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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 ↗5444 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.
- Hacker News · Posted on Hacker News · First archived
What it felt like when I was interviewed by an AI assistant ↗
Hacker News discussion.
- OpenAlex · Published · First archived
Artificial Intelligence—Coming to a City or a Desert Near You, Destroying the Planetary Economy and Environment ↗
The current fetish for artificial intelligence awaits the habitual fate of new transportation/communications technologies: wild over-investment in a financial bubble, sudden collapse, and eventual utility—consider the history of the bicycle, railway, radio, or internet. But unlike those technologies, whose ecological significance took decades to emerge, debates over artificial intelligence’s environmental impact are already underway, because construction of the data centers on which it depends has disclosed astounding needs for water and power. This article investigates the socioenvironmental…
- OpenAlex · Published · First archived
DFT Investigation on the Adsorption and Sensing Mechanisms of Transformer Fault Gases over MOF-Based Cu-BTC and ZIF-8 ↗
Real-time detection of the six characteristic gases dissolved in transformer oil (H2, CO, CO2, CH4, C2H4, C2H2), present from sub-ppm levels for C2H2 to several thousand ppm for CO2, is essential for early fault diagnosis in oil-immersed power equipment, yet conventional metal-oxide sensors require elevated operating temperatures and suffer from poor selectivity. Density functional theory is used here to evaluate two metal–organic frameworks, Cu-BTC and ZIF-8, as room-temperature sensing candidates for these gases. Adsorption energy, charge transfer, DOS, work-function variation and temperatur…
- OpenAlex · Published · First archived
Joint optimization framework for robot dance motion generation, aesthetic evaluation, and dynamic balance control based on conditional gans and safe deep reinforcement learning ↗
Artificial intelligence and intelligent algorithms provide effective technical support for generating complex robotic dance motions and maintaining stable execution under dynamic constraints. To address the separation among motion synthesis, aesthetic evaluation, and balance control in existing robot dance systems, this study proposes a joint optimization framework that integrates a conditional Generative Adversarial Network (cGAN), multimodal aesthetic scoring, and safe Deep Reinforcement Learning (DRL). A temporal motion representation model is first constructed to encode joint angles, angul…
- OpenAlex · Published · First archived
An Investment Analysis Research Enhanced Enterprise Development Algorithm for Bankruptcy Prediction ↗
Corporate bankruptcy prediction requires reliable optimization methods capable of identifying effective machine-learning configurations while maintaining robust global search performance. This study proposes an investment-analysis-inspired Enhanced Enterprise Development Optimizer (EED) for global optimization and KNN-based bankruptcy prediction. Rather than introducing additional generic perturbation operators, EED incorporates historical performance appraisal, covariance- and correlation-based search allocation, and drawdown-aware adaptive recovery into the original Enterprise Development Op…
- OpenAlex · Published · First archived
Putting Large Language Models into Environmental Context ↗
Abstract The emergence of large language models (LLMs) has fundamentally transformed information interaction, yet their application in environmental science and engineering (ESE) is hindered by multimodal data formats, dynamic context requirements, and hallucination risks. To transition from general-purpose models to domain expert agentic systems, this Perspective introduces a three-pillar framework for ESE-LLMs: domain perception, knowledge grounding, and action capability. Implementing these capabilities introduces potential pitfalls such as geographic representation bias, catastrophic forge…
- OpenAlex · Published · First archived
Artificial intelligence-assisted spatial correction of augmented reality projections; implementing radiation mapping during radioguided robotic surgery ↗
Abstract Translation of the drop-in gamma probe transformed radioguided robot-assisted prostate cancer (PCa) surgery. This study evaluated whether digitization of radiometric signals could allow conversion into more easier to interpret Augmented Reality radiation mapping (AR RM ) displays. 20 PCa patients were included who underwent robot-assisted sentinel node (SN)- and prostate-specific membrane antigen (PSMA) radioguided resections (24 SNs, 16 PSMA-avid nodes and 1 PSMA-avid primary tumor). Radiometric probe counts/s (dimensionless) synchronized with positional probe data allowed the retros…
- OpenAlex · Published · First archived
Machine Learning Classification and Adaptive Power Control for RGB Underwater Visible Light Communication ↗
Underwater visible light communication (UVLC) offers high data rates over acoustic and radio-frequency links but is highly sensitive to bubble-induced scattering. This paper presents an RGB wavelength-division-multiplexed UVLC system simulated in OptiSystem across clear-water, intermediate, and bubble-induced turbidity conditions. Eye-diagram features are classified using XGBoost, achieving 98.75% test accuracy and 96.18% (±2.17%) repeated cross-validation accuracy, statistically validated against SVM and Random Forest. A held-out intermediate-turbidity set, unseen during training, is correctl…
- OpenAlex · Published · First archived
Integrating Quantum Machine Learning and Ontology Matching for Efficient Semantic Web Services discovery ↗
Existing Semantic Web Service (SWS) discovery approaches based on classical machine learning suffer from limited representational capacity in high-dimensional semantic feature spaces and fail to fully exploit structured ontological knowledge for semantic alignment. This paper addresses these gaps by proposing a hybrid framework that integrates Quantum Machine Learning (QML) models with multi-type Ontology Matching to improve both the accuracy and semantic coverage of SWS discovery. In this paper, we discuss the use of quantum machine learning models and ontological matching techniques for the…
- OpenAlex · Published · First archived
Utilizing Artificial Intelligence in audiology and speech-language pathology education: Insights from ASLP students ↗
Background Emerging artificial intelligence-based technologies are increasingly influencing teaching and learning practices in healthcare education, offering new opportunities for student engagement, learning, and skill development. Objective The purpose of this study was to explore utilization of artificial intelligence in audiology and speech-language pathology education based on insights from students. Further, to explore their awareness, use, perceived benefits, barriers, and attitudes toward the integration of artificial intelligence. Method A cross-sectional online survey of 17 questions…
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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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