Articles tagged with Machine Learning
A groundbreaking AI model called NitroFusion creates images in seconds using modest hardware, eliminating the need for large computing resources. The open-source technology enables creative professionals and individuals to produce high-quality images affordably.
Researchers from Bar-Ilan University discover that classifying objects together through Multi-Label Classification can yield better results than detecting individual objects. This new method allows networks to learn correlations between object combinations, making them more recognizable in real-life applications such as autonomous vehi...
Researchers at Tokyo University of Science have developed a new method called black-box forgetting, which enables selective removal of unnecessary information from large pre-trained AI models. This approach enhances model efficiency and improves privacy by reducing computational resources and information leakage.
A new research paper published in Oncotarget introduces an innovative AI tool combining CT scans and body composition data to predict severe liver problems in primary sclerosing cholangitis (PSC) patients. The model achieved impressive results, correctly identifying at-risk patients with 97% accuracy.
Physicists at the University of Michigan have developed an algorithm that enables materials to learn and adapt, mimicking brain-like behaviors. This breakthrough has implications for the development of advanced materials with self-tuning properties.
A new grant will support a five-year investigation into the role of the brain's reward network in two neurodevelopmental conditions. Researchers will use high-resolution fMRI scans to analyze how the reward network interacts with other parts of the brain in autism, ADHD, and co-occurring autism and ADHD.
Researchers developed a deep-learning model to predict organoid development at an early stage, outperforming human experts in accuracy and speed. The model classifies bright-field images of organoids into three quality categories, indicating their potential for regenerative medicine applications.
A team of researchers has successfully identified a compound with record-breaking performance in film capacitors, a crucial component in renewable energy technologies. The breakthrough was achieved using a machine-learning technique that screened nearly 50,000 chemical structures to find the optimal material.
The US Naval Research Laboratory (NRL) will present its latest advancements in Earth and space sciences at the American Geophysical Union (AGU) Conference. NRL researchers will share their work on topics such as atmospheric data assimilation, ocean sciences, and geostationary ocean color.
A new study improves breast cancer risk prediction by analyzing up to three years of previous mammograms, identifying individuals at high risk 2.3 times more accurately than the standard method. The AI method considers subtle changes in mammogram images and holds up well across diverse settings.
Researchers used a machine-learning technique to accelerate discovery of materials for film capacitors, identifying a compound with record-breaking performance. The study aims to improve capacitor shielding properties and enhance energy savings in common electric power applications.
Dr Sanja Panovska has been awarded an ERC Consolidator Grant for her EXCURSION project, which aims to develop the first data-based model of the Earth's magnetic field over the last 780,000 years. The project will investigate geomagnetic excursions and their impact on technological infrastructure, environment, and climate.
Researchers analyzed 81 common household items for chemical makeup and exposure risks. The study used advanced chromatography and machine learning methods to identify chemicals that could pose negative health effects, such as synthetic antioxidant BKF, when exposure reached a certain threshold.
Researchers at Seoul National University have developed a machine learning-based design of experiments method that optimizes the performance and process conditions of organic thermoelectric devices, enabling efficient evaluation of key variables and reducing experimental time. The technology has the potential to significantly improve d...
The book examines AI's current advances, hurdles, and potential, emphasizing the need for science to maintain core norms and values. Experts advocate for human accountability and responsibility when using AI in research, highlighting the importance of transparent disclosure and attribution.
Researchers developed a platform to help AI learn complex tasks through nuance and real-time instruction, achieving up to a 30% increase in success rates. The GUIDE framework allows humans to provide ongoing, nuanced feedback, fostering incremental improvements and deeper understanding.
Dr. Sebastian Stich aims to create more efficient and adaptable machine learning models using collaborative learning approaches. The goal is to reduce computational power demands and costs, making it accessible to smaller players in fields like medicine.
Researchers have uncovered key insights about how liquid crystals transform between different phases using direct simulation and machine learning. This study provides a clearer understanding of the microscopic-level changes in these materials, which could lead to new possibilities for advanced materials development.
A new AI model, Spherical DYffusion, can predict climate patterns over 100 years in just 25 hours, making it 25 times faster than the state of the art. The model uses generative AI and physics data to achieve this breakthrough, reducing the need for supercomputers.
The Polymathic AI team has released two massive datasets for training artificial intelligence models to find and exploit transferable knowledge between seemingly disparate fields. The datasets include data from dozens of sources, covering astrophysics, biology, acoustics, chemistry, fluid dynamics, and more.
A recent study presents a machine learning technique for rapid CO retrieval from hyperspectral satellite data, providing insights into air quality and pollutant transport over East Asia. The approach outperforms traditional physical methods in terms of consistency and accuracy.
A machine learning algorithm developed by researchers at Aarhus University can identify patients at high risk of involuntary admission within six months. The study, based on electronic health record data, suggests that the algorithm can improve targeted treatment and prevention in psychiatric services.
Insilico Medicine has developed an innovative platform using generative AI to discover novel molecules. The company has nominated over 20 preclinical candidates and received IND clearance for 10 molecules.
A new study found that brain stimulation effectiveness is linked to individual learning abilities, not age. The study used atDCS on participants with optimal and suboptimal learning strategies, revealing accelerated accuracy improvement in suboptimal learners.
University of Toronto researchers developed a new computational method to study genome organization, uncovering previously unknown patterns in human chromosomes. The method uses machine learning to analyze high-throughput chromosome conformation capture data and sheds light on chromosomal re-organization leading to disease development.
NCSA launches Harbor, a fast storage service for global home and software directories, boosting Delta system performance by 400%. Consolidating file systems to VAST namespace improved uptime, load times, and user experience. With Harbor and VAST Data Platform, NCSA achieves scalable and efficient AI infrastructure.
Recent Nobel Prizes in physics and chemistry have recognized the convergence of AI with physics and chemistry, emphasizing the need for interdisciplinary research. Researchers advocate for nurturing AI-enabled polymaths to bridge the gap between theoretical advancements and practical applications.
A new AI tool generates realistic satellite images of future flooding, which can help communities visualize and prepare for approaching storms. The method combines a generative artificial intelligence model with a physics-based flood model, producing more accurate and realistic images than an AI-only approach.
A study found that ChatGPT provides higher fatality numbers when asked in Arabic compared to questions in Hebrew, highlighting the impact of user language on information dissemination. The researchers believe this has profound social implications, as it can shape perceptions of conflict and fuel biases.
The HumanTech Summit 2024 conference brings together experts in technological and social innovation, discussing key topics such as data security and interpersonal relationships. With a proven legacy of impact, the event has attracted over 1,400 participants and featured renowned keynote speakers from top universities.
The Mount Sinai Health System has received the 2024 CHIME Digital Health Most Wired recognition as a certified level 9 in acute care and level 8 in ambulatory care. The system leverages digital health solutions to enhance patient care and reduce administrative burden.
Researchers at Cold Spring Harbor Laboratory have devised a potential solution to the paradox of animal innate abilities using artificial intelligence. The genomic bottleneck algorithm allows for compression levels unseen in AI, enabling faster runtimes and potentially leading to more evolved AI systems.
Researchers successfully detect neutron participating in DVCS reaction using a new detector installed at Thomas Jefferson National Accelerator Facility. The experiment provides unprecedented insight into the distribution of partons inside neutrons, a crucial step towards understanding nucleon structure and spin.
Researchers at MIT have introduced a new algorithm that strategically selects the best tasks for training an AI agent, resulting in improved performance and reduced training costs. The technique outperforms existing methods by five to 50 times, making it more efficient and effective.
The São Paulo Advanced School on Disordered Systems will bring together students and researchers in complexity, bio-inspired applications, information science, and quantum materials. The school, supported by FAPESP, aims to establish a common forum for learning and discussing theories of general interest.
A global team, including Lehigh University researcher Xuanhong Cheng, is exploring molecular- and cellular-level changes in muscle tissue that could lead to better diagnostic tools and therapeutic options for CFS and long COVID. The team aims to develop noninvasive diagnostic tools using electrical signatures.
This systematic review analyzes 33 biological clocks used for aging and mortality quantification, categorizing them into epigenetic and phenotypic clocks. Epigenetic clocks demonstrate precision in estimating chronological age through DNA methylation, while phenotypic clocks predict mortality using easily measurable clinical variables.
A new study from Regenstrief Institute presents a low-cost, scalable methodology for early dementia risk identification. The technique uses machine learning to extract relevant phrases from medical notes, providing healthcare providers with an individualized dementia risk prediction or evidence of mild cognitive impairment.
The Margot and Tom Pritzker Prize for AI in Science Research Excellence recognizes exceptional contributions to artificial intelligence and scientific inquiry. Awardees will receive $50,000 and be honored at the University of Chicago and Caltech Conference on AI+Science.
The discovery provides a unique way to investigate the extreme phase of stellar evolution, bridging the gap between the earliest and final stages of binary star systems. This breakthrough could help explain cosmic events like supernova explosions and gravitational waves.
A new machine learning algorithm reconstructs wind fields quickly and accurately, even with limited observational data. This enhances storm forecasting and hazard preparedness by providing valuable data on a tropical cyclone's intensity and potential impact.
Researchers suggest that starting with smaller neural networks and using curriculum learning can improve performance and reduce the need for massive computing resources. This approach could lead to more resource-efficient and less energy-consuming AI systems.
Researchers developed a novel model predicting mood episodes using only sleep-wake pattern data from wearable devices. The study found daily changes in circadian rhythm are key predictors of mood episodes, offering new possibilities for tracking individual changes to prevent future episodes.
DeltaAI enables scientists and researchers to address the world's most challenging problems by accelerating complex AI, machine learning, and high-performance computing applications. The system will quadruple NCSA's AI-focused computing capacity and expand the NSF-funded advanced computing ecosystem.
Scientists have developed a methodology that can predict the most favorable binding sites of gold nanoparticles to five common human blood proteins. This breakthrough enables researchers to investigate how drug-carrying nanoparticles interact with blood proteins, which could lead to more effective cancer treatments.
Researchers found that membership inference attacks on large language models (LLMs) are not effective in measuring information exposure risks. The common method used to test LLM leaks suffers from ambiguity due to the fluidity of language, making it difficult to define a representative set of non-member candidates.
Scientists use machine learning-based classifiers to differentiate lung cancer and noncancer based on urinary miRNA ensembles. The study reveals high specificity and sensitivity in detecting early-stage lung cancer, offering new hope for improved patient outcomes.
Neuro-oncology experts developed guidelines to standardize AI use in brain cancer diagnosis and treatment. The recommendations aim to ensure reliable clinical trial results and protect patients.
A WVU research partnership with the DEA aims to improve fast and accurate identification of psychoactive substances like fentanyl. The Expert Algorithm for Substance Identification (EASI) will enable labs using different instruments to share data on chemical profiles, helping to identify drugs like fentanyl.
A recent machine learning study suggests that the association between gut bacteria and disease may be overstated. Instead, changes in microbial load were found to be a key factor in the presence of disease-associated microbial species. This discovery challenges current understanding of the gut microbiome's role in disease etiology.
Researchers developed a robot that identifies plants by measuring leaf properties with an electrode, achieving an average accuracy of 97.7% for ten different species. The device may revolutionize crop management and early disease detection, but its limitations need to be addressed.
The partnership aims to improve the speed and accuracy of flood damage assessments by combining multi-modal data sources and a novel vision language model. The system will enable first responders to quickly allocate resources and respond more efficiently to weather disasters.
A new study developed a machine-learning model to predict microbial load, the density of microbes in our guts. The model revealed that many factors can influence microbial load, including lifestyle, diseases, and medications.
NeuroMechFly v2 simulates how a fruit fly navigates through its environment while reacting to sights, smells, and obstacles. The model can track moving objects visually or navigate towards an odor source, while avoiding obstacles in its path, enabling researchers to study brain-body coordination and animal intelligence.
Researchers used machine learning to analyze data from 50 CF-LVAD patients and identified six prognostic factors that predicted post-implantation stroke. Lower levels of OxPhos proteins were associated with an increased risk of new strokes after implantation.
HemaChrome's machine learning-based technology enables instant and noninvasive measurement of blood hemoglobin levels from digital photos, facilitating point-of-care diagnostic tests. The collaboration with Global Health Labs aims to address anemia diagnosis gaps in low- and middle-income countries.
Researchers developed a machine learning framework that predicts inorganic pollutants in groundwater based on limited water quality samples. The model suggests 15% to 55% of sites may truly be risk-free, identifying critical gaps in groundwater quality datasets.
A group of student researchers developed a machine learning-based approach to better understand and represent the decline in global ocean oxygen levels. Their research showed that the world's oceans have lost oxygen at a rate of about 0.7% per decade from 1970 to 2010.
The university introduces Bachelor of Engineering in Robotics and Double Major Bachelor of Engineering Science in Process Engineering and Synthetic Chemistry to address global demand for roboticists. The programs incorporate AI-related elements and are designed to provide students with a unique skill set.
Researchers at Lehigh University are using advanced algorithms and cross-domain data to help cities predict human movement patterns, enabling better planning and preparedness for events and emergencies. The model will account for variations in data streams from different sources, such as cell towers, GPS, and financial transactions.