Deep learning will transform biology and medicine, but not in the way that many advocates think. Downloading ten thousand genomes and training a neural network to predict disease won't cut it. It is overly simplistic to believe that deep learning, or machine learning in general, can successfully be applied to genome data without taking into account biological processes that connect genotype to phenotype. The amount of data multiplied by the mutation frequency divided by the biological complexity and the number of hidden variables is too small. I’ll describe a rational “software meets bio” approach that has recently emerged in the research community and that is being pursued by dozens of young investigators. The approach has improved our ability to “read the genome”, and I believe it will have a significant impact on genome biology and medicine. I'll discuss which applications are ripe and which are merely seductive, how we should train models to take advantage of new types of data, and how we can interpret machine learning models.