Crystal structure prediction has long fascinated scientists. There has been intense investigation over the last century ranging from simplistic rules to data-driven predictions and, most recently, generative artificial intelligence tools developed by academics and now deployed at scale by private companies like DeepMind. The author describes the timeline of crystal structure prediction and how machine learning has supplemented and, in some cases, replaced traditional approaches. The video article compares generative models including variational autoencoders, generative adversarial networks, and diffusion models and describes new efforts to condition these models to achieve inverse design of new crystal structures. Specific examples of xtal2png and CrysTens representations were given.