AI-generated world: How Generative Adversarial Networks(GANs) are transforming whole industries today
Increasingly, the progress of AI has a significant impact on our lives.
Back in 2014, we first heard about the Generative adversarial networks introduced by Ian Goodfellow.
GANs are a machine learning algorithm that belongs to a family of generating models and are built on a combination of two neural networks, Generator which creates samples and Discriminator which tries to distinguish fake samples from real ones. 
The problem is similar to a counterfeiter vs. policeman competition, where first advances counterfeiting skills alongside increasing policemen expertise.
The usage of such technology allows you to generate photos that the human eye perceives as natural images. For example, an attempt to synthesize cats' photos mislead 67 % of experts who consider them to be real.
GAN technology is increasingly used to create content and data. For instance, creating pictures for an online store, avatars for games, or even virtual presenters for TV programs. GAN-generated synthetic data could even be used to teach other ML systems.
Another area where GANs were increasingly popular is face swap and transformation apps. Reface — Ukrainian app that allows automatically changing faces on photographs was names app #1 Face Swap app in AppStore
GANs can be used for various things, but what has caught the public attention is DeepFakes. Some experts conclude DeepFakes can have a tremendous impact on election manipulation, social engineering, automated disinformation attacks, identity theft, and financial fraud. 
From the commercial perspective, DeepFakes and GANs technology makes the biggest impact on the advertising industry.
It slowly removes the need to perform castings and hire models for advertising campaigns since AI can synthesize photos with given characteristics (hair colour, ethnicity, age, etc.).
It is financially beneficial, but it can also create a person's face that resembles you or your favourite celebrity, introducing additional pressure on you to buy or click. 
So, GAN is a potent tool, and as with each such technology, the main question is whether it will be used for good or bad.