It’s easy to think of deep learning as just something which chews on a load of data and makes a classification – the photo contains a dog; the audio waveform contains the word ‘hello’. But in recent years, a rapidly evolving approach called ‘Generative Adversarial Networks’ (GANs), which pits one AI against another to improve learning, has allowed deep learning to create new, highly-realistic outputs. The implications of the technology are huge, from virtual worlds through to rigorous testing of other machine learning technology, from patching gaps in datasets through to security. In this session we’ll look at how to design, train and use GANs, with practical examples from our AI research lab, the Digital Greenhouse. We’ll consider the improvements that have been made since GANs’ 2014 debut, and where they still fall short. We’ll conclude with some predictions of where this technology could head.