AI artists use a variety of techniques to create their artwork, which can range from simple colorization of images to more complex generative methods. Here are a few techniques commonly used by AI artists:

Style transfer: This involves taking the style of one image and applying it to another image. The technique involves using deep neural networks to extract the style and content of each image and then combining them to create a new image that has the style of one image and the content of another.

GANs: Generative Adversarial Networks (GANs) are a type of neural network that can generate new images that have similar features to a set of training images. GANs use two neural networks: a generator network that creates new images, and a discriminator network that tries to tell the difference between the generated images and the training images. Over time, the generator network learns to create images that are more and more like the training images.

DeepDream: This technique involves using a neural network to create surreal and abstract images by enhancing patterns and textures in an existing image. The technique works by amplifying the patterns in the image that the neural network recognizes.

Autoencoders: Autoencoders are a type of neural network that can be used to compress and decompress images. They work by encoding an image into a lower-dimensional representation and then decoding it back into the original image. Autoencoders can be used for a variety of tasks, including denoising and inpainting images.

Convolutional Neural Networks (CNNs): CNNs are a type of neural network that are commonly used for image recognition. They work by applying a set of filters to an image to extract features, which are then used to classify the image. CNNs can also be used for image generation by reversing the process: instead of classifying an image based on its features, the network can generate an image based on a set of features.

Recurrent Neural Networks (RNNs): RNNs are a type of neural network that are commonly used for sequential data, such as text or music. However, they can also be used for image generation by treating an image as a sequence of pixels. RNNs can generate images by predicting the value of each pixel in the sequence based on the values of the previous pixels.

These are just a few of the techniques commonly used by AI artists. As AI technology continues to advance, new techniques and methods are likely to emerge, making AI-generated artwork even more diverse and exciting.