Articles tagged with Machine Learning
Researchers questioned the Cascadia subduction zone's earthquake record, finding that turbidite layers showed no better correlation than random chance. The study suggests a need for further research on turbidite layers and their connection to past earthquakes.
Researchers at EPFL developed a next-generation miniaturized brain-machine interface capable of direct brain-to-text communication on tiny silicon chips. The MiBMI system can decode neural signals generated when a person imagines writing letters or words with high accuracy and low power consumption.
A recent study found that approximately half of FDA-approved AI medical devices are not trained on real patient data, sparking concerns about device accuracy. The researchers analyzed 500+ medical AI devices and discovered that many lacked clinical validation data, which is essential for ensuring the credibility of these technologies.
Automated systems with machine learning capabilities improve diagnostic accuracy and workflow efficiency in laboratory medicine. However, ensuring data security, preventing biases in ML models, and maintaining transparency are key concerns.
A new AI approach accurately links heat waves to global warming, estimating that record-setting heat waves could occur multiple times per decade under higher warming levels. The method uses actual historical weather data and machine learning to predict the magnitude of extreme events.
Researchers developed an approach to clearly separate physical and psychosocial components of pain, allowing for more targeted treatment. The new method combines measuring body signals, self-disclosure, and computerized evaluation to create two indices: one for physical component and one for psychosocial component.
Researchers developed a machine learning model to predict individuals with elevated autism spectrum disorder (ASD) risk using limited medical and background information. This study shows promise for early identification and potentially improving ASD diagnosis and treatment strategies.
A new AI model developed by Karolinska Institutet can predict autism in young children with an accuracy of almost 80% for those under two years old. The model uses a combination of limited information to identify patterns and strong predictors of autism, such as age of first smile and eating difficulties.
Researchers use AI algorithms to accelerate materials discovery, predicting polymer properties and generating new formulations. The technology has led to advancements in energy storage, filtration technologies, additive manufacturing, and recyclable materials.
Researchers developed a model to accurately predict the cycle lives of high-energy-density lithium-metal batteries using machine learning methods. The technique is expected to improve safety and reliability in devices powered by these batteries.
A wearable sensor supported by machine learning models can continuously monitor and quantify FOG episodes, providing an accurate picture of a patient's condition. This technology has the potential to support the development of new treatments and improve the lives of people with Parkinson's disease.
Using Explainable AI (XAI), researchers analyzed AI model predictions for antibiotic candidates, identifying critical molecular structures and variables. This helped improve predictive models, which can now be trained on what's truly important for activity.
Researchers developed a technique using videos and machine learning to quantify motor symptoms in early-stage Parkinson's disease. The approach achieved high accuracy in distinguishing between healthy individuals and those with early Parkinsonism, offering potential benefits for treatment outcomes.
Researchers created a data set of over 10 million documents to test detection ability in current and future detectors. They found that most detectors only work well in specific use cases and can be easily evaded by manipulating the text. The new tool, RAID, aims to provide a standardized benchmark for robust detection.
Researchers developed a new brain-computer interface that translates brain signals into speech with up to 97% accuracy, enabling a man with amyotrophic lateral sclerosis (ALS) to communicate with friends and family. The system was tested in real-time conversations with continuous updates, achieving high word accuracy rates.
A new study published in the Astrophysical Journal has found that galaxies in denser environments are up to 25% larger than isolated galaxies. Researchers used a machine learning tool to analyze millions of galaxies and found a clear trend: galaxies with more neighbors are also on average larger.
A new Diffusion-based Graph Contrastive Learning (DGCL) method constructs brain networks with unique features, capturing key connections and eliminating redundant ones. DGCL outperforms existing tools in terms of efficiency and prediction accuracy for brain disease analysis.
Researchers developed a framework called SigLLM that uses large language models to detect anomalies in time-series data. The approach converts time-series data into text-based inputs and can be deployed right out of the box, offering an efficient anomaly detection solution for complex systems.
A computer algorithm has achieved a 98% accuracy in predicting different diseases by analyzing the color of the human tongue. The proposed imaging system can diagnose various health conditions, including diabetes, stroke, and COVID-19, using a simple and affordable method.
Researchers at Carnegie Mellon University propose guidelines for using interpretable machine learning methods in computational biology to tackle complex problems. The guidelines address pitfalls such as relying on a single method and cherry-picking results, emphasizing the need for multiple approaches and human-centric considerations.
Researchers at the Broad Institute of MIT and Harvard developed a machine-learning approach to design better AAVs for gene therapy. The tool helps engineer capsids with multiple desirable traits, such as targeting specific organs or working in multiple species. About 90% of predicted capsids successfully delivered cargo to human liver ...
Researchers find humans can consistently detect water temperature through its sound, even when not consciously aware of it. Machine learning algorithms help classify thermal properties with high accuracy, revealing a complex sensory mapping skill.
New research finds that AI explanations can fuel a perception of fairness without being grounded in accuracy or equity. Humans were more likely to override AI recommendations when explanations highlighted gender rather than task-relevance, but this did not improve decision-making accuracy.
A recent study published in Nutrition and Health suggests that following a Mediterranean diet can lower perceived stress levels. The research, conducted by Binghamton University, found an association between consuming Mediterranean diet components and reduced mental distress.
A novel application of repurposed COVID-19 rapid antigen tests combines lateral flow assays with machine learning to evaluate coagulation status in cardiovascular care. The approach enables clinicians to perform immediate and accurate anticoagulant dosing adjustments using existing resources.
Researchers developed motion capture wearables with integrated machine learning, combining elasticity with high computing capabilities. The devices outperformed previous iterations in handling elongation and stretching, and demonstrated accurate performance in tasks like sign-language recognition.
Researchers have developed AI systems that use photos or videos to create simulations for training robots in real settings. The RialTo system generates highly accurate simulations of specific environments, while the URDFormer system creates generic simulations quickly and cheaply. These advancements aim to lower costs and increase acce...
A recent study uses machine learning to analyze 950 microbial genomes, identifying 2,194 potential toxins that could be used as new antimicrobials or biotechnological tools. The researchers also discovered four new toxins with enzymatic activities against different molecules.
The Ariel Data Challenge 2024 aims to extract faint exoplanetary signals from noisy space telescope observations, with a focus on overcoming noise sources like 'jitter noise'. The competition offers a unique chance for data scientists and AI enthusiasts to contribute to cutting-edge research in exoplanet atmospheres.
Researchers developed a novel clustering technique that considers both basic characteristics and target material properties, enabling the categorization of over 1,000 oxides into material groups. This approach uses machine learning to predict target properties and incorporates basic feature information into the analysis.
Researchers developed an AI model called GROVER that treats human DNA as a text, learning its rules and context to draw functional information about the DNA sequences. The tool has the potential to unlock the genetic code and advance personalized medicine.
A new study led by CU Boulder computer scientist Theodora Chaspari found that AI algorithms can be confused by natural variations in speech patterns between different genders and races. This can lead to underdiagnosis or misdiagnosis of mental health concerns like depression.
A research team at Iowa State University has developed artificial intelligence technology that can model and understand complex chemical reactions, including those involved in ammonia production. The technology uses reinforcement learning to identify the optimal reaction pathway, promising to reduce production costs and emissions.
A new study found that heteroresistance, a phenomenon where a tiny fraction of bacteria remain resistant to antibiotics, is also present in fungal bloodstream infections in bone marrow transplant patients. The research identified the specific species of fungi responsible and developed a machine learning model to detect this type of inf...
A new research consortium aims to improve the reliability of machine learning systems by using geometric methods to prevent adversarial attacks. The project, GeoMAR, will explore ways to feed neural networks with erroneous data during training to prepare them for real-world scenarios.
A new mapping tool from North Carolina State University uses machine learning and open-source satellite imagery to model flooding in urban environments. The model creates maps that predict urban area flooding, helping officials make informed choices about flood resiliency and prevention resources.
Researchers have introduced a new AI calibration method called Thermometer, which enables efficient calibration of large language models for various tasks. This technique leverages temperature scaling to adjust a model's confidence and can generalize to new tasks without requiring additional labeled data.
Researchers at Mayo Clinic used AI to analyze electroencephalogram (EEG) tests, identifying subtle brain wave patterns characteristic of cognitive problems like Alzheimer's disease. This technology has the potential to provide healthcare professionals with a more accessible tool for early diagnosis in communities without easy access to...
A collaboration of scientists, ethicists, and researchers aims to create a consensus definition for diverse intelligent systems, including AI, LLMs, and biological intelligences. The proposed approach will provide a common language for recognizing, predicting, manipulating, and building cognitive systems.
A recent study published in Frontiers in Immunology highlights the crucial role of tissue-resident memory T cells in non-small cell lung cancer. The research found that these cells can significantly impact patient outcomes and guide personalized treatment strategies, particularly those involving immunotherapy.
Researchers found that adults' faces can be matched to their names at above-chance levels, but not in children. Machine learning algorithms revealed greater similarity between adult faces sharing the same name. The study suggests a 'self-fulfilling prophecy,' where social expectations shape physical appearance over time.
A new device called computational random-access memory (CRAM) reduces energy consumption for artificial intelligence applications. CRAM enables true computation in and by memory, breaking down the bottleneck in traditional computing architecture.
A new study published in PLOS Digital Health found that machine learning models can reliably predict the disability progression of multiple sclerosis. The models were trained on data from 15,240 adults with at least three years of MS history and had an average accuracy of 0.71 ± 0.01.
Researchers at UVA School of Engineering and Applied Science developed artificial compound eyes that mimic praying mantis vision, offering improved depth perception and reduced power consumption by over 400 times compared to traditional systems.
A new AI model can identify certain stages of ductal carcinoma in situ (DCIS), a type of pre-invasive breast cancer, that are likely to progress to invasive cancer. The model uses imaging and machine learning to analyze tissue samples and determine the stage of DCIS based on cell arrangement and organization.
A study by Stanford University researchers reveals a previously unknown relationship between Sahara dust plumes and hurricane rainfall. Thicker dust plumes can lead to heavier rainfall, while thinner ones may suppress hurricane formation over the ocean.
A new automated system of monitoring and classifying persistent vibrations at active volcanoes can eliminate hours of manual effort. The system, based on machine learning, documents volcanic tremor, a continuous seismic signal indicating underground movement of magma or gas.
A new method combines machine vision, deep learning, and nonlinear conversion to increase information capacity in machine learning-based ultra-accurate information networks. The system can achieve low bit error rates and high data recognition accuracy even with complex light fields.
High levels of formaldehyde and aldehydes are emitted from new cars on hot summer days, exceeding national safety limits. A machine learning model has been developed to predict in-cabin concentrations of volatile organic compounds, potentially informing exposure assessments and intelligent car systems.
Researchers found that large language models perform poorly in high-stakes situations despite being better than smaller models, due to misalignment with human generalization function. Human generalization, which involves forming beliefs about others' abilities, plays a significant role in LLM performance and deployment.
A machine learning algorithm was trained to predict individuals with functional neurological disorder (FND) by analyzing their brain structure. The algorithm achieved significant above-chance accuracy in classifying FND participants against healthy controls and psychiatric samples, highlighting the importance of considering both brain ...
The University of Leicester is developing a method to shrink artificial intelligence algorithms for smarter spacecraft. The REALM project aims to demonstrate streamlined machine learning algorithms suitable for limited spacecraft power and computing performance.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
A study found that large language models, despite accuracy in medical exams, fail to consistently request necessary examinations and often deviate from treatment guidelines. In comparison to human doctors, AI diagnoses achieved lower accuracy rates, highlighting concerns about their suitability for everyday clinical practice.
Pusan National University researchers introduced FLIT-SHAP, an explainable machine learning approach that breaks down pollutant effects in mixtures. The tool revealed significant synergistic and antagonistic interactions, challenging current approaches to regulating pollutants.
Researchers at Duke University have broken through the performance wall of adaptive radar systems using convolutional neural networks, paralleling computer vision. They've released a large open-source dataset for other AI researchers to build upon their work, aiming to tackle industry needs like object detection and tracking.
A new study uses machine learning to analyze the genetic diversity of two amphibian species, finding that different processes shaped their evolution. The research suggests that population demographic events and contemporary landscape factors played a significant role in shaping the genetic variation of these species.
Researchers have developed PrISMa, a novel platform that seamlessly connects materials science, process design, techno-economics, and life-cycle assessment to identify effective and sustainable carbon capture solutions. The platform has been tested on over 60 real-world case studies, providing valuable insights for stakeholders.
Researchers at Weill Cornell Medicine used machine learning to define three subtypes of Parkinson’s disease, each with distinct driver genes and molecular mechanisms. These subtypes may suggest customized treatment strategies for patients, potentially targeting specific drugs such as metformin to slow down progression.
Scientists have developed a new technique that leverages X-ray photon correlation spectroscopy, artificial intelligence, and machine learning to create unique 'fingerprints' of materials. These fingerprints can be analyzed by neural networks to yield new information about material behavior under stress and relaxation.