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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5969 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Metabolic modeling: the link between AI and multi-omics data to accelerate the discovery of antibiotic-resistant traits in ESKAPE pathogens ↗
The development of predictive understanding of bacterial resistance to antibiotics is one of the greatest challenges of modern medicine. Genome-scale metabolic models (GEMs) are mathematical representations of the metabolic network of a given organism assembled from genomic data that predict cellular activity across many environmental and genetic contexts. Because a single model can be solved under thousands of conditions, GEMs generate data at scales that support downstream analysis with artificial intelligence tools, helping to elucidate how pathogens adapt to host environments, evade immune…
- OpenAlex · Published · First archived
DisMed-LLM: De-Identifying Spanish Medical Text with Large Language Models ↗
Abstract Patient privacy and compliance with regulations such as the General Data Protection Regulation (GDPR) necessitate the robust de-identification of clinical texts. This study investigates the feasibility, computational cost, and limitations of de-identifying Spanish medical records using training-free Large Language Models (LLMs) compared to a supervised baseline. We evaluated five instruction-tuned LLMs on two Spanish clinical corpora (DisMed and MEDDOCAN) and benchmarked them against a fine-tuned multilingual BERT (110M parameters). On a held-out test set, the supervised baseline achi…
- OpenAlex · Published · First archived
Multimodal deep learning architecture for gym exercise recognition and real-time posture error detection using LSTM and RAG-based expert chatbot ↗
The growing popularity of home fitness has led to an increase in home-based fitness training. Still, the lack of supervision by a trained professional has resulted in improper form, thereby increasing the risk of injury during exercise. Automated computer vision systems have also been developed to monitor exercise, but these systems rely on rigid joint-angle thresholds or require large annotated datasets of improper exercise. This paper discusses an artificial intelligence (AI) fitness coaching system that uses real-time pose-based analysis of exercise and a chatbot with expert knowledge for i…
- OpenAlex · Published · First archived
Retrofitting language models to operate over bytes ↗
Abstract Recent advances in artificial intelligence (AI) have largely been driven by large language models, deep neural networks that operate over discrete units called tokens. To represent text, most large language models use words or word fragments as the tokens, known as subword tokenization 1 . Subword tokenization obscures fine-grained information, which is problematic, especially for scientific data—such as computer code or biological sequences—where meaning depends on the individual characters or bytes 2 . Models that instead operate directly on the byte encoding of text avoid these lim…
- OpenAlex · Published · First archived
CyberRoBERTa: A Lightweight Transformer-Based Language Model for IoT Intrusion Detection ↗
With the accelerated proliferation of the Internet of Things (IoT) and its widespread use in sensitive areas such as healthcare, homes, and factories, security has become an increasingly critical concern. IoT remains one of the most challenging domains to secure, lacking the manageable scale, well-maintained software, and homogeneous environments of easily secured systems. Large language models (LLMs) have recently been applied to improve IoT security; however, most applications treat LLMs as “off-the-shelf” tools, creating a domain mismatch because model representations, tokenizers, and data…
- OpenAlex · Published · First archived
Teachers’ Perceptions of Generative Artificial Intelligence in Early Childhood and Primary Education: Barriers, Ethical Concerns, and Perceived Impacts ↗
The growing integration of Generative Artificial Intelligence (GAI) into education has intensified the need to understand teachers’ perceptions of its implementation in the early stages of schooling. This study aimed to examine the barriers, ethical concerns, and perceived impact of GAI among Early Childhood and Primary Education teachers in Spain. An interpretive qualitative design was adopted using an open-ended questionnaire completed by 183 teachers from 15 Spanish autonomous communities. Data were analyzed through thematic content analysis in NVivo 14, resulting in a coding framework comp…
- OpenAlex · Published · First archived
Machine learning and multi-model mechanistic analysis of CuTaS3/AgTaS3 heterostructures for photocatalytic and electrocatalytic hydrogen evolution ↗
Machine learning and mechanistic kinetic-thermodynamic analyses were applied to CuTaS 3 /AgTaS 3 heterostructures for renewable energy applications involving photocatalytic and electrocatalytic H 2 evolution, using existing experimental data without new experimentation. Gaussian Process Regression identified an optimal pH of ~9.2, consistent with the experimental optimum (pH 9.02, H 2 = 1430 µmol/g), with leave-one-out cross-validation R 2 = 0.902. Four independent computational models converged on the same alkaline pH range, constituting a multi-model consistency check rather than independent…
- OpenAlex · Published · First archived
Web and social event signals for AI-driven mobile network demand forecasting ↗
Background Mobile network demand is increasingly volatile due to collective human activities such as concerts, sports events, and public gatherings. Traditional capacity planning methods, largely based on historical network indicators, struggle to anticipate these transient and localized demand surges. Recent advances in social sensing and artificial intelligence suggest that web and social signals can provide early indicators of real-world events, enabling proactive, event-aware network management in future mobile network infrastructures. Methods This paper presents a combined analysis and em…
- OpenAlex · Published · First archived
AI‐Based PV Fault Detection: Benchmarking Indicators and Feature Selection Methods ↗
ABSTRACT This article presents a benchmarking of indicators for automated defect detection in photovoltaic modules based on I‐V curves. The methodology is structured in three stages. First, an ideal defect‐free I‐V curve is generated through simulation to provide a reference for comparison with experimentally measured I‐V data under standard test conditions. Second, several electrical and curve‐shape indicators are computed to quantify the deviation between simulated and measured curves. Electrical parameters are expressed as ratios between experimental and simulated values, whereas curve‐shap…
- OpenAlex · Published · First archived
Coupled adaptive neural network control for active suspension of deep-sea mining tracked vehicles under hydrodynamic effects and multiple parametric uncertainties ↗
Deep-sea mining tracked vehicles are subject to substantial parametric uncertainty from hydrodynamic effects, soft-seabed interaction, suspension nonlinearities, and actuator degradation. This paper proposes a coupled adaptive neural network (CANN) strategy for an 8-DOF six-wheel half-vehicle active suspension. Unlike channel-wise suspension controllers, the method reconstructs a two-dimensional body-level force-tracking error from bounce and pitch accelerations. A nominal backstepping law supplies the baseline generalized force, while a bounded-feature neural estimator with a composite filter…
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How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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