The history of ceramics is one of imitation and reproduction.
The apprentice obtains mastery of the craft through repetition, gradually improving their technique. Guided by a lifetime of working in the craft, the master examines each piece made by the student and throws away those deemed unsuitable.
The forger creates replicas and tests them in the marketplace. The connoisseur, informed by decades of experience dealing with antiques, judges the replicas. Those that are mistaken as authentic are sold, and the forger goes on to create even more convincing copies.
The “fake” vessels on this website have been created through a similar process of repetition, examination, and reinforcement. Except in this case, the entire procedure has taken place within machine-learning (ML) software known as a Generative Adversarial Network (GAN).
GANs consist of two parts: the Generator and the Discriminator. In a very general sense, the role of the Generator is similar to that of the apprentice and the forger, while the Discriminator plays the role of the Master or connoisseur. In a continuous feedback-loop, the Generator creates “fakes” that will be judged by the Discriminator as being “real” or “fake”, and both parts improve as time goes on. Eventually the Generator becomes a “Master” and can create the images on this website.
As extremely powerful ML software like StyleGAN are released and become more user-friendly, artists will have new tools with which to understand their craft and create new work.
Note: I am by no means a machine-learning expert. For those of you who are actually experts in the fields of AI and ML, I apologize in advance for poor generalizations and oversimplifications, and I hope that you will notify me of any mistakes. –Derek