Industries are utilizing generative AI in various ways to successfully generate new content. Learn about successful examples of this technology and how it’s expected to expand.
wo classes of AI systems contributing to current AI success stories are generative AI and discriminative AI. Generative AI systems create things, such as pictures, audio, writing samples and anything that can be built with computer-controlled systems like 3D printers. Discriminative systems identify things like people in pictures, words in speech or handwriting and — most importantly — what’s real vs. what’s fake. The two are paired in a generative adversarial network (GAN) model.
For example, a GAN for creating realistic yet fake yearbook photos might use a generative model to synthesize human faces and then pass them, along with real photos, through a discriminative model to see if it can tell which are fake and which are real. The exercise trains both models. The discriminator gets better at identifying fakes, as it is told which images were created by the generator. The generator gets better at creating realistic photos, as it is told which fakes the discriminator successfully identified.