Articles tagged with Machine Learning
Researchers at Cornell University used AI to investigate how reflection changes images, discovering clues like facial features and beards that can differentiate originals from reflections. The study has implications for training machine learning models and detecting faked images.
A new study by UCLA professors combines AI and folklore analysis to examine the storytelling elements of debunked conspiracy theories and actual news stories. The researchers found that conspiracy theories tend to form around specific elements that act as an adhesive, holding facts and characters together.
The researchers aim to create a machine-learning solution that combines pure data-driven reinforcement learning algorithms with domain knowledge. Their technique, Dino-RL, is designed to dynamically adjust to quickly detect and fix network problems without human intervention.
The increasing use of machine learning systems in public policy raises concerns about accountability, bias, and transparency. Explaining complex algorithms can reveal conflicting aims and implicit trade-offs in policy decisions.
Researchers found that humans can complement machine learning in correcting for biases. Vintage-specific skills and domain expertise are key attributes that help humans guide machines in mitigating bias. Human collaboration improves ML productivity but its impact on long-term productivity is unclear.
A machine-learning model analyzes histopathology slides to predict cancer prognosis and survival. The study, published in PLOS ONE, shows promise for improving cancer care.
A team of researchers from the University of Konstanz and Innsbruck explore the role of artificial intelligence in basic research, focusing on agency, creativity, and authorship. They aim to provide a conceptual framework for the development of AI methods in science.
Researchers developed an AI algorithm that uses the 'slowness principle' to estimate age and ethnicity by ignoring rapidly changing facial features. The system achieves impressive accuracy, outperforming even human experts in face recognition.
Researchers have improved Wi-Fi network operation and performance for the 5G/6G ecosystem by applying machine learning techniques. A new algorithm, ε-sticky, is proposed to reduce service disruptions and network instability, benefiting both stations that have found a suitable access point and those that haven't.
Artificial neural networks became unstable after continuous unsupervised learning, but exposure to Gaussian noise mimics slow-wave sleep stabilized them. This finding has implications for the development of biologically realistic AI systems.
A study by Oregon State University found that telepresence robots can enhance student engagement and expression in online classes. In contrast, instructors preferred teaching students in person, but valued telepresence robots as a remote learning solution over distance learning tools.
Researchers developed a novel AI-managed trading strategy that outperforms traditional methods, achieving greater gains and fewer losses. The proposed system utilizes convolutional neural networks to analyze layered images of current and past market data, leading to more accurate predictions and reduced randomness.
A new study by Carnegie Mellon University researchers has found the brain programs that code the sequence of steps in performing a complex procedure. The main findings were that each knot had a distinctive neural signature, so the researchers could tell which knot was being tied from the sequence of brain images collected.
A new NIST formula could significantly improve Wi-Fi and cellular system performance in unlicensed bands by selecting optimal frequency channels. The formula uses machine learning to maximize data rates and reduce interference, with computer simulations showing it can outperform exhaustive trial-and-error methods.
A recent study explores the application of deep learning in ecological resource research, addressing challenges such as multi-source/multi-meta heterogeneity and high dimensional complexity. The study highlights the potential of deep learning in connecting computer science with classical theoretical sciences in ecology.
A new AI-powered algorithm has revolutionized the analysis of deep sleep patterns by automating the detection of K-complexes. The tool, developed by Flinders University researchers, outperforms human scoring methods in speed and accuracy, providing a more comprehensive understanding of sleep health.
Researchers used AI to speed up the search for a key material in a new catalyst that converts carbon dioxide into ethylene with record efficiency. The resulting electrocatalyst has an 80% faradaic efficiency, a new record for this reaction, and shows promise for clean energy storage and carbon capture.
The human brain balances complexity and accuracy when processing patterns, with errors playing a crucial role in learning and cognition. The new model suggests that the brain constantly strives to represent things in simple terms, with participants showing quicker responses to sequences generated by modular networks.
Researchers developed an AI tool to automatically diagnose atrial fibrillation and five common ECG abnormalities, comparable to human diagnosis. The AI was trained on a large database of manually diagnosed ECGs and shows great potential for improved cardiovascular care in low-income countries.
Researchers at Carnegie Mellon University have developed a new AI-powered teaching interface that allows teachers to create intelligent tutoring systems in minutes, rather than hours. This innovation has the potential to increase the adoption of AI-based tutors and provide deeper insights into learning processes.
A team of scientists has developed a system utilizing image analysis and artificial intelligence to analyze the shape of large numbers of seeds from a single image. The trained model detected and segmented individual seeds with high accuracy and analyzed seeds of other crops, accelerating crop breeding and analysis.
Researchers successfully rebuilt bridge between experimental neuroscience and artificial intelligence learning algorithms by demonstrating a new accelerated brain-inspired learning mechanism. This mechanism outperformed commonly-used machine learning algorithms in handwritten digit recognition tasks with small datasets.
Fugu outperforms BBA in terms of least interruption time, highest image resolution and consistency of video quality, keeping viewers engaged for 5-9% longer
Researchers propose Anticipated Learning Machine (ALM) for precise future-state predictions based on short-term data. ALM efficiently reconstructs dynamics even with a small number of samples by constraining to a low-dimension space.
A team of scientists used machine learning to speed up the process of identifying optimal self-assembling peptides for biocompatible electronic devices. By screening 8,000 candidates, they were able to rank each design and pave the way for experimentalists to test the most promising ones.
Researchers used machine learning to analyze nationwide health checkup records and identified a reliable method for predicting diabetes patients. The model accurately predicted future incidence of diabetes with an overall accuracy of 94.9%.
A team of researchers used machine learning to study complex spin models, revealing key similarities between distinct phases. By training an AI on one model and applying it to another, they found that the algorithm could correctly classify phases and identify temperature transitions.
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.