Articles tagged with Machine Learning
A team of healthcare data scientists and doctors developed an AI system using machine learning algorithms to predict the risk of early death due to chronic disease. The system performed better than current standard approaches and showed promise in improving preventative healthcare.
Researchers found that children and teens learn about 2 bits per minute to acquire linguistic knowledge, filling a 1.5 MB floppy disk. This challenges assumptions that human language acquisition happens effortlessly.
Researchers used machine learning techniques on MRI brain scans to predict cognitive development in children aged 2. The study found that white matter connections at birth are highly predictive of future cognitive outcomes.
AI systems often employ 'Clever Hans' strategies that are not meaningful from a human perspective, but still achieve success. Researchers have developed explainable AI technology to identify these flawed strategies and identify more intelligent problem-solving approaches.
A new study from Carnegie Mellon University reveals that learning scientific information results in changes in the actual structure of memory-related areas of the brain. The researchers found evidence of microstructural, informational, and network change in the left hippocampus during the learning of organic compounds.
A recent NSF CAREER Award recipient aims to develop trustworthy machine learning from untrusted models, with a focus on safeguarding complex systems that piggyback on less-than-trustworthy technology. The grant will support work in creating tools to verify model safety and detect abnormal phenomena throughout the system's lifecycle.
Researchers develop AI algorithm to optimize traffic management in optical telecommunications networks, increasing efficiency by 30%. The new approach uses deep reinforcement learning and can learn autonomously without prior knowledge.
A study by German scientists uses AI to enhance climate and Earth system models, improving predictions for extreme events and seasonal changes. By combining physical modeling with machine learning techniques, researchers aim to create more accurate models that capture complex dynamic processes.
The Rhine-Main Universities Initiative Funding for Research supports the DeCoDeML network, combining expertise in deep continuous-discrete machine learning to tackle unresolved issues. Researchers from Mainz, Darmstadt, and Frankfurt will examine how machine learning can be made comprehensible to human understanding.
Researchers found that machine learning can identify customer needs from user-generated content (UGC) more accurately and efficiently than traditional methods. The approach uses machine learning to analyze UGC data and remove redundancies, followed by human analysis to formulate customer needs.
Game behavior can be analyzed to predict a person's personality features, including gender. Researchers used machine learning on large amounts of game data from the Steam gaming platform to make accurate predictions. The study shows that even limited information about gameplay and achievements can provide good predictive values.
A new machine learning methodology was developed by researchers at NCCR MARVEL to capture chemical intuition from partially failed trials. This approach helps chemists improve their synthesis conditions and create novel materials with higher surface areas.
Columbia engineers create a robot that learns what it is from scratch with zero prior knowledge of physics or motor dynamics. The robot uses deep learning to create a self-model, allowing it to adapt and learn from its own experiences.
The MIT robot uses a soft-pronged gripper, force-sensing wrist cuff, and external camera to see and feel the tower and its individual blocks. It learns from visual and tactile feedback, and adapts its moves in real-time to avoid toppling the tower.
A study published in PLOS ONE suggests that machine learning can provide an equally accurate and reliable prognosis for patients with cardiovascular disease, compared to traditional methods. The use of genetic programming reduces bias and human error, allowing complex associations to be made transparent to clinicians.
Researchers challenged a popular idea about how machine learning algorithms think by applying information theory to classification problems. They found that classifiers with many layers do not necessarily trade off between prediction and compression as previously thought.
A recent study from the University of Waterloo found that measuring AI's ability to learn is challenging due to the complexity of tasks. The researchers discovered that no mathematical method can determine whether an AI-based tool can handle a task or not, even with precise task descriptions.
A new study combines two forecasting methods with machine learning to estimate local flu activity, producing the most accurate estimates available, a week ahead of traditional healthcare reports. The approach, called ARGONet, outperforms earlier methods in more than 75% of states studied.
Researchers used artificial neural networks to learn atomic interactions from quantum mechanics, bypassing complex calculations. The ANN was used as a surrogate model to account for errors and improve predictions.
Researchers at Argonne National Laboratory are using machine learning algorithms to optimize engine simulations, significantly reducing design time and increasing accuracy. The project aims to create a more efficient and emissions-free combustion process, with potential applications in the automotive industry.
A research team led by Heng Huang aims to create a framework for secure and efficient multi-site collaborative big brain data mining. The project addresses computational challenges in analyzing complex brain disorders and genomics data.
Jakoah Brgoch aims to improve energy efficiency in LED lighting by developing new materials and algorithms. He will use machine learning to identify new phosphors and predict material behavior under temperature changes.
Researchers at PNNL developed a deep learning model that surpasses current cloud detection algorithms by nearly doubling precision and reducing processing time. The model's performance is promising for global forecasting applications.
Researchers developed a novel classification method combining brain imaging data from MRI and fMRI to recognize patients with benign epilepsy with centrotemporal spikes. This approach improved diagnosis accuracy, enabling early detection and treatment, which leads to better health outcomes for children. The study used a dataset of 40 B...
Japanese researchers have developed a technique for automatic anime colorization using deep learning, which is based on recent advances in machine learning approaches. The technique uses a combination of image segmentation and voting techniques to refine the colorization result.
Researchers developed a new machine learning method that allows AI to make classifications without negative data, a crucial component in traditional classification technology. This breakthrough enables AI systems to function effectively even when limited by data regulation or business constraints.
A new study from The George Institute for Global Health at the University of Oxford found that machine learning models can accurately predict the risk of emergency hospital admissions. By analyzing electronic health records, these models identified factors such as age, sex, and socioeconomic status to provide a more robust prediction t...
A team of computer scientists developed a novel deep learning-based approach to automatically generate realistic caricatures from portraits. Their method, called CariGANs, successfully generated clearer and more accurate caricature depictions that resemble hand-drawn artwork by professionals.
AI systems have a 'near unlimited capacity' for data processing and learning, potentially making diagnoses and surgical interventions more accurate. However, doctors will remain essential for holistic patient care, addressing individual preferences and social circumstances.
Researchers have discovered three types of attacks that can be launched using a computer's graphics processing unit (GPU) to spy on web activity, steal passwords, and break into cloud-based applications. The attacks work by exploiting vulnerabilities in the GPU's memory utilization and performance counters.
Researchers developed a new approach to interpret machine learning algorithms, revealing how they can produce nonsensical answers even when given meaningful inputs. By reducing inputs to the bare minimum required for correct answers, the method provides insights into algorithmic limitations and potential solutions.
Researchers at NASA's Goddard Space Flight Center are developing machine learning algorithms to analyze petabytes of data, extract new patterns and correlations, and improve science discoveries. The technology also helps monitor spacecraft health, prevent catastrophic failures, and assist engineers in designing new missions.
Researchers from Florida Atlantic University developed a machine learning model that detects Medicare fraud with high accuracy. By analyzing large datasets from Medicare Part B, they identified a unique process to map fraudulent providers, achieving a statistically significant detection rate.
A neural network mimics the fruit fly's visual system and distinguishes between individuals based on sight alone. The system achieved an F1 score of 0.75, surpassing human biologists' performance in a similar task.
Researchers will work together to develop tools and methods to manage machine learning risks, including methods to defend against adversarial inputs and develop robust training methods. The center aims to foster a long-term research community in trustworthy machine learning.
Researchers are using real-time learning to create a unified theory on how to optimize microgrid capacity through DC distribution. The team aims to create optimal power profiles for power electronic converters in DC distribution networks.
A proposed taxonomy categorizes conscience protections based on state or private actors, as well as provider conduct. Bioethicists must shape AI and machine learning to prevent moral pitfalls, including exacerbating human biases and weakening patient confidentiality.
The University of Pittsburgh has received a $1.2 million NSF grant to analyze electronic anesthesia records and prevent postoperative complications and death using machine learning and Big Data analysis. Dr. Heng Huang will develop a new deep learning algorithm to predict surgical outcomes based on historical patient data.
A novel machine learning framework developed by Virginia Tech researchers accelerates the discovery of new materials through computer simulations. The framework, which trains on the fly, enables faster development of accurate computational models of materials with potential biomedicine and energy applications.
The special issue brings together contributions from top academics and industry experts, highlighting the use of generative chemistry and GANs for de-novo molecular design. Experimental validation of generated molecules demonstrates high activity and selectivity in a novel inhibitor of Janus Kinase 3.
A study by Cameron Buckner using deep neural networks suggests that human knowledge stems from sensory experience, a school of thought known as empiricism. The networks demonstrate how abstract knowledge is acquired and can be used to understand complex tasks in neuroscience and psychology.
Researchers from MIT CSAIL developed a system that uses machine learning to determine if a news source is accurate or biased. The system achieved 65% accuracy in detecting factuality levels and 70% accuracy in detecting bias, using common linguistic features across the source's stories.
A study using machine learning and data from 550 children with varying learning difficulties identified four clusters of cognitive challenges that did not match previous diagnoses. The researchers found that working memory and phonological skills deficits often overlap, affecting both reading and math abilities.
New research reveals infants can acquire object categories using just a few labeled examples, sparking the process of categorization. This 'semi-supervised learning' strategy efficiently integrates all subsequent objects into their evolving category representation.
Scientists from the Salk Institute and UC San Diego use reinforcement learning to train gliders to navigate atmospheric thermals, reaching heights of 700 meters. The research highlights the role of vertical wind accelerations and roll-wise torques as navigational cues for soaring birds.
Researchers from UC San Diego use reinforcement learning to train gliders to navigate atmospheric thermals, achieving heights of 700 meters. The study highlights vertical wind accelerations and roll-wise torques as key cues for soaring birds, with implications for autonomous flying vehicle development.
The MIT-developed AI model can associate specific words with specific patches of pixels in an image, enabling real-time object highlighting based on spoken descriptions. This innovation holds promise for applications such as language translation and automatic image annotation.
A new AI program can analyze lung tumor images to specify cancer types and identify genetic changes, offering a potential solution for faster diagnosis and treatment. The tool achieved high accuracy rates in distinguishing between adenocarcinoma and squamous cell carcinoma, as well as identifying altered genes linked to lung cancer.
Researchers from UPNA won a second-place award at a scientific meeting for their work on modifying the machine learning process with Choquet integral, improving deep neural network performance in classification problems. The collaboration involved researchers from Brazil and institutions in Navarre.
Using deep learning algorithms, researchers have developed a system that forecasts aftershocks significantly better than random assignment. By analyzing earthquake data and physics-based models, they identified the second invariant of the deviatoric stress tensor as an important factor in predicting aftershock locations.
Researchers developed an AI system to detect tiny lung cancer tumors in CT scans, achieving 95% accuracy compared to human eyes at 65%. The system uses a connection-based approach similar to facial recognition software to identify patterns.
Xiaochen Guo, a Lehigh University professor, aims to improve data movement efficiency by revamping memory systems. Her goal is to proactively create and redefine locality in hardware, unlocking fundamental improvements for machine learning applications.
Researchers trained a machine learning algorithm to analyze microscopic radiation damage, achieving an accuracy of 86% compared to humans. The algorithm can process images faster and more efficiently than humans, making it a promising tool for developing safe nuclear materials.
Scientists developed a new machine learning method that can make accurate predictions for all possible conditions governed by the same physical dynamics. The method provides simple and interpretable descriptions of underlying physics, allowing for safer robot operation.
Researchers at the University of Alberta developed a machine-learning algorithm to analyze brain function images and predict schizophrenia diagnosis with 78% accuracy. The model also identified patients who would respond positively to antipsychotic treatment risperidone with 82% accuracy.
Researchers developed a technique to quickly teach robots novel traversal behaviors with minimal human oversight, enabling autonomous navigation in environments. The goal is to provide reliable robot teammates to the Soldier, allowing for faster task completion and enhanced situational awareness.
The researchers developed a novel synaptic architecture that could lead to a new class of information processing systems inspired by the brain. Prototype chips containing over one million nanoscale memristive devices were used to implement a neural network for detecting hidden patterns and correlations in time-varying signals.
A new approach uses machine learning to generate computer-generated X-rays to supplement real images, increasing the size of training sets for AI systems. This method improves classification accuracy for common and rare conditions by up to 40%, overcoming a challenge in applying artificial intelligence to medicine.
Researchers developed a deep-learning model to predict biological age of muscles and estimate the importance of genetic and epigenetic factors driving muscle aging. The study identified tissue-specific biomarkers of aging, which can be used to track the effectiveness of interventions.
A new type of personalized machine learning helps robots accurately assess children's engagement and interest during autism therapy, with a correlation score of 60% compared to human experts. This technology aims to augment human therapists with key information to personalize therapy content and create more engaging interactions.