Articles tagged with Machine Learning
Three researchers at Argonne National Laboratory, Prasanna Balaprakash, Karen Mulfort, and Zhang Jiang, have earned the DOE's Early Career Research Program awards. They will receive funding to advance their research in machine learning, molecular interactions, and advanced materials imaging.
PlinyCompute is a system designed for developing high-performance big data codes, offering faster implementation of complex object manipulation and library-style computations compared to Spark. The platform was developed by Rice University's DARPA-funded Pliny Project team, which aims to create sophisticated programming tools using mac...
Scientists developed a machine learning approach to predict microbial pathways, allowing for faster design and development of biofuels. The method accurately predicted biofuel production profiles, outperforming traditional kinetic models.
Researchers at the University of Sydney have developed a generalized method to predict epileptic seizures using data from non-surgical devices powered by AI and machine learning. The system can alert epilepsy sufferers within 30 minutes of the likelihood of a seizure, with an accuracy rate of up to 81.4%.
Researchers have developed an automated atom fabrication process using machine learning, paving the way for mass production of atom-scale devices. This breakthrough aims to reduce energy consumption by 1000 times and increase computation speed a hundredfold, making it a game-changing technology for the information age.
Researchers developed a brain-computer interface that uses mutual learning to improve control for tetraplegic individuals. The system allowed users to adapt and learn at their own pace, leading to significant improvements in performance.
Researchers at University of Lincoln develop machine learning algorithms for self-learning robots in hazardous nuclear sites, increasing capabilities in waste handling and site monitoring. The project aims to build systems that can adapt to unique radiation conditions using vision-guided robot grasping, manipulation, and cutting.
Researchers at Johns Hopkins Medicine have made significant discoveries about the cerebellum's role in learning and prediction. By studying monkey brains, they found that Purkinje cells communicate through simple spikes (predictions) and complex spikes (error feedback), organizing into small groups to learn together.
A new study from Columbia University uses an AI algorithm to analyze the full range of behaviors exhibited by the tiny pond-dwelling creature Hydra. By comparing its movements to neural firing patterns, researchers hope to gain insights into how its nervous system works.
Researchers at the U.S. Army Research Laboratory developed a new technology that allows soldiers to learn 13 times faster than conventional methods, potentially saving lives. This technique uses low-cost hardware and collaborative filtering to achieve a significant speedup in training compared to state-of-the-art systems.
Researchers at UCSB are developing a chip that uses ionic memristor technology to create a physically unclonable device, rendering it vulnerable to cyber attacks. The technology aims to prevent cloning and hijacking of devices in networks, making them ideal for securing IoT devices.
Researchers used a deep learning algorithm to classify real galaxies in Hubble images, achieving remarkable consistency in its classifications. The study found that the algorithm identified a specific mass range for the 'blue nugget' phase of galaxy evolution, which is followed by quenching of star formation in the central region.
Researchers will use naturalistic approach with video vignettes and head-mounted cameras to measure infant eye direction and analyze their visual attention. The study aims to understand how young learners generate data for optimal language learning.
The University of Surrey has developed a new algorithm that can compress large amounts of data from bridge monitoring systems, reducing the storage requirements for authorities. The K-SVD method achieves near-lossless reconstruction with less than 0.1% data loss, compared to other methods which require up to 50% of the original data.
A team of scientists has developed a method to discover new metallic glass alternatives using machine learning and accelerated experiments, reducing the discovery time from decades to hours. The approach enables researchers to quickly narrow down potential materials and get immediate feedback from AI models.
A team of scientists has developed a machine learning algorithm that can quickly identify new blends of ingredients for metallic glass, accelerating the discovery process by 200 times. The method uses data from thousands of experiments to pinpoint potential materials and has significant implications for the future of materials science.
A groundbreaking study by Gladstone and Google AI uses deep learning to analyze cell images, identifying features that humans can't detect. The method uncovers important information that was previously impossible or problematic for scientists to obtain.
A team of scientists, including those from Google, developed a computer program that can identify structures in unstained brain cells. The program learned to spot features such as cell nuclei, dead cells, and specific types of brain cells by analyzing stained images.
Researchers used machine learning to classify abnormal protein activity in tumors, identifying potential 'hidden responders' who may benefit from specific therapies. The study combined genetic data with machine learning approaches to predict response to inhibitors affecting cancer cells with overactive Ras signaling.
A bioinformatics professor and his colleague used GitHub as their writing platform to collaborate on a paper about deep learning in biology and medicine. The paper has been massively rewritten and revised by online collaborators, resulting in over 40 co-authors.
Scientists at Bar-Ilan University discovered that learning in the brain occurs in dendrites, not just synapses. This new finding suggests a faster and more efficient learning process, with implications for current treatments and machine learning algorithms.
Researchers at Massachusetts General Hospital developed an artificial intelligence technique, AUTOMAP, that enables the production of high-quality images in less time and with lower doses. This approach uses deep learning to automatically determine the correct image reconstruction algorithm, allowing for instant feedback during scanning.
The UTSA researchers have created a cloud-based learning platform for artificial intelligence that aims to teach machines to learn like humans. This platform can help AI agents automatically detect threats in network traffic and improve their performance in discovering and thwarting new attacks.
Researchers developed a visible neural network, DCell, that uses real-world cellular behaviors and constraints to predict cellular growth. The system can simulate cellular growth nearly as accurately as a real cell grown in a laboratory.
Machine learning techniques can reconstruct a quantum system based on relatively few experimental measurements, allowing scientists to thoroughly probe complex systems exponentially faster than conventional methods. This method benefits the development of quantum computers and other applications of quantum mechanics.
Researchers developed an AI-based computational tool to screen patients for common retinal diseases, achieving over 95% accuracy in diagnosis. The tool also showed promise in diagnosing childhood pneumonia with over 90% accuracy, highlighting its potential applications in healthcare.
Computer scientists and materials researchers developed a more accurate and objective method for classifying steel microstructures. The method uses machine learning to analyze microscope images and achieve accuracy of around 93%, surpassing conventional methods which only achieved 50% correct classification.
Researchers create a new approach to machine learning using a single-layer neural network that can analyze images with limited training data. The algorithm, called MS-D, requires far fewer parameters than traditional methods and has the ability to learn from a remarkably small set of images.
Researchers at Princeton University have developed a software tool using machine learning to predict reaction yields, reducing time and cost for synthesizing new medicines. The tool, which can handle up to four reaction components, uses random forest models to accurately forecast yields for thousands of reactions.
Researchers at U.S. Army Research Laboratory and University of Texas at Austin developed a new algorithm called Deep TAMER to train robots using human feedback. The algorithm enables robots to learn tasks in a short amount of time with accurate critique, improving performance on complex tasks like Atari Bowling.
Researchers trained machine learning models on radiologist reports to identify clinical concepts in CT scans, achieving an accuracy of 91%. This technology will help develop artificial intelligence to diagnose diseases and improve patient care.
Researchers used machine learning to decode brain activity during a simple task of distinguishing between BA and DA syllables. The results show that the brain uses specific regions for mental associations related to the task, not just for processing information.
Researchers trained neural networks on thousands of images from simulated high-energy particle collisions to identify key features. The networks achieved up to a 95% success rate in this analysis. Machine learning algorithms will next be applied to actual experimental data to further advance our understanding of the universe's mysteries.
Researchers from Innsbruck and Vienna teams used artificial intelligence to design new quantum experiments, leveraging a projective simulation model and reinforcement learning. The AI-agent performed tens of thousands of experiments, discovering novel structures that could be tested in the lab.
A new algorithm developed by Kirk Bansak et al. uses machine learning and optimal matching to align refugees with suitable employment locations based on their individual skillsets. The results show that the algorithm boosts employment success in both the US and Switzerland, with gains of up to 71% compared to current practices.
A new method of securely communicating between multiple quantum devices has been developed, enabling a large-scale, un-hackable quantum network. The approach uses quantum laws to ensure security and can work for any device, regardless of manufacturer, bridging the gap between theory and practical implementation.
Researchers have developed a machine learning model to predict geothermal heat flux beneath the Greenland Ice Sheet, revealing an anomalously high heat flux in northern regions. The study uses 22 geologic variables to improve ice-mass loss and global sea-level rise predictions.
Researchers developed codes MENNDL and RAVENNA to efficiently design and train neural networks, generating and training up to 18,600 networks simultaneously. This enables the training of highly accurate networks in a fraction of the time, with applications in self-driving cars, intelligent robots, and scientific experiments.
Researchers at Osaka University developed a technique called lexical acquisition through implicit confirmation to enable AI to learn words in the flow of dialogue. This method allows computers to acquire knowledge about unknown words during conversations with humans, making them smarter and more responsive.
Researchers at IIT-Istituto Italiano di Tecnologia focus on iCub's evolution from its origin to date, showcasing hardware and software co-evolution. The robot's current version enables crawling, sitting, balancing, and recognizing objects, with a sensitive full-body electronic skin system.
Researchers at University of Helsinki develop a new privacy-aware machine learning method that enables accurate modeling using private user device data. This method ensures limited information on each data subject is revealed, making it ideal for protecting sensitive health and human behavior data.
The Conversational Intelligence Challenge Finals saw six teams compete, with two winning teams developing modules for processing different scenarios and a command module that decides which routine should step in. The competition aimed to work out approaches for chatbot evaluation and human-to-machine dialogue collection.
A new collaborative engineering project funded by NSF aims to make numerical computation of departure rates and route choice faster, enabling rapid rerouting and diversion. The project uses machine learning to develop statistical models of traffic flow, potentially reducing congestion by seconds, minutes or hours ahead of time.
Youth football players undergo brain changes, including alterations in the default mode network, after a single season of play. This study suggests that repeated subconcussive impacts can have a lasting effect on brain health.
Researchers at UCLA developed a deep-learning-based technique to reconstruct holograms for microscopic images, producing better results than current methods. This approach could aid in diagnosing abnormalities in medical images and improve optical microscopy for medical diagnostics.
Researchers at Numenta propose a new theory for how the brain learns models of objects through movement, pairing sensory input with location signals. The theory predicts that even the first levels of processing in the brain are learning and recognizing complete objects.
CANDLE, a scalable deep learning framework developed by Argonne's Exascale Deep Learning and Simulation Enabled Precision Medicine for Cancer project, was recognized with the award. The framework has achieved impressive results, including explaining over 92% of variance in drug response.
Researchers efficiently used Stampede2's 1024 Skylake processors to complete a 100-epoch ImageNet training with AlexNet in 11 minutes, setting the fastest time recorded to date. The Layer-Wise Adaptive Rate Scaling (LARS) algorithm enabled this breakthrough, allowing for larger-than-ever batch sizes and adaptive learning rate adjustments.
A study published in Biodiversity Data Journal uses deep learning techniques to differentiate between similar plant families with high accuracy. The researchers trained neural networks on digitized herbarium specimens, achieving up to 99% accuracy in distinguishing between two challenging species.
Researchers from Lehigh University and Columbia University have developed a new testing approach for deep learning platforms used in self-driving cars, malware-detection, and other systems. Their method, called DeepXplore, exposes thousands of unique incorrect corner-case behaviors, enabling faster identification and fixing of errors.
A new approach brings transparency to self-driving cars and other self-taught systems by automatically error-checking neural networks. Researchers found thousands of bugs missed by previous techniques, activating up to 100% of network neurons and improving accuracy up to 99%.
Researchers found that mothers alter their voice timbre when speaking to infants, regardless of native language. This consistent shift enables computers to distinguish between infant- and adult-directed speech.
Scientists developed a machine-learning method to predict molecular behavior, which can aid in developing new pharmaceuticals and enhancing emerging battery technologies. The method combines physics, chemistry, and machine learning, allowing it to simulate complex chemical behavior within molecules.
Researchers develop a quantum perovskite material that exhibits adaptive response to repeated proton insertion and removal, resembling brain's desensitization. This property enables effective programming of the material like a computer.
Carnegie Mellon University's Center for Human Rights Science has received a $100,000 grant from Open Society Foundations to explore the positive impact of emerging technologies on human rights. The grant will enable the center to catalog and share developing technologies with the broader human rights community.
Researchers have developed a web app capable of producing 3D facial reconstruction from a single 2D image. The technique, using Convolutional Neural Networks, allows for arbitrary facial poses and expressions, with over 400,000 users already trying it out.
Researchers have developed a new method to simulate infrared spectra using artificial neural networks, reducing simulation time from thousands of years to minutes. This breakthrough enables the analysis of complex chemical systems and paves the way for widespread adoption in various fields.
The study uses image data to reconstruct the cell cycle of white blood cells and the progress of diabetic retinopathy, demonstrating the method's capability in handling continuous biological processes. The software also identifies individual categories and assigns measured data to clusters when data is not part of a continuous process.
A computer science approach using machine learning predicts the time remaining before a fault fails by analyzing acoustic signals emitted during laboratory-created earthquakes. The technique identifies new signals, previously thought to be low-amplitude noise, that provide forecasting information throughout the earthquake cycle.
The Lehigh project aims to build bridges between optimization experts, learning theorists, and statisticians to advance machine learning. With a $1.5 million grant, the team will develop innovative educational pathways and state-of-the-art mathematical tools for data science, promoting long-term research and training activities in the ...