Generative AI is a subset of artificial intelligence that focuses on creating new data, such as images, text, or audio, rather than simply analyzing existing data. It operates through generative models, algorithms trained on large datasets to learn patterns and structures.
One common type of generative model is the Generative Adversarial Network (GAN) which consists of two neural networks: a generator and a discriminator. Another type of generative model is the Variational Autoencoder (VAE) which aims to encode data into a compact representation and then decode it to generate new instances.
Transformers, a model architecture that has gained popularity, are also used in generative AI. They utilize self-attention mechanisms to process data sequences, making them practical for tasks like text generation. Models like GPT (Generative Pre-trained Transformer) have shown remarkable performance in generating coherent and contextually relevant text.
This fascinating article by Towards Data Science explains the topic in-depth and answers the questions around "How does Generative AI work?" to complete beginners. Dive into the basics of how AI systems create new content that closely resembles existing data, and discover the applications of Generative AI across various fields.
Full article here.