Articles tagged with Machine Learning
Diagnostics.AI has launched the industry's first fully-transparent machine learning platform for clinical real-time PCR diagnostics, delivering algorithmic transparency and per-test auditability. The platform is CE-IVDR certified and backed by over 15 years of experience and millions of successfully processed samples.
A refined artificial intelligence (AI) tool has shown promise for objective evaluation of patients with facial palsy. The 'fine-tuned' model demonstrated substantially lower error rates and improved keypoint detection in every area of the face, including areas of asymmetry.
A new machine learning tool called GLUCOSE helps doctors manage blood sugar levels in patients recovering from heart surgery by recommending tailored insulin doses. The model outperformed experienced clinicians in keeping blood sugar levels within a safe range, despite using only current patient data.
Aerial robots are limited to manipulating rigid objects, but Lehigh University researcher David Saldaña aims to expand their capabilities with an adaptive controller and reinforcement learning. His research has potential applications in construction, disaster response, and industrial automation.
Tandemn, a distributed-GPU network, aims to lower costs and barriers to GPU access while providing owners with possible users for their underutilized resources. The startup secured $1.5 million in funding from private investors after winning the Cozad New Venture Challenge.
Researchers from Empa developed machine learning algorithms to optimize laser-based manufacturing techniques, reducing preliminary experiments by two-thirds. They also implemented real-time optimization using field-programmable gate arrays (FPGAs) for improved welding processes.
Researchers developed a machine learning model called DreaMS to analyze previously unknown molecules. The model significantly accelerates the analysis and uncovers unexpected chemical similarities between substances.
Researchers have developed a machine learning tool that identifies metabolism-related molecular profile differences between colorectal cancer patients and healthy individuals. The tool, called PANDA, shows promise as a noninvasive method of diagnosing colorectal cancer and monitoring disease progression.
Derek Leben's book 'AI Fairness' offers a philosophical framework to evaluate and mitigate biases in AI algorithms. The author argues that principles like autonomy, equal treatment, and equal impact should guide the design of fair AI systems.
OneCareAI applies AI and supercomputing to detect early stroke risk from ECG data obtained with smartwatches, offering a non-invasive solution for personalized risk assessments. The technology has the potential to be extended to other cardiovascular diseases, opening opportunities for scalability.
Researchers used machine learning to classify older adults in Japan by physical and cognitive functions. Five functional subtypes were identified, including severe multicomponent and moderate physical types, which showed higher risks of death and hospitalization respectively.
The application of AI in public health informatics offers potential to revolutionize data collection, disease surveillance, decision-making, and interventions. However, its implementation faces technical, ethical, and operational challenges related to health equity, privacy, security, and bias.
Computer scientist An Wang receives a $1M NSF CAREER grant to leverage cloud computing resources for efficient machine learning model training. Environmental engineer Bridget Hegarty receives a grant to develop safe and effective biocontrol for water systems using bacteriophages.
A new study develops advanced machine learning models tailored to Canadian data, offering precise predictions for e-bus energy use under varying climates and heating systems. The research reveals that tree-based models deliver the highest accuracy in predicting energy consumption, with a mean absolute error of just 0.09–0.1 kWh/km.
Researchers at Virginia Tech have designed a new metallic material alloy with superior mechanical properties, leveraging data-driven frameworks and explainable AI. This breakthrough accelerates the discovery of advanced metallic alloys, offering insights into materials' structure-property relationships.
Researchers at Duke University have developed a new framework called HUMAC that enables robots to collaborate like humans by teaching them Theory of Mind. After just 40 minutes of guidance, robot teams exhibited strong collaborative behaviors and achieved high success rates in simulations and physical tests.
Developers of educational tools focus on technical challenges, while educators are concerned with broader impacts, such as inhibiting critical thinking skills and exacerbating systemic inequality. Researchers recommend designer-centered approach to facilitate development of educator-centered edtech.
Researchers found that vision-language models fail to understand negation words like 'no' and 'not', which can cause incorrect diagnoses. This limitation affects multiple choice question answering with negated captions, with accuracy dropping below random chance.
New York University researchers developed a novel process using machine learning to reveal groups of genes governing nitrogen use efficiency in plants like corn. The study aims to help farmers improve crop yields and minimize fertilizer costs.
A new AI-driven tool can forecast acute child malnutrition in Kenya up to six months in advance with high accuracy, enabling timely interventions. The model integrates clinical data and satellite information to identify emerging risk areas, providing a game-changing solution to address public health emergency in the country.
A University at Buffalo-led study proposes using AI-powered handwriting analysis to identify spelling issues, poor letter formation, and other indicators of dyslexia and dysgraphia. The work aims to augment current screening tools and provide an early detection tool for these neurodevelopmental disorders.
A new deep learning model, ENDNet, significantly enhances subgraph matching accuracy by identifying and neutralizing extra nodes that interfere with the matching process. This improves performance in pattern recognition tasks across various fields, including drug discovery and natural language processing.
A new AI model can predict protein location in human cells with high accuracy, enabling faster diagnosis of diseases like Alzheimer's and cancer. By combining protein sequence analysis with computer vision, the model can pinpoint proteins' locations at the single-cell level.
A new study uses explainable machine learning to predict metastasis and mortality in children with medulloblastoma, identifying key immune factors driving risk. The model provides a transparent approach to prognostic accuracy and supports personalized clinical decision-making.
A study found that large language models generalize language patterns in a surprisingly human-like way through analogy, relying on stored examples and drawing analogies when dealing with unfamiliar words. The models behave as if they have formed a memory trace from every individual example of every word encountered during training.
A new AI-based method accurately sorts cancer patients into groups with similar characteristics before treatment and similar outcomes after treatment. The approach showed better performance than standard statistical and machine learning methods in predicting treatment outcomes from health record data.
Researchers developed an AI-based music creation support system called Amuse, which converts user inputs into harmonic structures to support composition. The system has high potential as a creative companion for musicians and is centered on the creator's initiative.
A team of researchers at Rice University developed a new strategy for identifying hazardous pollutants in soil using light-based imaging and machine learning algorithms. The approach can detect toxic compounds like PAHs and PACs even when no experimental data is available, addressing a critical gap in environmental monitoring.
A Carnegie Mellon University researcher critiques an article on large language models' ability to analyze sensitive discourse, including hate speech. The commentary highlights the need for a nuanced approach to human-AI collaboration in critical discourse analysis.
Researchers employed Bayesian neural networks to fit photonuclear cross-sections with remarkable reliability, outperforming traditional methods like TENDL-2021. The approach demonstrated superior accuracy in describing low-energy thresholds and high-energy tails, particularly for sparse or biased data.
A new study from the University of Surrey explores how metaverse platforms like Roblox and ZEPETO are changing consumer engagement forever. Digital doppelgängers, using AR and VR technologies, create immersive experiences that enhance brand engagement and emotional connections.
A new microscopy method, LICONN, developed by ISTA scientists and Google Research, can reconstruct mammalian brain tissue with all synaptic connections between neurons. This technique uses standard light microscopes and hydrogel to achieve high resolution and opens up possibilities for visualizing complex molecular machinery.
Researchers have discovered nearly 35,000 publicly downloadable deepfake image generators, downloaded over 15 million times since late 2022, primarily targeting women. The study highlights the need for robust technical safeguards and regulatory approaches to address the creation and distribution of these harmful AI models.
Large Language Models (LLMs) like Mixtral can grade student responses quickly, but often rely on shortcuts and assume students understand topics. Researchers found that LLMs are more accurate when provided with human-made rubrics, which include specific rules for grading. This suggests that AI can be used to streamline grading processe...
A new study from McGill University suggests AI can revolutionize conservation efforts by enabling data-driven decision making. The research identifies key ways AI can close biodiversity knowledge gaps, including species identification and distribution mapping.
Researchers have used machine learning to study the melting of layered materials, discovering a complex two-step process that contradicts prior theories. The team identified changes in topological excitations as the key to understanding the unexpected melting behavior, enabling predictions up to 12 material layers.
The journal is inviting submissions on AI applications in biomedical engineering, focusing on medical device development, personalized diagnostics, and patient outcome prediction. Accepted articles will be published as part of a themed section showcasing current research on AI applications in biomedical engineering.
A new study published in the Journal of the National Cancer Institute suggests that artificial intelligence can help detect interval breast cancers earlier, potentially reducing their rates by 30%. The research used AI software to analyze mammograms and identify subtle signs of cancer that were missed by radiologists.
Researchers used generative AI to design diverse mitochondrial targeting sequences, achieving a 50-100% success rate in yeast, plant cells, and mammalian cells. The AI-generated sequences showed improved targeting abilities compared to existing ones, with potential applications in metabolic engineering and therapeutics.
A recent study by the University of Surrey suggests that hotel managers may need to adapt to AI-driven system management, shifting their focus from controlling to coaching staff. Effective communication, emotional intelligence, and creativity are key strategies for managers to navigate this transformation.
Researchers at MIT developed a technique to improve the reliability of conformal classification, which can produce impractably large prediction sets. By combining test-time augmentation with conformal prediction, they reduced prediction set sizes by up to 30 percent while maintaining probability guarantees.
A Dartmouth team's AI model recognized Navajo with near-perfect accuracy, identifying related languages such as Apache and Native Alaskan languages. The study suggests that this technology could be a bridge to including smaller languages in online translation services.
Researchers developed an AI system to classify tiny powdery pollen grains produced by fir, spruce, and pine trees, enhancing speed and accuracy. The tool can aid allergy sufferers, urban planners, farmers, and wildlife conservation efforts.
Researchers developed a novel vote-based model for accurate hand-held object pose estimation, addressing issues with existing approaches. The new framework achieves significant improvements in accuracy and robustness, enabling robots to handle complex objects and advancing AR technologies.
A team of researchers used machine learning to analyze changes in astrocyte cells' structure, shedding light on heroin addiction and relapse. The study, published in Science Advances, found that specific subpopulations of astroglia exhibit more pronounced morphological changes during drug use.
A new study finds that AI tools can significantly improve the readability of online patient education materials, making them more accessible for patients. The researchers used three large language models to optimize the readability of materials without compromising accuracy, resulting in improved scores and reduced word counts.
A study by University of East Anglia compared 145 real student essays with 145 ChatGPT-generated ones, finding that AI essays were coherent but lacked engagement markers like questions and personal commentary. This reflects the limitations of AI in replicating human writing's conversational nuance.
A new review advocates for building confidence in AI applications by implementing robust data governance frameworks, enhancing transparency, and involving stakeholders. The authors emphasize the importance of addressing ethical implications and ensuring equitable access to AI-driven innovations in clinical oncology.
Kavraki's interdisciplinary research in robotics and biomedicine has been recognized for its impact on manufacturing, space exploration, and medicine. Her work bridges theory and application, with contributions to novel robot motion planning, personalized cancer treatments, and drug discovery.
César A. Uribe, a Rice University professor, has received an NSF CAREER Award to develop mathematical tools for decentralized learning in AI and data science. His research aims to create more efficient networks of computers that can process massive amounts of data without relying on centralized coordination.
Researchers have developed a neuro-quantum leap in finding optimal solutions, leveraging Fowler-Nordheim annealers to discover new and unknown solutions. NeuroSA's structure is neuromorphic, with a search behavior determined by FN annealer, making it powerful for solving complex optimization problems.
Researchers develop mathematical modeling to predict aflatoxin outbreaks in Texas using remote sensing satellites and soil properties. The model has the potential to save farmers billions of dollars in losses by providing early risk prediction and targeted prevention strategies.
Researchers at Rice University developed a new machine learning algorithm that excels in interpreting light signatures of molecules, materials and disease biomarkers. The tool can detect subtle signals in optical spectroscopy, enabling faster medical diagnoses and sample analysis.
Researchers found that pretrained foundation models outperformed a standard baseline model in identifying nonmelanoma skin cancer from digital images. The models achieved accuracies of up to 92.5% in distinguishing between NMSC and normal tissue, significantly improving upon an older architecture.
A new study found that artificial intelligence can better estimate the prevalence of physical abuse in children seen in emergency departments. The AI model correctly identified child abuse in over 35% of cases, outperforming traditional methods that relied solely on diagnostic codes entered by providers or administrative staff.
Researchers identified three subtypes of senescent skin cells with distinct shapes, biomarkers, and functions. The study found that one subtype, C10, becomes more prevalent with age and is harder to kill with existing drugs.
Researchers at ETH Zurich developed a method to specifically reduce uncertainty in AI responses by enriching general language models with additional data from relevant subject areas. The SIFT algorithm uses relationship vectors to identify closely related information, resulting in more reliable answers and reduced computational overhead.
The Simons Collaboration on Ecological Neuroscience (SCENE) is a 10-year program that will support projects aiming to uncover how the world shapes representations in the mind and brain. By integrating sensory and motor information, researchers hope to discover fundamental principles of cognition applicable across species.
Researchers developed an AI model called Odor Generative Diffusion (OGDiffusion) to automate fragrance creation, generating essential oil blends based on user input of scent descriptors. The system produced fragrances that met people's expectations in human sensory tests.
Machine learning (ML) techniques can identify materials with high synthesis feasibility and suggest suitable experimental conditions. Computational models derived from thermodynamics and kinetics enhance predictive performance and interpretability of ML models, optimizing experimental design and increasing synthesis efficiency.