Quality4.0 -- Transparent product quality supervision in the age of Industry 4.0

Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry

In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety. Improving the automated fault diagnosis methods using data and machine learning-based models is a central aspect of intelligent fault diagnosis (IFD). A major challenge in IFD is to develop realistic datasets with accurate labels needed to train and validate models, and to transfer models trained with labeled lab data to heterogeneous process industry environments. However, fault descriptions …

面向工业级 CRM 系统中增强型 ASR 模型的弱监督技术

Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems

针对通用预训练 ASR 模型难以适配行业语音识别的问题，提出弱监督微调行业特定 ASR 模型的方案，提升其在工业级 CRM 中的辅助效果并已实际应用。

LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering

The predominant approach to visual question answering (VQA) relies on encoding the image and question with a "black-box" neural encoder and decoding a single token as the answer like "yes" or "no". Despite this approach's strong quantitative results, it struggles to come up with intuitive, human-readable forms of justification for the prediction process. To address this insufficiency, we reformulate VQA as a full answer generation task, which requires the model to justify its predictions in natural language. We propose LRTA [Look, Read, Think, Answer], a transparent neural-symbolic reasoning f…

用C4描述代理AI系统：来自行业项目的启示

Describing Agentic AI Systems with C4: Lessons from Industry Projects

本文基于行业项目经验，提出针对代理AI系统风格的文档系统，通过风格导向的建模词汇和分层描述技术，实现透明且可持续的架构文档。

Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI

As research and industry moves towards large-scale models capable of numerous downstream tasks, the complexity of understanding multi-modal datasets that give nuance to models rapidly increases. A clear and thorough understanding of a dataset's origins, development, intent, ethical considerations and evolution becomes a necessary step for the responsible and informed deployment of models, especially those in people-facing contexts and high-risk domains. However, the burden of this understanding often falls on the intelligibility, conciseness, and comprehensiveness of the documentation. It requ…