Articles tagged with Machine Learning
HSE economists found that AI models like ChatGPT and Claude tend to play 'too smart' and lose in strategic thinking games by assuming a higher level of logic in people than is actually present. The study replicated results from previous human participant experiments, showing LLMs adapt to opponents with varying levels of sophistication.
A new study analyzed over 460,000 scientific abstracts to identify key themes, trends, and research gaps in aging research. The study found a growing separation between basic biological studies and clinical research, highlighting the need for integration to translate laboratory discoveries into medical applications.
The new program focuses on advancing foundational research in AI, including innovation in language models and algorithmic efficiency. Google will support research grants, scholarships for students, and educational initiatives at the TAU Center.
Researchers developed RNACOREX, a new open-source software tool that identifies gene regulation networks in cancer. The tool analyzes thousands of molecules simultaneously to detect key interactions, providing an interpretable molecular map that improves understanding of tumors.
Researchers developed a tool to quickly identify resistant strains of S. aureus using genomic profiles and machine-learning models. The approach is based on gene-content information rather than highly detailed genomic profiles, making it more practical for real-life clinical contexts.
Researchers used machine learning to combine clinical data with biological markers, achieving 89.58% accuracy in predicting patient survival. The study identified key markers such as E2F8, WDR77, and hsa-miR-495-3p, which were associated with tumor growth and cancer development.
Researchers developed a machine learning framework, DeePKS, to improve density functional theory calculations for CO adsorption on metal surfaces. The framework achieves near-hybrid functional accuracy with high efficiency, enabling the exploration of complex catalytic systems.
Researchers from HIRI analyzed over 24,000 bacterial genomes to demonstrate that common machine learning methods can lead to biased results. They found that conventional approaches may fail to capture true resistance signals and limit accuracy in new strains.
A new AI framework uncovers simple, understandable rules governing complex dynamics in nature and technology. The AI generates equations that accurately describe complex systems, revealing hidden variables that govern their behavior. This approach offers scientists a new way to leverage AI for understanding complex systems.
A new study from the University of East Anglia finds that AI tools excel at speed and clarity, but human translators remain crucial for tone, cultural nuance, and natural sound. Generative AI can handle parts of video translation, but consumers still notice when something feels off.
A novel machine learning framework predicts protein binding on gold nanoclusters, revealing chemical principles governing biomolecule–gold interactions. The model enables scalable design of effective nanomaterials for biomedical applications.
Researchers developed an electronic nose that can detect and identify two common indoor mold species using nanowires. The e-nose measures changes in electrical resistance to gas molecules interacting with a sensing material, proving its potential for fast and objective monitoring of indoor air quality.
Researchers developed a transfer learning-enabled framework to predict mechanical properties of particle-reinforced aluminum matrix composites. The model achieves high predictive accuracy, addressing the challenges of limited data availability in traditional machine learning algorithms.
Researchers have launched a new multimodal medical dataset, Observer, capturing anonymized, real-time interactions between patients and clinicians. The dataset links video, audio, transcripts, and electronic health records to study subtleties like body language and environmental factors affecting care.
A new study warns that AI is racing ahead of safety checks in GP clinics, putting patients at risk. The research found that many GPs use AI tools without thorough evaluation or regulatory oversight, carrying risks like automation bias and loss of important social details.
Researchers developed an AI model that can detect coronary microvascular dysfunction using a common electrocardiogram, outperforming previous models in diagnostic tasks. The model can accurately identify a condition often missed in emergency department visits, providing a cost-effective and non-invasive way to diagnose serious heart co...
An AI-driven robotic assembly system allows people to build physical objects by simply describing them in words. The system uses generative AI models to build a 3D representation of an object's geometry based on the user's prompt, and then iterates on the design based on feedback from the user.
The University of Oklahoma researcher is working on a project using inverse design techniques and AI to create advanced materials that can quickly switch between conductive and insulating states. The goal is to reduce uncertainty in the discovery process and create a scalable material-design methodology.
A Harvard-designed bio-logger captures high-fidelity audio of sperm whale codas, which are later analyzed by machine learning models to uncover structured communication. Recent results show that sperm whales have their own alphabet and use vowels and diphthongs in their language.
A team of MIT engineers developed a deep-learning model that predicts how individual cells will fold, divide, and rearrange during a fruit fly's earliest stage of growth. The model achieved 90% accuracy in predicting the movement of 5,000 cells over the first hour of development.
A new method has been developed to generate valid confidence intervals for problems involving data that vary across space, helping researchers trust the results of certain experiments. This work can be applied to fields like environmental science and epidemiology.
A new dataset and model improve the efficiency of machine-learning interatomic potentials and their applicability to different chemical elements and material classes. The PET-MAD model uses a compact and denser dataset of 95,595 structures and an original neural network architecture, achieving robust simulations with minimal fine-tuning.
A new study used deep learning and large-scale computer simulations to identify structural differences in synthetic cannabinoid molecules that cause them to bind to human brain receptors differently from classical cannabinoids. Researchers found that these substances often trigger the beta arrestin pathway, leading to more severe psych...
Researchers at Florida Atlantic University have developed a deep learning model that detects and evaluates Alzheimer's disease (AD) and frontotemporal dementia (FTD) using EEG brainwave analysis. The model achieved over 90% accuracy in distinguishing individuals with dementia from cognitively normal participants.
Researchers developed an AI tool to identify patients with undiagnosed Alzheimer's disease using electronic health records, addressing underdiagnosis and healthcare inequities. The model achieved sensitivity rates of 77-81% across diverse populations, promoting fairness while maintaining high accuracy.
Researchers at the University of Washington have developed smart headphones that use AI to detect conversation cadence and isolate participants in a noisy soundscape. The prototype was tested with 11 participants, who rated filtered audio more than twice as favorably as baseline sound.
The University of Texas at Dallas has partnered with Tech Mahindra to facilitate collaboration on artificial intelligence (AI) innovation, skill development, and research. The partnership will provide opportunities for students and faculty to advance AI technologies, data science, and cybersecurity.
Researchers have developed an AI-driven method to uncover genetic interactions driving cancer progression. The approach highlights complex interplay between genes allowing malignant cells to gain momentum and reveals previously hidden cancer drivers.
A new advance in EEG imaging technology has improved mapping for epilepsy surgery, providing an accurate and non-invasive method. Pathological HFOs were found to be the most accurate biomarker for identifying epileptogenic brain regions, allowing for precise localization of seizure onset.
The Jackson Laboratory's CARDIOVERSE project uses AI, stem cells, and genetic variation to predict drug safety before human trials. The initiative aims to reduce cardiotoxicity and improve patient stratification in clinical trials.
A breakthrough AI system called OmniPredict can predict human pedestrian behaviors with unprecedented accuracy, revolutionizing self-driving cars and urban mobility. The model combines visual cues with contextual information to anticipate pedestrians' next moves, reducing the risk of accidents and improving traffic safety.
The MIT team developed a new AI-based controller that enables the robot to follow gymnastic flight paths, such as executing continuous body flips. The robot's speed and acceleration increased by 450% and 250%, respectively, compared to previous demonstrations, making it comparable to insects in terms of agility.
Researchers developed instance-adaptive scaling framework that uses process reward model to estimate difficulty of question, enabling LLMs to spend more compute on promising solution paths. This approach achieves comparable accuracy with existing methods while reducing computational cost by up to one-half.
A Kobe University team has introduced a new method using deep learning for creating tailored simulations that respect physical laws while being computationally efficient. The approach shows superior accuracy in simulating diverse physical systems, including those with chaotic behavior.
Researchers at MD Anderson have made significant discoveries in the treatment of rare bile duct cancers, with zanidatamab showing promising results. Additionally, a study identified RASH3D19 as a target to overcome treatment resistance in KRAS-mutant cancers.
A new study by Cambridge University Press & Assessment and Microsoft Research found that traditional learning activities like making notes remain critical for students' reading comprehension and retention. Note-taking, either alone or combined with large language models (LLMs), was more effective in helping students understand and reme...
The EBRAINS Summit 2025 will bring together experts to assess how neuroscience can drive medical progress, digital innovation, and responsible data use. Preliminary results from the EPINOV clinical trial, integrating virtual brain technology for epilepsy surgery planning, will be presented.
University of Missouri researchers are combining in-home sensor technology with artificial intelligence to monitor daily changes in ALS patients' health. The system uses machine learning to estimate a patient's score on the ALS Functional Rating Scale Revised, predicting potential problems before they occur.
Researchers at Pusan National University have developed an AI-powered design methodology for gerotor pumps, achieving a 32.3% increase in average flow rate and reducing pressure fluctuation by 53.6%. This innovation has the potential to improve engine durability, lubrication, and cooling, leading to quieter operation and increased reli...
A study compares five DNA foundation language models across 57 diverse datasets to identify their strengths and weaknesses in predicting gene expression, identifying genomic components, and detecting harmful mutations. The findings highlight the importance of selecting appropriate models based on specific genomic tasks.
A new AI system can accurately reconstruct hand muscle activity without sensors, enabling precise estimation of fine motor control. The technology has potential applications in sports science, rehabilitation, and human-machine interaction.
Researchers developed FocalCodec, an audio tokenization method that compresses speech efficiently, preserving its meaning and sound quality. The system was tested with positive results, showing nearly identical reconstructed speech to original recordings.
Researchers developed a machine learning tool, SAMP-Score, to identify compounds that induce senescence in p16-positive cancer cells. The tool successfully identified a promising candidate, QM5928, which triggers senescence without killing cancer cells.
A team of researchers has developed a detailed open map of emerging technologies, grouping 23,000 plus technologies into a multi-level map. The Cosmos 1.0 framework uses machine learning to analyze Wikipedia pages, books, and patents.
Researchers developed a reduced order model that accelerates calculations by identifying key features in flow data, enabling faster testing of geometry parameters for efficiency gains. The team plans to make their extensive database and model available online for other research groups.
Researchers introduced a method to make photonic circuits more adaptable without sacrificing compatibility, enabling the creation of practical photonic quantum neural networks. The approach achieved a classification accuracy above 92 percent in experimental tests, demonstrating its potential.
Large language models can learn to mistakenly link certain sentence patterns to specific topics, causing unexpected failures on new tasks. This shortcoming could reduce the reliability of LLMs used in safety-critical domains like handling customer inquiries and generating financial reports.
The National Center for Supercomputing Applications (NCSA) has received the 2025 HPCwire Readers' and Editors' Choice Awards for its outstanding research in artificial intelligence and energy systems. NCSA's premier supercomputing systems Delta and DeltaAI were utilized in two different domains, including a novel AI-based approach to m...
A survey of 134 young European family physicians found moderate overall readiness for AI, with varying levels of knowledge about current applications and usage. The study suggests a need for training and curricula tailored to primary care to address uneven readiness and low day-to-day use.
A new interpretable machine learning framework predicts syngas composition in biomass-plastic co-gasification, identifying key factors controlling product distribution. The model supports improved syngas quality, reduced experimental workload, and more efficient process optimization.
Researchers at the University of Utah have developed a new model to predict the snow-to-liquid ratio, which varies widely in the Western United States. By training a random forest model on high-quality data from 14 mountain sites, they were able to explain nearly half of the variability in snow density compared to existing methods.
A study at Technical University of Munich found that AI-simplified CT reports reduced reading time to two minutes and improved patient comprehension. Patients rated the simplified texts as more helpful and informative, with 82% finding them easier to read and understand.
A new report warns that AI can worsen health disparities in brain disease diagnoses if proper safeguards are not implemented. Researchers call for diverse perspectives, AI education, and strong governance to ensure equitable implementation.
PRX Intelligence, a new open access journal from APS, publishes high-impact research on AI and machine learning advancing physical sciences. The journal accepts submissions starting Feb 2026, with flexible article formats and no publication charges for 2026 manuscripts.
A study of 258 published novelists in the UK found that half believe AI will replace their work entirely. The majority also report using AI for non-creative tasks like information searches and editing text written without AI. However, many express concerns about copyright laws not being respected and originality being lost.
Researchers have developed a system called CytoDiffusion that uses generative AI to study the shape and structure of blood cells. The system can accurately identify normal blood cell appearances and spot unusual or rare cells that may indicate disease, outperforming existing systems in tests.
A team of scientists at the University of Tokyo has developed an automated, high-throughput system that uses machine learning to analyze droplets of biofluids for disease diagnosis. The technology relies on imaging drying processes to distinguish between normal and abnormal samples.
The initiative aims to decipher the basic function of human genomic sequences, enabling personalized diagnosis and therapy. The partnership will analyze genomic regions to identify underlying mechanisms that contribute to disease and uncover potential therapeutic targets.
Researchers at ETH Zurich developed an immersive digital co-pilot to support conservators in restoring historic buildings. The tool uses spatial computing technologies and augmented reality to provide structural analysis insights, enhancing decision-making and disseminating heritage knowledge.
Researchers developed a new approach called SEAL that enables large language models (LLMs) to update themselves permanently. This allows LLMs to learn from user input and improve their performance on tasks like question-answering and pattern-recognition. The technique improves accuracy and enables smaller models to outperform larger ones.