Articles tagged with Machine Learning
The Stowers Institute has appointed its first AI Fellow, Sumner Magruder, to harness the potential of artificial intelligence in biological research. He will collaborate with researchers to design new algorithms and unlock insights from large datasets.
Researchers develop AI-powered methods for modeling the Gulf of Mexico's dynamics, achieving higher accuracy for short-term predictions and emulating 10-year dynamics without hallucinations. This breakthrough drives forward critical management of natural resources in the U.S. and Mexico, advancing AI technology in earth sciences.
A study published in the British Journal of Health Psychology reveals that commercial fitness apps can have negative themes such as quantifying diet and physical activity challenges, oversimplified algorithms, and aversive emotional responses. The findings suggest a need for user-centered design prioritizing wellbeing over rigid goals.
A new study from McGill University found that increased physical activity in older adults with cardiovascular conditions is linked to greater social support and access to greenspace. Brain imaging revealed a connection between brain connectivity and exercise behavior change.
Researchers at UC San Diego developed a new method for fine-tuning large language models with significantly less data and computing power. This approach updates only the necessary parts of the model, reducing costs and improving generalization.
Researchers developed voltage-matrix nanopore profiling to accurately classify proteins in complex mixtures based on their electrical signatures. The method reveals molecular individuality and compositional differences without labeling or modifications, holding promise for disease diagnosis and real-world bioanalytical applications.
A team of researchers at the University of Waterloo developed a framework that uses mathematical tools and machine learning to rigorously check and verify the safety of AI-driven systems. The framework has been tested on challenging control problems and matched or exceeded traditional approaches.
Researchers at HUN-REN Szegedi Biológiai Kutatóközpont have developed an AI-powered platform for automated 3D cell culture analysis, enabling high-precision screening of cellular models. The technology removes the limitation of throughput in personalized medicine, allowing for fast and accurate analysis of clinical samples.
A machine learning model developed by Dr. Lan Mu's team at Tianjin University of Commerce predicts biochar yield and nutrient content with stunning accuracy, unlocking smart soil solutions for healthier soils, cleaner ecosystems, and smarter farming.
Researchers develop geophysical-machine learning tool that estimates soil strength parameters using limited borehole data, enabling continuous subsurface characterization. The approach reduces the need for expensive and time-consuming drilling in challenging terrains.
The team aims to build an anonymized database representative of the whole population by collecting two vivid memories from participants. The findings will inform new ways to help people remember in more detail and understand human memories across the lifespan.
Researchers developed a machine learning-driven design for a high-energy NASICON cathode that surpasses previous materials in terms of specific capacity, average operating voltage, and rate capability. The new cathode addresses sustainability concerns by replacing toxic vanadium with more environmentally friendly elements.
Researchers at University of California San Diego have developed an AI-based method to target cancer stem cells, which can spread and resist therapy. The approach leverages machine learning to identify treatment targets and restore function to genes, leading to the self-destruction of cancer cells.
Lehigh University researchers used machine learning to compare bone marrow extracted from the hip and shoulder, finding six proteins that distinguish between the two extraction sites. This study may lead to standardized BMAC extraction protocols and personalized treatments based on protein concentrations.
Recent research found that large language models are not yet able to consistently fool humans in conversations. They struggle with using discourse markers, opening and closing features, and subtle imitations. Despite rapid development, key differences between human and artificial conversations will likely remain.
Researchers from MIT and the MIT-IBM Watson AI Lab have introduced a new training method that enables vision-language models to localize personalized objects in a scene. By using carefully prepared video-tracking data with contextual clues, the model is better able to identify the location of a specific object in a new image.
A landmark study analyzed health data from over 600,000 patients across 10 countries to assess patient risk for non-ST-elevation acute coronary syndrome (NSTE-ACS). The AI-powered model GRACE 3.0 predicts risk more accurately and guides personalized treatment decisions.
A new large language model, LassoESM, has been developed to predict lasso peptide properties, enabling the acceleration of rational design for biomedical applications. The model was trained on thousands of lasso peptide sequences and demonstrated accurate prediction of various properties.
VFF-Net applies label-wise noise labelling, cosine similarity-based contrastive loss, and layer grouping to improve image classification performance compared to conventional forward-forward networks. The algorithm reduces test errors on various datasets, enabling lighter and more brain-like training methods that make AI more sustainable.
The conference explores how generative AI is reshaping kidney medicine through AI-driven diagnostics, data integration, and omics analysis. Key findings include the use of LLMs to transform diagnostic precision, patient management, and research design.
Scientists from Japan developed a theoretical framework that explains how collective cells can perform complex tasks. The key is distributed information processing and reinforcement learning in the environment.
A new AI-powered tool, EZSpecificity, can predict the best enzyme-substrate combination for various applications. The tool outperformed existing models in accuracy, especially for halogenase enzymes.
Researchers developed an AI system, InfEHR, that links unconnected medical events over time, revealing diagnostic insights. The system transforms millions of scattered data points into actionable patient-specific diagnostic insights.
Researchers are developing a precision phage platform to restore microbiome balance and combat antibiotic-resistant diseases. The MIGHTY project aims to harness phages as targeted antimicrobials, leveraging AI and machine learning methods to identify effective phage combinations.
A new project aims to develop a computationally efficient model that accurately predicts how additive manufacturing process parameters influence the solidification microstructure of binary alloy solidification. This will enable optimization of additively manufactured parts with confidence in critical industries.
A new AI tool, SpectroGen, uses generative AI to quickly assess material quality by generating spectra in less than one minute. It can replace traditional methods that take several hours or days, improving productivity and efficiency in industries such as manufacturing and pharmaceuticals.
A new project aims to enhance workforce readiness in molecular bioscience by creating open-access resources and modules tailored to student needs. The Molecular Data Education Hub will host instructional materials and case studies for instructors to implement into their courses.
MetaSeg achieves the same segmentation performance as U-Nets but requires 90% fewer parameters, making medical image segmentation more cost-effective. The new approach leverages implicit neural representations to quickly adjust to new images and decode accurate labels.
The platform allows users to create personalized LLM-based chatbots, serving as a 24/7 teaching assistant or scraping campus websites to find needed resources. Illinois Chat has already been utilized in various departments and research groups, providing valuable feedback to make it more practical.
A new smartphone-based AI system accurately predicts avocado firmness and internal quality with high accuracy, enabling consumers to avoid overripe avocados. The technology has the potential to assess the ripeness and quality of other foods, reducing global food waste by 50% by 2030.
A new machine learning framework integrates experimental features with microbial biofilm data to optimize bioelectrodechlorination, predicting pollutant degradation rates with high accuracy. The approach reduces reliance on exhaustive laboratory testing while enhancing remediation efficiency and can be adapted for other bioelectrochemi...
Researchers at Texas A&M University have developed a new framework to protect industrial processing facilities from cyber threats. The framework identifies vulnerabilities, detects abnormal activity in real-time, and provides safeguards and mitigation strategies to maintain safe operations.
FastTrack uses machine learning force field and 3D PES sampling to predict atomic migration barriers accurately within minutes, overcoming limitations of traditional methods. It enables quantitative high-throughput screening of ion transport across vast material spaces.
Researchers at MIT have found a hidden atomic order in metals that changes their properties, including mechanical strength and heat capacity. The discovery reveals a new physical phenomenon explaining the persistent patterns and provides a simple model to predict chemical patterns in metals.
Researchers developed an AI-based generative approach to discovering technology opportunities from patent maps using machine learning. The system translates patent vacancies into human-readable text, enabling the identification of untapped technologies and facilitating innovation forecasting.
A new study uses general-purpose AI to classify real cosmic events and explain its reasoning without complex training. The model achieved approximately 93% accuracy and provided plain-English explanations for every classification.
The research team's Cord-Approx strategy coordinates drivers to assign different street parking spots using statistical predictions and an optimal matching algorithm. This approach significantly reduces parking search time, with drivers finding a spot in 6.7 minutes on average.
Florida International University researchers have developed a defensive system called SHIELD that can detect and neutralize cyberattacks on drones in real time. The system allows the drone to finish its mission while providing robust recovery mechanisms, making it crucial for securing commercial drone use across industries.
Researchers at MIT have developed a 3D-printable aluminum alloy that is five times stronger than traditionally manufactured versions. This breakthrough could lead to lighter and more efficient aircraft parts, such as fan blades in jet engines, reducing energy consumption and costs.
Paul Boutros, a pioneer in using AI and machine learning to analyze cancer data, is appointed as the new director of the National Cancer Institute-designated cancer center at Sanford Burnham Prebys. The cancer center will leverage computational tools to personalize therapies for cancer patients.
Scientists at MIT developed a method to predict how plasma in a tokamak will behave during rampdown, achieving high accuracy with limited data. This new model could significantly improve the safety and reliability of future fusion power plants.
A team of researchers from Worcester Polytechnic Institute has developed a new approach to producing hydrogen using plasma technology and metal alloys. The method reduces energy consumption and carbon emissions compared to traditional methods, making it more environmentally friendly and potentially affordable.
A team of researchers developed a computational method that can design intrinsically disordered proteins with desired properties. The work uses automatic differentiation to optimize protein sequences and leverages molecular dynamics simulations for precision. This breakthrough has the potential to reveal new insights into diseases like...
Researchers developed a new imaging method using multiphoton microscopy to rapidly identify pancreatic neuroendocrine tumors with high accuracy. Machine learning algorithms achieved 80.6% accuracy, while convolutional neural networks outperformed with accuracies ranging from 90.8% to 96.4%.
A new study from the University of Gothenburg reveals that trawling restrictions have led to a significant increase in marine life, particularly among filter-feeding species like mussels and soft corals. However, heat-sensitive species are declining at shallow depths due to warmer water temperatures, driven by climate change.
A machine learning approach enhances the treatment of livestock manure, predicting phosphorus distribution and recovery. The process converts biowaste into hydrochar and a nutrient-rich liquid, reducing environmental pollution and supporting sustainable agriculture.
Researchers argue that AI can strengthen pandemic preparedness by detecting emerging diseases earlier. By combining data from humans, animals, and the environment, AI can reveal patterns and provide insights into potential pathogens.
Researchers at Tohoku University utilized AI to develop strategies for water treatment and air pollution control. The study highlights innovative approaches using machine learning in material screening and global distribution simulation of pollutants.
A new 'future-guided' AI method developed at the University of California, Santa Cruz, has shown significant improvements in predicting seizures using brain wave data. The technique operates with two deep learning models working together, improving predictions further into the future by transferring knowledge.
Liberate AI project develops an AI model capable of predicting long-term outcomes and potential complications in ischemic stroke patients. The model will be trained using Swarm Learning technology, aiming for explainability and transparency, while striking a balance between these features and accuracy.
A new AI tool called Waldo can scan social media data to discover personal reports of harmful side effects of popular health products. The tool achieved an accuracy of 99.7% in detecting adverse events, outperforming general-purpose chatbots.
Researchers at Champalimaud Centre for the Unknown used machine learning techniques to show that mice's facial movements reflect their hidden thoughts. This discovery could offer unprecedented insight into brain function and potential new research tools.
A new clinical model integrating CT signatures predicts HCC risk more accurately than existing models, stratifying patients into high-risk and low-risk groups. The study enhances individualized surveillance and improves patient outcomes for cirrhosis patients.
The European Lighthouse on Secure and Safe AI (ELSA) network has grown to include 41 members, expanding its expertise in pressing AI research topics and fostering knowledge exchange. The network's founding members are renowned for their work on Safety, Security, Artificial Intelligence, and Machine Learning.
Researchers at Institute of Science Tokyo developed a new framework for generative diffusion models by reinterpreting Schrödinger bridge models as variational autoencoders. This approach reduces computational costs and prevents overfitting, enabling more efficient generative AI models with broad applicability.
Researchers have developed a new method to boost energy transfer in magnesium batteries using amorphous materials. The approach uses machine learning to simulate the behavior of ions within these materials, leading to significant improvements in rate of energy transfer.
Using embryoid models made of stem cells, researchers uncovered important processes mimicking the first moments of human development. The team applied AI neural networks to thousands of images collected with confocal fluorescent microscopy to detect features and protein marker expression data.
Researchers developed an AI-based system to streamline medical image segmentation, allowing users to mark areas of interest and predict segmentations with decreasing user input. The tool has been shown to outperform state-of-the-art tools in image segmentation tasks, enabling faster study completion and cost reduction.
A cross-sectional study found that FDA-cleared AI-enabled medical devices lack standardized efficacy, safety, and risk assessment, highlighting the need for dedicated regulatory pathways and post-market surveillance to address these challenges.
The book offers novel human-centered AI techniques to address social intelligence challenges, including multimodal approaches and explainable AI designs. Researchers can integrate artificial and human intelligence to tackle complex social challenges.