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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 ↗5986 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
Assessment of Lane Marking Retroreflectivity Using Vision-Based ADAS Data: A Double Machine Learning Approach ↗
Abstract Continuous monitoring of pavement marking quality is essential to ensure lane visibility and guidance for both human drivers and advanced driver assistance systems (ADAS), while standard photometric surveys are costly and infrequent. In such a framework, this study assessed whether the availability of vision-based ADAS lane keeping data can support scalable, maintenance-oriented marking monitoring when calibrated against standard measurements. A multi-source dataset was developed by spatially fusing camera lane keeping data, certified section-level retroreflectivity statistics from th…
- OpenAlex · Published · First archived
Enabling AI Agents for Semantic 3D City Models through Automated Domain Context Generation ↗
Semantic 3D city models offer rich urban information yet remain largely inaccessible to non-expert users due to their complex schemas, deep class hierarchies, and heterogeneous data structures. Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and code generation. But applying them to domain-specific databases requires contextual knowledge that far exceeds what can be manually written and maintained. This paper presents a framework that automatically generates domain context from CityGML data stored in 3DCityDB instances. The framework dec…
- OpenAlex · Published · First archived
A Cognitive Design System Approach to Enhancing Cognitive Alignment and Difficulty Control in Gen-AI Item Generation ↗
Automatic item generation (AIG) has been transformed by generative AI (Gen-AI), offering high efficiency and flexibility. However, post-hoc explanations of item difficulty do not provide the a priori control necessary for robust psychometric quality. This proof-of-concept study integrates Gen-AI with the cognitive design system approach grounded in information-processing theory. A structured prompt template operationalized difficulty manipulations across three cognitive components (Encoding, Inference/Mapping, Response) derived from verbal analogical reasoning theory. Three Gen-AI models (Clau…
- OpenAlex · Published · First archived
A method for generating urban flood control emergency plans based on large language models and graph retrieval enhancement ↗
ABSTRACT Urban flood control emergency plans are crucial for addressing flood disasters and are essential for safeguarding lives and property as well as ensuring high-quality urban development. Existing generation methods often rely on shallow retrieval, which can lead to fragmented knowledge; moreover, the generation of long texts lacks structural constraints, frequently resulting in the omission of key elements. To address this, this study proposes a framework for generating urban flood control emergency plans that integrates LLMs with graph retrieval. First, a flood control knowledge graph…
- OpenAlex · Published · First archived
The Impact of AI Anthropomorphism on Citizens' Willingness for Emergency Co‐Production: Evidence From a Survey Experiment ↗
ABSTRACT Although AI has garnered considerable attention for optimizing the efficiency of emergency governance, traditional emergency AI primarily focuses on the implementation of technical functions and often engages in human–computer interaction merely as an instrumental tool. It fails to address citizens' psychological perception needs in emergency scenarios by simulating human‐like traits, which Alimits its effectiveness in motivating citizens' willingness for emergency co‐production. To fully unlock the value of AI in emergency cooperation, this study, centred on post‐disaster rescue scen…
- OpenAlex · Published · First archived
Teacher authority, responsibility, and platform governance in generative AI-supported classrooms ↗
Abstract Public debate on large language models (LLMs) in education often asks whether artificial intelligence will replace teachers. This article argues that this question misses a more important shift: in postdigital classrooms, teachers’ authority is not disappearing but being reorganized. As generative AI reduces the centrality of teachers’ authority based on knowledge possession, teachers are increasingly expected to exercise three other forms of authority: epistemic-arbitration authority, pedagogical-orchestration authority, and normative-accountability authority. They must judge the rel…
- OpenAlex · Published · First archived
Using AI to assign building archetypes to individual buildings for enhanced resolution and accuracy in urban digital twin applications ↗
Urban digital twins benefit from granular, building-level data, yet classifying city quarters into meaningful typologies remains a challenge. This paper presents a machine learning-based approach to automatically assign a building archetype to individual buildings using 3D CityGML data including building functions as the sole input. The archetypes range from detached single-family housing to industrial and business parks, and are defined by geometric properties and locational context rather than socio-economic factors, ensuring broad applicability across research domains. Because the method re…
- OpenAlex · Published · First archived
A standardized AI score to benchmark educational chatbots ↗
The rapid integration of AI chatbots into education has created a need for objective methods to evaluate their pedagogical performance. This study introduces an AI Score, a composite metric designed to benchmark educational chatbots across four criteria: their initial performance, their robustness, their self-correction ability and their lack of reliability. The AI Score is calculated using a weighted formula and validated through a standardized test comprising highly discriminant multiple-choice questions. To validate the test, six platforms—ChatGPT, Copilot (Studio, then Chat), NotebookLM, G…
- OpenAlex · Published · First archived
Neofeudalism, transition, and AI ↗
This piece engages Arvidsson and Bonini's discussion of workhouses and platforms as a way of approaching questions of transition from capitalism. It defends the idea of neofeudalism, developing it in light of AI.
- OpenAlex · Published · First archived
Accelerated discovery of thermostable mRNA–lipid nanoparticle vaccines using data-efficient AI ↗
The instability of mRNA−lipid nanoparticles (LNPs) necessitates ultra-cold storage, limiting global distribution and their broader application in advanced delivery systems. Solid-state, water-free formulations enhance thermostability and enable integration into emerging delivery modalities such as microneedle patches. Previous efforts to stabilize mRNA−LNPs have been constrained by narrow formulation scope and low-throughput screening methods. Here we introduce Algorithm-Guided Experimental design for lipid Nanoparticle Thermostabilization (AGENT), an artificial intelligence (AI)-driven framew…
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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.
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