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2,395 papers
This paper introduces a novel framework for knowledge representation and reasoning that synergistically combines the strengths of neural networks and symbolic logic. The core claim is to construct a system capable of both learning intricate patterns from data using neural networks and performing precise reasoning and i...
Background Artificial intelligence (AI) and digital health technologies are increasingly shaping how public healthcare services are planned, delivered, and governed. In Egypt, national digital health reforms have created an urgent need to assess whether public healthcare institutions have the administrative readiness,...
Artificial-intelligence agents increasingly participate in the production, interpretation, circulation, and reuse of claims on which organizations act. This paper develops AI-nterprise, a nascent design theory and normative reference model for the human-agent enterprise: an organization in which people and artificial a...
This paper explores a novel approach to reasoning about temporal relationships by integrating temporal logic with neural networks. The core idea is to train a neural network to understand and apply the rules of temporal logic, thereby enabling it to reason more robustly and reliably about events and their temporal depe...
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Abstract Neurosymbolic AI and Zero Trust Architecture (ZTA) are increasingly proposed together as a combined approach to securing cloud-edge IoT continuums in federal and critical infrastructure sectors, on the premise that neurosymbolic systems can supply the explainable, robust threat detection that continuous-verifi...
A structured narrative review using predefined searches of Web of Science Core Collection and Scopus to synthesize empirical XAI applications across six water-research domains, with emphasis on method selection, model and data compatibility, explanation reliability, and operational implementation.
Abstract Cloud-edge Internet of Things (IoT) continuums increasingly rely on artificial intelligence to detect intrusions and enforce zero-trust policy at scale, yet purely neural detectors remain difficult to audit and purely symbolic engines struggle to generalize across noisy, heterogeneous telemetry. Building on a...
AI systems are designed to decline certain requests to avoid providing harmful or inaccurate information. However, refusals can unintentionally reduce users’ impressions of and trust in AI. Prior work has examined what an AI should explain when refusing a request, but offers no guidance on which AI should deliver the e...
Background Accurate discrimination between benign and malignant breast masses is essential for biopsy decisions and individualized management. This study aimed to develop an interpretable machine-learning model integrating artificial intelligence (AI)-structured digital breast tomosynthesis (DBT) mass features with per...
This research data package supports the manuscript entitled “Emission-prioritized explainable AI for auditing circular-economy and carbon-neutrality integration in project environmental assessment documents”. It contains processed document-level features, sentence-level evidence, country-sector indicators, topic/model...
Abstract—Abstract—Explainable AI methods such as SHAP,which is a tool for generating Shapley Additive explanations are widely used in health and fitness applications to give reasons for predictions , but their reliability is usually studied on static datasets. Most fitness apps collect user data incrementally, leading...
The review combines recent research findings with guidance on new investigation tracks for developing all-encompassing AI systems which both spread far-reaching outcomes and follow sustainability requirements.
Earth AI
GPT-Lab
· gpt-lab.eu
Oct 14, 2026
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post Building trustworthy AI: The role of AI harnesses, intent contracts and evals appeared first on GPT-Lab.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Professor Deb Roy contends there should be someone who is “answerable” for every piece of text in public life, no matter its origins.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
GPT-Lab
· gpt-lab.eu
Oct 12, 2026
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
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