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 ↗5572 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
Enhancing Coastal Safety with AI: A Lightweight Deep Learning Framework for Rip Current Segmentation via Architectural Co-Design and Model Compression ↗
Rip currents pose significant threats to coastal safety and to autonomous marine vehicles operating nearshore. However, deploying high-precision monitoring models on resource-limited platforms remains challenging. This paper proposes a comprehensive framework that combines a novel architecture and compression methodology. First, we introduce RipSegNet, integrating a re-parameterized backbone network to efficiently extract multi-scale features from amorphous, low-contrast rip currents; a content-aware upsampling neck structure to preserve diffuse boundaries; and a lightweight shared convolution…
- OpenAlex · Published · First archived
Predicting the Risk of Proteinuria Based on Blood Plasma Fourier‐Transform Infrared Spectroscopy and Machine Learning Algorithms From Pregnant Women and Newborns ↗
ABSTRACT Proteinuria, defined by an abnormally elevated concentration of proteins in the urine, serves as a critical biomarker for the early detection and diagnosis of preeclampsia. Elevated proteinuria levels adversely affect both maternal and foetal health, increasing the risk of preterm birth and low birth weight. This study focused on the identification of proteinuria in blood samples through Fourier‐transform infrared (FTIR) spectroscopy as a diagnostic tool for real‐time diagnosis. Blood plasma collected during birth was evaluated in four study groups: pregnant controls ( n = 12), pregna…
- OpenAlex · Published · First archived
Predicting unplanned readmissions in neurological patients: A large-scale AI-driven analysis using CHAID decision trees ↗
Background Unplanned readmissions in neurology signify care gaps, increasing costs, and morbidity. This study aimed to develop and internally validate an AI model to predict unplanned readmissions among neurological patients in Jordan. Method and materials We conducted a retrospective analysis of 2,795 adult neurology admissions (2022–2024). Patients’ diagnosis was grouped and analyzed based on the International Classification of Diseases and Related Health Problems, Tenth Revision. Nine prediction models were compared using Electronic Health Record (EHR) data. The Chi-square Automatic Interac…
- OpenAlex · Published · First archived
A Scoping Review of Vibe Coding and AI-Assisted Programming in University Computing Education ↗
Background: Generative AI coding tools are increasingly used in university computing education, including emerging practices commonly described as vibe coding. However, evidence on their educational effects is scattered across studies that differ in interaction patterns, instructional designs, assessment practices, and learning contexts. Methods: We conducted a scoping review using a PRISMA-guided process and synthesized 37 studies published between 2022 and July 2026. Five research questions examined student–AI interaction patterns, learning outcomes, task-level instructional strategies, asse…
- OpenAlex · Published · First archived
An AI assisted four layer teaching model for computational thinking cultivation evaluated in a 14 week classroom pilot ↗
Computational thinking has become a foundational competency for 21st-century learners, yet effective and scalable approaches to its cultivation remain insufficient. This study addresses three persistent gaps in the literature: the lack of systematically integrated AI-assisted pedagogical architectures, the inadequate operationalization of personalized learning paths for computational thinking, and the reliance on single-dimensional evaluation metrics. We propose a four-layer AI-assisted teaching model that, building on the conventional resource–adaptation–interaction–evaluation lineage of inte…
- OpenAlex · Published · First archived
The AI CEO: A conceptualisation and outline for future research ↗
Artificial intelligence increasingly contributes to strategic work, yet research has not conceptualised an AI role that contributes to this work at the top of the firm. Drawing on role theory and research on managerial work, we conceptualise this role as the AI CEO: a role in which an AI system performs the chief executive's strategic work. We develop two configurations of the role and propose how augmenting or automating the human CEO affects firm performance, relative to a human CEO acting alone. Cases from practice show that existing systems already perform this work but are rarely integrat…
- OpenAlex · Published · First archived
Integrating generative artificial intelligence facilitates faculty interaction network and role transition in a biostatistics curriculum ↗
In a two-year action-research study within an undergraduate biostatistics curriculum for nursing, pharmacy, and medical biotechnology students, we examined how generative artificial intelligence (GenAI) facilitates educators to design, supervise, and evaluate learning. Students used GenAI first as a technical tutor and then as a partner in knowledge co-creation in case-based statistical problem-solving. Teachers’ reflection records and 4697 LINE messages during the study period were analyzed using qualitative coding, GenAI-assisted semantic analysis, and social network analysis. Teachers’ role…
- OpenAlex · Published · First archived
Physics-constrained liquid neural network for wind-induced vibrations prediction of hanger cables on long-span suspension bridges ↗
Wind-induced vibrations (WIVs) of hanger cables on long-span suspension bridges could accelerate fatigue in suspension bridge suspension cables, damage damping devices, and other serviceability issues. The existing high-fidelity fluid-structure simulations are costly for real-time capable monitoring and purely data-driven approaches often violate basic physical laws and struggle to generalize under changing wind conditions. Prediction of hanger cable WIVs under strong non-stationarity and cross-domain distribution shifts has long faced challenges such as insufficient generalization, extrapolat…
- OpenAlex · Published · First archived
AI-Based Smart Honeypot System for Cyber Attack Intelligence ↗
The rapid growth of digital technologies has increased the number of cyberattacks targeting online systems and user data. Traditional security methods such as firewalls and intrusion detection systems are often unable to handle advanced and evolving threats effectively. To address this issue, this paper proposes an AI-Based Smart Honeypot System for Cyberattack Intelligence that combines machine learning, behavioral analysis, and deception techniques for detecting malicious activities in real time. The proposed system monitors user activities such as login attempts, typing behavior, session ti…
- OpenAlex · Published · First archived
Neural network-based multipartite entanglement classification with prior guidance from quantum uncertainty relations ↗
Quantum entanglement is a crucial resource in quantum information processing, yet its efficient, scalable and robust classification in multipartite systems remains theoretically challenging. Although supervised machine-learning has been applied to this task, most existing methods still suffer from high measurement costs, computational consumption, and weak noise robustness. In this work, by incorporating multipartite uncertainty relations as prior guidance, we propose a neural network approach to classify distinct Stochastic Local Operations and Classical Communication (SLOCC) multipartite ent…
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.