The Data Edge: AI, Procurement and FM
AI & Human Collaboration
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๐๏ธ ๐๐ต๐ฒ ๐ฑ๐ฎ๐๐ฎ ๐ฒ๐ฑ๐ด๐ฒ โ ๐ฑ๐ฎ๐๐ฎ ๐พ๐๐ฎ๐น๐ถ๐๐ ๐ถ๐ป ๐ฎ๐ถ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ In this episode, Erwin and Stephanie delve into the complexities of data quality in AI projects, emphasizing that messy data often leads to costly mistakes. They explore how human-AI collaboration and understanding the limitations of models like LLMs are crucial for success. ๐ ๐๐๐ฌ ๐ง๐ข๐ฃ๐๐๐ฆ * The common misconception that first data categorization is 100% accurate โ and why errors are part of the process * The reality of achieving high data quality and near automation (up to 95%) in data processing * Expectations vs. reality: Why clients sometimes expect AI to be a 'magic bullet' and how to set realistic goals * The importance of contextual knowledge and communication to improve model accuracy * Methodologies for training AI models as 'new employees', including leveraging human expertise and internal knowledge * A real-world construction project: data categorization challenges, including language issues (tablets as lozenges) * Differentiating LLMs like ChatGPT from specialized machine learning models * The role of human-AI cooperation in improving data quality and operational efficiency * Creating a knowledge center for clients through ongoing data training and model refinement * The value of building IP within organizations by developing tailored data solutions and models โฑ๏ธ ๐ง๐๐ ๐๐ฆ๐ง๐๐ ๐ฃ๐ฆ 00:00 Introduction: The impact of messy data on industry costs 00:30 Setting the stage: From data quality to correction hiccups 01:14 Why initial categorization often isn't perfect โ and it's normal 02:02 The misconception of AI producing perfect results immediately 02:50 Achieving high data quality and near automation possibilities 03:17 Managing client expectations around AI and data processing 04:05 Importance of communication about processes and contextual insights 05:14 When models don't perform as expected: Training methodologies 05:45 Example project in construction: Data categorization challenges 06:47 Using dashboards to identify and fix misclassified data 08:11 Language nuances affecting classification (e.g., tablets as lozenges) 08:58 Differences between LLMs like ChatGPT and task-specific ML models 10:16 The core distinction: General language models vs. specialized models 12:11 Why consistency and rule-based training are vital 13:24 Human-AI collaboration enhancing data accuracy 14:02 Implementing biases and industry knowledge to improve models 15:19 Building an organization's IP through data and model development 16:21 Potential for transparency: Sharing system rules with clients 17:05 Recap: Differentiating AI types and combining human expertise 18:18 Closing: Key takeaways on data, AI, and IP in projects
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