Articles tagged with Machine Learning
A team of ORNL researchers aims to use deep learning to identify patterns in scientific data that alert scientists to potential new discoveries. They plan to leverage ORNL's Titan supercomputer and develop novel high-performance computing methods.
Researchers at Disney Research developed an algorithm that can automatically recognize soccer formations and defensive strategies from player tracking data. The algorithm outperformed conventional methods in identifying dynamic player roles and coordinated team behavior, with applications beyond sports.
Researchers have developed a new algorithm that allows AI to collect error reports and correct them immediately, without affecting existing skills. This enables robots to learn from their mistakes and spread new knowledge amongst themselves.
Kolachalama's research aims to improve cardiovascular treatments by developing models for smarter artery care and improving drug-coated angioplasty balloons. The $231,000 Scientist Development grant will support his three-year study on the mechanisms of drug-coated balloon therapy.
A new AI system developed by MIT researchers reduces online video rebuffering and improves quality, adapting to different network conditions. The system achieves higher-quality streaming with less rebuffering than existing approaches.
A new study proposes that cultural activities, such as language use, affect our ability to collect data, make connections, and infer behavior. The research reveals that the brain's limited working memory can be beneficial in some cognitive tasks, unlike our closest relatives, chimpanzees.
Researchers used active machine learning to discover new conditions for synthesizing gigantic polyoxometalate molecules. The algorithm outperformed human experimenters, covering a broader range of the 'crystallization space' and discovering unexpected crystals.
A team of researchers from the University of Texas at Austin has developed novel approaches to information retrieval that leverage artificial intelligence, crowdsourcing, and supercomputing. Their method combines input from multiple annotators to determine the best overall annotation for a given text, improving accuracy in extracting d...
Researchers at the University of British Columbia have developed a breakthrough algorithm called DeepLoco that enables computer characters to learn complex motor skills like walking and running through trial and error. The system uses deep reinforcement learning to allow characters to respond to their environment without hand-coding st...
Researchers found that babies as young as 7 months can learn a second language with just one hour of play-based exposure per day. The study used an English-language method and curriculum in Madrid's public infant-education centers, showing significant improvements in English comprehension and production.
Researchers developed a new algorithm that can turn audio clips into highly-realistic videos of people speaking, using available public domain video footage. This technology has potential applications in improving video conferencing and creating realistic virtual reality experiences.
The University of Texas at San Antonio is developing an artificial neural network called NFrame to monitor and detect 'bad behavior' in computer systems. The system will learn normal behaviors and flag anomalies, allowing it to predict potential issues and prevent security breaches.
Researchers aim to develop a system that can explain AI decisions to humans, making autonomous vehicles and robots more trustworthy. The project will utilize real-time strategy games like StarCraft to train AI players that can provide natural language explanations.
Researchers at Tsinghua University outline recent advances on nonparametric Bayesian methods, regularized Bayesian inference, scalable algorithms, and system implementation to tackle the challenges of Big Data. They also discuss connections with deep learning and highlight the need for human expertise in devising appropriate features a...
Machine learning techniques are successfully applied to image-based diagnosis, disease detection, and prognosis in medical imaging. The review focuses on denoising methods using machine learning approaches to develop a systematic decision for diagnosing and prediction of medical images.
The Biogerontology Research Foundation is helping to develop artificial intelligence for accelerated drug discovery in aging and age-related diseases. Researchers have made significant progress in using deep learning-based approaches to characterize biomarkers of ageing and predict the chronological age of patients.
Researchers have developed a method that enables computers to infer psychologically plausible models of individuals from limited data, improving understanding of human behavior. This breakthrough could lead to more accurate predictions and adaptations in human-robot interaction and adaptive interfaces.
Researchers trained AI models to identify TB on chest X-rays, achieving a 96% accuracy rate. The models' performance was improved when combined with expert radiologist interpretation, increasing the diagnosis accuracy to nearly 99%.
Researchers found that machine learning programs can acquire cultural biases from online language patterns, affecting tasks like image categorization and automated translations. This study highlights the importance of identifying and addressing bias in AI systems to promote fairness and equality.
Researchers at Stanford University have created a deep learning algorithm that can accurately predict the toxicity of chemicals and associate drugs with side effects using just six data points. This breakthrough could help chemists choose promising candidates and accelerate drug development.
Researchers at UTA developed OnTask, a software tool that offers timely and personalized feedback to help students adjust their studying throughout the course. The online tool uses data about student activities and suggests strategies to increase confidence and success.
Researchers have developed a machine learning model that can predict the outcome of cellular interactions and design new cancer treatments. The Stampede supercomputer enabled the team to run billions of simulations, allowing them to identify patterns in the data and create a system capable of predicting laboratory results.
Researchers used deep learning and machine learning techniques to analyze S&P 500 data, achieving statistically significant and economically substantial returns. The findings challenge the efficient-market hypothesis and suggest AI can excel in times of high volatility.
A study of Google DeepMind's access to millions of NHS patient records reveals concerns over data privacy and regulation. The researchers argue that the original agreement lacked transparency and suffered from an inadequate legal basis, serving as a cautionary tale for public sector bodies and private tech firms.
A machine learning application powered by artificial intelligence has been developed to provide evidence-based answers to frequently asked questions in interventional radiology. The system enables real-time communication between clinicians and patients, improving the quality of care.
A new automated method based on deep learning techniques analyzes detailed game data to create models of how a typical player would behave in a given situation. This allows for the comparison of actual player behavior with predicted ghostly behavior, providing valuable insights into defensive athletic performance.
Researchers have developed software to decode digital brain data, enabling rapid progress on the ability to monitor neural activity and understand learning, memory, and cognitive functions. The collaboration has reduced processing time from days to less than a second.
A new UT Dallas study derived optimal policies and data-driven techniques for firms to learn about demand and adjust capacity. The study's main finding is that production managers need to maintain a careful balance between observing the demand and changing the capacity.
Swiss researchers use neural networks to challenge the resolution limit of telescopes, recovering features that were previously invisible. The technique, inspired by a generative adversarial network, achieves better results than previous methods, such as deconvolution, and has vast potential for future astronomical observations.
A Northwestern University and Los Alamos National Laboratory team developed a novel workflow to design new materials with useful electronic properties. By combining machine learning and density functional theory calculations, they created design guidelines for ferroelectricity and piezoelectricity.
A new project by Upside Energy and Heriot-Watt University aims to use machine learning and distributed AI to manage storage assets and provide real-time energy reserves. The goal is to relieve stress on the grid and reduce reliance on traditional power stations.
Researchers have identified nearly two-dozen solid electrolytes that could replace volatile liquids in smartphones and laptops. The AI-powered approach allows for rapid screening of materials, identifying the most promising candidates for further study.
Researchers are preparing to tackle an onslaught of petabytes of complex data from sophisticated experiments, including CERN's Large Hadron Collider. To keep up with the challenge, experts propose developing exascale supercomputers and smarter networks, as well as reengineering software to adapt to future hardware developments.
A team of researchers from the University of Toronto has developed a novel machine learning approach to determine whether planetary systems are stable or not. This method is 1,000 times faster than traditional methods and can provide valuable information about exoplanets, including their mass and orbital eccentricity.
Researchers at U of T Engineering developed an AI algorithm that learns directly from human instructions, exceeding conventional training methods by 160% and outperforming its own training by 9%. The algorithm's potential lies in applying heuristic training to fields like medicine and transportation.
Researchers developed a machine learning classifier to discover membrane-active peptides with diverse sequences. The approach identified new peptides with broad biomedical implications, including immunotherapy and anticancer therapeutics.
Researchers at Numenta compared their biologically-derived HTM sequence memory to traditional machine learning algorithms, demonstrating comparable prediction accuracy. The new paper highlights the algorithm's properties, including continuous online learning and robustness to sensor noise, making it ideal for streaming data applications.
Researchers developed a new AI system that uses web search to extract data from plain text, outperforming traditional machine learning methods by up to 10%. The system learns to generate search queries and gauge relevance, then extracts relevant data from online texts.
Researchers at UTA are developing an AI system that assesses children's executive function skills, recognizing patterns of inattention and hyperactivity. The system provides recommendations for effective intervention and monitoring progress over time.
A new data-cleaning tool called ActiveClean analyzes a user's prediction model to identify mistakes and update the model as it works. By minimizing human error, ActiveClean improves model accuracy and reduces statistical biases, making it an essential tool for building better prediction models.
Researchers have discovered a way for machines to learn about natural or artificial systems by observing them, eliminating the need for prior knowledge. This breakthrough could lead to advances in technology, including predictive human behavior and algorithm development for detecting abnormalities.
Researchers developed a machine learning system that detects differences in accelerometer data between individuals with muscle tension dysphonia and controls. The system improved after receiving voice therapy, suggesting potential for wearable devices providing real-time feedback.
Researchers developed a method to identify impoverished areas using high-resolution satellite imagery and machine learning. The approach outperformed existing methods and could be used to map poverty worldwide at low cost.
Researchers used direct neural recordings and brain stimulation to study visual word form area's role in reading. They found that this area codes knowledge about learned visual words, enabling accurate discrimination of similar words.
Researchers led by Josef van Genabith are working on two EU-funded projects to improve automatic machine translation, particularly for complex languages like Latvian and Czech. They aim to use 'deep learning' to recognize patterns in large text datasets and learn from them.
Researchers have developed an AI-powered MRI technique that can detect early forms of dementia, including mild cognitive impairment and Alzheimer's disease. The technique uses machine learning to analyze perfusion maps created by arterial spin labeling (ASL) imaging, with high accuracy in distinguishing between patients.
Computer vision systems can now learn to recognize objects they have never seen before by analyzing word use and contextualization, reducing the need for thousands of labeled images. This new learning paradigm, called semi-supervised vocabulary-informed learning, was developed by Disney Researchers using a large dataset of English words.
Automated camera system improves sports broadcasts by learning from human operators and achieving smoother footage without jerkiness. The system uses a new approach called imitation learning, which repeats multiple times and analyzes deviations to learn from human mistakes.
A team of researchers led by Sridhar Mahadevan is applying deep learning methods to analyze large amounts of scientific data from Mars. They hope to develop a practical tool for handling vast amounts of data created by various scientific instruments, including the NASA Curiosity rover.
Scientists from Insilico Medicine used deep neural networks to predict therapeutic use of large numbers of drugs from gene expression data, achieving 54.6% accuracy in class prediction. The study also found that many misclassified drugs had dual use, suggesting potential for drug repurposing.
Insilico Medicine presents research on applying deep learning to biomarker development and cosmetics applications at INNOCOS World Beauty Innovation Summit. The company's app RYNKL evaluates anti-aging interventions using machine learning methods, minimizing animal testing.
A University of Washington team developed a highly capable five-fingered robot hand that can perform dexterous manipulation and learn from its own experience. The hand uses machine learning algorithms to model physics and plan actions, allowing it to adapt to new tasks without human intervention.
Researchers explore machine learning's potential to enhance plastic surgery with algorithms for predicting burn healing times and suggesting reconstructive approaches. The field also holds promise for improving microsurgery, craniofacial surgery, hand and peripheral nerve surgery, and aesthetic surgery outcomes.
Researchers at Numenta Inc. have published a new theory on how networks of neurons in the neocortex learn sequences, providing a breakthrough in understanding neural circuits.
Researchers at Carnegie Mellon University have identified specific neural systems used to encode new scientific concepts. The study shows that the brain repurposes existing neural structures to form new knowledge, enabling humans to learn abstract ideas.
Researchers at Tel Aviv University have developed a cutting-edge solution for radiologists using Deep Learning technologies. The lab has created tools to facilitate computer-assisted diagnosis of X-rays, CTs and MRIs, freeing up time for complex cases.
A novel approach, called machine unlearning, enables the removal of data without retraining a computer learning system from scratch. This method is crucial for increasing security and protecting user privacy, especially in the face of cyber-attacks and data breaches.
Researchers developed a web-based platform using artificial neural networks to answer crossword clues more accurately than existing products. The system can understand words, phrases, and sentences by leveraging definitions in dictionaries and Wikipedia.
Carnegie Mellon University's Jing Lei and Ryan Tibshirani have been awarded NSF CAREER grants for their cutting-edge research projects in large-scale data analysis and nonparametric estimation. Their work aims to advance statistical inference with complex high-dimensional data.
Todd Gureckis, NYU associate professor, awarded PECASE for pioneering research on human cognition and machine learning. The award recognizes his innovative work in comparing human intelligence to intelligent algorithms.