Articles tagged with Machine Learning
Researchers at Ruhr-University Bochum used artificial intelligence to predict the structure of thin films, reducing the need for extensive experiments. The team developed a generative model that can generate images of the surface of a layer under specific process parameters, enabling the identification of optimal material formulas.
A team of Berkeley Lab cosmologists, led by George Stein and Uros Seljak, developed a code that best identified a mock signal hidden in simulated particle-collision data. Their efficient machine learning tool, called sliced iterative optimal transport, can run on a simple desktop or laptop computer.
A new AI-driven system, DeepSPM, demonstrates fully-autonomous Scanning Probe Microscopy (SPM) operation, allowing for optimal data acquisition and quality assessment without human supervision. This breakthrough enables long-term SPM operation and bridges the gap between nanoscience, automation, and artificial intelligence.
Researchers at University of Münster develop AI tool to predict reaction outcomes using molecular structures, enabling accurate predictions for yields and stereoselectivities. The model can be applied to diverse reactions and is expected to significantly change the approach to chemical syntheses.
A new computer algorithm inspired by the mammalian olfactory system rapidly learns patterns and identifies smells even with strong sensory interference. The algorithm is applied to a neuromorphic computer chip, Loihi, which can learn to identify patterns or perform tasks a thousand times faster than traditional methods.
A new study finds that machine learning algorithms can accurately diagnose mastitis origin and reduce mastitis levels on dairy farms. The technique achieved a classification accuracy of 98% for environmental vs contagious mastitis and 78% for lactation vs dry period environmental mastitis.
Professor Gregory Ditzler is developing mathematical models and algorithms to recognize patterns and identify relevant features in machine learning. His research aims to prevent security threats in autonomous vehicles and other applications.
Rice University researchers developed a cost-saving alternative to GPU acceleration called SLIDE, which uses general-purpose CPUs without specialized hardware. The algorithm outperforms traditional back-propagation training with hash tables, reducing computational overhead and enabling faster deep learning on CPUs.
Researchers use machine learning to accelerate analysis of buried interfaces and edges in materials, creating stronger, more energy-efficient materials. The technique pairs atom probe tomography with machine learning to extract composition profiles and compare them to actual ground truth.
Research finds poor connectivity between brain hubs rather than specific regions causes learning difficulties. Children with well-connected hubs have either specific cognitive difficulties or none at all, while poorly connected hubs lead to widespread and severe problems.
Researchers at USC developed personalized learning robots for children with autism, which could autonomously gauge engagement in long-term therapeutic interventions. The robots achieved 90% accuracy in detecting a child's interest in tasks.
Professor Mary-Anne Williams from the University of Technology Sydney wins a Google TensorFlow Faculty Award to develop educational content with TensorFlow 2.0 and design machine learning experiences. The award aims to support responsible AI technologies that people can understand and trust.
A new AI algorithm developed by University of Illinois researchers accurately predicts corn yield using deep learning and convolutional neural networks. The approach incorporates various topographic variables, soil electroconductivity, nitrogen treatment rates, and seed application to optimize crop management decisions.
A team at Stanford University developed a machine learning-based method that accelerates battery development for electric vehicles, reducing testing times from almost two years to 16 days. The approach optimizes the charging process, finding better protocols to test and predicting battery performance based on only a few charging cycles.
Army researchers developed a new algorithm that enables collaborative and communication-efficient deep learning, reducing the need for centralized data pooling. The algorithm decreases communication overhead by up to 70% without sacrificing performance accuracy or learning rate.
A new study uses artificial intelligence to identify groups of disease-related genes from huge amounts of gene expression data. The researchers found that the AI model discovered relevant patterns that agree well with biological mechanisms in the body, suggesting potential applications in precision medicine and individualized treatment.
NYU assistant professors Anna Choromanska, Christine Constantinople, and Daniele Panozzo have been awarded Sloan Fellowships for their innovative research in machine learning, brain science, and partial differential equations. The fellowships provide $75,000 over two years to support their research.
Researchers at Texas A&M University developed a tool to identify the source of errors caused by software updates using deep learning. The algorithm, which analyzes performance counters, can find bugs in a matter of hours instead of days.
Researchers are exploring a human-centric approach for 6G communications, emphasizing the need for secure, affordable, and accessible networks that protect users' mental and physical health. The technology will also require innovative solutions such as decentralized blockchain networks and artificial intelligence to enhance performance.
Researchers found that novelty activates dopamine neurons, promoting associative learning in animals and humans. This discovery has implications for improving learning strategies and designing more efficient machine learning algorithms.
A study published in Perspectives on Behavior Science found that AI models can accurately interpret behavioral data, outperforming a popular visual-aid tool. This could lead to better decision-making and tailored interventions for individuals with developmental disabilities, mental health issues or learning difficulties.
A research team from HKU developed a novel deep learning approach to predict disease-associated mutations in metal-binding sites. The approach uses spatial features and physicochemical sequential features to train a model, achieving an AUC of 0.90 and accuracy of 0.82.
A new study by Oxford University Press USA reveals that machine learning can predict long-term risks of heart attack and cardiac death. The research used machine learning to assess cardiovascular risk factors in subjects, aligning accurately with actual events over a 15-year period.
Researchers harness cyber security techniques to give control to those targeted, without resorting to censorship. Algorithms can identify potential hate speech and provide a score for its likelihood, allowing users to view or delete unseen content.
The study found that Norwegian, Swedish, and Danish languages use topicalization to move sentences elements to the front, making them stand out from other languages. This feature allows speakers to emphasize certain words without changing the overall meaning of the sentence.
Researchers are developing a framework to assess the 'intelligence' of AI systems by grading them on problem-solving skills and adaptability. The AIQ test will evaluate systems based on accuracy, time taken, and data requirements.
Researchers have developed a new approach called MACH, which reduces the training resources required for large-scale machine learning models. By dividing data into smaller buckets and using compressed sensing, the system can process 70 million queries and 49 million products in minutes, compared to hours or days with traditional methods.
The article highlights the need for regulators to prioritize continuous monitoring and risk assessment in managing AI/ML-based medical technology. The authors suggest that less emphasis should be placed on planning for future algorithm changes, and instead focus on developing new processes to identify and manage associated risks.
Researchers created an artificial intelligence tool to identify neutrophils primed for NETosis, a process where white blood cells expel inflammatory DNA into circulation. The new technology allows scientists to measure NETosis in different diseases and test drugs that may inhibit or promote the process.
Scientists at the University of Tsukuba created an AI program called MC-SleepNet to automatically classify mouse sleep stages, achieving 96.6% accuracy and high robustness against noise in biological signals. This system can significantly assist researchers by automating data annotation, accelerating research on sleep patterns.
Researchers employ neural networks to predict molecular bond energies, reducing computational cost and improving accuracy. The combination of AI and quantum chemistry calculations provides an efficient tool for quickly predicting molecular bond energies in complex systems.
Argonne researchers used a machine learning algorithm to relate known molecular structures to larger data sets, reducing computational costs while maintaining precision. The approach improved the accuracy of predictions about battery electrolyte candidates, enabling scientists to identify potential materials for next-generation batteries.
Researchers at MIT developed a model that learns a compact state representation for soft robots, optimizing movement control and material design parameters. This enables 2D and 3D soft robots to complete tasks quickly and accurately in simulations.
Researchers have developed a deep machine learning algorithm that can predict the quantum states of molecules, enabling faster design of drug molecules and new materials. The algorithm can process complex quantum chemical data in seconds on a laptop or mobile phone, revolutionizing computational chemistry and molecular physics.
Researchers studied mouse brain activity while learning tasks, finding neural networks become more focused and selective over time. The team developed computational models to inform decision-making neuroscience, revealing the role of inhibitory neurons in cognition.
Researchers used physics-informed generative adversarial networks (GANs) to model subsurface flow in the Hanford Site, achieving exaflop performance. The approach enabled estimation of hydraulic conductivity and hydraulic head with high accuracy, overcoming the limitations of traditional methods.
A machine learning model identified patients at risk of requiring kidney replacement therapy, leading to a significant improvement in dialysis initiation rates. The system calculates weekly risk scores and alerts clinicians to optimize treatment decisions, resulting in better patient outcomes.
A novel attention-based deep learning method automatically learns clinically important regions on whole-slide images to classify them. The new approach outperformed the current state-of-the-art approach that requires detailed annotations for its training.
An international team of researchers has been awarded a $10 million European Research Council Synergy Grant to develop machine learning algorithms for enhancing Earth observation datasets. They will also develop machine-learning-based parametrizations for clouds and land-surface processes to improve climate modeling.
Researchers develop LOGAN, a deep neural network that can transform shapes between unpaired domains, enabling automatic translation of objects like chairs to tables. The method learns unique features and preserves key characteristics during transformations.
The company has proposed a new family of prior distributions: TRIP, which improves Fréchet Inception Distance for GANs and Evidence Lower Bound for VAEs. The model was experimentally validated in cells and animals, demonstrating its potential for accelerating drug discovery.
Researchers at Princeton University explore adversarial tactics applied to artificial intelligence, which can trick systems into causing gridlock or revealing sensitive information. Machine learning systems are vulnerable to data poisoning and evasion attacks, which can compromise their performance and safety.
A new algorithm combines the capabilities of two spacecraft instruments, enabling lower-cost and higher-efficiency space missions. The virtual super instrument uses deep learning to analyze ultraviolet images and synthesize useful scientific data.
Researchers at Mainz University explore fundamental aspects of artificial intelligence using machine learning techniques and interdisciplinary approaches combining physics, biology, and materials sciences. The goal is to understand why modern systems are successful and develop better machine learning methods.
Researchers developed a machine learning method to enhance optoacoustic imaging quality without sacrificing it. The approach uses sparse data, allowing for reduced sensor numbers and improved diagnosis accuracy, facilitating clinical decision-making.
Researchers are developing a form of cybersecurity inspired by human biological systems, detecting and addressing threats in their earliest stages. The team is also offering training and research opportunities to students from underrepresented backgrounds.
A recent systematic review and meta-analysis suggests that artificial intelligence can detect diseases from medical imaging with similar accuracy to health-care professionals. However, the true power of AI remains uncertain due to limited high-quality studies, and researchers call for higher standards of research and reporting.
ORNL's labwide AI Initiative applies machine learning and deep learning to tackle complex problems in materials science, disease diagnosis, and cybersecurity. The lab's powerful computing resources and expertise enable researchers to develop new technologies and extract insights from massive datasets.
Automated machine learning techniques improve efficiency and accuracy in analyzing heart function on cardiac MRI scans. The study, conducted in the UK, found that AI can analyze a scan in approximately four seconds with similar precision to experts.
A team of researchers, led by Hagit Shatkay, is developing computational methods to accelerate discovery in astroparticle physics, a crucial step towards understanding dark matter. By analyzing noisy sensor data from an underground experiment, the team aims to detect and identify dark-matter particles.
A team of scientists from Skoltech and Moscow Institute of Physics and Technology studied the movements of 19 esports players, including professionals and amateurs. The results show that machine learning methods can accurately predict a player's skill level in 77% of cases, with professional players moving more than beginners.
Researchers use deep neural networks to simulate light-induced molecular reactions on long time scales, accelerating computation by up to 19 years. This method enables better understanding of biological processes like carcinogenesis and ageing, with potential applications in material ageing and photosensitive drugs.
Researchers from Skoltech found that professional players move around more often and intensely than amateurs, while sitting still during game events. Machine learning methods correctly predicted a player's skill level in 77% of cases.
Danish researchers at Aarhus University are developing an AI system to detect market manipulation and fraud in global stock exchanges. The project, called DISPA, aims to replace manual sampling with automated analysis of trading activity.
Researchers use TDA to inject knowledge of real world into neural networks, reducing training time and increasing intelligibility. This approach enables machines to focus on meaningful features and improve performance in tasks like face recognition.
Researchers developed a deep learning model that extracts patterns from gene locations and functions to identify disease associations. The KAUST model achieves better accuracy than state-of-the-art methods by combining multiple datasets and incorporating graph convolutional networks.
Researchers trained an AI model using human-generated clickbait data, resulting in improved performance compared to other systems. The study found differences in headline creation between humans and machines, highlighting the need for high-quality training data to improve machine learning models.
Researchers developed a neural network model using machine learning to predict Universe structure formation. The new model is more accurate than existing analytic methods and efficient enough for large-scale simulations.
Researchers believe that studying animal brains can improve AI's ability to tackle complex tasks like dish-washing. By understanding how biological neural networks work, AI systems may be able to overcome barriers and achieve superhuman performance.
A team of scientists at Bar-Ilan University has developed a new type of ultrafast artificial intelligence algorithm based on the slow dynamics of brain function. This breakthrough outperforms traditional machine learning algorithms in various fields.