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 ↗6055 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
A strategic framework for transitioning from smart city to AI city in the Incheon free economic zone (IFEZ) ↗
Abstract The emergence of artificial intelligence (AI) as a foundational driver of urban systems necessitates a structural evolution beyond conventional smart city paradigms. In this context, the objective of this paper is to examine how the Incheon Free Economic Zone (IFEZ), South Korea, can be conceptualized and structured as a district-scale AI city model that moves beyond connected smart cities toward AI cities. The study is guided by the assumption that this transition requires not only technological upgrading, but also the coordinated integration of urban AI architecture, governance mech…
- OpenAlex · Published · First archived
Seq2DomML: Protein domain boundary prediction from bacterial protein sequences using a machine learning framework ↗
Abstract Purpose The identification of protein domain boundaries is important for designing stable single-domain constructs for protein structural solution. For proteins without experimentally determined structures, boundary selection often relies on conserved domain annotations, predicted structures, or iterative truncation screening. Seq2DomML is developed as a sequence-based machine learning framework for predicting residue-level domain partitions in bacterial proteins without requiring structural coordinates or homologous domain assignments as input. Methods Structural domain annotations f…
- OpenAlex · Published · First archived
Neurodiversity and generative AI in higher education: A sequential multi-method qualitative study of students’ interactions, motivations, and strategies ↗
Neurodivergent students have been largely overlooked in research examining student experiences with generative AI (GenAI) in higher education, creating critical knowledge gaps about inclusive educational technology use. This sequential multi-method qualitative study examined how neurodivergent students (NDS) and neurotypical students (NTS) interact with and experience GenAI in academic contexts. Using a sequential design, we employed a virtual revisit think-aloud protocol with 24 university students (12 NDS, 12 NTS) completing an academic task using Microsoft Copilot, followed by focus groups…
- OpenAlex · Published · First archived
Machine learning-based energy management and bidirectional power control for battery-fed electric vehicle traction drive ↗
Abstract Efficient energy management in a battery-fed electric vehicle (EV) traction system requires coordinated control of propulsion power, battery operating conditions, Direct Current (DC)-link voltage, and regenerative braking under rapidly changing driving conditions. This study develops a machine learning (ML)-based energy-management and bidirectional power-control framework integrating a lithium-ion battery, bidirectional direct current–direct current (DC–DC) converter, DC link, inverter-fed Permanent Magnet Synchronous Motor (PMSM), battery-state estimation, and regenerative-braking co…
- OpenAlex · Published · First archived
Physics-Informed Explainable Machine Learning for Hybrid State of Health and Remaining Useful Life Estimation of Lithium-Ion Batteries ↗
Accurate health assessment is essential for the reliable and cost-effective operation of lithium-ion battery energy storage systems. This study presents a physics-informed explainable machine-learning framework for hybrid State-of-Health (SoH) estimation and Remaining Useful Life (RUL) prediction under heterogeneous simulated operating and degradation conditions. A multi-cell degradation model represents capacity fade, resistance growth, thermal and operational stress, and nonlinear aging acceleration. Hybrid SoH combines capacity- and resistance-related health information, while end of life (…
- OpenAlex · Published · First archived
Applied AI for Intelligent Die and Tooling Lifecycle Management in Aluminium Manufacturing ↗
The manufacture of aluminium products by extru-sion, die casting and related machining relies on tooling whose condition affects quality, scrap and production continuity. This narrative review examines how applied artificial intelligence can connect design, production, inspection and maintenance into an intelligent tooling lifecycle. It draws on 33 research publications dated 2020–2026, comprising the original 30-publication corpus and three publications added through a targeted update. Separate guidance from the Project Management Institute informs an adaptation of Cognitive Project Managemen…
- OpenAlex · Published · First archived
Data-Driven Identification of GelMA Hydrogel Formulations with Bioink-Relevant Properties Using Design of Experiments (DoE) and Machine Learning (ML) ↗
Abstract Gelatin methacryloyl (GelMA) hydrogels are promising bioink candidates for extrusion-based 3D bioprinting, but their synthesis and formulation conditions must be optimized to achieve suitable gelation, printability, and mechanical stability. In our study, a design-of-experiments (DoE) and machine-learning-guided workflow was developed to optimize GelMA synthesis and identify bioink-relevant formulations. Two synthesis variables, the methacrylic anhydride-to-gelatin ratio (0.05–0.30 mL/g) and reaction time (1–4 h), were investigated in phosphate-buffered saline at pH 7.4 to determine t…
- OpenAlex · Published · First archived
Generative LLM for adaptive taxonomy-informed multi-label classification in construction safety ↗
Accurate hierarchical multi-label classification of construction accident narratives is vital, yet traditional methods struggle with the Occupational Injury and Illness Classification System (OIICS), which contains over 2000 labels across four attributes with four-level hierarchies and is subject to taxonomic updates. This paper proposes the Adaptive Generative Construction Classification (AGCC), a taxonomy-informed generative framework that reformulates coding as structured sequence-to-sequence generation. Each attribute is predicted at a variable depth using leaf codes, while taxonomy-inform…
- OpenAlex · Published · First archived
The hidden cost of an image: Quantifying the energy consumption of Artificial Intelligence image generation ↗
With the growing adoption of Artificial Intelligence (AI) image generation, in conjunction with the ever-increasing environmental resources demanded by AI, we are urged to answer a fundamental question: What is the environmental impact hidden behind each image we generate? In this research, we present a comprehensive empirical experiment designed to assess the energy consumption of AI image generation through diffusion models. Our experiment compares 17 state-of-the-art image generative models by considering multiple factors that could affect their energy consumption, such as model quantizatio…
- OpenAlex · Published · First archived
A Comparative Analysis of Generative AI for Architectural Visualization: A Quantitative Comparison Using Objective Evaluation Metrics and the Architectural AI Score (AIS) ↗
This study presents a quantitative metric-based comparison of four state-of-the-art text-to-image generative AI systems for architectural visualization: OpenAI Image Generation, Imagen 4, Midjourney v8.1, and Stable Image Ultra. The models were evaluated using the Architectural Prompt Dataset (APD-50), a controlled, expert-informed prompt set consisting of 50 architectural prompts across five typologies: Modern Residential Buildings, Public Buildings, Religious and Cultural Architecture, Interior Design, and Facade and Sustainable Architecture. For each prompt, four images were generated per m…
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.