Exploring AI Modeling: Key Concepts and Applications

Data Integrity

Data integrity is crucial for effective AI implementation, as highlighted by Jaeden Schafer and Andrew Madsen. They discuss how inconsistent data definitions across business units can lead to significant issues when AI models are applied. Andrew emphasizes the importance of a strong data foundation, noting that AI can exacerbate problems if built on poor-quality data 1. He explains that AI can help categorize data and build semantic layers, aligning technical and business perspectives, but warns against relying on AI as a silver bullet 2.

AI is not magic; it's just math underneath. If you build it on top of junk, it's not going to be helpful.

--- Andrew Madsen

   

Synthetic Data

The use of synthetic data in AI models is debated, with Andrew Madsen expressing skepticism about its effectiveness. He argues that synthetic data can perpetuate errors, likening it to "the blind leading the blind," and suggests there is ample real data available for training AI models 3. However, he acknowledges the potential need for synthetic data in specific scenarios, such as financial services where real data is scarce. Jaeden and Andrew also discuss the future of open-source AI models, predicting a shift towards open-source solutions that could rival proprietary models in reliability and performance 4.

I think we'll get to the point where open source is just as good or better than the proprietary models.

--- Andrew Madsen