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. The three families differ in how they learn the distribution of the training data and how they sample from it. A GAN trains the generator against the discriminator in an adversarial loop, pushing synthesized samples toward the distribution of the real data. A VAE learns a probabilistic latent space, then decodes points from that space into new samples — a structure that gives smoother interpolation but typically blurrier outputs than a GAN. A Transformer-based generator, by contrast, models data sequentially: it predicts the next token (or patch, in vision models) given the prior context, and the self-attention layers let it weigh long-range dependencies inside that context. In practice, the choice of architecture follows the data. Discrete sequence data — natural language, code, protein sequences — tends to favour Transformer decoders. Continuous data with strong spatial structure — images, video frames — has been served by GANs, VAEs, and more recently diffusion models that share the same underlying idea of learning a data distribution and sampling from it. The common thread across all of them is that the model is not retrieving stored examples; it is producing new outputs whose statistical structure matches what it learned during training. This fascinating article by Towards Data Science explains the topic in-depth and answers the questions around “How does Generative AI work?” for 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. Credits: Towards Data Science Image: Freepik.com
