AI art is generated through the use of artificial intelligence algorithms. These algorithms can take many forms, but the most common type used to generate art are neural networks.
One popular approach for creating AI art is to use a type of neural network called a Generative Adversarial Network, or GAN. This type of network consists of two parts: a generator and a discriminator. The generator is trained to create new, synthetic images, while the discriminator is trained to distinguish between real and generated images. During training, the generator and discriminator are "adversaries," with the generator trying to produce images that can fool the discriminator, and the discriminator trying to correctly identify which images were generated and which are real.
Another way is to use a type of neural network called Variational Autoencoder (VAE). It learns to generate new data by recognizing patterns in the input data and then creating new data with similar patterns.
There's also the possibility of using reinforcement learning algorithms that can create unique artworks based on the given instructions or set rules.
In any case, it is important to have a good dataset of artwork to train the neural network, and some fine-tuning on generated results to make sure they are aesthetically pleasing.
It's worth mentioning that creating AI art is a multidisciplinary field, including computer science, mathematics, art, and design.