Articles tagged with Machine Learning
Researchers used X-ray microscopy to analyze lithium batteries and employed machine learning to speed up the learning curve about process that shortens battery life. Infrared microscopy also goes off-grid with new technique, enabling time-sensitive experiments and broadening biological spectromicroscopy scope.
A team of scientists developed a deep learning model that improves air quality estimates by combining satellite and ground-based observations. The model achieved higher spatial and temporal resolution, enabling more accurate predictions of nitrogen dioxide levels in the Los Angeles area.
A machine learning study of rock art in Arnhem Land, Australia, has reconstructed the chronology of artistic styles using over 14 million images. The analysis revealed a link between style similarity and time, showing that styles closer in age were also more similar in appearance.
Southwest Research Institute is developing an AI-based integrated corridor management decision support system for the Tennessee Department of Transportation. The system uses machine learning algorithms to optimize traffic flow and improve collaboration among transportation agencies.
A recent study by researchers at the University of Johannesburg shows how AI can forecast municipal solid waste in a large African city. By using machine learning algorithms and combining data from various sources, including census data and landfill site records, the team was able to predict the city's waste management needs until 2050...
Researchers developed an AI model to detect COVID-19 in chest X-rays with high accuracy, using a large dataset of other X-ray images as a starting point. The tool has the potential to assist doctors in identifying, measuring the severity and classifying the disease.
Researchers developed microswimmers that can change direction by heating tiny gold particles, then learned to navigate through a virtual environment via external control and virtual rewards. The findings suggest an optimal speed is key to navigation, with implications for autonomous tasks and collective behavior in biological systems.
Osaka University researchers use deep learning to improve mobile mixed reality generation, enabling the automatic removal of obstructions and addition of greenery. This technology may revolutionize green architecture and city revitalization by providing real-time visualizations.
MCG faculty adapted traditional curriculum to provide an online Pandemic Medicine Elective, covering topics from SARS-CoV-2 to professional responsibility. Students logged over 6,000 service hours through projects like mask delivery and contact tracing.
A team of RIT researchers is working on developing an artificial intelligence system that can learn over time and play the popular video game Starcraft II. This project has the potential to advance practical solutions such as self-driving cars, service robots, and other real-world applications.
A new educational curriculum in longevity medicine for physicians has been developed, outlining the benefits of promoting healthspan and lifespan. The course provides a comprehensive introduction to theoretical and practical basics of longevity medicine, including molecular mechanisms, biomarkers of aging, and geroprotector regimens.
Researchers at UOC used AI to analyze urban scenes and identify patterns that may lead to accidents, suggesting that complexity and layout are key factors. The technology aims to aid traffic authorities in reducing the likelihood of accidents by providing real-time hazard warnings and optimizing traffic flow.
Researchers develop compact optical machine-learning decryptors that process information at the speed of light without consuming power. These devices can be integrated on CMOS chips and have a neuron density of over 500 million neurons per square centimeter.
Researchers have successfully demonstrated a significant speed-up in robot learning time using quantum physics, enabling machines to learn faster and make better decisions. This breakthrough has promising implications for the development of autonomous systems.
Researchers at SUTD developed a new connection between exploration-exploitation trade-off in multi-agent AI systems and Catastrophe Theory. This discovery aims to improve the performance of AI systems, such as robotic space missions and healthcare management.
Researchers at The University of Tokyo used artificial intelligence to show that T helpers in the adaptive immune system act like a neural network, optimizing responses to pathogens. The study may lead to improved vaccine development and stronger immune responses.
A new deep-learning algorithm, CARRL, is designed to help machines build a healthy skepticism of their measurements and inputs. By combining reinforcement-learning algorithms with deep neural networks, researchers created an approach that outperformed standard machine-learning techniques in scenarios with uncertain and adversarial inputs.
The study uses natural language processing to extract adverse effects from Spanish health records, highlighting a major health problem and improving clinical decision making. The system learns more effectively with larger corpora, contributing to closing the gap in clinical text mining across languages.
Artificial intelligence has already shown promise in pathology, including image classification and diagnosis of diabetic retinopathy. Future developments aim to further enhance diagnostic accuracy and improve patient outcomes through augmented intelligence. The authors emphasize the need for careful validation, performance monitoring, ...
Professor Sun's research focuses on deep learning and meta-learning for recognizing images and videos. Her team is working on a food app that uses AI to track nutrition and achieve a healthy diet, but faces challenges due to cultural diversity.
A new AI tool developed at the University of Gothenburg uses deep learning to analyse microscope images, extracting more details and information than traditional methods. The tool, called Deep Track 2.0, simplifies data generation and allows for real-time analysis and customised information retrieval.
A new bioinformatics tool called PlasmidHawk has been developed by Rice University researchers to track the origin of synthetic genetic code. The tool uses a sequence alignment-based approach and was found to outperform recent deep learning approaches in lab-of-origin prediction, achieving 76% accuracy.
Researchers found that exposure to new experiences dampens established brain connections, allowing for flexible strategy encoding. Novelty triggers neural mechanisms that facilitate learning from new tasks and rules.
A machine learning algorithm helps differentiate between benign and premalignant polyps in CT colonography data, with a sensitivity of 82% and specificity of 85%. The findings suggest a role for machine learning-derived algorithms in boosting the effectiveness of CT colonography as a screening tool for colorectal cancer.
Yingyan Lin, an assistant professor at Rice University, has received a $400,000 NSF CAREER Award to develop more efficient deep learning hardware accelerators. Her goal is to push forward ubiquitous intelligent devices and green artificial intelligence, addressing the gap between complex algorithms and limited resources.
Sun and Tong's project aims to make AI workflows more shareable and replicable. The researchers will further develop the open-source GeoWeaver system into a stable operational platform for NASA's EOSDIS archive.
Researchers developed a deep learning model that accurately predicts lung cancer patients' survival expectancy, significantly better than traditional machine learning models. The model can analyze large amounts of data to understand how various factors affect lung cancer survival periods.
Researchers at Wyss Institute and Google Research used machine learning to design highly diverse AAV capsid variants that can evade neutralizing antibodies. The approach produced over 57,000 variants with improved functional diversity, potentially leading to improved gene therapies and reduced immunogenicity.
A novel machine learning algorithm developed by Princeton physicist Hong Qin accurately predicts planetary orbits without using traditional physics laws. The technology has potential applications in predicting plasma behavior in fusion facilities, challenging the fundamental role of theories in science.
Researchers used metabolomics and machine learning to identify biomarkers for COVID-19 diagnosis and risk assessment. The study found 26 biomarkers that differed between mild and severe illnesses, potentially revealing new clues on how SARS-CoV-2 affects the body.
Researchers showed that deepfake detectors can be defeated by inserting inputs called adversarial examples into every video frame, which cause AI systems to make mistakes. The attack still works after videos are compressed. Key findings include high success rates of over 99% for uncompressed and 84.96% for compressed videos.
Researchers at Geisinger Health System developed an AI algorithm using echocardiogram videos to predict mortality within a year. The model outperformed other clinically used predictors and improved cardiologists' prediction accuracy by 13 percent.
A new study published in the Proceedings of the National Academy of Sciences presents a computationally-based modeling approach that simulates infant language learning. The researchers found that infants do not learn consonant- and vowel-like phonetic categories, but rather learn to distinguish between speech sounds in a more nuanced way.
A new machine learning model, integrated with CT scans of the lungs, surpasses benchmarks in predicting disease severity and supports hospital resource management. Additionally, Owkin develops models to discover coronavirus epitopes that may improve future vaccine efficacy.
Naked mole-rats form distinctive, colony-specific chirps that convey individual's social membership and are culturally transmitted across generations. These dialects change when a queen dies and young pups learn the dialect of their adoptive groups.
A new study published in Nature Communications found that deep learning models surpass standard machine learning approaches in analyzing brain imaging data, generating more accurate representations of the human brain. This is particularly beneficial for complex problems requiring large datasets and advanced analysis.
Researchers developed a multi-fidelity graph network approach to predict material properties with improved accuracy, enabling predictions for disordered materials. The new method reduced mean absolute errors by 22-45% compared to traditional approaches.
Researchers at UCLA have developed Diffractive Deep Neural Networks (D2NNs) for all-optical object classification, achieving higher accuracy than individual constituent D2NNs and digital AI models. The success of the ensemble learning approach demonstrates the power of combining multiple predictions to obtain a more accurate prediction.
New research identifies a risk of collusive pricing behavior when AI algorithms set prices without observing competitor prices. This can lead to monopolistic price effects and supracompetitive market outcomes. The study suggests that independent AI algorithms can result in these negative consequences.
The University of Texas at San Antonio's MATRIX AI Consortium has received over $1 million in research funding to develop novel brain-inspired lifelong learning algorithms. Inspired by the honeybee brain, these algorithms aim to close the performance gap between modern AI systems and biological systems.
A joint research team developed DeepTFactor, a deep neural network predicting transcription factors from protein sequences. The tool uses three parallel convolutional neural networks and predicted 332 transcription factors of Escherichia coli K-12 MG1655.
A new study published in the Endocrine Society's Journal of Clinical Endocrinology & Metabolism uses machine learning to predict gestational diabetes in Chinese women. The researchers analyzed nearly 17,000 electronic health records and found that low body mass was associated with an increased risk of gestational diabetes.
A new AI-powered microscope can rapidly image large tissue sections with cellular resolution, potentially during surgery, to find the answer. The DeepDOF microscope uses deep learning to train a computer algorithm to optimize image collection and post-processing.
A new machine learning algorithm has classified over 2,300 supernovae with an accuracy rate of 82%, using real data from the Pan-STARRS1 Medium Deep Survey. The classifier was trained on a subset of supernovae with spectra and then applied to the remaining data, achieving high accuracy rates.
Researchers at the Salk Institute have created a computational model of brain activity that simulates how humans adapt to new situations. The model, which incorporates the concept of 'gating' to control information flow, outperforms previous models and mimics human mistakes seen in patients with prefrontal cortex damage. This breakthro...
The hexxed app, a mobile game, helps researchers compare human vs machine problem-solving strategies. Players must figure out rules on the fly, resembling AI's approach, allowing scientists to create a benchmark for human intelligence.
Computer simulation is crucial for developing human-interactive smart robots, enabling safer, faster, and more efficient design and control. By analyzing the biology of soft animal structures, researchers can construct virtual proving grounds to understand robot behavior and optimize performance.
Researchers developed a new method combining label-free imaging with artificial intelligence to study live cells over time. The technique allows for the estimation of cell attributes without using toxic fluorescent dyes.
A new machine learning approach offers important insights into catalysis by providing a tool to design efficient catalytic processes. The Bayeschem model explains how catalysts interact with different intermediates and determines the optimal bond strengths.
A new machine learning algorithm, TranSEC, uses traffic datasets from UBER drivers and publicly available sensor data to map street-level traffic flow over time. This creates a big picture of city traffic, allowing for near-real-time analysis and predictive modeling.
An AI model developed at Duke University successfully identified patients with Alzheimer's disease from retinal images, suggesting its potential as a predictive tool. The study provides proof-of-concept for machine learning analysis of certain types of retinal images to detect the neurological disease in symptomatic individuals.
Insilico Medicine introduces Molecular Sets (MOSES), a benchmarking platform for generative chemistry models, enabling easy comparison and evaluation of new models against existing approaches. The platform provides a curated dataset, metrics, and baselines for assessing model performance.
A new study examines the 'word-of-machine' effect, where consumer preference for AI recommenders is influenced by the importance of utilitarian versus hedonic attributes. When utilitarian features are emphasized, consumers prefer AI over human assistance, while hedonic features lead to a preference for humans.
Researchers exploring the nature of AI failures reveal 'adversarial examples' may not be intentional mistakes. Instead, they might be 'artifacts' created by interactions between network and data patterns. This rethink suggests that misfires could offer useful information if interpreted correctly.
Researchers developed an AI-powered method to detect Parkinson's disease from retinal images, identifying smaller blood vessels as key features. The approach is less costly and more accessible than traditional brain imaging techniques.
KAUST researchers developed a holistic approach using design of experiments and machine learning to identify the greenest method for producing a popular metal organic framework material called ZIF-8. This process reduced waste and energy consumption by optimizing multiple variables simultaneously.
A team of researchers has introduced Deep Potential Molecular Dynamics (DPMD), a new machine learning-based protocol that can simulate over 100 million atoms per day. The protocol achieves ab initio accuracy and was recognized with the ACM Gordon Bell Prize for its achievement in high-performance computing.
USC researchers have developed a system that lets robots autonomously learn complicated tasks from a very small number of imperfect demonstrations. The system uses signal temporal logic to evaluate the quality of each demonstration, allowing robots to learn more intuitively and adapt to human preferences.
Researchers developed a new electronic chip that brings together imaging, processing, machine learning, and memory in one device powered by light. The prototype achieves brain-like functionality and can be used to enable smarter and smaller autonomous technologies like drones and robotics.
Researchers developed machine learning frameworks that guarantee robots' performance in unfamiliar settings, with a guaranteed success rate of 88.4% in obstacle avoidance trials. The approach expands generalization theory to robotics, providing more broadly applicable guarantees on robot control policies.