The document describes how to generate and review images using machine learning. To generate images, a model learns from original images, searches topics to generate new images on, and iterates generating and scoring images until a threshold is passed. To review generated images, both input and generated images are disassembled into pixels, quantified by color, analyzed for tendencies, which are then compared between input and generated images to find the generated image most similar to the original tendencies.