Articles tagged with Machine Learning
A team of New York University scientists created a computer model that can represent and generate human-like goals by learning from how people create games. The AI model successfully captured the ways humans develop new goals and generated its own playful goals indistinguishable from human-created ones.
Researchers at Cornell University have developed a new method for mapping poverty using national surveys, big data, and machine learning. The approach translates Earth observation data into actionable terms for policymakers, providing more accurate estimates of poverty lines.
A comprehensive analysis of Bluesky reveals its structure, polarization, and political leanings. The platform's users, particularly those from X (formerly Twitter), exhibit left-leaning tendencies, as evident in the majority of shared links and content. The study also highlights how Bluesky diverges on the Israel-Palestine conflict topic.
Researchers leveraged a large language model to classify artwork types without pre-prepared training data, achieving high accuracy. This approach enables performance comparable to conventional machine learning methods while reducing human effort and time required for data organization.
Researchers at Columbia University developed a way for robots to autonomously model their own 3D shapes using a single camera, enabling them to understand and adapt to their movements. This breakthrough allows robots to overcome damage to their bodies, making them more reliable and resilient for various applications.
Researchers developed ItpCtrl-AI, a transparent AI framework that reads chest X-rays like a radiologist, providing accurate diagnoses and increasing trust in medical technology. The framework uses a gaze heat map to show the computer where to search for abnormalities and what section of the image requires less attention.
Ben Jones, a UTA physicist, has been recognized for his contributions to developing advanced instruments used in particle physics research. His work focuses on uncovering the origin of neutrino mass and sheds light on fundamental physics at extremely small scales.
The partnership aims to develop AI-driven tools to improve investment decisions, enhance system stability through intelligent forecasting, and deploy smart optimisation algorithms. The collaboration seeks to address key challenges in smart energy storage by integrating Trinasolar's expertise with NTU's leading research.
Ebru Demir aims to study how groups of AI-driven microswimmers move in biological fluids for potential applications in drug delivery, fertility treatments, and other medical fields. Her research combines artificial microswimmers with machine learning to uncover the underlying physics governing their movement.
Researchers from Aarhus University found a machine-learning algorithm that can predict patients with high risk of developing schizophrenia or bipolar disorder within the next five years. The study analyzed electronic health record data and identified key factors, including words describing symptoms in clinical notes.
Researchers at Cornell University have developed a new method using underwater microphones and machine learning to estimate the population of North Atlantic right whales. The study demonstrates how this method can aid in the conservation of these critically endangered species by providing real-time monitoring capabilities. By analyzing...
A study published in Science Bulletin has created a comprehensive tree density map of China, estimating 142.6 billion trees across the country, with varying densities by region and ecosystem type.
A recent study has identified that the neuronal subtype responds best to immunotherapy, while other subtypes exhibit lower response rates. The researchers developed a machine-learning algorithm using large public data sets to predict treatment response based on tumor mutational burden and immune cell infiltration.
Researchers used machine learning to predict diagnostic transition to schizophrenia and bipolar disorder, with schizophrenia being notably easier to forecast. By analyzing routine clinical data, the study suggests a possible approach for early intervention and personalized treatment plans.
Researchers created printed fabric sensors that can detect tiny skin movements for sleep disorder monitoring. The smart pyjamas achieved an accuracy of 98.6% in identifying six different sleep states, including nasal and mouth breathing, snoring, and teeth grinding.
Researchers at the University of Navarra present a new prediction methodology to improve fairness and reliability of AI models used in critical decision-making. The methodology optimizes parameters of reliable machine learning models, reducing inequalities related to sensitive attributes.
Researchers are using machine learning to enable autonomous control of particle accelerators, opening up new possibilities for commissioning and operating high-power accelerators. The technology has been successfully applied to the CAFe2 superconducting segment, achieving global trajectory adaptive control.
A new study by the University of Birmingham found that everyday stress, loneliness, and childhood trauma are significant risk factors for dissociation in teenagers and young adults. The research identified four key risk factors, with everyday stress being the most significant.
Researchers found a remarkable X-ray flash in archived Chandra Observatory data, hinting at possible explanations: X-ray burst, magnetar flare, or new cosmic event. The discovery showcases the potential of AI for scientific breakthroughs in astronomical archives.
Researchers have developed software tools that analyze student behavior in real-time, assessing how well students develop and use collaborative problem-solving skills. The tools can modify the game to improve learning outcomes by identifying patterns of behavior associated with specific learning outcomes.
Researchers developed an AI system to control airflow on wing surfaces, reducing turbulent separation bubbles by 9%. The system uses deep reinforcement learning to adapt to airflow dynamics and enhance the effectiveness of experimental technologies.
A new study from the University of South Australia found that most people trust AI in situations where the stakes are low, such as music suggestions. However, those with poor statistical literacy or little familiarity with AI were just as likely to trust algorithms for trivial choices as they were for critical decisions. The study also...
Researchers used a remote seismic station to detect and characterize an earthquake swarm in American Samoa, producing a new catalog of events. The technique employed, combining single-station data with deep-learning models, can be useful in sparse monitoring regions.
Researchers developed mini biohybrid rays using cardiomyocytes and rubber, demonstrating improved swimming efficiencies approximately two times greater than previous biomimetic designs. The application of machine-learning directed optimization enabled an efficient search for high-performance design configurations.
A novel machine learning-based approach enhances glacier lake depth estimation accuracy, addressing traditional methods' limitations. The method integrates ICESat-2 satellite data with multispectral imagery from Landsat-8 and Sentinel-2, providing a high-precision solution for large-scale monitoring of supraglacial lakes.
Researchers at Rutgers University have developed an AI tool that combines whale monitoring and environmental data to predict endangered whale habitat. The tool guides ships along the Atlantic coast to avoid critically endangered North Atlantic right whales, preventing deadly accidents and informing conservation strategies.
The international workshop brings together leading researchers from around the world to discuss machine learning applications in high-energy particle physics, quantum mechanics, and material discovery. The event highlights the pivotal role of machine learning in advancing research in the physical sciences.
A groundbreaking study analyzed data from over 78,000 cancer patients to identify nearly 800 genetic changes impacting survival outcomes. The research also discovered genes significantly associated with survival in various cancers, such as breast, ovarian, skin, and gastrointestinal cancers.
The new method enables accurate detection of polycyclic aromatic hydrocarbons (PAHs) and their derivatives (PACs) in placental samples, providing critical insights into maternal and fetal health. This breakthrough could inform public health measures and improve fetal and maternal health outcomes.
Researchers at San Francisco State University have developed a step-by-step machine learning tutorial to detect antibiotic resistance in patients. The team used a publicly available dataset to train four popular machine learning models, which can be easily accessed through Google Colab. The tutorial aims to make machine learning access...
A new machine learning model, NAS-WD, has improved the accuracy of detecting 'woody breast' in chicken meat to 95%, allowing for better quality assurance and customer confidence. The model uses hyperspectral imaging to analyze complex data from images, enabling more accurate detection than traditional methods.
Researchers developed Torque Clustering, an unsupervised learning method that efficiently uncovers patterns in vast datasets without human guidance. The algorithm outperforms traditional methods, offering a potential paradigm shift for robotics and autonomous systems.
A new method developed by Osaka Metropolitan University accurately predicts housing prices in Osaka City, with neighborhood perception being a key factor. The approach achieves nearly 75% accuracy by combining existing property data with machine-learning-processed street view images.
Researchers at Incheon National University have developed a new AI-powered solution to improve high-speed users' connectivity in 5G and 6G networks. The method significantly reduces errors and improves data reliability by prioritizing key parameters such as angles and delays.
A new AI-based tool can translate a person's thoughts into continuous text without requiring language comprehension, and it can be trained in under an hour. The system was developed by adapting a previous brain decoder to a new person using short, silent videos.
InsectNet uses machine learning to identify over 2,500 insect species at 96% accuracy, providing critical information for farmers and researchers. The app can be fine-tuned for specific regions, making it useful for agricultural challenges worldwide.
A new quantum-classical approach has been developed for designing photochromic materials, accelerating the discovery of novel compounds. The method identified five promising candidates with key properties essential for photopharmacology applications.
The SETI Institute invites researchers to apply for a postdoctoral fellowship focused on developing advanced AI tools for exoplanet discovery. The successful candidate will work with Dr. Vishal Gajjar and his team to uncover subtle signals in massive datasets using machine learning and anomaly-detection techniques.
A recent study emphasizes the urgent need to address bias in generative AI systems, which can distort outcomes and erode public trust. The research suggests that developing and deploying ethical, explainable AI is crucial to ensure fairness and transparency in critical decision-making areas.
Researchers used AI to find adalimumab as potential treatment for iMCD, a rare disease with poor survival rate and few treatment options. The treatment has shown promising results in saving the life of a patient who was on hospice care.
Researchers developed a machine learning tool that screens for comorbid depression and anxiety disorders using acoustic voice signals. The study confirmed that a one-minute verbal fluency test can reliably identify subjects with comorbid AD/MDD, who tend to use simpler words and exhibit reduced variability in phonemic word length.
Yann LeCun, NYU's Courant Institute of Mathematical Sciences professor, has been selected as a winner of the 2025 Queen Elizabeth Prize for Engineering for his groundbreaking research on artificial neural networks. His work enabled machines to process and learn from vast amounts of data in ways previously unimaginable.
Researchers found that a machine learning predictive model leveraging EHR data outperformed screening surveys in identifying patients with health-related social needs. The models demonstrated biases, particularly towards White, non-Hispanic patients.
Dental implant surgeries require optimal mechanical stress levels for successful bone healing and long-term implant success. Researchers are developing a hybrid biomechanical model using machine learning to provide precise, patient-specific predictions of mechanical stress.
A team of MIT engineers has developed a training method for multiagent systems that can guarantee their safe operation in crowded environments. The method enables agents to continually map their safety margins, allowing them to scale up to any number of agents while maintaining system safety.
Researchers developed SPACIER, an open-source software that integrates machine learning with molecular simulations to design high-performance optical polymers. The tool surpassed the empirical limits of refractive index and Abbe number in a proof-of-concept study, demonstrating its practical potential.
A new machine learning approach developed by UCR researchers enables the detection of environmental patterns in large datasets generated by LIGO, improving data quality and reducing noise. This technology has potential applications in particle accelerator experiments and complex industrial systems.
Dr. Girish N. Nadkarni, a pioneering physician-scientist, has been named Chair of the Windreich Department of Artificial Intelligence and Human Health and Director of the Hasso Plattner Institute for Digital Health at Mount Sinai. He will lead efforts to advance AI research, education, and clinical translation.
Researchers found that training AI agents in a less noisy environment can lead to better performance than traditional methods. The indoor training effect suggests that constructing simulated environments with specific noise levels can improve AI learning.
Researchers developed ProtET, an AI model leveraging multi-modal learning to controllably edit proteins through text-based instructions. This approach enhances functional protein design across domains like enzyme activity, stability, and antibody binding, promising real-world applicability in biomedical research.
The open-source AI model analyzes medical images, generates detailed reports, and answers clinical questions to streamline diagnostics and improve accuracy. BiomedGPT aims to democratize healthcare and reduce disparities amongst patients by providing easily accessible data to bolster underserved hospitals.
Researchers developed MUNIS, a deep learning tool that predicts CD8+ T cell epitopes with high accuracy, potentially accelerating vaccine development. The tool was validated using experimental data from influenza, HIV, and EBV, demonstrating its potential to streamline vaccine design.
A collaborative study investigates how advanced AI tools can make it easier to evaluate interventions for ageing, providing personalised recommendations. The researchers identified eight critical requirements for effective AI-based evaluations and found that following specific guidelines improved the quality of the recommendations.
Vision-Language Models (VLMs) inherit biases from uncurated datasets, leading to poor group robustness and biased predictions. Researchers are exploring strategies to mitigate these biases in discriminative models, but generative tasks like image captioning and image generation require attention.
A recent study has employed machine learning algorithms to improve the accuracy of flood season rainfall predictions. The findings show that combining climate system numerical models with ML-based correction methods results in substantial improvements, increasing prediction scores by up to 7.87%.
Neuromorphic computing is poised to emerge into full-scale commercial use, driven by the need for energy-efficient solutions. The review article proposes strategies for building large-scale neuromorphic systems that can tackle complex real-world challenges.
Researchers designed nano-architected materials with exceptional strength-to-weight and stiffness-to-weight ratios, overcoming stress concentration issues. Machine learning optimized geometries led to over double the strength of existing designs.
Researchers applied machine learning methods to analyze large datasets of individual cells, achieving better results than classical learning methods. Self-supervised learning improved performance, especially in applications with large single-cell data sets.
A new hybrid machine learning model predicts ultimate axial strength of CFRP-strengthened CFST columns with high accuracy, enabling safer and more efficient designs. The model can be used to optimize construction processes and enhance the safety of structures at a lower cost.
The new tool will enhance the DLA's supply chain management capabilities, reduce operational disruptions, and bolster readiness. Quantum Research Sciences' technology will provide predictive capabilities and automate obsolescence management processes.