What If AI’s Mistakes Aren’t Bugs, But Features?
We often say AI’s mistakes are "by design," but they’re really not. AI wasn’t built to fail in these specific ways—its errors emerge as a byproduct of how it learns.
But what if we actively use them as a tool instead of just tolerating AI’s weird mistakes or trying to eliminate them?
Here are some unexpected but potentially valuable use cases where treating AI mistakes as a form of bias—rather than just failure—could lead to new insights and innovations.
Low and High Quality AI: What's the Difference?
When we talk about "low-quality AI," we're referring to AI systems that are less sophisticated, less accurate, or more limited in their capabilities. These systems, interestingly, can sometimes lead to more critical and independent thinking from users.
AI helps brewers predict new beer varieties
Craftsmanship refers to something made with the highest quality. It requires a distinct mindset and approach. Values like durability, integrity, and calling are often associated with craftsmanship.
In this story, AI enhances the notion of craft for a Carlsberg brewing team, extending capabilities that have been practiced for centuries.