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Woodpecker Software Testing
Woodpecker Software Testing
May 14, 2026 · Artificial Intelligence

Why AI Is Harder to Test and How to Build Robust Security Pipelines

As AI moves into finance, healthcare, and autonomous driving, real incidents expose the limits of traditional testing, prompting a shift toward AI security testing that tackles exploding input spaces, untraceable logic, and runtime drift through adversarial robustness, fairness audits, jailbreak checks, and supply‑chain verification, all integrated into CI/CD pipelines.

AI security testingCI/CD integrationadversarial robustness
0 likes · 8 min read
Why AI Is Harder to Test and How to Build Robust Security Pipelines
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 16, 2026 · Artificial Intelligence

Evidence Mining for Explainable AI: Methods and Applications

The talk introduces evidence‑mining techniques that extract supporting information from input text to improve model explainability, discusses the shortcut‑learning pitfalls of existing methods, and presents a new approach that enhances reliability and integrates with large‑model chain‑of‑thought compression for more interpretable, efficient reasoning.

AI researchevidence miningexplainable AI
0 likes · 4 min read
Evidence Mining for Explainable AI: Methods and Applications
Woodpecker Software Testing
Woodpecker Software Testing
Mar 15, 2026 · Artificial Intelligence

Why 95% of AI Models Fail: A Deep Dive into Model Evaluation Techniques

The article explains that a high‑accuracy model alone does not guarantee a deployable AI system; it details how inadequate evaluation leads to most production failures and presents a comprehensive, multi‑dimensional evaluation framework—including distributional robustness, fairness, explainability, temporal stability, and efficiency trade‑offs—plus practical CI/CD pipelines and common pitfalls.

AI quality assuranceFairness AuditModel Evaluation
0 likes · 7 min read
Why 95% of AI Models Fail: A Deep Dive into Model Evaluation Techniques
AI Frontier Lectures
AI Frontier Lectures
Feb 10, 2026 · Artificial Intelligence

Can an 8B Model Outperform GPT‑4 in Faithfulness Detection? Inside FaithLens

FaithLens is an 8‑billion‑parameter model that surpasses GPT‑4.1 and other large models on twelve hallucination‑detection benchmarks while providing high‑quality natural‑language explanations, thanks to a novel data‑synthesis pipeline, three‑dimensional filtering, and rule‑based reinforcement learning.

LLM hallucinationefficient inferenceexplainable AI
0 likes · 12 min read
Can an 8B Model Outperform GPT‑4 in Faithfulness Detection? Inside FaithLens
Kuaishou Tech
Kuaishou Tech
Jan 28, 2026 · Artificial Intelligence

BLM‑Guard: Explainable Multimodal Ad Moderation Using Chain‑of‑Thought and Policy‑Aligned RL

The paper introduces BLM‑Guard, an explainable multimodal ad‑moderation framework that combines interleaved‑modal chain‑of‑thought reasoning with a policy‑aligned reinforcement‑learning reward to detect hidden cross‑modal violations in short‑video ads, and presents a new benchmark that demonstrates state‑of‑the‑art performance across multiple risk scenarios.

Benchmarkad risk detectionchain-of-thought
0 likes · 12 min read
BLM‑Guard: Explainable Multimodal Ad Moderation Using Chain‑of‑Thought and Policy‑Aligned RL
HyperAI Super Neural
HyperAI Super Neural
Nov 20, 2025 · Artificial Intelligence

From 9,874 Papers to 15,000 Structures: MOF‑ChemUnity Rebuilds MOF Knowledge for Explainable AI

MOF‑ChemUnity constructs a scalable, extensible knowledge graph that links millions of MOF names and synonyms to over 15,000 crystal structures using LLM‑driven entity matching, enabling accurate, explainable AI‑assisted material discovery, water‑stability prediction, expert recommendation validation, and graph‑enhanced retrieval across diverse applications.

Graph RAGKnowledge GraphMOF
0 likes · 17 min read
From 9,874 Papers to 15,000 Structures: MOF‑ChemUnity Rebuilds MOF Knowledge for Explainable AI
Architecture & Thinking
Architecture & Thinking
Sep 12, 2025 · Artificial Intelligence

How Knowledge Graphs Turn Large Language Models into Trustworthy Experts

Integrating structured knowledge graphs with generative AI provides traceable, explainable, and high‑precision reasoning across domains such as medicine, finance, and law, through techniques like Retrieval‑Augmented Generation, graph neural networks, and adaptive planning, dramatically reducing hallucinations and boosting expert‑level performance.

AI hallucinationGraph Neural NetworkKnowledge Graph
0 likes · 12 min read
How Knowledge Graphs Turn Large Language Models into Trustworthy Experts
AIWalker
AIWalker
Jun 24, 2025 · Artificial Intelligence

How Multimodal Fusion Accelerates Paper Publication: Key Insights and Resources

The article surveys 117 recent multimodal‑fusion papers, classifies them into improvement‑based and combination‑based approaches, highlights representative works such as TimeXL, OGP‑Net, MMR‑Mamba and FusionSight, and provides a free collection of papers, classic models and code repositories for researchers.

AI researchComputer VisionDeep Learning
0 likes · 8 min read
How Multimodal Fusion Accelerates Paper Publication: Key Insights and Resources
21CTO
21CTO
Nov 17, 2024 · Artificial Intelligence

Why Large Language Models Threaten Component‑Based Software Development

Software developers must confront the challenges of large language models, which lack composable, testable components and transparency, raising issues of explainability, safety, legal ownership, and sustainability, and the article proposes building testable, interchangeable components to enable truly explainable AI.

AI ethicscomponent testingexplainable AI
0 likes · 8 min read
Why Large Language Models Threaten Component‑Based Software Development
JD Retail Technology
JD Retail Technology
Feb 26, 2024 · Artificial Intelligence

Explainable AI Forecasting and End-to-End Inventory Management in JD's Smart Supply Chain

The article details JD’s smart supply‑chain innovations, describing an explainable AI forecasting method that boosts prediction accuracy while maintaining interpretability, and an end‑to‑end inventory management model based on multi‑quantile RNNs that improves replenishment decisions, reduces costs, and enhances overall operational efficiency.

Supply Chainexplainable AIforecasting
0 likes · 14 min read
Explainable AI Forecasting and End-to-End Inventory Management in JD's Smart Supply Chain
Baidu Tech Salon
Baidu Tech Salon
Dec 14, 2023 · Artificial Intelligence

Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview

The Baidu Research Institute’s 2023 Paper Sharing Session featured eight cutting‑edge papers—from semi‑supervised web‑search ranking and hierarchical reinforcement learning for autonomous intersections to spatial‑heterophily graph networks, a unified XAI benchmark, differentiable neuro‑symbolic KG reasoning, and novel stochastic‑gradient and neural‑field loss analyses—showcasing advances across AI, data mining, and computer vision.

Knowledge GraphsNeural Fieldsartificial intelligence
0 likes · 10 min read
Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Oct 20, 2023 · Operations

How Transparent AI Boosts Trust in AIOps: Explainable Root‑Cause Solutions

This article examines the rapid growth of the Chinese IT operations market, explains why AIOps faces trust challenges due to opaque deep‑learning models, and presents AsiaInfo's transparent‑model and post‑hoc explanation engine together with three concrete explainable root‑cause analysis methods, concluding with future outlooks for trustworthy AIOps.

AI trustOperationsRoot Cause Analysis
0 likes · 13 min read
How Transparent AI Boosts Trust in AIOps: Explainable Root‑Cause Solutions
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Aug 6, 2023 · Artificial Intelligence

Explaining Image Recognition: Logistic Regression and Convolutional Neural Networks

This article introduces the principles of image recognition, compares traditional logistic regression with convolutional neural networks, demonstrates their implementation using Python code, visualizes model weights, and explains key concepts such as padding, convolution, pooling, receptive fields, and multi‑layer feature extraction.

convolutional neural networkexplainable AIimage recognition
0 likes · 12 min read
Explaining Image Recognition: Logistic Regression and Convolutional Neural Networks
DataFunTalk
DataFunTalk
Aug 4, 2023 · Artificial Intelligence

Self‑Explaining Natural Language Models: Collaborative Game Rationalization and Solutions for Spurious Correlations

The article reviews the growing importance of model explainability in high‑risk domains, analyzes the challenges of large language models, introduces the collaborative game‑theoretic RNP framework, and presents three mitigation strategies—Folded Rationalization, Decoupled Rationalization, and Multi‑Generator Rationalization—along with experimental results and future research directions.

Collaborative RationalizationLipschitz ContinuitySelf-Explaining Models
0 likes · 15 min read
Self‑Explaining Natural Language Models: Collaborative Game Rationalization and Solutions for Spurious Correlations
DataFunTalk
DataFunTalk
Apr 28, 2023 · Artificial Intelligence

Causal Inference and Uplift Modeling for Insurance Recommendation and Explainability

This article explains how uplift sensitivity prediction, Bayesian causal networks, and decision‑path construction are applied to improve insurance product, coupon, and copy recommendations on the Fliggy platform, detailing modeling approaches, evaluation metrics, and practical outcomes of the causal inference framework.

AB testingBayesian networksInsurance Recommendation
0 likes · 16 min read
Causal Inference and Uplift Modeling for Insurance Recommendation and Explainability
Python Programming Learning Circle
Python Programming Learning Circle
Mar 21, 2023 · Artificial Intelligence

A Survey of 10 Python Libraries for Explainable AI (XAI)

This article introduces Explainable AI (XAI), outlines its importance, describes a step-by-step workflow, and reviews ten Python libraries—including SHAP, LIME, ELI5, Shapash, Anchors, BreakDown, Interpret‑Text, AI Explainability 360, OmniXAI, and XAI—providing usage examples and code snippets.

Pythonexplainable AImachine learning
0 likes · 12 min read
A Survey of 10 Python Libraries for Explainable AI (XAI)
DataFunTalk
DataFunTalk
Nov 4, 2022 · Artificial Intelligence

Explainable Knowledge Graph Reasoning: Background, Advances, Motivation, Recent Research, and Outlook

This article reviews explainable knowledge graph reasoning, covering its background, core concepts, downstream applications, major reasoning methods, motivations for interpretability, recent advances such as hierarchical and Bayesian reinforcement learning, meta‑path mining, and future research directions.

Knowledge Graphexplainable AIgraph reasoning
0 likes · 18 min read
Explainable Knowledge Graph Reasoning: Background, Advances, Motivation, Recent Research, and Outlook
Model Perspective
Model Perspective
Oct 31, 2022 · Artificial Intelligence

Understanding SHAP: How Shapley Values Explain Black‑Box Models

This article explains the SHAP (Shapley Additive Explanation) method, its theoretical foundations in game theory, the computation of Shapley Values, various algorithmic approximations like TreeSHAP and DeepSHAP, practical code examples, and the strengths and limitations of using SHAP for model interpretability.

Model InterpretationSHAPShapley Values
0 likes · 11 min read
Understanding SHAP: How Shapley Values Explain Black‑Box Models
NetEase Yanxuan Technology Product Team
NetEase Yanxuan Technology Product Team
Aug 22, 2022 · Industry Insights

How We Built a Transparent, Explainable Traffic Allocation System for E‑Commerce

This article details the design and implementation of a transparent, explainable traffic‑decision system for an e‑commerce platform, covering background challenges, directional principles, selection and targeting methods, PV value estimation, allocation algorithms, and the supporting data‑engineering and visualization infrastructure.

PV estimationalgorithmic fairnessexplainable AI
0 likes · 22 min read
How We Built a Transparent, Explainable Traffic Allocation System for E‑Commerce
AntTech
AntTech
Jul 18, 2022 · Artificial Intelligence

Trusted AI Research at Ant Group: Advances in Computer Vision, Watermark Defense, Robust Machine Learning, and Explainable NLG

Ant Group’s security labs present a series of cutting‑edge AI research achievements—including hierarchical multi‑granular classification for computer vision, watermark‑vaccine defenses, multi‑modal document understanding, robust and explainable machine learning, and logic‑driven data‑to‑text generation—highlighting their commitment to trustworthy and secure AI applications.

AI SafetyComputer VisionData2Text
0 likes · 12 min read
Trusted AI Research at Ant Group: Advances in Computer Vision, Watermark Defense, Robust Machine Learning, and Explainable NLG
DataFunTalk
DataFunTalk
Jan 3, 2020 · Artificial Intelligence

Survey of Machine Learning Model Interpretability Techniques

This article provides a comprehensive survey of model interpretability in machine learning, covering its importance, evaluation criteria, and a wide range of techniques such as permutation importance, partial dependence plots, ICE, LIME, SHAP, RETAIN, and LRP, along with practical code examples and visualizations.

ICELIMEPDP
0 likes · 39 min read
Survey of Machine Learning Model Interpretability Techniques
DataFunTalk
DataFunTalk
Feb 20, 2019 · Artificial Intelligence

Recommendation Reasoning and Its Path Toward Future AI

This article explores why recommendation systems need reasoning, how recommendation reasoning connects to future strong AI, discusses explainability, causal inference, graph-based reasoning, and the philosophical underpinnings of AI, while also reflecting on practical examples from Hulu's recommendation platform.

Future AIRecommendation Systemscausal reasoning
0 likes · 25 min read
Recommendation Reasoning and Its Path Toward Future AI
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 13, 2019 · Artificial Intelligence

How Graph Neural Networks are Revolutionizing E‑commerce Recommendations

This article explores how cognitive computing combined with graph neural networks and text generation enables large‑scale interest mining, interpretable embeddings, and multi‑modal recommendation in e‑commerce, outlining platform implementations, explainable methods, and future directions for AI‑driven consumer engagement.

E-commerce AIRecommendation Systemscognitive computing
0 likes · 9 min read
How Graph Neural Networks are Revolutionizing E‑commerce Recommendations
Hulu Beijing
Hulu Beijing
Jun 8, 2018 · Artificial Intelligence

How Hulu Leverages AI for Video Recommendation, Content Understanding, and Ads

The article reviews Hulu’s 2018 iQIYI keynote on AI video applications, detailing how AI drives personalized recommendations, content analysis through computer vision and NLP, ad targeting across visual, linguistic, and semantic layers, and outlines the platform’s machine‑learning architecture and future directions.

AIHulucontent understanding
0 likes · 6 min read
How Hulu Leverages AI for Video Recommendation, Content Understanding, and Ads