Articles tagged with Machine Learning
Researchers at Cold Spring Harbor Laboratory used machine learning concepts to study the immune system's learning process, finding that the system achieves negative selection through generalization. This process allows for the correct deletion of self-reactive T cells despite encountering only a small fraction of the body's self-peptides.
A novel AI model has been developed that can recognize yoga poses with high accuracy, paving the way for more effective digital coaching tools and movement-monitoring applications. The model achieved accuracy levels of over 93% during testing, significantly outperforming previous models.
Research from the University of Birmingham and other institutions found that interacting with AI-powered customer service robots can reinforce or alter a consumer's self-perception. The study explores how mirroring and mimicry can lead to a 'robotoid humanness' where consumers become more like robots, raising ethical concerns.
A new study shows that AI can discover novel strategies that humans can adopt and preserve across generations. The study found that AI agents can discover optimal strategies that are difficult for humans to find, and that these strategies can be passed on and maintained over time. This discovery has significant implications for the rol...
Researchers found that hedge funds that effectively bet against public sentiment outperformed those that rode sentiment, earning a 0.4% monthly premium. This result persists even after controlling for fund characteristics and economic risks.
Researchers developed an end-to-end machine-learning guided workflow to create high-performing materials for separating methane from nitrogen. The new zinc-based metal-organic frameworks (MOFs) provide state-of-the-art gas adsorption and separation while reducing costs and increasing efficiency.
Researchers used machine learning to analyze elemental composition of biochar and found hydrogen-to-carbon ratio and oxygen content to be key predictors of persistent free radicals concentration and radical type. The study provides a data-driven framework for linking elemental properties to biochar reactivity and environmental risks.
Portland State University is leading a national research team using artificial intelligence to lower the cost of finding geothermal energy. The ARISE project, supported by the US Department of Energy, aims to narrow the range of estimated costs by at least 10% through machine learning and data analysis.
Multi-source data-driven machine learning is transforming lung cancer diagnosis, treatment, and prognosis by analyzing complex medical data. The review highlights the innovative applications of this technology in early screening, personalized treatment optimization, and dynamic prognostic risk stratification.
Professor Fioretto's team develops AI-driven methods for autonomous power grid topology control, improving resilience and efficiency. The Genesis Mission Platform provides access to advanced AI models and high-performance computing resources.
A team of researchers developed a reusable magnetic sensing platform combining surface-enhanced Raman scattering with machine learning to detect trace uranyl ions. The system maintained its detection limit even in complex aquatic environments, with strong selectivity and resistance to interference.
Researchers developed an AI model that analyzes routine whole histopathology images to predict cancer subtype, genetic mutations, and survival outcomes across 32 solid cancers. The model achieved a strong predictive accuracy score for TP53 mutation detection and demonstrated the ability to infer RNA expression levels and tumor taxonomy.
Researchers combined AI, genetics, and gut microbiome analysis to shed light on intestinal fibrosis in Crohn's disease. They identified a shared set of 43 key genes linked to disease progression and found that bowel fibrosis is driven by ongoing immune activation, damage to the intestinal lining, and changes in gut bacteria.
Researchers developed AI-based methods to analyze microscopic tissue images and molecular information, revealing genetic and protein profiles of tumor clones. The approach enabled the identification of distinct cell populations within a tumor, which differ in gene activity and protein function.
Researchers have developed a machine learning model that can help clinicians assess uncertain variants in prenatal genetic testing, providing more accurate diagnoses and clearer information for families. The approach uses tissue-agnostic episignatures to overcome limitations in epigenetic testing.
A new review article discusses how artificial intelligence can predict disease trajectories and enable precision medicine strategies for inflammatory bowel disease. AI-based systems can standardize interpretation of endoscopic images, detect mucosal healing, and support recognition of dysplasia in patients with long-standing colitis.
Assistant Professor Yingxue Zhang's project aims to develop urban AI models that can efficiently process vast amounts of human-generated data to optimize commute times, traffic safety, and more. The model will utilize offline reinforcement learning to tackle spatial-temporal dynamics in urban life.
A new blood test, ADLiB, combines genetic clues with machine learning to identify patients most likely to have lymphoma. The platform analyzes cell-free DNA and prioritizes patients who need a tissue biopsy, helping clinicians diagnose the disease more quickly.
Hyunsoo Lee, an SNU undergraduate, presents research in generative visual computing at leading conferences NeurIPS, CVPR, and ECCV. His work spans image editing, human motion, and 3D content generation, leveraging pretrained generative models to produce consistent outputs.
Researchers developed a machine learning framework that predicts microbial contamination and estimates potential public health risks from routinely measured water quality indicators. The approach, called ML-QMRA, achieved high accuracy in predicting pathogen concentrations and their associated health risks.
Non-invasive approaches are expanding options for assessing portal hypertension, with elastography techniques and biochemical markers showing high sensitivity and specificity. AI-powered predictive models combine clinical data to improve diagnosis, but should not replace invasive HVPG, which remains the gold standard.
Researchers found that next-generation reasoning LLMs o3-mini and DeepSeek-R1 reproduced racial and gender stereotypes in generated clinical content. The models overrepresented Black populations in stereotypically associated conditions and exaggerated the majority gender, mirroring issues previously observed in GPT-4.
Researchers developed a method for generating personality assessment questionnaires with ChatGPT from any source text, including the DSM-5 and an astrology textbook. The generated questionnaires showed high internal consistency within personality clusters and could predict life outcomes like depression, anxiety, and well-being at level...
UniSpec delivers lossless LLM acceleration without retraining while adapting automatically to different hardware platforms and multilingual workloads. The framework achieves up to 2.6× faster inference than existing methods across multiple models, hardware, and languages.
Jiaqi Ma's $660,307 grant aims to develop tools for understanding how individual components of training data affect large AI systems. This project will improve the performance and reliability of widely used technologies like language models and recommendation systems.
Researchers have developed a new photonic architecture that enables scalable spatiotemporal interleaving networks for high-density integrated photonic convolution. The SPIN (Spatiotemporal Photonic Interleaving Network) framework reduces waveguide complexity and increases programmability in wavelength-domain interleaving, enabling comp...
Tianjun Sun's research develops better ways to measure human behavior and abilities, ensuring AI systems are accurate, fair, and trustworthy. Her work combines psychological measurement with AI, aiming to ground AI assessments in scientific standards.
Researchers at Harvard John A. Paulson School of Engineering and Applied Sciences developed an AI recommendation model that incorporates reinforcement learning to adjust to the uniqueness of each user. This approach improved human-AI performance over traditional one-size-fits-all decision support.
A new project supported by DARPA will study AI systems to determine how to train them to withstand failures, attacks, and unexpected situations. The goal is to develop self-improving AI for safety, enabling AI systems to recognize weaknesses in their reasoning and improve behavior over time.
A team of researchers at North Carolina State University has created a novel approach to optimize vaccine distribution by combining machine learning with column generation. This method accelerates run-time for the optimization model by 79.1% while maintaining high-quality solutions.
A Singapore team developed a machine-learning tool that accurately predicts liver cancer recurrence after surgery, outperforming the TNM staging system. The tool identifies two biologically distinct patterns of recurrence, enabling personalized approaches to risk prediction and targeted therapies.
A new study by researchers at Johns Hopkins University highlights a significant disconnect between those who use AI health tools and those who create and fund them. The study reveals that key stakeholders have fundamentally different definitions of value, usability, and cost, creating systemic barriers to technology adoption.
Researchers developed a novel AI framework that optimizes investment decisions directly while accounting for risk. The study found that conventional forecasting-based approaches were outperformed by the decision-focused model in terms of risk-adjusted performance and wealth accumulation.
A global assessment found that only 63.3% of lakes worldwide meet good water quality standards, exposing nearly half of the world's population to potential health risks associated with freshwater security. Strong regional disparities were observed, with Europe and North America faring better than Asia, South America, and Africa.
A new study combines satellite imagery, environmental data, and habitat surveys carried out by citizen scientists to create detailed maps of lowland heathland. The approach enables conservationists to identify small pockets of heathland that can often be missed by broader national mapping products.
Researchers have created an AI model that accurately predicts cardiac index, a metric used to evaluate heart function, using non-invasive sensors on patient skin. The system demonstrates potential for accessible cardiovascular assessment beyond major hospitals and specialized clinics.
Researchers developed a physics-based framework to predict temperature-driven VOC emissions from automotive paint sludge. Higher temperatures increase the release rate of VOCs, with moderate changes leading to substantial increases in quantity and speed of diffusion.
Researchers developed an inverse-design framework to optimize magnonic crystal design, identifying unconventional lattice structures with large band gaps. The approach enables the exploration of previously unexplored material systems and device dimensions, paving the way for high-speed spin-wave computing and energy-efficient devices
Researchers at MIT developed a new technique called VLASH that allows robots to predict their future position, enabling smoother motions and quicker reactions. This breakthrough doubles the speed of robots performing tasks like pick-and-place and boosts performance in dynamic activities.
The review explores how integrating Federated Learning (FL), Reinforcement Learning (RL), and Natural Language Processing (NLP) can overcome modern NLP system limitations, such as protecting user privacy and adapting to changing environments. The study presents a unified framework that combines FL, RL, and NLP as three co-equal pillars.
Researchers at KAIST have developed Stable-GFlowNet, a new AI safety verification framework that uncovers seven times more hidden vulnerabilities in AI than existing methods. The technology is expected to serve as a foundation for developing safer and more trustworthy generative AI models.
Researchers at Duke University have developed a method to systematically develop novel probiotic and prebiotic combinations to maintain gut health and treat gastrointestinal diseases. The approach uses machine learning and automation to explore complex interactions between microbes, nutritional sources, and the environment.
Researchers have developed an AI tool that can detect online propaganda in Kinyarwanda, a Bantu language spoken by 350 million Africans. The dataset, called KinyaProp, provides examples of misinformation in Kinyarwanda for large language models to learn from and recognize.
A KAIST research team developed a next-generation world model that learns executable theories from observation alone. The Neural Theorizer (NEO) model discovers reusable primitives and composes them into executable programs to explain new situations.
The health tech industry is evolving with AI-powered wearables that enable real-time data interpretation, reducing centralized infrastructure demands. Pharmaceutical AI tools like NoHarm automate reviews, freeing up resources for medication errors. These innovations transform healthcare, improving care and patient outcomes.
Researchers at Harvard John A. Paulson School of Engineering and Applied Sciences have developed a new AI framework called Orla that streamlines building and running AI workflows. In tests, Orla reduced computing costs and response times without sacrificing quality.
A team of researchers at Harvard and Max Planck Institute have developed three new functional components for photonic microchips using an inverse design algorithm. The compact designs are about 500 times smaller than conventional designs and offer a path toward higher-performance integrated light technologies.
The City University of New York has received an $18.1 million NSF award to create a cloud-programmable national laboratory that uses artificial intelligence and robotics to speed the discovery, design, and production of advanced bio-inspired materials. Researchers nationwide will have remote access to automated tools for developing sus...
Researchers have developed an AI-powered framework that combines multiple AI technologies with automated experiments to accelerate the discovery of advanced energy materials. The '4th+ paradigm' approach enables near-atomic-level accuracy in predicting material properties and rapidly analyzing experimental data.
Gert Aarts, a renowned physicist, has been awarded 1.58 million euros in funding from the Wübben Foundation Science to establish an Advanced Professorship at Bielefeld University. He will focus on linking theoretical physics with machine learning and expanding research on strongly interacting matter.
Researchers used AI and single-cell technology to study the 3D genome in brain cells from individuals with Alzheimer's disease. They found increased compartment mingling, reduced gene activity, and altered brain cell organization. The study identifies 3D genome organization as a key layer of Alzheimer's biology.
Researchers designed an end-to-end workflow to identify new blue OLED materials using AI and quantum chemistry. They developed a virtual library of over 19,000 molecules and used machine learning to select promising candidates, which were then experimentally evaluated and found to have high color purity and efficiency.
A Tulane University team is using AI to discover new superconductors, which could improve the nation's electrical grid, medical imaging, and quantum computing. The project combines high-fidelity calculations, physics-aware AI, and experimental measurements to accelerate discovery.
Researchers used machine learning to analyze thousands of automated experiments and accurately predict how new material compositions will respond to heat, identifying the most promising materials. This approach gives scientists a roadmap for developing more durable perovskite solar cells that can withstand real-world operating conditions.
A novel cross-modal fusion framework integrates low-altitude drone RSI with ground robot LiDAR-inertial measurement unit (IMU) odometry to create accurate digital models of orchards. The system achieved localization accuracy on the order of a few centimeters, demonstrating robustness to seasonal variations and long-term drift.
Researchers developed an AI framework for detecting and managing microplastics in wastewater treatment systems. The system uses computer vision and machine learning to predict removal efficiency and identify pollution sources. While AI can complement chemical analysis, major challenges remain before these tools can be widely deployed.
Researchers developed a framework to integrate AI into hospitals, emphasizing patient care, staff experience, and economic sustainability. The Total Mission Value framework aims to ensure high-quality patient care remains the top priority amidst AI's transformative potential.
Exposure to even moderate levels of multiple air pollutants during critical stages of pregnancy may increase the risk of preterm birth. The study found that a mixture of ozone and fine particulate matter posed the strongest relationship to early preterm birth.
Researchers developed an AI platform, PeptiVerse, to predict key properties of peptides, enabling early assessment of drug potential. The open-source platform allows users to evaluate ordinary and chemically modified peptides, streamlining the discovery process.
A new approach combines AI with high-resolution mass spectrometry and toxicology databases to predict biological effects of environmental chemicals. This framework helps researchers prioritize candidates for laboratory testing and health risk assessment.