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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 ↗5938 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 Regulatory Governance Framework for AI-Driven Financial Fraud Detection in US banking integrating OCC, SR 11-7, CFPB and FinCEN compliance requirements for model development, validation and monitoring lifecycles ↗
US financial institutions deploying AI-based fraud detection face a fragmented compliance landscape spanning four regulatory frameworks – OCC Bulletin 2011-12, SR 11-7, the CFPB AI circular and FinCEN BSA/SAR requirements – with no integrated governance life cycle connecting these requirements to model development, validation and monitoring practice. This paper presents the Regulatory Governance Framework for AI-Driven Financial Fraud Detection (RGF-AFFD), a three-tier governance architecture empirically anchored in a multi-study empirical program and corroborated by independent published evid…
- OpenAlex · Published · First archived
Advanced design of highly efficient graphene-based Ti-GaSb-Si based solar absorber for mining related applications optimized using machine learning ↗
The solar absorber is a necessity in renewable energy applications; it has been widely used and investigated in various materials and layer configurations. Analysis of the design and materials, with height as a parameter, can be developed for effective solar absorber design as a renewable energy solution. With the four diamond shapes with the graphene purpose in the new solar absorber design, it is named FDGSA for several thermal conversion applications in the current research. The three materials' composition with Ti–GaSb–Si (titanium, gallium antimonide, and silicon) has been used for the de…
- OpenAlex · Published · First archived
Deep underground energy storage solutions for data centers driven by artificial intelligence energy consumption ↗
The rapid development of artificial intelligence (AI) has driven an unprecedented surge in electricity consumption by data centers (DCs). Although renewable energy and ground-based energy storage technologies are being widely adopted, they still face inherent limitations in scalability, safety, structure and stability. This review proposes deep underground energy storage (DUES) as a new approach and discusses three specific technologies: hydrogen storage, compressed air energy storage, and flow batteries. Firstly, it is found that DUES can align with AI load curves. The three technologies span…
- OpenAlex · Published · First archived
A cognitive network framework for intelligent 5G systems with AI-based traffic analysis and adaptive optimization ↗
Abstract This paper describes the design of a secure and sustainable communication framework that is specifically targeted for interactive mobile learning platforms and the data of the users are protected through effectively applying lightweight cryptographic methods, characterizing an adaptive risk based authentication model for improving the access control, and utilizing a deep learning based encrypted traffic classifier for real time anomaly detection and the supporting energy-aware strategies of the system include intelligent task offloading and bandwidth-adaptive content delivery so these…
- OpenAlex · Published · First archived
Human Spokesperson Versus Artificial Intelligence Avatar and Authorship Disclosure in Digital Advertising: Effects on Performance Metrics ↗
Virtual influencers generated by artificial intelligence have emerged as a strategic tool in digital advertising, although evidence regarding their actual effectiveness in market campaigns remains limited to laboratory experiments based on self-report measures. This study examines the effect of advertising spokesperson type (human versus AI-generated avatar) and authorship disclosure (stating or not stating the origin of the advertisement) on the performance metrics of an actual campaign on the Meta platform. Through a field experiment with a 2 × 2 factorial design (spokesperson type × authors…
- OpenAlex · Published · First archived
Classification of real plastic waste using machine learning-assisted laser-induced breakdown spectroscopy ↗
Plastic type classification using laser-induced breakdown spectroscopy (LIBS) was evaluated using actual waste samples under non-optimized experimental conditions designed to approximate laser-sample variability encountered in industrial operation. The study addresses challenges in plastic sorting crucial for enhancing recycling efficiency and mitigating $$\:{co}_{2}$$ emissions and environmental harm from plastic waste overflow. Held-out testing using physically separate real-waste specimens was performed using data from standard materials for modeling and data from real waste for the test. T…
- OpenAlex · Published · First archived
Neural Network‐Based Runtime Monitoring and Control for Unknown Nonlinear Systems ↗
ABSTRACT Real‐time system monitoring and stabilization become especially challenging when the system dynamics are unknown. This article introduces a novel design for monitoring and stabilizing unknown nonlinear systems with measurement noise. The design utilizes a generic modelling framework by decomposing the system into a known tunable linear component and an unknown nonlinear component, which is approximated using a deep neural network (DNN) with bounded errors. A robust interval observer is designed to produce tight state bounds for the original system state, with the centre of the interva…
- OpenAlex · Published · First archived
Deployment-oriented evaluation of large language models for Arabic emotion classification ↗
Large Language Models (LLMs) are increasingly used for emotion classification, yet Arabic evaluations remain limited and typically emphasise predictive performance alone. This study presents a deployment-oriented evaluation framework for Arabic emotion classification that complements conventional accuracy-based assessment with measures of output validity, operationalised through coverage and unknown rate, together with strict versus covered evaluation. The evaluation is conducted on 1002 manually annotated Arabic sentences spanning five emotion categories, with very high inter-annotator agreem…
- OpenAlex · Published · First archived
AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulness ↗
The proliferation of Artificial Intelligence (AI) in workplaces is transforming how we work. While existing research on human-AI collaboration at work often prioritizes performance, less is known about their experiential outcomes. Through interviews with 24 employees across Information Technology (IT), service-based, and healthcare sectors, this paper examines AI’s impact on job satisfaction via perceptions of job decency and meaningfulness, now and in the future. Our results reveal that the anticipated impact of AI on overall job satisfaction varies with the occupational domain, with differin…
- OpenAlex · Published · First archived
From OSS to Open Source AI: an Exploratory Study of Collaborative Development Paradigm Divergence ↗
AI development is embracing open-source paradigm, but the fundamental distinction between AI models and traditional software artifacts may lead to a divergent open-source development paradigm with different collaborative practices, which remains unexplored. We therefore bridge the knowledge gap by quantifying and characterizing the differences in the collaborative development paradigms of traditional open source software (OSS) and open source AI models (OSM), and investigating the underlying factors that may drive these distinctions. We collect 1,428,792 OSS repositories from GitHub and 1,440,…
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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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