Articles tagged with Machine Learning
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.
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 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.
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 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.
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.
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 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 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 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 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 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.
A new study from McGill University uses machine learning-guided virtual reality simulators to accurately assess the capabilities of neurosurgeons. The researchers found that these AI-powered tools can predict the level of expertise with 90% accuracy, enabling more efficient and effective mentorship.
The KDD Cup 2019 competition featured three tracks tackling societal challenges such as transportation and malaria. Teams won prizes of up to $15,000 by applying machine learning tools to complex problems.
Researchers at Cincinnati Children's Hospital Medical Center designed an AI-powered system to streamline clinical trial recruitment. The Automated Clinical Trial Eligibility Screener (ACTES) reduces patient screening time by 34 percent and improves enrollment by 11.1 percent compared to manual screening.
Researchers used machine learning to design novel polymers with superior heat transfer properties. The method achieved outstanding prediction performance even with limited data sets, leading to the identification of promising 'virtual' polymers.
A team of UCI researchers developed a deep reinforcement learning algorithm called DeepCubeA, which can solve the Rubik's Cube in under 20 moves, outperforming human solvers. The algorithm works on other combinatorial games and demonstrates symbolic, mathematical, and abstract thinking capabilities.
Researchers developed Deep-CEE, a deep learning technique to speed up finding galaxy clusters. The novel approach uses AI models trained on images to identify galaxy clusters, replacing manual analysis by astronomer George Abell.
A new machine learning approach enables researchers to encode quantum mechanical laws into neural nets, simulating molecular motion billions of times faster than conventional methods. This breakthrough advances research in fields like drug development, protein simulations, and reactive chemistry.
Artificial intelligence is being incorporated into a first-year mass communications class at Lehigh University, equipping students with skills to adapt and shape AI. The new approach aims to prepare students to report on AI's impact on journalism and help shape its future.
A Rutgers University study uses artificial intelligence to control a robotic arm that efficiently packs boxes, saving businesses time and money. The system develops software and algorithms for robust motion and real-time monitoring to detect failures.
A new AI model, D3M, generates complex 3D simulations of the universe in milliseconds, achieving accuracy comparable to high-accuracy models. The breakthrough enables researchers to explore various cosmic scenarios without sacrificing accuracy.
Dartmouth researchers developed an algorithm to measure brain activity patterns and assess conceptual understanding in students. The method produced neural scores that significantly predicted individual differences in performance on concept knowledge tests, highlighting the brain's role in processing complex information.
A study by Tokyo Institute of Technology researchers explores the connection between biological evolutionary open-endedness and recent studies in machine learning. They propose combining neural networks with artificial life ideas to create autonomous systems that invent or discover new things.
Three young scientists, Murad Mamedov, et al., receive $150,000 for their novel methods in understanding the human immune system. The Michelson Prizes recognize groundbreaking research using genomics, AI, and machine learning to transform human health.
A machine learning platform called AirSurf-Lettuce uses computer vision and deep learning to categorize lettuce crops in fields, measuring quantity, size, and location. This technology can help reduce yield loss up to 30% by providing precise harvest times and improving crop management decisions.
A new machine learning approach for low-dose CT imaging has been shown to perform as well as, or better than, traditional iterative techniques in an overwhelming majority of cases. The method allows radiologists to fine-tune images according to clinical requirements, enabling faster and more accurate scans.
Nanoengineers developed new graph network-based models that accurately predict material properties, outperforming existing AI technology in complex tasks. The MEGNet models can learn relationships between elements and overcome data limitations in materials science, enabling rapid discovery of transformative materials.
Researchers investigating the effects of Internet-based learning on university students, finding mixed results in acquiring domain-specific knowledge and difficulties with critical thinking. The study also highlights the role of algorithms in shaping online learning environments.
Scientists from the University of Bristol and ETH Zurich have developed an interactive VR software framework that enables humans to train machine-learning algorithms using 'on-the-fly' quantum mechanics calculations. This allows for high-quality training data generation, improving machine learning models and accelerating scientific dis...
A multi-institution research team, led by Worcester Polytechnic Institute's Eric Young, is developing a biosecurity tool to identify genetically engineered organisms in the environment. The tool uses unique DNA signatures to distinguish between engineered and naturally occurring microorganisms.
A new framework called Learn to Grow has been developed to enable artificial intelligence systems to better learn new tasks while forgetting less of what they have learned regarding previous tasks. This framework allows AI systems to retain previous skills and apply them to new tasks, improving performance and efficiency.
Researchers at Osaka University developed an automatic diagnosis system using deep learning and MEG, achieving high accuracy in classifying patients with neurological diseases. The system outperformed conventional methods using waveforms, offering a promising approach for clinical practice.
Researchers have developed a new framework that enables deep neural networks to learn new tasks while minimizing the loss of previously learned information. The Learn to Grow framework demonstrates improved performance in both new and old tasks, with backward transfer occurring when learning a new task enhances previous task accuracy.
Researchers at the University of Texas at Austin developed an AI agent that can gather visual information and reconstruct a full 360-degree image of its surroundings. The agent uses deep learning to choose the most informative shots, similar to how humans would take pictures in different directions based on prior experience.
A study by the European Society of Cardiology found that machine learning algorithms can accurately predict heart attacks and deaths with over 90% accuracy. By analyzing 85 variables from imaging data, the algorithm identified patterns correlating to death and heart attack, surpassing human performance.
A team of researchers at Virginia Tech has created a new system to efficiently distribute data processing tasks across thousands of servers in supercomputers, achieving balanced loads and improved performance. The novel technique uses machine learning to predict task types and amounts, allowing for optimized load balancing.
Researchers have developed a brain-machine interface that can generate synthetic speech by controlling a virtual vocal tract based on brain activity. The technology has the potential to restore fluent communication in individuals with severe speech disabilities, including those with paralysis and neurological diseases.
The US Department of Energy has announced $20 million in funding for artificial intelligence research, with a focus on improving grid operation and management. The projects aim to develop faster grid analytics, better asset management, and sub-second automatic control actions to reduce costs and avoid grid outages.
A workshop published a roadmap for AI in medical imaging, highlighting key research themes and prioritizing foundational machine learning research. The report emphasizes the need for collaboration among professionals, funding agencies, and institutions to develop innovative imaging technologies.
Dr. Blake Richards has made significant contributions to mathematical models of learning and memory in the brain, providing insights into the neurobiological basis of animal and human intelligence. His work explores the neural basis of deep learning and its potential to revolutionize our understanding of the brain.
Researchers at Virginia Tech use drones and AI to complement human searchers, analyzing historical data from over 50,000 lost person scenarios. The system aims to provide large-scale data for better decision-making, addressing niche problems in the search process.
A machine learning model can reproduce the swarming behavior of locusts by integrating methods from philosophical action theory and quantum optics. The 'Projective Simulation' learning model was successfully applied to a locust's specific swarming behavior, demonstrating its potential for realistic application to biological systems.
Artificial intelligence can amplify human capabilities, reducing systemic glitches and errors in medical decision-making. Machine learning models can analyze vast amounts of data to identify patterns predictive of outcomes and help diagnose diseases.
Researchers developed a system to automatically identify and classify violin bow gestures, providing real-time feedback for students. The system achieved 94% accuracy in identifying bowing techniques, enabling practical learning scenarios for music education.