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 ↗5376 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.
- GitHub · Repository created · First archived
juliobsz/skills ↗
An agent skill that builds award-caliber websites from one line
- Hugging Face · Model / dataset date · First archived
Vxtzq/CrowdGPT — dataset update ↗
# CrowdGPT dataset **🥳🥳 ~400B tokens so far 🥳🥳** Current data from: https://huggingface.co/datasets/openbmb/Ultra-FineWeb-L3 , https://huggingface.co/datasets/HuggingFaceFW/fineweb and https://huggingface.co/datasets/codeparrot/github-code-clean Description: CrowdGPT dataset files converted to .bin format to be accessed directly as shards.
- OpenAlex · Published · First archived
Physics-Guided Multi-View Transformer with Block Attention Residuals for Cross-Condition Railway Bearing Fault Diagnosis ↗
Railway bearings experience speed- and load-dependent distribution shifts that complicate fault diagnosis. This study proposes a Physics-Guided Multi-View Transformer with Block Attention Residuals (PGMV-BAR). Characteristic-order prior tokens sequentially query envelope-order and time–frequency features and remain in the fusion sequence with a separate condition token. A two-stage strategy combines masked reconstruction, contrastive consistency, prior alignment, and supervised fine-tuning. Evaluation follows a source-only cross-condition protocol. Beijing Jiaotong University–Rail Autonomous O…
- OpenAlex · Published · First archived
Advancements in Tactile Biosensors: The Role of Additive Manufacturing and Machine Learning in Biomedical Applications ↗
Tactile biosensors areimportant for biomedical applications that require sensing mechanical stimuli like pressure, strain, and vibration. This review considers developments in tactile biosensor designs through additive manufacturing (AM) and machine learning (ML). The review defines four sensor mechanisms, piezoelectric, piezoresistive, strain gauge, and capacitive, and summarizes their working principles, materials, and biomedical relevance. Various additive manufacturing methods, such as direct ink writing, fused deposition modeling, stereolithography, and selective laser sintering, are disc…
- OpenAlex · Published · First archived
High-Information-Density Knowledge Graph Retrieval Augmentation for Large Language Models ↗
Retrieval-Augmented Generation (RAG) mitigates hallucination and knowledge staleness in large language models (LLMs) by injecting external knowledge into the context window, yet its effectiveness is rigidly constrained by attention dilution and inference cost. Existing knowledge-graph-enhanced methods follow a sequential “retrieve-then-compress” paradigm in which retrieval and compression are optimized independently, preventing the compressor from rectifying redundancy and noise introduced during retrieval and making it difficult to achieve globally efficient information allocation under a lim…
- OpenAlex · Published · First archived
Research on the Evolution Characteristics and Collaborative Detection Strategies for Bottom Oil-Spill Fires in Oil-Immersed Transformers ↗
Conventional single-type detectors generally face difficulties in achieving both early warning and fire-source localization for rapidly developing bottom oil-spill fires occurring in large oil-immersed transformers. In this study, a 500 kV transformer bottom oil-spill fire model is established to analyze the evolution of temperature field, smoke density, CO and CO2 concentrations, and thermal radiation, and to compare the response characteristics of various detectors. A stable high-temperature zone forms within 30 s after ignition, exhibiting a central temperature of 950–1000 °C and a distinct…
- OpenAlex · Published · First archived
Hierarchical Safe and Robust UAV Path Planning Based on Large Language Models ↗
Unmanned aerial vehicle (UAV) path planning is widely applied in fields such as Internet of Things (IoT) environmental monitoring, intelligent transportation, low-altitude logistics, and autonomous inspection. With the rapid development of UAV intelligence, natural language task planning powered by Large Language Models (LLMs) has emerged as a promising research hotspot. However, in safety-critical flight missions, LLMs suffer from issues such as unstable structured outputs, insufficient understanding of complex constraints, and unreliable responses to dangerous inputs, which can jeopardize th…
- OpenAlex · Published · First archived
Transformer-Convolution Pyramid Network for Multi-Class Kidney, Tumor, and Cyst Segmentation ↗
Background/Objectives: Chronic kidney diseases are fatal disorders, and early treatment is crucial for reducing mortality. Segmenting kidneys, tumors, and cysts from computed tomography scans has a significant role in assessing these diseases’ progression, determining their severity, and planning surgeries. However, accurate segmentation is challenging for many reasons. The objective of this study is to develop a hybrid network that can overcome these challenges. Methods: The proposed network was designed using (i) a patch-joining and swin transformer block to capture global features efficient…
- OpenAlex · Published · First archived
Artificial intelligence-based analysis of digital media engagement and user sentiment using machine learning techniques ↗
Abstract Digital media datasets consist of both text content and platform, campaign, temporal, and engagement data, but precomputed sentiment and engagement data can lead to target leakage and overestimation of model performance. This study introduces the Hybrid Textual Sentiment Engagement Learning Model (HTSELM), a leakage-controlled late-fusion model for sentiment and engagement analysis. The experiments are conducted with the machine-generated Social Media Engagement Dataset, which has 28 raw columns and 12,000 records. Deterministic Affective Proxies are removed prior to training for the…
- OpenAlex · Published · First archived
When AI Is—and Is Not—Needed: AI Literacy and Computational Thinking in Primary STEM ↗
Artificial intelligence (AI) is increasingly present in children’s lives, yet exposure does not necessarily lead to an accurate understanding of how AI works. This study examined whether a LEGO-based integrated STEM intervention could improve third-grade students’ AI literacy and computational thinking, particularly their ability to distinguish machine learning from rule-based automation and reason about training data, model error, and technology choice. An embedded mixed-methods, quasi-experimental pretest–posttest control-group design was conducted with 93 students from four intact classroom…
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.