Articles tagged with Machine Learning
Researchers at UC Davis developed a brain-computer interface that translates neural activity into speech in real time. The technology allows individuals with ALS to communicate more naturally and inclusively, with 60% of synthesized words intelligible to listeners.
Researchers have developed a new method to physically restore original paintings using digitally constructed films that can be removed if desired. The process uses a polymer film mask printed on a very thin film and aligned to an original painting, which takes around 3.5 hours from start to finish.
Researchers at MIT developed a machine learning-based adaptive control algorithm that enables autonomous drones to adapt to unknown disturbances like gusting winds. The system achieves 50% less trajectory tracking error than baseline methods in simulations.
Nach01, a large language model-based foundation model, leverages structural and spatial data for diverse chemical tasks. The model is now available on AWS Marketplace, streamlining access for researchers and pharma companies to build and apply large-scale generative models.
The SWIFTT project will explore practical and technical challenges of using AI and satellite data to monitor bark beetle outbreaks. Forest professionals, researchers, and remote sensing experts will discuss the interplay between remote sensing, machine learning, and traditional forestry knowledge.
The book, co-authored by 29 contributors from over ten countries, offers an introduction to machine learning and deep neural networks for complex quantum problems. It serves as a timely guide for PhD students and researchers looking to apply modern machine learning methods to quantum physics and chemistry.
The Rice University team created a soft robotic arm capable of performing complex tasks using smart materials, machine learning, and an optical control system. The arm is guided and powered remotely by laser beams without any onboard electronics or wiring.
The journal JMIR Human Factors is inviting submissions for a new theme issue focusing on human factors in health care education, management, and knowledge translation. The issue aims to explore current and emergent educational and training aspects of human factors, including digital competencies for healthcare professionals.
The virtual teaching assistant (VTA) provides personalized feedback to individual students even in large-scale classes. The system, which automatically vectorizes a large volume of course materials and uses them as the basis for answering students' questions, has been shown to significantly reduce the burden on TAs.
Professor Shalom Lappin argues that tech companies' dominance in AI development poses a threat to public benefit and environment. He calls for comprehensive international regulation, intellectual property reform, and preparation for workforce disruption to address these challenges.
A new computer model based on artificial intelligence is being developed to help doctors treat stroke patients. The model will use data from the German Stroke Registry and local brain images to predict long-term outcomes and potential complications, enabling doctors to make informed decisions about therapy.
Researchers have developed a hyperspectral imaging technique that can detect microplastics in soil with impressive accuracy, paving the way for faster and non-invasive monitoring methods. The MCT-HSI system has been shown to achieve over 93% detection accuracy across tested levels of microplastic concentrations.
This study identifies ageing-related differentially expressed genes (ARDEGs) as potential biomarkers for heart failure with preserved ejection fraction (HFpEF). The ARDEGs were found to be associated with metabolic and immune functions, suggesting a critical role in immune regulation in HFpEF.
Researchers developed an algorithm that lets a robot think ahead and consider thousands of potential motion plans simultaneously, solving multistep manipulation problems in a matter of seconds. The new method enables robots to rapidly determine how to manipulate and pack items without damaging them, even in narrow spaces.
A recent study found that AI-generated memes scored higher than those made by humans alone, but human-AI collaborations excelled in creativity and shareability. Human involvement was crucial for creating top-rated memes with strong emotional resonance.
Researchers used proactive and transfer learning strategies to mitigate data shifts in AI models for hospital applications. They found that models trained on one hospital type performed better than those trained on all hospitals using transfer learning.
Ashish Sharma's dissertation developed fundamental advances in natural language processing to positively impact mental health. His AI-supported tools have been used by over 160,000 people, with more than fifty percent of users reporting a household income of less than $40,000 per year.
Researchers found that brain's dopamine neurons encode a map of possible future rewards across time and magnitude, guiding adaptive behavior in uncertain environments. This biological insight aligns with recent advances in AI, particularly distributional RL algorithms, which learn from reward distributions rather than averages.
Doctors rate GPT-generated educational materials as clear, accurate and complete as human-authored versions, but easier to read. The AI-generated materials were translated into five languages for a randomized assessment.
A University of Maine study evaluated over 7,000 anonymized medical queries and found that AI models performed well on factual and procedural queries but struggled with 'why' and 'how' questions. Human clinicians consistently outperformed AI in terms of emotional engagement and empathetic nuance.
Stanford researchers have developed a machine learning approach to design proteins that can target specific genomic sites without triggering immune responses. By combining three independent algorithms, the team created zinc finger DNA-binding domains with improved functionality and lowered immunogenicity.
A team of researchers combined artificial intelligence and statistical modeling to analyze language patterns in three major sections of the Bible. They distinguished between three distinct scribal traditions spanning the first nine books of the Hebrew Bible, known as the Enneateuch. The model also determined the most likely authorship ...
Researchers successfully reproduced high-pressure synthesis reaction of superhydrides using a machine learning model, revealing a unique reaction pathway involving surface melting, hydrogen absorption, and solidification. This breakthrough deepens understanding of high-pressure physico-chemical processes and holds promise for easier de...
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 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.
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 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.
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.
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.