep learning-based approach to predict SC2 battle outcomes by considering army composition and terrain type. The proposed model leveraged parameter sharing, enabling it to analyze complex factors accurately and make predictions.