Hey there! In this blog post, I’m diving into some fascinating AI topics. We have AI saving me an hour in my daily reading routine, real-world insights from AI engineers, and a look at a cool project called UniHDSA that’s revolutionizing document structure analysis with a unified approach. Plus, catch a glimpse of smarter AI models! Check it out for some amazing tech insights!

• How AI Shaves an Hour Off My Daily Reading Routine:
• AI Engineering in the real world: What does AI engineering look like in practice? Hands-on examples and learnings from software engineers turned “AI engineers” at seven companies
• chadmed/triforce:
• Qwen2.5-VL-32B: Smarter and Lighter:
• UniHDSA: A Unified Relation Prediction Approach for Hierarchical Document Structure Analysis: Document structure analysis, aka document layout analysis, is crucial for understanding both the physical layout and logical structure of documents, serving information retrieval, document summarization, knowledge extraction, etc. Hierarchical Document Structure Analysis (HDSA) specifically aims to restore the hierarchical structure of documents created using authoring software with hierarchical schemas. Previous research has primarily followed two approaches: one focuses on tackling specific subtasks of HDSA in isolation, such as table detection or reading order prediction, while the other adopts a unified framework that uses multiple branches or modules, each designed to address a distinct task. In this work, we propose a unified relation prediction approach for HDSA, called UniHDSA, which treats various HDSA sub-tasks as relation prediction problems and consolidates relation prediction labels into a unified label space. This allows a single relation prediction module to handle multiple tasks simultaneously, whether at a page-level or document-level structure analysis. To validate the effectiveness of UniHDSA, we develop a multimodal end-to-end system based on Transformer architectures. Extensive experimental results demonstrate that our approach achieves state-of-the-art performance on a hierarchical document structure analysis benchmark, Comp-HRDoc, and competitive results on a large-scale document layout analysis dataset, DocLayNet, effectively illustrating the superiority of our method across all sub-tasks.