Articles tagged with Machine Learning
A new study found that girls struggle to master AI due to low confidence and limited institutional support. To overcome this, schools should provide more female role models and create a supportive classroom environment.
A new study reveals that soil carbon residence time governs riverine dissolved organic matter's age, with climate, hydrology, and soil processes controlling carbon cycling in rivers. The research provides a high-resolution global atlas of riverine DOC, showing that ancient carbon sources are locally important but modern terrestrial org...
The UK-led OpenBind initiative has released its first publicly available dataset and predictive AI model, accelerating the discovery of new medicines using artificial intelligence. The release showcases high-quality, standardized experimental data and a trained predictive model, enabling researchers worldwide to drive the next generati...
The rise of consumer-facing health AI assistants is transforming healthcare access, offering users personalized medical workspaces and real-time lab result interpretation. However, concerns around data privacy and the risk of misdiagnosis highlight the need for caution in this rapidly evolving landscape.
The new AI model uses genetic mutation patterns to trace ancestral relationships between species, including humans and mosquitoes. The tool can predict when gene pairs last shared a common ancestor and is faster than traditional statistical methods.
Researchers at ETH Zurich created a machine-learning-based map showing Europe's most beautiful and scenic regions. The study found that excluding these areas would reduce wind energy potential but increase costs per unit of electricity generated.
A new algorithm called MEERKAT reduces the amount of data shared between large language models, boosting performance and reducing power consumption. By sharing only 0.1% of the model's parameters, MEERKAT shrinks communications by over 1000 times.
Researchers discovered that simple artificial intelligence tools can bypass security techniques meant to protect authentic content from use in deepfakes and facial identity theft. The study found that attackers can easily defeat existing image protection using off-the-shelf AI models and simple commands.
Researchers developed a new framework, 'Mollifier Layers,' to tackle challenging inverse PDEs. This advance could benefit fields such as genetics and weather forecasting by inferring hidden forces that produce observable patterns.
A recent study reveals that individuals with higher education or income are more aware of and use AI tools, exacerbating social inequalities. The researchers recommend increasing engagement with AI-related topics through outreach campaigns, educational programs, and community workshops to reduce this new digital divide.
The new framework, published in PNAS Nexus, offers guidance for building human-AI teams that are effective, accountable, and aligned with human values. It focuses on reasoning, memory, and attention as core processes that can be distributed across people and AI systems.
The study demonstrates that the T1GRS tool can identify children and adults at high risk for Type 1 diabetes earlier than current methods, enabling preventive measures before the disease develops. The researchers grouped individuals into four sub-types based on genetic features, each with unique clinical profiles and outcomes.
New research from West Virginia University finds that judges are adopting generative artificial intelligence in courtrooms, but remain committed to human control over judicial decision-making. Judges use AI for administrative tasks like document summarization and case organization, but prioritize legal reasoning and final judgment.
A large language model exceeded physician baselines in clinical reasoning tasks, including emergency room decisions and identifying likely diagnoses. The study highlights the need for rigorous prospective trials to evaluate the impact of AI on clinical practice.
Researchers at the University of Houston have developed an AI-driven framework to extract and analyze historical flood insurance maps, uncovering significant changes in flood hazard areas. The study reveals that flood risks have expanded in two areas and reduced in one, with critical consequences for resilience and exposure.
A new study analyzing over 40,000 comments in the public record found that Americans strongly support the federal government's reclassification of cannabis to a less dangerous Schedule III. The majority cited therapeutic benefits and economic impacts as motivations for rescheduling.
Researchers at MIT developed a technique to overcome memory constraints and communication bottlenecks in federated learning, enabling faster and more accurate AI model training. The new framework, FTTE, uses a subset of model parameters and an asynchronous approach to reduce lag time and improve training performance.
A new paper highlights concerns over autonomous AI-based drug prescribing, citing complex questions about the role of the FDA in regulating these technologies. The use of AI sandboxes to test medical tools before market release is also being questioned.
New research from the University of Kansas found varying approaches to AI use in journalism classes across US institutions. The study suggests that a more consistent approach could better serve education and practice, but inconsistent policies may confuse students. Researchers recommend clearer guidelines from accrediting bodies.
Researchers emphasize the need for more thorough frameworks to ensure AI-enabled robots embody human values. The field should focus on three complementary lines of defense: rules that shape robot decisions, checks that monitor behavior, and safety reasoning.
A new study suggests that an AI tool analyzing facial changes can serve as a prognostic biomarker for cancer prognosis. The researchers found that patients with higher biological aging rates had lower chances of survival, and the effect was strongest when photos were taken over longer intervals.
MIT researchers have created an 'EnergAIzer' method that generates reliable results in seconds, allowing data center operators to optimize resource allocation and reduce energy waste. The tool leverages patterns from AI workloads and software optimizations to provide fast but accurate power estimates.
Researchers developed a machine learning approach to analyze Fermi surface images, identifying compositions with significant changes and nodal lines. The method accurately detects outliers, enabling efficient screening of large datasets for desirable electronic properties.
Researchers propose two physical embedding solutions to improve machine learning force fields' accuracy and stability in organic systems. The first method uses adaptive bond length sampling, effectively covering high-energy bond length regions prone to simulation collapse. The second method employs top-down model correction using physi...
A recent study explores how response timing affects people's use and evaluation of AI systems, challenging the assumption that faster is always better. Participants rated slower responses as more thoughtful and useful, highlighting a subtle but powerful feature of human psychology.
Researchers have developed SmartDJ, an AI-powered editor that allows users to reshape audio experiences with simple words. The system uses language models and diffusion models to interpret high-level requests and generate edited outputs.
Researchers developed a microfluidic platform that squeezes individual breast epithelial cells to measure their mechanical age, revealing an unexpected insight: older cells are stiffer and at higher risk of cancer. The AI-powered platform provides a non-genetic test for women with unknown genetic risks.
Researchers developed an AI method to automate charge transition line extraction from charge stability diagrams, enabling high-efficiency single-electron region definition and virtual gate configuration. This breakthrough aims to scale up quantum computing by handling vast numbers of qubits beyond human capability.
Researchers developed an AI-powered methodology to identify and count target viruses more efficiently than previous techniques. The new approach uses electrochemical impedance spectroscopy and machine learning to separate signals from noise, enabling quick and accurate readings across a wide range of titers.
Researchers are close to building a 'virtual gut' capable of predicting responses to diet, drugs, and microbiome-based therapies. A new review outlines an analytical framework for host-microbiome multi-omics studies, covering preprocessing, feature selection, data integration, predictive modeling, and evaluation.
Computer scientist Micheal Lones warns that using generative AI in machine learning increases the risk of malicious cyberattacks, data leaks, and bias against underrepresented groups. He advises developers to manually evaluate LLM-generated code and outputs to mitigate these risks.
Recent AI tools offer surgeons assistance in complex decision-making by analyzing donor hearts and providing a data-driven approach. This could lead to increased efficiency in the donor process, reducing the likelihood of hearts going unused due to time constraints.
A new study by the University of Technology Sydney and Massachusetts General Hospital/Harvard Medical School found that dietary patterns and a history of appendix removal are strongly associated with Alzheimer's risk. The research suggests that a healthy gut microbiome plays a crucial role in protecting the brain from neurodegeneration.
A recent study from Lancaster University reveals that AI systems like ChatGPT can learn to mirror human impoliteness, potentially escalating into verbal violence. The research tested ChatGPT's ability to respond to real-life impolite interactions, finding it often produces more impolite behavior than humans.
A new technique uses a single image to forecast solar panel energy production and maximize output. The method estimates the amount of energy that will be produced based on the angle of the sun, shadows, reflections, and weather patterns, allowing for more accurate placement and optimization of solar panels in urban areas.
Researchers developed an AI model called Path-IO that uses machine learning to predict responses to immunotherapy for patients with metastatic non-small cell lung cancer. The model accurately stratified patients into high-risk and low-risk groups, with patients in the high-risk group having double the risk of death or disease progression.
Researchers developed a machine learning model using DNA methylation patterns to predict cancer origin in patients with unknown primary. The model correctly identified the cancer type in about 95% of cases, suggesting it could support personalized treatment decisions.
Artificial intelligence models provide personalized advice, but may perpetuate negative stereotypes about people with autism. Researchers found that up to 70% of the time, AI discourages those with autism from socializing.
A team of scientists from NTU Singapore has developed a new biochip that, when paired with Artificial Intelligence (AI), can detect quickly and accurately extremely small amounts of microRNAs. The device can cut detection time from hours to 20 minutes.
A research team at Saarland University has developed an AI-assisted method to determine temperature distribution inside a running electric motor in real time, without additional hardware. The system uses motor-condition data extracted from electromagnetic fields and can detect thermal overload and optimize power regulation.
A review article highlights a deep learning-driven CNN approach for detecting and classifying dynamic road obstacles, achieving high accuracy in obstacle identification and classification. The proposed architecture shows strong performance, but real-world deployment requires continued evaluation across larger and more varied scenarios.
A custom-built AI system helped uncover how bacterial communities organize themselves, showing that early moments of a biological transition carry more information than previously considered. The findings bring new insight into the relationship between genotype and phenotype in Myxococcus xanthus.
A team of researchers developed a machine learning framework to optimize laser settings for printing crack-susceptible superalloys. The algorithm reduced internal crack density by 99% and increased the metal's high-temperature strength, surpassing traditional cast components.
Researchers developed a new training technique, HarmonyGNN, to improve the accuracy of graph neural networks in heterophilic graphs. The framework achieved state-of-the-art performance on four heterophilic graphs with accuracy improvements ranging from 1.27% to 9.6%.
A novel imaging reconstruction framework, TT-PADM, enables high-quality photoacoustic tomography imaging even under limited-view and sparse-view acquisition constraints. This breakthrough technique reduces the number of required acoustic transducers without compromising image quality.
The study uses adaptive machine learning force fields to track sodium metal-electrolyte reactions, achieving a 71% speedup over ab initio molecular dynamics while retaining comparable accuracy. The approach identifies key components of the solid electrolyte interphase, including Na2O and NaOH, which influence its stability.
A new study reveals that AI systems mimic the structure of human judgment but with a more rigid, rule-based approach. The researchers found biases in AI judgments, especially across demographic traits, highlighting the need for awareness and understanding how these systems 'think'.
Researchers identified patient-reported symptoms associated with GLP-1s, including menstrual changes, fatigue, and temperature-related complaints, that may not be fully captured in clinical trials or drug labeling. Nearly 4% of Reddit users reported reproductive symptoms, and fatigue was the second most common complaint.
Researchers from Tohoku University examined the role of materials databases in supporting modern artificial intelligence tools used in materials science. They found that database architecture can directly affect AI model performance and reliability. The study aims to improve database quality, connectivity, and develop new AI systems th...
Scientists at the University of Virginia Health System have developed a suite of AI-powered tools, called YuelDesign, YuelPocket and YuelBond, to transform how new drugs are created. These tools can design drug molecules tailored to fit their protein targets exactly, even accounting for protein flexibility.
A new machine-learning method detects sudden changes in fluid behavior, improving simulation capabilities for everyday applications like weather prediction and nuclear reactor safety. This enables faster design testing, real-time adjustments, and reduced computational burden.
Gladstone Institutes investigator Ryan Corces receives $750,000 to investigate unknown genetic variants contributing to Alzheimer's disease. He aims to identify new drivers and therapeutic targets using artificial intelligence and CRISPR tools.
Researchers developed Sandook, a software-based system that tackles three major sources of performance-hampering variability simultaneously. The two-tier architecture optimizes task distribution for the overall pool while faster schedulers on each SSD react to urgent events.
The Alliance for Clinical Trials in Oncology is enrolling adolescent and young adult cancer patients in various trials, including genetic services and treatment studies. These trials aim to address longstanding gaps in care and improve outcomes for AYAs with cancer.
NTU Singapore aims to integrate AI into 40% of its undergraduate courses by 2030. Half will use personalized learning, while the other half will teach students how to build and deploy AI agents. Students will have access to Google's premium AI tools and computing credits to create their own agents.
A new 3D printing preview tool, VisiPrint, uses AI to generate aesthetically accurate previews of fabricated objects, reducing the need for multiple reprints and waste. The system considers material properties, layer height, and nozzle path to create realistic simulations.
A new testing framework, Scalable Experimental Design for System-level Ethical Testing (SEED-SET), balances measurable outcomes and qualitative values like fairness. The system uses a large language model to capture stakeholder preferences and identifies scenarios where AI systems align with human values.
AI models exhibit emergent intelligence through specialization and cooperation, unlike physical systems where individual components reflect similar information, according to Bar-Ilan University research. This finding has implications for neuroscience and challenges traditional notions of intelligence in AI.
Researchers propose a foundational framework to help multi-agent, connected systems decide what information they can trust before acting. The 'cy-trust' concept assigns a numerical trust value between 0 and 1 to data from other agents based on sensing, context, network behavior, and past experience.
The Association for Computing Machinery has published its inaugural issue of ACM AI Letters, a premier venue for rapid and timely AI research. The journal aims to bridge the gap between traditional conferences and journals, featuring short, peer-reviewed contributions that accelerate knowledge dissemination across academia and industry.