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 ↗6073 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
AI-driven multimaterial top-down digital light processing for cell-laden multilayered vascular-like tubular constructs ↗
ABSTRACT Replicating the multiscale and multimaterial architecture of native vascular networks remains a major challenge in tissue bioprinting and regenerative medicine. Conventional fabrication methods struggle to reproduce the structural and functional complexity of vascularized tissues, limiting the development of physiologically relevant constructs. In this work, we build upon our previously developed AI-driven multimaterial digital light processing (DLP) bioprinting platform and its computational optimization framework to demonstrate its application to the fabrication of complex, cell-lad…
- OpenAlex · Published · First archived
Genetic algorithm-enhanced neural network for parameter optimization of quantum key distribution ↗
Quantum Key Distribution (QKD) parameter optimization is crucial for maximizing secure key rates under realistic finite-key conditions. However, the commonly used Local Search Algorithm (LSA) exhibits clear limitations in protocols with non-convex objective functions or extremely narrow feasible regions. This paper proposes a neural-network framework enhanced by a Genetic Algorithm (GA) for QKD parameter optimization as a practical alternative to LSA-based data generation. We develop a customized GA for QKD optimization to construct higher-quality training datasets, and combine it with a multi…
- OpenAlex · Published · First archived
Spiking neural transformer based confidentiality preserving framework for breast cancer detection in histopathology images ↗
Breast cancer diagnosis by medical imaging requires accuracy and high sensitivity to patient information. Even though artificial intelligence has made major progress in the field of histopathological image analysis, there are still limitations such as noise in the image, structural variability, and class imbalance, low interpretability of deep learning models, high computational complexity, and privacy risks. To overcome these limitations, this paper suggests a Confidentiality-Preserving Spiking Neural Transformer for Breast Cancer Detection (CP-SNT) to secure and robustly detect breast cancer…
- OpenAlex · Published · First archived
Integration of Artificial Intelligence into Architectural Education: An Online Model Proposal for Instructor Training for Studio Courses ↗
Generative Artificial Intelligence (GenAI) is driving a fundamental paradigm shift in architectural design, transitioning from deterministic drafting to algorithmic curation. While the Architecture, Engineering, and Construction (AEC) sector rapidly adopts these tools, academic curricula face a “Techno-Instructional Void.” This gap risks inducing a “Zero Order Thinking State” (ZOTS)—a cognitive passivity rooted in Cognitive Load Theory, where students uncritically accept unbuildable machine hallucinations. To address this, we applied a Design and Development Research (DDR Type 2) methodology t…
- OpenAlex · Published · First archived
An Interpretable Machine Learning Framework for Predicting Quality Outcomes in Printing Production Systems ↗
This study proposes an interpretable machine learning framework for predicting multiple quality outcomes in a printing production process characterized by complex and nonlinear relationships. The analysis is based on a real-world industrial dataset including key process parameters, namely Ink Weight, Top Coat Weight, Printing Speed, Varnish GC, and Temperature, along with two quality indicators: Solvent Residue and Isooctane Ratio. To capture nonlinear patterns and interaction effects, several machine learning algorithms, including K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosti…
- OpenAlex · Published · First archived
Heterogeneous multi-AUV cooperative area search with an LLM-assisted hierarchical planning framework ↗
In heterogeneous multi-AUV cooperative area search, existing methods often decompose the task region before assigning sub-regions to individual AUVs. However, coverage costs associated with platform capability differences, time-energy coupling, and temporal coordination constraints are often insufficiently considered during region decomposition, which may lead to suboptimal partitions and capability-task mismatches. To better account for these factors, a Voronoi-based region parameterization method is developed to formulate region decomposition and task allocation as a continuous optimization…
- OpenAlex · Published · First archived
STARS: Static Analysis-Guided Assertion Synthesis using Large Language Models ↗
Automated unit test generation promises to reduce the cost of software quality assurance, and hence, is attracting attention from both academia and industry. Yet, generating assertions that are executable, meaningful to developers, and able to catch faults remains an unsolved challenge. Existing approaches either randomly enumerate assertions that are plausible based on static program analysis without considering whether they naturally fit the test prefix or query an LLM to generate assertions based on local context only, such as the test prefix and the focal method. However, we observe that l…
- OpenAlex · Published · First archived
On the use of machine learning in damage identification of offshore jacket wind turbine: a virtual case study ↗
Abstract This study presents a high-fidelity, simulation-based Finite Element – Operational Modal Analysis – Machine Learning (FE–OMA–ML) framework for damage diagnosis of a jacket-type offshore wind turbine support structure. Finite-element-simulated acceleration responses are processed using Enhanced Frequency-Domain Decomposition (EFDD) to identify modal frequencies and damping ratios, which are then used as machine-learning input features. A progressive stiffness-degradation dataset covering 13 structural conditions and multiple sensor configurations is constructed to represent the gradual…
- OpenAlex · Published · First archived
Beyond symmetric models: explaining satisfaction through AIPA and machine learning approaches in the airline industry ↗
The airline industry is facing intense competition, where customer satisfaction (CS) is a critical determinant of long-term success. Therefore, it is essential to identify the critical factors that drive CS. Drawing on three-factor theory of CS, this study aims to examine the asymmetric effects of airline service attributes on CS. Moreover, the analysis also considers the segmentation between full-service carriers (FSCs) and low-cost carriers (LCCs). To achieve this, 8033 Skytrax reviews from 15 European airlines were analyzed using Asymmetric Impact-Performance Analysis (AIPA), further suppor…
- OpenAlex · Published · First archived
Experimentally Validated Adaptive Digital Twin for AI-Driven Fault Diagnosis and Predictive Health Monitoring of Multi-Machine Electric Drive Systems ↗
This paper presents an adaptive digital twin framework for intelligent fault diagnosis and predictive health monitoring of a coupled multi-machine electric drive system. The experimental platform consists of two synchronous motors and a synchronous generator arranged in an interconnected electromechanical configuration and instrumented through the Lucas-Nülle laboratory platform. A physics-based digital representation is integrated with experimental measurements to reproduce the electrical and mechanical behavior of the drive system under different operating conditions. Physical-to-digital res…
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.