Articles tagged with Machine Learning
Researchers from RIKEN Center for Quantum Computing have used machine learning to perform efficient quantum error correction using an autonomous system that can determine the best corrections despite being approximate. Machine learning plays a crucial role in addressing large-scale quantum computation and optimization challenges.
A team of scientists from Ames National Laboratory developed a new machine learning model that predicts Curie temperatures of new material combinations. This breakthrough discovery is crucial for designing high-performance magnets with reduced critical materials.
Researchers developed methods to predict CCS values using machine learning and computer models, offering a faster alternative to experimental determination. The study's findings provide a foundation for measurements using portable ion mobility spectrometers in the future.
The article explores machine learning (ML) applications in chemistry, highlighting its potential to accelerate research and provide innovative solutions. Key findings include the development of ML models for retrosynthesis, atomic simulation, and heterogeneous catalysis, as well as the need for open ML contests to nurture young talent.
Researchers from Bar-Ilan University improved AI classification tasks by choosing the most influential path to the output, rather than learning with deeper networks. This approach can enhance existing architectures and pave the way for improved AI systems without additional layers.
A team of researchers at the University of Waterloo and Dalhousie University have developed a method for forecasting short-term disease progression using limited data. The Sparsity and Delay Embedding-based Forecasting model, or SPADE4, uses machine learning to predict epidemic progressions with high accuracy.
A new study by North Carolina State University found that artificial intelligence performs better when it chooses diversity over lack of diversity. The AI was able to increase its accuracy up to 10 times more than conventional AI in solving complicated problems.
A new method can detect drivers' attention levels from their eye movements, enabling the development of more effective takeover signals. The study found that drivers who are engrossed in on-screen activities take longer to respond to warnings, highlighting a potential safety concern.
A team of researchers has discovered a particularly efficient molecular structure for solar energy storage materials, which could lead to more efficient solar energy harvesting. The new molecules were identified by screening over 400,000 molecules with the help of machine learning and quantum computing.
A team of researchers from the University of Zurich and Intel has developed an AI system called Swift that can beat human champions in drone racing. The autonomous drone achieved the fastest lap, winning multiple races against three world-class champions, but human pilots proved more adaptable to changing conditions.
A novel study from the University of South Australia identified 84 features that could signal increased cancer risk in a dataset of 459,169 UK Biobank participants. The study found several biomarkers linked to cancer risk, including urinary microalbumin and high levels of cystatin C.
A deep learning approach has unveiled a significant change in the characteristics of global daily precipitation for the first time. The research found that on more than 50% of all days, there was a clear deviation from natural variability in the daily precipitation pattern since 2015.
A machine learning model has been developed to distinguish the composition ratio of solid mixtures of chemical compounds using only photographs. The model was trained on a small dataset and achieved accuracy roughly twice that of human experts.
Researchers have developed a novel neural network approach to design brand new proteins with unique arrangements and dynamic functionalities. The method combines attention neural networks with graph neural networks to predict existing protein properties and envision new proteins that nature has not yet devised.
A new AI-powered triage platform uses machine learning and metabolomics data to predict patient disease severity and length of hospitalization during a viral outbreak. The platform integrates routine clinical data, patient comorbidity information, and untargeted plasma metabolomics data to drive its predictions.
Scientists have successfully measured the speed of molecular charge migration in a carbon-chain molecule, revealing a movement of several angstroms per femtosecond. The study used a two-color high harmonic spectroscopy scheme with machine learning reconstruction to achieve a temporal resolution of 50 as.
Researchers at Singapore Management University aim to create a robust machine learning system capable of correctly identifying Singapore's multiracial food. The project focuses on addressing biases in current systems and developing an algorithm that can recognize a wide range of dishes, including those beyond popular online trends.
Researchers at MIT developed a method to simplify the process of whole-body manipulation for robots, enabling them to reason efficiently about moving objects. The technique uses AI and smoothing to reduce the number of decisions required, making it possible for robots to adapt quickly in complex environments.
A new study by the University of Illinois and USDA-Agricultural Research Service has identified the key factors influencing sweet corn yield. The analysis found that seed source is a significant variable, with processors having a choice over which hybrids to use, and high nighttime temperatures also impact yield.
The University of Missouri is launching a five-year, $3 million doctoral training program to prepare the next generation of scientists and engineers for emerging fields like materials science and data science. The program aims to empower future workers with both technical expertise and data-driven insights.
A new study found ChatGPT to be nearly 72 percent accurate across all medical specialties and phases of clinical care. It was also 77 percent accurate in making final diagnoses. However, the model struggled with differential diagnosis, which is a crucial aspect of medicine.
A new AI tool predicts certain forms of esophageal and stomach cancer at least three years prior to diagnosis. The K-ECAN tool uses basic EHR data to identify high-risk patients, who may benefit from earlier screening and preventative measures.
Researchers developed a deep-learning model to assess CXR images for probable COVID-19 severity. The model achieved an area under the receiver operating characteristic curve of 0.78 when predicting intensive care need within 24 hours.
Researchers developed a modified bandit Q-learning algorithm that aims to learn optimal Q values for every state-action pair, balancing exploitation and exploration. The scheme relies on photonic systems to enhance learning quality, accelerating parallel learning through conflict-free decision-making.
A new study reveals that using big data and machine learning can improve antimicrobial resistance surveillance in livestock production. The research found correlations between environmental variables, microbial communities, and antimicrobial resistance, suggesting multiple routes for improving surveillance.
Researchers at the University of Pennsylvania School of Engineering and Applied Science have discovered dozens of small protein sequences with antibiotic qualities in extinct organisms like Neanderthals and Denisovans. They then synthesized these molecules using artificial intelligence and tested their efficacy against pathogens.
Researchers developed a novel method using Google Trends to assess player popularity and demonstrated its improvement in predicting market value. The method involves calculating six indicators of popularity that can be compared among players, improving accuracy when incorporated with other factors.
Researchers used AI and mobility data to enhance air pollution models, improving accuracy by an average of 17.5% and identifying hotspots with high PM2.5 levels. This integrated approach can inform targeted health alerts and safety measures for areas with poor air quality.
A team led by Dr. Zixiang Xiong at Texas A&M University aims to understand the fundamental limits of learned source coding, a machine learning-based data compression method. They hope to develop more powerful compression methods for efficient use of wireless communication and less energy consumption by mobile devices.
Researchers propose a hypothesis that astrocytes, non-neuronal cells in the brain, can perform core computation as transformers, providing insights into human brain function and machine learning success. This discovery could spark future neuroscience research and help explain transformer performance across complex tasks.
A new study shows that a rule-based natural language processing tool successfully identified patients with unstable access to transportation, food insecurity, social isolation, financial problems, and signs of abuse or exploitation. The tool performed better than deep learning algorithms in identifying these social determinants of health.
Scientists at Max-Planck-Institut für Eisenforschung developed a machine learning model that enhances predictive accuracy in alloy design, uncovering new corrosion-resistant compositions. The model combines numerical and textual data, enabling the identification of optimal alloy formulas.
Researchers used AI to accurately classify four subtypes of Parkinson's disease from patient-derived stem cells, with one subtype reaching an accuracy of 95%. The study suggests that personalized medicine and targeted drug discovery could be possible using this approach.
Researchers used machine learning algorithms to analyze ChatGPT-generated and human-written Japanese texts, finding that the AI's style could be distinguished with high accuracy. The study suggests a new method for detecting AI-generated content in academic papers written in Japanese.
A team of researchers from the University of Cambridge developed a way to incorporate human error into machine learning systems, improving their performance in handling uncertain feedback. However, they found that even with uncertainty accounted for, hybrid systems still perform worse than standalone machine learning models.
The new tool, SnorCall, analyzes unsolicited calls to shed light on robocall trends and types of scams. It extracted information from over 232,000 robocalls, including phone numbers used in scams, helping regulators and law enforcement take action.
A machine learning model found that background parenchymal enhancement (BPE) on breast MRI is an indicator of breast cancer risk in women with extremely dense breasts. Women with dense breasts are at a higher risk of developing breast cancer compared to those with fatty breasts.
Researchers have developed a new explainable AI model to reduce bias and enhance trust in machine learning-generated decisions. The Pattern Discovery and Disentanglement (PDD) model can predict medical results with rigorous statistics and explainable patterns, leading to more reliable diagnoses and better treatment recommendations.
Researchers developed an AI model called OncoNPC that can analyze genetic data to predict cancer type and origin. The model accurately classified at least 40% of tumors with unknown origin, leading to a 2.2-fold increase in eligible patients for targeted treatments.
Critically ill children on ventilator support can experience patient-ventilator asynchrony (PVA), which worsens outcomes. Researchers are using machine learning to develop a common set of definitions and measurements for PVA in pediatric patients, aiming to minimize risks and improve clinical outcomes.
Researchers created a self-supervised AI model called GedankenNet that learns physics laws and thought experiments to reconstruct microscopic images. The model successfully reconstructed human tissue samples and Pap smears from holograms without relying on real-world experiments or data.
Researchers found that clinical strains of Aspergillus fumigatus differ significantly from environmental strains in amino acid synthesis. The fungus appears to shape the lung microbiome to its advantage, surviving on vital metabolites produced by other microorganisms.
AI-powered clinical decision support systems can enhance patient care, but doctors lack necessary skills to interpret and act on risk predictions. To address this gap, medical education and training need to incorporate explicit coverage of probabilistic reasoning tailored to CDS algorithms.
A radiomic-based model using T2-weighted MRI data achieved high accuracy in diagnosing pediatric Crohn disease, outperforming expert radiologists. The model was ensembled with clinical data to further improve performance.
Researchers developed a generative AI tool, AniFaceDrawing, to assist users in creating high-quality anime portraits. The tool uses a sketch-to-image framework and employs stroke-level disentanglement to match raw sketches with latent vectors of the generative model.
Researchers at University College London found that humans can only reliably detect fake speech 73% of the time, and this ability improves only slightly with training. The study's findings raise concerns about the potential for deepfakes to be used by criminals and nation-states to cause harm.
A machine learning system capable of learning diverse tennis skills from broadcast video footage has been created by a research team led by Simon Fraser University's Jason Peng. The system can generate long-lasting matches with realistic racket and ball dynamics between two physically simulated characters.
Researchers propose leveraging high-value information to overcome statistical and computational challenges in reinforcement learning. By accessing valuable observations, agents can improve strategies without trial and error, making the learning process more efficient and effective.
Researchers have discovered a way to utilize nonlinear scattering media for optical computing and machine learning. They created a novel theoretical framework involving third-order tensors, which can represent the complex relationships between input and output signals. This breakthrough has potential applications in real-world settings...
A study by University of Toronto researchers found that child language development and language evolution share a common cognitive foundation, based on a core knowledge base. The team built a computational model that predicts word meaning extension patterns across languages and time scales, highlighting the role of visual, associative,...
Researchers designed machine learning models to identify children at risk of self-harm, finding that incorporating more data points and diagnostic codes improved detection rates. The models were particularly effective for detecting underrepresented groups, such as Black and Latino youth.
A new study uses machine learning to analyze data from DrugAge, a database of chemical compounds modulating lifespan in model organisms. The researchers create four types of datasets to predict whether or not a compound extends the lifespan of C. elegans, using features such as compound-protein interactions and Gene Ontology terms.
Researchers at Monash University developed a co-training AI algorithm that can effectively mimic human oversight in medical imaging. The algorithm achieved an average improvement of 3% compared to state-of-the-art approaches using limited annotated data, enabling AI models to make more informed decisions and uncover accurate diagnoses.
Researchers have developed a computational tool to compare large datasets and predict immune responses to disease, potentially leading to better vaccines. The new algorithm, designed by La Jolla Institute for Immunology scientists, uses machine learning to identify underlying patterns in immune system data.
An analysis of English Twitter data reveals a 17-fold increase in daily FGM conversations on International Day of Zero Tolerance, suggesting opportunities for social media education. At least 200 million women and girls have undergone FGM, leading to short- and long-term health consequences.
Hang aims to develop general-purpose robots that can handle complex physical interactions without requiring perfect input from sensors or extensive instructions. His project seeks to improve robotic manipulation tasks by reducing assumptions about how the robot acts in real-world conditions.
The Fengyun-4A satellite in collaboration with a machine learning model generated a detailed PV resource map for China, providing new insights into the country's solar energy potential. This advancement sets a new standard for solar resource mapping, empowering decision-makers to make informed choices for a sustainable future.
A new editorial explores the potential of machine learning to enhance early cancer detection in primary care, leveraging extensive patient data and improving risk stratification accuracy. The authors emphasize the need for responsible implementation, collaboration, and validation across diverse populations.
Researchers developed a tool called SKILL that enables AI agents to learn 102 distinct tasks by sharing knowledge in parallel, reducing the time needed to master new skills. The technology has potential applications in medicine, education, and other fields where vast knowledge is required.
The study evaluates recent research on artificial intelligence-generated molecular structures from the perspective of medicinal chemists, recommending guidelines for assessing novelty and validity. Insilico Medicine's recommendations aim to improve the process of generating and evaluating novel AI-generated drugs.