THE INTERNET IS THINKING
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 ↗6025 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
Development and implementation of an AI-based handwriting recognition tool for math education: improving students’ attitudes and computational thinking ↗
Handwriting recognition for mathematical expressions remains challenging in educational settings, especially when solutions combine Korean text and symbolic notation. This study reports the design and classroom implementation of an AI-based handwriting annotation tool that uses the Mathpix OCR API to convert handwritten solutions into LaTeX and allows students to correct errors. An eight-session program integrated mathematics for AI (functions, loss functions, gradients, and neural networks) with data annotation and human-in-the-loop debugging. Eighty-three 12th-grade students at a high school…
- OpenAlex · Published · First archived
Artificial intelligence use, creative thinking disposition, and metacognitive awareness among EFL learners: a structural equation modelling approach ↗
Artificial intelligence is increasingly embedded in English as a foreign language (EFL) learning, yet its cognitive implications depend on how learners engage with it rather than on its use alone. This study examined the structural relationships among differentiated patterns of AI use, creative thinking disposition, and metacognitive awareness among undergraduate EFL learners. Using a quantitative cross-sectional correlational design, data were collected from 713 learners and analysed through structural equation modelling. AI use was conceptualised as cognitive-support use, creative-extension…
- OpenAlex · Published · First archived
The GPU Resilience Lottery: Understanding and Taming Real-World Hardware Errors in Heterogeneous AI Infrastructure ↗
The booming LLM demand is driving a rapid increase in the scale and heterogeneity of modern AI infrastructure. To achieve high availability for sustained LLM workloads, infrastructure providers must contend with frequent and severe hardware errors. Without a holistic resilience understanding, providers may inadvertently schedule AI jobs onto GPUs with high error risks and mitigate errors with significant costs, further degrading cluster availability. As a result, AI jobs are forced to ‘win’ a GPU resilience lottery for interruption-free execution. However, betting on a winning ticket, i.e., se…
- OpenAlex · Published · First archived
LLM See, LLM Do: Enhancing Repository-Aware Code Generation in LLMs Through Logical Context and Usage Knowledge ↗
Large language models (LLMs) have significantly advanced automatic code generation. Although they have demonstrated remarkable ability in writing standalone code, they still struggle with real-world repository-level coding tasks due to the lack of repository-specific knowledge. Existing studies typically employ retrieval-augmented generation (RAG) approaches to retrieve relevant context to guide LLMs’ generation. However, these approaches fail to ensure the logical relevance and the usability of retrieved results, leading to suboptimal code generation. To address this problem, we propose LogiC…
- OpenAlex · Published · First archived
A machine-learning model for predicting sand production onset under true triaxial stresses based on CFD-DEM simulation dataset ↗
Predicting and managing sand production has become a critical research focus in geo-energy systems such as natural gas hydrates, petroleum extraction, geothermal energy, and underground hydrogen storage due to the safety risks associated with sand production during and after extraction processes. This work identifies the dependence of the critical drawdown pressure gradient of sand production on particle size distribution, true triaxial stresses, and slot width. Based on Particle Flow Code (PFC) software, a validated computational fluid dynamics and discrete element method (CFD-DEM) numerical…
- OpenAlex · Published · First archived
Negotiating generative AI in online and distance higher education: lecturers' task-selective adoption in Yogyakarta, Indonesia ↗
Purpose This study examines how lecturers in Yogyakarta, Indonesia, adopt and negotiate the use of generative artificial intelligence (AI) in academic work, focusing on perceived usefulness, task-selective use, ethical ambivalence and institutional readiness. Design/methodology/approach A convergent mixed-methods design combined an online survey of 120 lecturers from public and private universities in Yogyakarta with semi-structured interviews with eight lecturers. Survey data were analyzed descriptively and compared across age, academic rank and institution type using the Kruskal–Wallis H tes…
- OpenAlex · Published · First archived
Machine learning-guided screening of urine cultures for urinary tract infection diagnosis according to AMCLI guidelines ↗
Urinary tract infections (UTIs) are among the most prevalent bacterial infections, and their diagnosis relies on the accurate interpretation of urine cultures. However, manual evaluation is time-consuming, operator-dependent, and susceptible to diagnostic variability. In this study, we propose a machine learning–guided pipeline for the automatic classification and segmentation of urine culture images, aligned with the most recent diagnostic guidelines. A dataset of 115 original chromogenic agar plate images, each corresponding to a unique patient, was acquired under standardized conditions and…
- OpenAlex · Published · First archived
A machine learning framework for uncertainty aware predictive optimization in smart environment and financial systems ↗
This research presents a novel Machine Learning Driven Methodology for Predictive Optimization (ML-DMPO). The framework introduces a theoretically grounded integration of uncertainty-aware predictive analytics with constraint-aware prescriptive optimization through a differentiable joint optimization objective. Unlike traditional decoupled approaches, ML-DMPO propagates predictive uncertainties directly into decision quality assessments. The methodology was validated through dual case studies spanning smart environments and financial systems. In smart building energy management, ML-DMPO achiev…
- OpenAlex · Published · First archived
Processes of Patterning: AI, Literacy, and the Epistemological Challenge ↗
Abstract This article develops a conceptual analysis of what occurs when central dimensions of literacy are outsourced to Generative Artificial Intelligence (GenAI) systems. Classical conceptions of literacy, sociocultural theories of mediated learning, and postdigital perspectives on knowledge practices, provide the theoretical foundation to further the argument that contemporary AI tools reshape the developmental processes through which judgement, independent thinking, and internal structures of meaning are formed. Using the Swedish AI‑based study platform Tentaklar as an example, we show ho…
- OpenAlex · Published · First archived
AI-enabled marketing analytics for SMEs: multimethod evidence on application potential and adoption conditions ↗
Abstract Artificial Intelligence (AI) is transforming marketing analytics by enabling advanced data processing, predictions, and decision support. However, small and medium-sized enterprises (SMEs) often face substantial barriers to AI adoption due to limited organizational, technological, and human resources. To bridge theoretical and practical perspectives, this study employs a hybrid approach combining a systematic literature review of 49 studies with an empirical survey of more than 100 SMEs. The findings identify organizational, technological, and user-related conditions, as well as AI ap…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.