Generative models

 for example, that a generative model trained on real images of faces can output new synthetic images of similar faces. For more details on how these models work, see Ian Goodfellow’s awesome NIPS 2016 tutorial write up. The architecture he introduced, generative adversarial networks (GANs), are particularly hot right now in the research world because they offer a path towards unsupervised learning. With GANs, there are two neural networks: a generator, which takes random noise as input and is tasked with synthesising content (e.g. an image), and a discriminator, which has learned what real images look like and is tasked with identifying whether images created by the generator are real or fake. Adversarial training can be thought of as a game where the generator must iteratively learn how to create images from noise such that the discriminator can no longer distinguish generated images from real ones. This framework is being extended to many data modalities and task.

https://medium.com/@NathanBenaich/6-areas-of-artificial-intelligence-to-watch-closely-673d590aa8aa#.fr334umzj