What is generative AI?

Generative AI, short for Generative Adversarial Network (GAN), is a type of artificial intelligence (AI) that involves a system of two neural networks, the generator and the discriminator, working in tandem to produce and evaluate data. It’s a framework used in unsupervised machine learning.

Here’s a breakdown of the components:

Generator: This network creates new data instances that resemble a given dataset. For example, it might generate images, text, audio, or other types of content based on patterns it has learned during training.

Discriminator: This network evaluates the generated data and compares it with real data from the dataset. It tries to distinguish between the generated data and real data.

The process involves constant competition between the generator and the discriminator, with the generator attempting to produce data that is increasingly indistinguishable from real data, and the discriminator improving its ability to tell real data from generated data. The iterative back-and-forth process of the generator trying to improve its generated data and the discriminator refining its ability to differentiate real from fake data eventually results in the generator producing data that closely resembles real data from the dataset.

Generative AI has found applications in various domains, including image and video synthesis, natural language processing (NLP), art creation, music composition, and more. It has significantly impacted creative fields and has the potential to revolutionize how content is generated and customized in the future.