Articles tagged with Machine Learning
The journal argues that AI can process vast datasets to detect patterns and predict climate impacts, improving pollution tracking, climate modeling, and agricultural planning. AI-driven modeling can also help policymakers evaluate tradeoffs and anticipate unintended consequences, supporting more informed decision-making.
A team of researchers has developed DopFone, an app that uses a smartphone's speaker and microphone to estimate fetal heart rate with high accuracy. The system was tested on 23 pregnant women and showed promising results, with an average error of 2 beats per minute.
The semiconductor industry is shifting from compute to memory as ultra-large AI models demand higher performing chips. SK hynix is increasing bandwidth by a factor of 1.5x every two years, while GlobalFoundries uses AI to improve process control and manage diverse manufacturing processes.
Researchers used machine learning techniques to compress a large model of the visual cortex, creating smaller versions that predict neural responses with high accuracy. The compact models revealed specific computational patterns in how neurons detect important features, offering insights into how visual information is processed.
A new study finds that fewer than 250 fossils are required to train an image-based AI algorithm, a significant improvement on previously thought numbers. The discovery could greatly speed up the identification process in vertebrate paleontology, where most fossils are fragmented and difficult to analyze.
Researchers have developed a smaller and simpler AI model that accurately predicts neural responses to visual stimuli in macaque brains. The compact model reveals unique neuron preferences for features like edges and colors, shedding light on how the brain processes information.
Researchers at Linköping University have developed an AI model that can accurately determine the time of death from blood samples, providing crucial information in murder investigations and police work. The method uses metabolites to analyze changes in small molecules over time, outperforming current methods.
Researchers developed a machine learning-guided strategy to design advanced biochar materials that remove phosphorus efficiently while lowering treatment costs. The study provides a practical pathway for restoring eutrophic waters at large scale.
A study reveals that identical photons in optical circuits exhibit Hopfield Network behavior, enabling associative memory mechanisms similar to the human brain. The research finds a fundamental limit to memory capacity, with quantum coherence allowing correct retrieval but transitioning to disorder as data volume increases.
Researchers at Linköping University have developed an AI-boosted electronic nose that can detect ovarian cancer from blood plasma samples with high accuracy. The method uses machine learning to identify patterns specific to the disease, making it a promising tool for early detection and improved survival rates.
Researchers developed a new model to predict long-term survival after heart failure in elderly Japanese patients. The Top-20 XGBoost model incorporates physical function metrics, rivaling the importance of traditional cardiovascular risk factors, and provides a more accurate estimation of mortality risk.
Epic Games has acquired Meshcapade, a Max Planck startup that develops solutions for creating and animating digital humans. The technology, based on the SMPL body model, enables realistic human movement and expression in 3D.
A new machine learning model interprets leg motion as expended energy, providing a more accurate measure of calories burned. The device has been shown to have double the accuracy of commercial smartwatches and activity trackers.
A new study by the University of Cambridge found that many AI bots lack basic safety disclosures, including transparency about their abilities and potential risks. The 'AI Agent Index' revealed a significant transparency gap, with only four out of thirty agents publishing formal safety and evaluation documents.
A study found that patients value AI-based interpretation for routine situations due to its speed and convenience, while remote video interpretation is preferred for emotionally sensitive or high-stakes conversations. Patient trust, autonomy, and perceived control drive interpreter preferences.
A team from MIT and UC San Diego has developed a new method to uncover hidden biases, moods, and abstract concepts in large language models (LLMs). The approach identifies these connections within the model and allows for manipulation of the concept in generated answers.
Researchers found that dosed nonlinearity improves model performance in various tasks, especially with limited data. Nonlinear units function like flexible switches, adapting linear processing modes based on context.
A study by RIKEN researchers identifies a MYCN-driven biomarker that predicts the risk of liver cancer. The biomarker, known as the MYCN niche score, uses machine learning to analyze gene expression patterns and indicates whether a tumor-free liver is at high risk for developing tumors.
The journal explores the convergence of computational biology, artificial intelligence, and healthcare innovation, with a focus on precision medicine and enhanced patient care. Submissions are accepted from researchers, clinicians, and technologists on topics such as AI in medicine, computational genomics, and drug discovery.
Researchers at UCSF and Wayne State University found that generative AI tools can perform orders of magnitude faster than human teams in analyzing health data. Junior researchers paired with AI generated viable prediction models in minutes, outperforming experienced programmers in hours or days.
Researchers developed an AI-based tool called BIOPREVENT to identify patients at higher risk for chronic GVHD and dying from transplant-related causes. The tool combines immune biomarkers, clinical data, and machine learning to create a personalized risk estimate over time.
China's youth faces a growing mental health crisis, but AI platforms like DeepSeek offer promising solutions to bridge the gap. These platforms leverage natural language processing and generative AI to provide round-the-clock support tailored to Chinese society.
Researchers found that wild Yellow-naped Amazon parrots use complex vocalizations with syntax, collocates, and a large repertoire of notes when fighting for territory. The team identified over 450 calls in warble duets, revealing precise rules governing the birds' communication.
Researchers developed an AI-based system to generate high-resolution soybean yield maps across Brazil, leveraging knowledge from U.S.-based models through transfer learning. The approach achieved strong predictive performance without using municipal-level yield data, improving estimates for this key agricultural region.
A University of Houston professor has found that tree-like thin films release heat at least three times better than traditional methods, enabling more efficient cooling in AI data centers. The discovery demonstrates the power of physics-aware AI design for validating high-impact cooling solutions.
Scientists have identified specific patterns of brain chemical activity in honey bees that predict how quickly individual bees learn new associations. The findings may help explain why humans learn at different speeds and provide implications for understanding brain disorders.
Researchers at the University of Pennsylvania have developed HoloRadar, a system that enables robots to reconstruct hidden 3D spaces beyond their line of sight using radio waves processed by AI. This capability can improve safety and performance in driverless cars and cluttered indoor settings.
A new study published in The Lancet Respiratory Medicine found that prehospital emergency intubation of high-risk trauma patients improves 30-day survival by 10.3% and could save 170 lives each year in the UK. Prehospital intubation needs to be administered by an advanced critical care team.
Researchers at Nagoya University developed an AI system called YORU that recognizes animal behaviors with over 90% accuracy. The system combines real-time video capture with optogenetics to selectively target brain cells driving specific behaviors, offering a major breakthrough in social behavior studies.
Rice University scientists create a detailed map of the Alzheimer's brain using hyperspectral Raman imaging and machine learning. The findings show that chemical changes are unevenly distributed across the brain and extend beyond amyloid plaques, revealing broader metabolic differences between healthy and diseased brains.
New machine-learning tools significantly improve phishing site detection accuracy, surpassing 95% precision/recall. The study evaluates ten classifiers across three public datasets using URL, domain, and content features.
A recent NSF grant will support the development of new diagnostics and predictive models for understanding self-competition and weak asymmetry in turbulent flows. The project aims to uncover hidden patterns that current models miss, leading to improved simulations in weather forecasting, climate modeling, and engineering design.
Researchers at Oregon State University have developed a deep learning-based model for rapid bacterial contamination detection, eliminating misclassifications of food debris. The enhanced model can reliably detect bacteria in three hours and has the potential to prevent outbreaks and protect consumer health.
Researchers analyzed over 25,000 YouTube comments on wildlife videos to find only 2% calling for conservation efforts. Despite the low number, experts believe there is an opportunity for improved conservation messaging on social media.
Drexel researchers create machine learning program that integrates qualitative and quantitative data to identify gentrification in Philadelphia neighborhoods. The program, trained with data from thousands of images and focus groups, accurately identifies new-build gentrification with 84% accuracy.
Researchers used machine learning to simulate gold nanoclusters' behavior under realistic conditions, revealing structural changes and polymer-like chain formation. The study demonstrates the accuracy of the machine learning potential in understanding nanomaterials' properties.
Researchers at the Netherlands Cancer Institute developed a deep learning model PARM that predicts gene regulation with unprecedented accuracy. The model enables the prediction of functional impact of regulatory mutations in specific cell types, opening new paths for cancer diagnostics and patient stratification.
A new study from George Mason University reveals that AI-driven antidepressant treatment can be less effective for African American patients due to the use of general population data. The study suggests that incorporating additional patient demographics, such as race and ethnicity, can improve the tool's effectiveness.
Researchers at Queen Mary University of London have developed an AI system called Auto MV that can generate complete music videos directly from full-length songs. The system uses a multi-agent approach to analyze the musical structure and lyrics, plan scenes, and generate images and video clips.
Researchers at MIT developed a generative AI model called DiffSyn that suggests promising synthesis routes for complex materials like zeolites. By using this model, scientists can test millions of theoretical materials in under a minute, accelerating the materials discovery process.
The portable optical sensor uses machine learning to analyze spectral patterns and estimate grape ripeness directly on the vine. This technology promises significant business benefits for winemakers by providing non-destructive, real-time insight into grape ripeness, reducing labor and time required for sampling and analysis.
The University of Ottawa has launched the Ottawa Medical Artificial Intelligence Research Institute (OMARI), a center for research, education, and innovation in medical Artificial Intelligence. Led by Dr. Khaled El Emam, OMARI aims to facilitate cross-cutting collaborations and sharpen the university's competitive edge in AI-driven hea...
Researchers at Boston Children's Hospital have developed a novel mass spectrometry tool called FLEXITau to analyze brain tissue from 203 patients with various tauopathies. The study identified 145 post-translational modifications and 195 cleavage sites across tau, providing a precise molecular roadmap for diagnostics and drug development.
The FIU-Florida Heart Research Foundation Center for Innovation in Cardiovascular Health brings together experts in various fields to uncover the fundamental drivers of cardiovascular disease. The center will focus on AI-powered diagnostics, regenerative tissue technologies, and workforce development.
Researchers developed a machine learning framework that accurately predicts and optimizes biochar production from algae, identifying temperature as the dominant control on biochar yield. The model achieved strong agreement with experimental results and was able to pinpoint key factors influencing biochar production.
A new study using AI-powered BioBERT model accurately identifies fungal lifestyles, switching between helpful partner for plants to aggressive decomposers. The tool has nearly 90% accuracy and can scan thousands of papers in minutes, flagging species that may switch roles.
A new AI-driven model predicts patient risk of suffering a major adverse cardiac event (MACE) with higher accuracy than traditional predictive models. This improvement in prognostic risk assessment could help personalize individual care plans and maintain quality of life for patients.
Researchers have developed a new AI tool called VASCilia that provides unprecedented 3D views of cochlear hair cells. The tool accelerates the imaging process by 50-fold, allowing scientists to analyze cells with greater precision and accuracy. This advancement offers new insights into hearing loss caused by damaged inner ear hair cells.
Researchers have developed a passive, solar-powered orbital data center that can scale AI computing and reduce environmental impact. The system leverages decades of research on 'tethers' and could host thousands of computing nodes to replicate terrestrial data centers.
A recent study from Binghamton University School of Management reveals that focusing on human-robot collaboration can generate additional economic value and improve a company's ability to capture a greater share of the competitive market. By leveraging robots in collaborative settings, organizations can foster a positive sense of commi...
Researchers at Chungnam National University have developed an AI model that uses deep learning to predict stable defect configurations in materials. The model, trained on data generated by conventional simulations, can generate results in milliseconds rather than hours, accelerating the material design process.
The Institute of Science and Technology Austria (ISTA) has received a significant donation to advance trustworthy AI technology. The €5 million gift from Garrett Camp will support fundamental research in artificial intelligence, focusing on interdisciplinary collaboration and long-term impact.
The Center for AI Responsibility and Research will build upon Binghamton research advancing AI for the public good. The new center is supported by a $30 million commitment from Tom Secunda and $25 million from Gov. Hochul and the NY State Legislature.
A large-scale study reveals that generative AI models have reached the threshold of average human creativity, but the most creative individuals still outperform even the best AI systems. The study also highlights the importance of human guidance and parameterization in modulating AI creativity.
Researchers found that complexity, including ground truth, real-world complexity, and stakeholder involvement, are key factors in reducing AI biases. By accounting for these complexities, developers can create more fair AI models.
Researchers created a computational model that combines physiological signals, sensory input, and word information to construct human emotions. The model achieved an agreement rate of about 75% when compared to participants' self-reported emotional evaluations.
Researchers developed an autonomous AI network technology that enables multiple AI systems to spontaneously collaborate and share knowledge to discover new materials. This collaboration improves exploration efficiency and paves the way for the creation of numerous new materials.
The review synthesizes over a decade of research on ammonia inhibition in anaerobic digestion, highlighting emerging tools to stabilize biogas production from nitrogen-rich wastes. Biological and material-based solutions, digital technologies, and synthetic biology are discussed as potential mitigations.
Researchers developed an AI-based system that accurately detects whip sounds in horse racing, achieving detection rates of up to 70% in audio data. The system's ability to process audio in real-time and its reliance on high-frequency components make it a promising tool for improving animal welfare and fair competition.
A chemist proposes a framework for shared model proteins to improve reproducibility and coordination in protein science. The proposal includes five widely used proteins and aims to establish minimal reporting requirements and curated reference datasets.