Emergence of Generative AI

While researchers were busy teaching machines to understand and translate sequences—like converting English paragraphs into French sentences—another exciting frontier was unfolding in parallel: how can machines not only understand but also create new, original content? Before neural network–based approaches became dominant, classical probabilistic models such as Hidden Markov Models (HMMs)HMMs captured sequential dependencies using hidden states and transition probabilities, serving as an early stepping stone for language and time-series analysis. were widely used to tackle such tasks. However, while effective for many applications, HMMs lacked the capacity to capture long-range dependencies and generate new content—a gap that modern generative models would eventually fill.

Imagine listening to your favorite song repeatedly. Over time, you understand its rhythm, the instruments, and even the mood it creates. Imagine if you could compose a new song that carries the same essence without copying it exactly. Early researchers in AI asked a similar question: “Can a machine learn from examples and then generate something new that feels authentic?”

Before modern breakthroughs like VAEs and GANs, pioneering generative models such as Restricted Boltzmann Machines (RBMs), PixelCNN, and WaveNet paved the way. These early methods tried to capture the underlying patterns in data by learning compressed representations—much like sketch artists who study numerous examples (faces, handwritten digits, etc.) and distill their essential features. While effectively summarizing data, these models often struggled to produce new, diverse samples.

Consider a sculptor who studies hundreds of statues. Over time, the sculptor learns the key features of a particular style, so much so that they can carve a new statue that captures the essence of that style without copying anyone’s work exactly. Generative models operate on a similar principle: they learn the underlying patterns and distributions of data and then use that knowledge to produce novel outputs.

In this lesson, we’ll explore two landmark approaches in generative modeling—variational autoencoders (VAEs) and generative adversarial networks (GANs). Both represent breakthroughs in how machines can generate images, text, and even music, and they form the backbone of what we now call generative AI.

What are variational autoencoders (VAEs)?

Before we explore variational autoencoders, let’s briefly understand what a classic autoencoder is. Although it has “encoder” and “decoder” parts—similar terms to those we encountered for sequence models—the purpose is entirely ...