Most AI systems don’t fail when things are normal; they fail in rare, unpredictable situations. One idea stuck with me from my recent podcast conversation: building AI for the real world is less about making models smarter and more about making systems reliable when things go wrong. What’s interesting is that a lot of the engineering effort goes into handling edge cases, the scenarios that rarely happen, but matter the most when they do. It changes how you think about AI entirely. It’s not just a model problem; it’s a systems problem. Curious how others here think about this: Are we focusing too much on model performance and not enough on real-world reliability? submitted by /u/vitlyoshin
Originally posted by u/vitlyoshin on r/ArtificialInteligence