Generative methods allow a computer to automatically distill the essence of a dataset and then produce novel examples that are indistinguishable from the original data. That's the promise, but getting there has been difficult. This talk focuses on recent advances in generative adversarial networks (GAN), describing ideas that have finally enabled the synthesis of credible high-resolution images. It also covers recent work by NVIDIA (StyleGAN) that makes the image generation more controllable by borrowing ideas from style transfer literature, and also leads to an interesting, unsupervised separation of high-level attributes (e.g. pose or identity in case of human faces) and inconsequential variation in the images (exact placement of hair, etc.).