Articles tagged with Machine Learning
The book offers novel human-centered AI techniques to address social intelligence challenges, including multimodal approaches and explainable AI designs. Researchers can integrate artificial and human intelligence to tackle complex social challenges.
Researchers developed a platform called CRESt that incorporates insights from literature, chemical compositions, and imaging to optimize materials recipes. CRESt uses robotic equipment for high-throughput testing and large multimodal models to further optimize materials recipes.
The partnership aims to provide cutting-edge professional development to public sector and critical infrastructure leaders across Canada, equipped with strategic skills to outthink and outmaneuver threats. The initiative strengthens Canada's cyber resilience by enhancing national cyber resilience.
Researchers at Chalmers University of Technology have developed new simulation methods using machine learning to understand halide perovskites, a promising material for efficient solar cells. The study provides insights into the structure and behavior of formamidinium lead iodide, helping to address its instability issues.
Scientists at Seoul National University have developed a framework to manipulate emergent behavior in animal groups and robot swarms. The approach uses physics-informed AI to learn local interaction rules, enabling the control of collective patterns such as rings, clumps, and flocks.
Researchers at FAU have developed a smarter AI framework that can manage complex systems with unequal levels of authority and adapt to imperfect information. The framework, based on reinforcement learning and game theory, reduces unnecessary computation while maintaining system stability and optimal strategy outcomes.
A multidisciplinary team led by Natasha Vermaak investigates developing structural materials resistant to high-frequency thermomechanical loads for rotating detonation engines. The project aims to address the lack of established materials solutions for extreme thermomechanical loadings, enabling advancements in propulsion systems.
A wearable device called a-Heal optimizes each stage of the wound healing process using AI and bioelectronics, delivering medication or an electric field for personalized treatment. Initial preclinical results show the device speeds up the healing process by 25% compared to standard care.
Researchers at MIT developed a technique called SCIGEN that steers generative AI models to create promising quantum materials by following specific design rules. The approach led to the synthesis of two actual materials with exotic magnetic traits.
A recent study demonstrates significant gains in forecast accuracy using a hybrid Shanghai Typhoon Model, which combines the strengths of both physics-based and machine-learning weather prediction models. The model achieved substantially lower track errors than state-of-the-art models during Typhoon Danas in 2025.
Researchers developed MoBluRF, a two-stage motion deblurring method for NeRFs, achieving high-quality 3D reconstructions from ordinary blurry videos. The framework outperforms state-of-the-art methods and is robust against varying degrees of blur, enabling smartphones to produce sharper and more immersive content.
A new study from IU researchers found that simple screening questionnaires performed better than advanced machine learning methods in measuring social needs. However, each method had gaps, particularly with financial strain, which was harder to identify.
A deep learning model achieved up to 98% accuracy in distinguishing autistic from neurotypical participants, providing clear insights into brain regions most influential to its decisions. The model could benefit autistic people and clinicians by offering accurate and explainable results to inform assessment and support.
A new AI model trained on routine ECG data accurately predicts patients at risk for life-threatening complications after surgery, outperforming current risk scores. The model achieves 85% accuracy and has the potential to transform decision-making and risk calculation for both patients and surgeons.
Researchers developed an AI-driven algorithm that can predict nearly 70% of hot flashes before they're felt. The Embr Wave wearable device will incorporate this technology to mitigate symptoms and provide meaningful relief.
Researchers developed computational tools to analyze ecological data, identifying functionally equivalent species across different ecosystems. These tools use optimal transport distances to compare network structures, allowing for large-scale monitoring of ecosystem health and guiding conservation efforts.
Researchers are developing a detection system to identify pre-ignitions in hydrogen internal combustion engines (H2-ICE) using machine learning algorithms and onboard sensors. The project aims to address the challenges associated with H2-ICE pre-ignition, which can degrade engine performance and compromise its mechanical integrity.
Researchers developed a custom speech recognition system trained on Supreme Court hearings, reducing transcription errors by up to 9% compared to leading commercial tools. The AI tool semantically matches paragraphs with timestamps, allowing users to scroll through judgements and instantly watch relevant exchanges from the hearing.
A new study developed a flood-forecasting AI that can be tuned for any country, reducing errors in national flood prediction programming. The hybrid model combining the AI with the National Water Model was four to six times more accurate, improving forecast accuracy and potential economic impacts of floods.
Moffitt researchers develop machine learning model to predict urgent care visits for lung cancer patients, incorporating patient-reported outcomes and wearable sensor data. The study shows improved prediction accuracy and provides insights into the interaction of symptoms, sleep quality, and lab results with risk.
The TCT AI Lab equips clinicians with skills to integrate AI into clinical practice, advancing precision, efficiency, and patient outcomes. The initiative is made possible by a generous grant from the Jon DeHaan Foundation.
Researchers from Bielefeld University explore how humans and machines learn differently, highlighting the importance of bridging cognitive science and AI research. The study shows that machines generalize differently than humans, making it crucial for successful human-AI collaboration.
A new paper highlights the importance of human historians in capturing emotional complexity behind world events as AI struggles to accurately represent Holocaust survivors' experiences. Historians possess skills that AI lacks, including the ability to capture human suffering and preserve fracture and silence.
Biochar, a carbon-rich material, is gaining attention for its ability to improve soils, clean water, and capture carbon. Machine learning models can predict biochar yield and pollutant removal efficiency with over 90% accuracy, accelerating its development.
Ves-GAN, an unsupervised vessel-targeted denoising framework, significantly improves LDCTA imaging by minimizing noise while preserving intricate vascular structures. This innovation enhances diagnostic accuracy in cardiovascular disease diagnosis, directly impacting patient outcomes.
A new study predicts an increase in western US wildfires sparked by lightning strikes, with 98% of the region seeing more risk days by 2060. The western US is expected to see a significant rise in lightning days, with areas like Oregon and Idaho experiencing up to 12 more days per summer.
Researchers at Mount Sinai are developing an AI-based predictive tool that can analyze complex sleep study data to predict cardiovascular event risk and treatment response among individuals with obstructive sleep apnea. The findings could help clinicians make more informed recommendations about OSA treatment for their patients.
A new AI-powered method has reduced antibody discovery time from weeks to under a day, offering a scalable approach that minimizes data bottlenecks and accelerates research. This breakthrough could transform pandemic response and therapeutic development, particularly during health emergencies where rapid response is critical.
Experts weighed in on the current state of artificial intelligence (AI) in cancer care, noting both its potential for improving efficiency and its challenges. Many expressed excitement over AI's possibilities for helping support an overburdened oncology workforce and accelerating the pursuit of new cures.
Researchers are decoding animal decision-making using glass knifefish, exploring the trade-off between gathering information and acting on it. The study, funded by the NIH, aims to understand how animals make decisions in uncertain environments and may lead to breakthroughs in robotics and medicine.
Researchers at Politecnico di Milano developed photonic chips for training physical neural networks, eliminating digitisation requirements. This allows for faster, more robust, and efficient network training using light signals.
Researchers used AI models with integrated clinical and claims data to predict chronic kidney disease (CKD) progression to end-stage renal disease (ESRD). The study found that the models outperformed single data source models and reduced racial bias. The findings can inform likelihood and management of CKD, supporting targeted interven...
Researchers have developed a silicon chip that uses light to perform convolution operations for AI, reducing energy consumption and increasing speed. The chip achieves near zero energy performance, leap forward for future AI systems.
Researchers have developed a new light-based chip that cuts power consumption for image recognition tasks by up to 100 times, using lasers and microscopic lenses fabricated onto circuit boards. This breakthrough enables faster performance and potentially strain-free AI systems.
The Global RETFound initiative aims to develop the world's first globally representative AI foundation model in medicine, addressing health inequalities and AI bias. The project involves a diverse dataset of over 100 million fundus photographs sourced from 65+ countries.
Researchers harness AlphaFold 3 to predict how T cells recognize peptides, opening avenues for precision immunotherapy and vaccine design. The approach enables in silico identification of immunogenic epitopes that could serve as vaccine targets.
A recent breakthrough in MXene nanomaterials could enable bespoke design tailored by AI. Researchers have discovered the thermodynamic forces underlying their unique structure and behavior, setting parameters necessary for AI-guided design.
Researchers at UCR have developed a method to preserve AI safeguards in open-source models by retraining internal structure to detect and block dangerous prompts. The approach avoids external filters or software patches, instead changing the model's fundamental understanding of risky content.
Researchers used AI to identify four times more earthquakes than earlier tools and pinpoint previously unknown faults in the region. The study expands seismicity recorded by monitoring stations from 2022 to 2025, revealing two faults converging under the town of Pozzuoli west of Naples.
A CISPA researcher has been awarded an ERC Starting Grant to tackle the issue of data leaks in large AI models. The project aims to develop new methods for protecting private training data, making it a crucial step towards ensuring trust in artificial intelligence.
A global study surveyed 14,000 patients across 43 countries, finding that those in poorer health were more likely to reject AI. Patients preferred explainable AI and wanted clinicians to make final decisions.
The Wits MIND Institute has received a $1 million boost from Google.org, enabling it to drive next-generation breakthroughs in natural and artificial intelligence. The partnership aims to advance the scientific understanding of both natural and artificial intelligence, foster breakthrough research and technological innovation.
A new study uses machine learning to optimize the adsorption capabilities of biochar for dye removal, identifying optimal conditions for maximum efficiency. This research has significant implications for addressing water pollution and achieving environmental sustainability.
Researchers developed an AI model that makes individualized treatment recommendations for atrial fibrillation patients, potentially revolutionizing stroke and bleeding prevention. The model reclassified up to half of AF patients from receiving anticoagulants under current guidelines.
Researchers at EPFL developed BindCraft, an open-source AI platform that uses AlphaFold2 to generate novel binders with desired functional properties. The platform reduces the need for high-throughput screening and makes protein design more democratized.
A new AI tool has identified over 1,000 'questionable' scientific journals, including those that charge hundreds or thousands of dollars to publish research without proper vetting. The AI system uses six criteria to evaluate journals, including editorial board composition and website quality.
A former diplomat warns that algorithms lack empathy and intuition, which are essential for successful negotiations. However, AI can streamline diplomacy and amplify human aspirations when used carefully. Diplomats need training in AI ethics and global cooperation to ensure equal access and deployment.
An interdisciplinary team analyzed data from 31 sub-Saharan African countries, identifying key socioeconomic factors associated with maternal and child health services use. The study found that socioeconomic status plays a strong link in accessing these life-saving services.
A team of computer scientists at UC Riverside has developed a method to erase private and copyrighted data from artificial intelligence models without needing access to the original training data. The approach enables AI models to 'forget' selected information while maintaining functionality with remaining data.
SMU's latest project aims to improve AI-driven defect detection in aircraft surface inspections, tackling challenges like inconsistent lighting conditions and limited training data. The project uses knowledge from large vision-language models to spot defects accurately and adapt to changing environments.
Researchers Marie Teich and Wilmer Leal develop a formal framework to analyze metaphors, confirming they are enduring linguistic and cognitive structures. The study reveals two significant metaphorical processes: mappings from concrete to abstract topics and the emergence of new mappings between domains.
A new study published in Clinical Gastroenterology and Hepatology shows that AI analysis can accurately identify mucosal ulceration as well as human reviewers, providing a more objective and reproducible assessment of Crohn's disease. The findings have potential implications for education, drug development, and automated care.
Researchers at the University of Vaasa developed smart packaging that can detect subtle color changes in printed packages, enabling cost-effective solutions for industries like food and beverage, healthcare, and logistics. This technology provides a human-eye accurate and environmentally friendly alternative to electronic sensors, pavi...
ConcreteSC technology achieves significant speed boosts and improved efficiency in next-generation wireless networks. The innovation integrates user tasks into communication processes, reducing computational complexity and increasing semantic meaning.
A new Mayo Clinic AI tool, UNISOM, has shown promising results in identifying early signs of clonal hematopoiesis of indeterminate potential (CHIP), a condition that raises the risk of leukemia and heart disease. The tool helps detect CHIP-related mutations in standard genetic datasets, opening new avenues for research and discovery.
Simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models for certain climate scenarios. However, simple models are more accurate when estimating regional surface temperatures, while deep-learning approaches excel at local rainfall estimation.
A new reporting checklist introduces key methodological details to improve reproducibility and transparency in AI-based automated image analysis. The guidelines will support clear communication of methods and reduce cognitive bias, promoting the translation of AI tools into routine pathology workflows.
Researchers at Uppsala University developed an AI model that can accurately predict battery ageing, leading to longer life and enhanced safety for electric vehicle batteries. The model reduces the need for sensitive vehicle data and provides a detailed picture of chemical processes inside batteries.
A new Concordia study reimagines parcel delivery by integrating electric vehicles, autonomous delivery robots, and self-service lockers to prioritize high-value customers like Amazon Prime members. The innovative model cuts route and vehicle use costs by up to 53% compared to traditional methods.
Researchers developed a systematic solution combining high-throughput calculations and machine learning to find high-performance materials. Thermal expansion enhances thermoelectric performance by reducing lattice thermal conductivity and increasing the Seebeck coefficient.