Frequently asked questions
What is generative AI?
Generative AI, or generative artificial intelligence, refers to various algorithms and models with the ability to create new images, video, audio, text, and other content based on what it has learned from training data. They are made possible through the use of generative adversarial networks (GANs) and other machine-learning techniques.
What is a generative adversarial network (GAN)?
A GAN is a type of neural network commonly used to facilitate generative AI. GANs consist of two discrete networks that are pitted against each other: A generator, which creates an image, video, or text; and a discriminator, which evaluates the generated media and determines whether it is real or fake.
Over time, the generator model is able to learn from the discriminator in order to get better at creating media that cannot be easily identified as fake.
What other generative models are used?
While GANs commonly underpin generative AI models, other models also exist. Other important models include variational autoencoders (VAEs), neural radiance fields (NeRFs), and diffusion models.