Artificial Intelligence (AI) and Language Models: AI Art Tools

AI generative tools are based on machine learning techniques that can learn from data and produce outputs that are not explicitly programmed. Some of the common techniques used by AI generative tools are:

• Generative adversarial networks (GANs): These are composed of two neural networks that compete with each other. One network tries to generate realistic outputs, while the other network tries to distinguish between real and fake outputs. The result is a network that can produce high-quality and diverse outputs that are indistinguishable from real ones.
• Variational autoencoders (VAEs): These are neural networks that can encode inputs into latent variables and decode them back into outputs. The latent variables capture the essential features of the inputs and can be manipulated to generate new outputs that are similar but not identical to the inputs.
• Transformer models: These are neural networks that can process sequential data, such as text or audio, using attention mechanisms. They can learn long-term dependencies and generate coherent and fluent outputs based on a given prompt or context.

All AI detection tools perform imperfectly, and may make mistakes. It can often be a good idea to test a suspicious image on multiple platforms, to see if there is consensus. Even this can sometimes be misleading. It is important to bring in context and other evidence when making such judgements. 

• Illuminarty

• Umm-maybe

• Optic AI or Not