Articles tagged with Artificial Intelligence
Researchers created a computational model that combines physiological signals, sensory input, and word information to construct human emotions. The model achieved an agreement rate of about 75% when compared to participants' self-reported emotional evaluations.
Researchers developed a wearable Revoice device that decodes speech signals and emotional cues to enable seamless real-time communication in people with post-stroke dysarthria. The device achieved a word error rate of 4.2% and sentence error rate of 2.9%, showing promising results for stroke rehabilitation.
The ADVANCE-6G, TEST-6G, and INES projects have provided IMDEA Networks with robust experimental platforms to tackle the technological challenges of future networks. The Institute has strengthened its research capabilities in advanced 5G and future 6G networks.
A team of researchers used Dungeons & Dragons as a testing ground for Large Language Models, evaluating their performance in long-term tasks. The models showed quirky behaviors but excelled in staying 'in character' and determining correct actions, with Claude 3.5 Haiku performing the best.
A new study reveals AI is being used to recreate deceased people's voices, faces, and personalities without consent, raising questions about ownership, exploitation, and power. The study identifies three dominant ways AI brings back the dead, including spectral labor, where digital remains are extracted and monetized.
Researchers developed AI-powered in silico labeling to analyze cell images without staining, preserving cell health. The system leverages context, such as cell shape and position, to accurately stain rare processes like cell division.
Researchers developed an energy learning hyper-heuristic algorithm to tackle complex task assignment challenges in heterogeneous UAVs. The algorithm outperforms traditional methods in convergence speed and solution quality, enabling efficient completion of tasks while avoiding obstacles.
Researchers have discovered the ExHOS nanomachine, which controls constitutive exocytosis by delivering spherical molecular packages to the cell surface. This process is essential for preserving cell fitness and vital functions such as communication and growth.
The University of Cincinnati College of Medicine has been awarded a $1.1 million AMA Transforming Lifelong Learning Through Precision Education grant to develop an AI-powered system for personalized learning and feedback. The project aims to improve the quality of medical education by providing real-time feedback on clinical reasoning ...
Developed by Mass General Brigham researchers, the autonomous AI system achieves high accuracy in detecting cognitive impairment using routine clinical documentation. The system includes five specialized agents that critique each other and refine their reasoning, just like clinicians would in a case conference.
A new study predicts train-induced environmental vibration using physics-based numerical simulations and limited field data, outperforming conventional models. The method improves existing workflows, reducing dependence on expensive field measurements.
Researchers at IIT develop optical microscopy technique that combines polarization and dark-field microscopy to observe cells with high contrast, preserving their natural conditions. The next step involves using AI to enrich images with molecular information related to diseases.
A chemist proposes a framework for shared model proteins to improve reproducibility and coordination in protein science. The proposal includes five widely used proteins and aims to establish minimal reporting requirements and curated reference datasets.
The article reviews AI's potential to improve mental health diagnoses, monitoring, and treatment while addressing unique challenges such as biased training data and patient privacy concerns. The authors argue for human-centered approaches and coordinated oversight to ensure responsible AI deployment in mental health care.
Engineers at the University of Pennsylvania have discovered that foams exhibit internal motion resembling deep learning in AI systems. The study suggests a common mathematical principle underlying both foams and AI training, with implications for designing adaptive materials and understanding biological structures.
Researchers developed a new technique called CLASSIC that enables large-scale testing of complex DNA circuits in human cells. The approach uses artificial intelligence and machine learning to analyze vast numbers of complete circuits at once, providing scientists with a clearer picture of the rules governing genetic part behavior.
AI tools enable scientists to publish more papers, receive citations, and become leaders earlier, but also narrow the scope of science by concentrating attention on data-rich domains. This 'lonely crowds' phenomenon reduces interaction among researchers, leading to overlapping research and a contraction in knowledge extent.
Scientists created biologically realistic artificial cilia using hydrogel, enabling precise control over their motion. The tiny structures can be powered by low-voltage electrical signals and have shown remarkable durability and versatility.
Oliver Zielinski's contributions to oceanography have significantly advanced understanding and stewardship of the ocean through innovative research and leadership. He has led applications of artificial intelligence for environmental monitoring and mentored early-career scientists.
Researchers developed EnCompass, a framework that enables programmers to improve agents' performance by allowing easy recovery from errors and testing different search strategies without major code rewriting. This improves the organization of core logic in complex AI systems.
Researchers have decoded the logic of microRNA strand selection using AI, revealing a conserved and programmable mechanism governing gene regulation. The study found that this decision follows conserved rules rather than chance, with mammalian microRNAs showing a strong bias towards a single dominant strand.
A team of researchers from Keio University has developed a novel system that uses Gaussian process regression to model and reproduce complex human motions. This enables robots to adapt to touch and interact with objects in dynamic environments, improving their dexterity and versatility.
Researchers develop a framework that mirrors human expert assessment to verify AI-generated answers, filtering out clear mistakes and identifying nuanced gaps. The system saves thousands of working hours and highlights the importance of contextual knowledge and internal documentation.
Large language models (LLMs) provide treatment recommendations for early-stage hepatocellular carcinoma (HCC) that align with clinical guidelines. However, their performance is limited in late-stage disease, where they prioritize tumor factors over liver function. LLMs should be used as a supplement to clinical expertise.
Researchers developed ZENN, an AI framework that recognizes and adapts to hidden differences in data quality. By using thermodynamic principles, the model distinguishes meaningful signals from uncertainty and noise.
Researchers developed PDJA, a novel AI-based attack framework that amplifies vulnerability and bypasses defenses in multi-agent reinforcement learning systems. The approach improves attack effectiveness and cross-layer vulnerability exploitation, opening new opportunities for evaluating the robustness of AI-driven autonomous systems.
A study published in PNAS found that over 60% of pairs of damaging genetic variants can restore enzyme activity when combined, challenging long-held assumptions about genetics. This phenomenon, known as intragenic complementation, has implications for rare disease diagnosis and treatment.
Researchers at Colorado State University used AI to modify antibodies into stable intrabodies that can visualize histone modifications in real-time. This allows for better understanding of gene expression and its relationship with cancer and other disorders. The team created 19 new antibody-based probes with a 70% success rate, signifi...
A machine-learning-based algorithm developed by Tokyo Metropolitan University researchers can accurately count sister chromatid exchanges (SCEs) in chromosomes, giving a more objective measurement. The accuracy rate is 84%, which could help diagnose disorders like Bloom syndrome with greater consistency.
Artificial intelligence is transforming water quality management by detecting subtle biological changes and triggering timely responses. AI-based sensors can analyze signals directly in the field for near real-time water quality assessment, predicting harmful algal blooms and pathogen outbreaks.
Researchers at Duke University have created a new method to use analog radio waves to boost energy-efficient edge AI, enabling devices to run powerful AI models without heavy chips or distant servers. The approach, called Wireless Smart Edge networks (WISE), achieves nearly 96% image classification accuracy while consuming significantl...
Shaun Gleason, director of the Partnerships Office at Oak Ridge National Laboratory, has been appointed to Tennessee's AI Advisory Council. He brings decades of experience advancing the use of AI technologies in various fields, including scientific discovery and manufacturing.
A new report by HEPI and Taylor & Francis explores the potential of AI to advance translational research and accelerate its real-world application. The report finds that AI has the potential to strengthen the UK's translational research system, but careful implementation, governance, and investment are necessary.
A new digital and legally binding fingerprint developed at the University of Copenhagen makes products impossible to counterfeit. Royal Copenhagen is among the first brands in the world to use this solution, resulting in immediate transparency across their distribution chain.
The Mount Sinai Health System has launched an AI-powered clinical trial-matching platform, PRISM, to help connect patients with life-saving trials. The platform uses machine learning to match patients with trials based on their electronic health records and diagnosis.
The collaboration aims to integrate AI-powered analytics with clinical expertise to accelerate data-driven cancer care. MD Anderson researchers will leverage SOPHIAs AI technologies to develop bioinformatics pipelines for rapid RNA-sequencing data interpretation.
The Naval Research Laboratory launched a remote sensing experiment to improve AI applications in hyperspectral imaging, providing a unique spectral fingerprint for each pixel. This will help detect subtle material differences and observe environmental change with unprecedented accuracy, particularly in coastal and aquatic environments.
A new study from Mayo Clinic introduces MedEduChat, an EHR-integrated AI agent providing accurate and patient-specific prostate cancer education. The tool delivers clear explanations based on each patient's health record, improving confidence and understanding of diagnosis and treatment options.
A new framework proposes managing AI-related risks by treating workers as co-designers of workplace transformation. The framework integrates job crafting, participatory oversight, and expanded WHS definitions to address relational harms and promote human labour values.
Researchers at University of Waterloo discover workaround for 'no cloning' problem in quantum computing by encrypting quantum information as it's copied. This breakthrough enables redundant and encrypted quantum cloud services, a crucial step towards building quantum computing infrastructure.
Researchers created an AI model to design peptides targeted by proteases, which are overactive in cancer cells. The peptides can be detected in urine, revealing specific types of cancer. This technology could lead to at-home tests for various cancers.
Researchers found that postgraduate students selectively engage with GenAI tools when revising critical reading reports, focusing on key analytical dimensions. This strategic engagement is driven by academic goals, supervisor demands, and career aspirations.
The use of generative AI in software development can lead to increased productivity, but also creates technical debt and hasty coding if not managed properly. Companies must prioritize fixing technical debt, develop clear guidelines for AI-assisted coding, and train developers on the hazards of AI coding.
Generative AI is increasingly used in classrooms, raising concerns about its impact on student learning. Researcher Gerd Kortemeyer explores the benefits and drawbacks of AI in physics education, suggesting that it can be a helpful tool when integrated into teaching methods that focus on critical thinking and collaboration.
The SHASAI project addresses cybersecurity risks in AI systems from design to real-world operation, combining secure hardware and software, risk-driven engineering, and real-world validation. The project will demonstrate its methods and tools in three diverse scenarios, supporting Europe's efforts to promote trustworthy AI.
A new synthesis of seismic research shows that AI combined with physical principles is rapidly transforming how scientists image the Earth's shallow and crustal structures. The study highlights the need for physics-guided AI frameworks that balance computational efficiency with interpretability.
Jundong Li, an associate professor at the University of Virginia, has received the 2025 Tao Li Award for his significant contributions to data mining and machine learning. His research focuses on developing models that can extract actionable insights from structured data, particularly graphs.
The first Editorial of 2026 discusses AI's impact on the scientific community. The journals use select AI tools, with encouraging results in data sharing among published research articles.
Recent progress in designing lighter, faster, and more resource-efficient multimodal AI systems is highlighted in a comprehensive survey. Vision token compression techniques and emerging architectures like mixture-of-experts selectively activate model components to increase capacity without proportional increases in computation.
A new AI model using deep transfer learning accurately predicted spoken language outcomes in deaf children after cochlear implants. The 'predict-to-prescribe' approach allows for earlier intensified therapy for children forecast to struggle with spoken language.
A research team at Huazhong University of Science and Technology developed an Ag/Sb2O3/Au molecular-crystal memristor array for real-time edge computing in smart power grid inspections. The device enables ultra-low-power switching, supporting non-volatile and volatile modes for tasks like image convolution and transient event detection.
A study published in EMBO Molecular Medicine has identified a combination of statins and phenothiazines that shows promise in treating aggressive neuroblastoma. The drug combination was found to impede tumour growth and improve survival rates in laboratory trials with mice.
The new framework reduces processing time by an average of 87.7% compared to the state-of-the-art approach, achieving cache hit rates between 70% and 90%. It demonstrates linear scalability, handling high query loads efficiently.
A team of researchers at Osaka Metropolitan University has created two models to detect and fix common labeling errors in large radiographic collections. The models achieved high accuracy rates for body-part classification and projection/orientation detection, showing promising results for improving deep-learning models in medical AI.
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.
Researchers developed an AI-based noninvasive tool to predict oropharyngeal cancer spread, identifying patients who should receive aggressive treatment or undergo de-intensification. The tool improved risk stratification, enabling more accurate predictions of survival and cancer spread.
A new study finds that large language models like ChatGPT significantly boost paper production, especially for non-native English speakers. However, this increase in AI-written papers makes it harder to separate valuable contributions from low-quality work.
Researchers at the University of Arizona developed a mesh sleeve that monitors leg movements using AI to detect early warning signs of frailty. The device sends only the analyzed results, reducing transmission and internet requirements.
As chatbots become more sophisticated, they are likely to generate and spread gossip, leading to reputational damage, shame, and social unrest. Drs. Joel Krueger and Lucy Osler warn that this 'feral' AI gossip can be particularly damaging due to its ability to operate unconstrained by human norms.