Articles tagged with Artificial Intelligence
George Mason University researchers Zhi Tian and Yue Wang are securing $125,000 in funding from the Virginia Innovation Partnership Authority to develop an artificial intelligence-based solution for multi-access edge computing. The project aims to enhance network security and improve efficiency of collaborative machine learning.
Researchers at Lancaster University examine the use of AI in the food sector, highlighting the need for trusted data collaboration to reduce waste and increase sustainability. They also warn about potential ethical issues and unexpected consequences of new technology.
Researchers at Rice University are creating a 3D-printed smart helmet with embedded sensors to protect soldiers' brains against kinetic or directed-energy effects. The program aims to modernize standard-issue military helmets by incorporating advances in materials, image processing, artificial intelligence, and energy storage.
Researchers have developed a new method that uses deep neural networks to predict extreme heat waves with unprecedented accuracy, up to two weeks before they occur. This breakthrough has significant implications for risk management, planning, and warning systems, which will greatly improve public safety and support public policies.
The review found that datasets lack images and information about patients with darker skin, which may lead to inaccurate diagnoses. The research highlights the need for better representation of diverse skin tones in training AI models.
A systematic review of AI studies on mechanical ventilation found that many were testing early technology, but more work is needed for transparency and bias avoidance. The review recommends improving data availability and reporting standards to facilitate the translation of AI into improved patient care.
Researchers trained a GAN to generate novel refractory high-entropy alloys with specific properties, surpassing human intuition and guesswork in material design. The model produces alloy compositions in milliseconds, offering a promising tool for determining suitable materials.
Researchers created novel frog models to replicate polycystic kidney disease, allowing for real-time observation of disease processes. AI analysis enabled rapid assessment of disease in the tiny animals.
Researchers used reinforcement learning to control a small particle moving in a double-well system, achieving accurate control despite noisy measurements. The method shows promise for future applications in quantum technologies and AI.
A new machine learning-based algorithm has been developed to identify adolescents who have experienced suicidal thoughts and behavior. The algorithm, applied to a large dataset of survey responses from over 179,000 high school students in Utah, shows high accuracy in predicting individual adolescents at risk.
A new study explores the problem of shortcuts in a popular machine learning method and proposes a solution that can prevent shortcuts by forcing the model to use more data. By removing simpler characteristics and asking the model to solve the task two ways, researchers reduce the tendency for shortcut solutions and boost performance.
Researchers at University of Missouri and University of Chicago develop an artificial material that can respond to its environment, make decisions, and perform actions not directed by humans. The material uses a computer chip to control information processing and convert energy into mechanical energy.
Scientists have developed a software that adds missing sugar components to protein models created with AlphaFold, enabling more accurate structural predictions. This breakthrough has the potential to revolutionize workflows in biology, allowing scientists to understand proteins and their mutations faster than ever.
A new algorithm has been developed to train spiking neural networks, mimicking the human brain's structure and function. This approach enables these powerful, fast, and energy-efficient systems to solve complex tasks like image classification with high precision.
A new visual analytics tool, Sibyl, was developed to help child welfare specialists understand machine learning predictions. The tool uses bar graphs to show how specific factors of a case contribute to the predicted risk that a child will be removed from their home within two years.
A new study by Georgia Institute of Technology researchers found that automation and AI may have a uniformly positive impact on worker well-being, but only partially. The results show that workers facing automation risk experience less stress but worse health and minimal job satisfaction. Notably, high-risk occupations like receptionis...
A new study maps out risk factors associated with misleading information and proposes practical ways to manage them in the business world. The research aims to bridge the divide between academic research and real-world practice of cyber risk management.
Researchers at Tohoku University developed a smart chair with pressure sensors that detect workers' movements to predict low back pain progression. By analyzing small body motions, they identified a common motif that can be used to forecast worsening LBP throughout the day.
Researchers use physical reservoir computing to teach robots to think like humans by simulating brain signals. The system enables goal-directed behavior without additional learning, highlighting a potential breakthrough in AI development.
The project aims to address rising healthcare costs, health disparities, and expands digital health through advances in AI. It also trains students from different disciplines to develop and apply data science techniques.
Researchers explored superoscillations for nanoimaging and nanometrology, achieving subwavelength focusing and imaging beyond the traditional diffraction limit. This technology combines with deep learning algorithms to increase resolution and accuracy in micro-nano displacement detection.
A new study reveals that high-performing AI next-word prediction models resemble the function of language-processing centers in the human brain. The models' activity patterns closely match those seen in the brain during language tasks, suggesting a potential connection between AI and human language processing.
A new project aims to empower people who are blind to independently review and protect their personal visual content from accidental privacy leaks. Researchers have developed novel computer vision algorithms that can detect sensitive information in images and videos, allowing users to blur or remove private content before sharing.
Researchers found that humanlike chatbots raise unrealistic expectations, leading to lower satisfaction and purchase intentions in angry customers. To mitigate this effect, companies should deploy non-humanlike chatbots in customer service situations where customers tend to be angry.
A new NLP model developed by researchers at Harbin Institute of Technology achieves higher AUC scores than existing models. The Heterogeneous Graph-Based Sequential Multi-Grained Information Aggregation Framework (HGM-GIF) uses a combination of word-level, event-level, and sentence-level information to improve stock market predictions.
A recent study published in Nature Machine Intelligence challenges the long-held assumption that accuracy and fairness are mutually exclusive in machine learning. Researchers found that optimizing models for accuracy does not necessarily compromise fairness, particularly when adjustments are made to data, labels, and scoring systems.
The new AI-CARING initiative aims to develop robotics and artificial-intelligence systems to help seniors manage daily activities, including medication schedules and meal preparation. The project also seeks to grow employment opportunities and educational programs in the field of AI-robotics, with UMass Lowell as a key partner.
The AI optimization improves the motor's power factor, reducing disruptions to the power grid. The optimized motor shows excellent performance, with improved efficiency and increased torque while drawing less current.
Experts from the UK and US are calling for AI to advance research and develop new therapies for the growing global deafness crisis. Nearly 2.5 billion people will experience some degree of hearing loss by 2050, with inadequate care for many affected individuals.
Researchers at Osaka University developed a deep neural network to accurately determine qubit states despite environmental noise. The novel approach may lead to more robust and practical quantum computing systems.
A new MIT study suggests that pedestrians choose routes that point most directly toward their destination, even if those routes are longer. This strategy, known as vector-based navigation, may have evolved to allow the brain to devote more power to other tasks.
A new AI-based computer algorithm created by Mount Sinai researchers can identify subtle changes in electrocardiograms to predict heart failure. The algorithm was 94% accurate at predicting healthy ejection fractions and 87% accurate at detecting weakened hearts, offering a promising alternative to traditional diagnosis methods.
A team of bioinformaticians at Friedrich Schiller University Jena developed a method to identify small active substance molecules using machine-learning methods. They successfully identified 11 new, previously unknown bile acids in mice using this approach.
Researchers developed a new risk measure, Universal Influenza-like Transmission (UnIT) score, that outperformed existing models in predicting weekly case count forecasts. The model used 10 years of influenza hospitalization data to identify patterns and improve COVID-19 spread predictions.
A new study by MIT researchers has found that blind and sighted readers have sharply different takes on what content is most useful to include in a chart caption. The study created a four-level framework for evaluating charts, which could help develop more effective tools for automatically generating captions and alternative text.
Researchers at Johns Hopkins University have developed a non-invasive optical probe to understand the complex changes in tumors after immunotherapy. Using Raman spectroscopy and machine learning, they identified key features that indicate how tumors respond to treatment, showing promising results for predicting patient response.
A new AI-powered algorithm, GEM, has been developed to quickly identify genetic causes of serious disease in newborns. The technology leverages machine learning and natural language processing to analyze vast amounts of genomic data and clinical records, achieving an accuracy rate of 92% compared to existing tools.
A new UCF project aims to improve telehealth medicine by leveraging artificial intelligence to enhance healthcare training and diagnostic reasoning. The research will focus on implementing AI and new technologies into telehealth, tracking physicians' and patients' communication, and recording diagnosis accuracy.
A new study from the University of Gothenburg introduces an AI-based method to develop faster, cheaper, and more reliable information about cells using microscopy. This approach eliminates the drawbacks of traditional fluorescence microscopy by providing accurate results without damaging cells or inhibiting processes.
The new system, using over-current driven LED lights, improves image brightness and color consistency, reducing motion blur and variability caused by sunlight. The prototype showed an average decrease of 85% in standard deviation for hue-saturation-value channels compared to auto-exposure settings.
A team of researchers from the University of Illinois Urbana-Champaign used advanced machine learning to model the physico-chemical properties of a molten salt compound called FLiNaK, enabling accurate atomic-scale reproduction and prediction of behavior under specific reactor conditions. This computational framework can help character...
A team of researchers has created a virtual fitting room system using AI and a bespoke robotic mannequin. The system can digitize garments in two hours and synthesize photorealistic images, allowing users to try on clothing in real-time.
Researchers developed a novel machine learning algorithm to identify previously unknown air pollutant mixtures linked to poor asthma outcomes in children. The study found that early exposure to individual and mixed pollutants can lead to longer-term problems with asthma, affecting about seven percent of US children.
The Human Brain Project's Scientific Conference presents abundant scientific achievements in neuroscience, brain medicine, and technology. Renowned experts discuss past, present, and future of brain research, highlighting the role of HBP in driving innovation.
A team of researchers from Osaka University has designed a sulfonated polyaniline network for reservoir computing, achieving 70% accuracy in speech recognition tasks. The device uses an electrochemical approach and has potential applications in the development of artificial intelligence devices.
Researchers at the University of Waterloo developed AI technology that can identify players by their jersey numbers in hockey videos with high accuracy. The system uses multi-task learning and a large dataset of over 54,000 images from NHL games to recognize sweater numbers.
A study found that people living with overweight or obesity experience lower levels of care across eight quality markers, including emotional support and fast access. Healthcare professionals often make rude comments about weight, contributing to poor experiences.
Researchers at GlaxoSmithKline and CCDC combined proprietary and published datasets to train machine learning models for predicting stable polymorphs in new drug candidates. The approach leverages the large volume and variety of data in the Cambridge Structural Database, resulting in more confident predictions and improved model accuracy.
Researchers at Karolinska Institutet developed an AI-based tool to improve breast cancer diagnosis and predict recurrence risk. The method divides patients with grade 2 tumours into high-risk and low-risk sub-groups, enabling personalized treatment.
Researchers aim to create a flexible security feature that learns from past cyberattacks and requires minimal human intervention. They'll collaborate with device developers to share solutions and improve future responses to attacks.
Researchers developed a dynamic respirator that modulates pore size in response to changing conditions like exercise and air pollution. The device features an AI-powered system that adjusts filtration characteristics wirelessly, providing improved breathability and comfort.
A new AI system developed by NYU and NYU Abu Dhabi researchers achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. The system helps decrease false-positive findings and requested biopsies while maintaining sensitivity.
A new study using machine learning uncovers 'genes of importance' in plants that help them grow more efficiently with less fertilizer, reducing economic and environmental costs. The approach also predicts additional traits in plants and disease outcomes in animals.
A new study shows an artificial intelligence tool improved radiologists' ability to correctly identify breast cancer in ultrasound exams, with a 37% increase in accuracy. The tool also reduced the number of tissue samples needed to confirm suspect tumors by 27%.
China's space transportation systems have made significant leaps in recent decades, with advancements in launch vehicles, propulsion systems, and artificial intelligence. The country aims to become a powerful space nation by the mid-21st century, with plans for manned missions to the Moon and Mars.
SUTD researchers develop sensor that assigns dirt score to areas based on visual and tactile analysis, allowing for more efficient exploration of complex spaces. The sensor is integrated with a smart algorithm that directs the robot to focus on areas with high dirt probability.
A team of researchers, led by University of Houston associate professor Ryan Kennedy, has received a $750,000 NSF grant to create an algorithm-accountability benchmark. The project aims to establish general ways of analyzing algorithms and studying their impact on public policy decisions.
Researchers developed an AI algorithm that can assess climate change tipping points, predicting rapid or irreversible change in systems. The new approach can detect features of tipping points common to various complex systems, including ecology, epidemiology, and markets.
Researchers developed a feature selection algorithm that uses boosting to select relevant features from high-dimensional data sets. The algorithm outperforms other methods in terms of accuracy and number of features used, making it more scalable and explainable.
Researchers at WVU aim to characterize autism patients' behaviors using behavior-tracking technology. They plan to use artificial intelligence to identify traits associated with ASD, which could lead to more accurate diagnoses and personalized interventions. The study also has the potential to improve early detection in young children.