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explainable ai

2,229 papers

#explainable ai Open access Sep 2026

666: KLEOPATRA DIE APOKALYPSIS IOANNOU ALS PALIMPSEST DER TATEN OCTAVIANS

The ‘666 = Cleopatra’ calculation: The AI considers this evidence extremely strong Francesco Carotta’s recent use of artificial intelligence to examine his long-standing linguistic and historical hypotheses represents an important shift: it transforms a controversial theory into one that can, at least in principle, be...

A.P.J. Hendriks Francesco Carotta · 0 citations
#explainable ai Open access Sep 2026

AI-Based Electronic Control Units for Software-Defined Vehicles: A Structured Review of Architectures, Techniques, and Hardware Implementations

The automotive electrical/electronic (E/E) architectureis undergoing a fundamental transformation from looselycoupled, function-specific electronic control units (ECUs)toward domain-centralized, zonal, and fully centralizedcomputing architectures, forming the foundation of emerg-ing software-defined vehicles (SDVs). In...

Shirshendu Roy · 0 citations
#explainable ai Dataset Open access Sep 2026

Agentic Ontology of Work (AOW)

A platform-agnostic semantic model for intelligent, autonomous, and governed enterprise work: 25 classes and 33 relationships across four layers (Perception, Cognition, Execution, Assurance), including a five-level Assurance Level scale for governed autonomy. Published as a whitepaper and as an OWL 2 ontology with a JS...

Manish Garg · 0 citations
#explainable ai Open access Sep 2026

Printing Neurons: When Engineers Merged Machines with the Human Brain

In April 2026, engineers at Northwestern University reported printed artificial neurons that generate electrical signals realistic enough to activate living neurons in mouse brain tissue. This article explains, for a general technical audience, what artificial neurons are and why flexible, biocompatible neural interfac...

Areeba Ghaffar · 0 citations
#explainable ai Open access Sep 2026

Ontropy: Ontological Orientation as a Solution to the Problem of Individuation

This paper reconstructs Ontropy (Ontropi / Yönelim Ontolojisi I) as a systematic philosophical research program centered on the category of ontological orientation. Beginning from a priority argument establishing Being as ontologically prior to certainty, the system poses the classical problem of individuation in forma...

Ali Asaf Karamollaoğlu · 0 citations
#explainable ai Open access Sep 2026

LEGAL CONTRACT ANALYZER - CLAUSE / RISK HIGHLIGHTER

The Legal Contract Analyzer — Clause/Risk Highlighter is an AI-powered document analysis system designed to help users understand and review contracts more efficiently. Legal contracts can contain lengthy clauses, complex terminology, obligations, conditions, deadlines, and potential areas of concern. Manually reviewin...

M Swathi, C Nikita, D Radhika et al. · 0 citations
#explainable ai Open access Sep 2026

Predicting and Explaining Economic Losses from Natural Disasters: A Post-Hoc Machine Learning and Explainable AI Approach

The results demonstrate that event-aware validation materially strengthens the methodological reliability of post hoc disaster-loss modelling while highlighting persistent limitations in cross-domain transferability and catastrophic-loss prediction.

Tarık Talan, Teyfik Giray, Adem Korkmaz et al. · 0 citations
#explainable ai Open access Sep 2026

子空间稳定、轴语义易位与连续谱:跨城市路网形态主成分表征的测量条件审计 (Stable Subspaces, Variable Axes, and the Morphological Continuum: Auditing Measurement Conditions in Cross-City Street-Network PCA)

【目的】 跨城市路网形态比较常依赖开放地理空间数据(OSM)与主成分分析(PCA)构建低维表征,但既有研究多预设离散分类范式,且普遍忽视数据质量异质性、空间尺度推移与特征共线性对主成分轴语义的构造性干扰。本文旨在检验跨城市路网形态主成分表征的前提假设是否成立,对数据适用性、空间尺度与特征共线性等关键测量条件实施系统审计,评估低维形态...

Wenhao Wang · 0 citations
#explainable ai Sep 2026

The Socio-Technical Architecture of Teacher Education (STATE): decoupling agency from infrastructure in digital teacher education

Teacher education in the Global South faces increasing pressure to integrate advanced digital technologies, yet motivation alone does not guarantee implementation. Grounded in Socio-Technical Systems theory, this study proposes the Socio-Technical Architecture of Teacher Education (STATE) framework to explain this disc...

Sanjay Ranjan, V. P. Joshith · 0 citations
#explainable ai Open access Sep 2026

666: KLEOPATRA DIE APOKALYPSIS IOANNOU ALS PALIMPSEST DER TATEN OCTAVIANS

The ‘666 = Cleopatra’ calculation: The AI considers this evidence extremely strong Francesco Carotta’s recent use of artificial intelligence to examine his long-standing linguistic and historical hypotheses represents an important shift: it transforms a controversial theory into one that can, at least in principle, be...

A.P.J. Hendriks Francesco Carotta · 0 citations
#explainable ai Book Sep 2026

Geospatial Artificial Intelligence for Urban Flood Risk Assessment and Resilience Planning

Urban flooding is escalating because of climate change, rapid urbanization, land-use change, and inadequate drainage, demanding advanced spatial decision-support systems. Geospatial Artificial Intelligence (GeoAI), integrating GIS, remote sensing, and deep learning, has become a powerful approach for flood risk assessm...

Shashikant Nishant Sharma, Rakesh Kumar · 0 citations

From tech blogs

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Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

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