Emergence of Generative AI

While encoder–decoder models showed that machines could understand and translate sequences, the next question was even more ambitious: Can machines also create? Early probabilistic models, such as Hidden Markov Models (HMMs), captured short-term patterns but struggled with long-range dependencies and creativity.

Think of listening to your favorite song on repeat. You learn the rhythm and melody, but true creativity means composing a new tune in the same style, not just replaying the old one. This became the challenge: could machines learn patterns from data and generate something new that feels authentic?

Early generative models

Before the breakthroughs that shaped modern generative AI, researchers explored earlier models that tried to capture the patterns and distributions within data. These approaches laid important groundwork, showing that machines could not only analyze inputs but also attempt to produce new outputs.

• Hidden Markov Models (HMMs): Used widely in speech recognition, they modeled sequences by capturing short-term transitions between states but struggled with long-range context.

• Restricted Boltzmann Machines (RBMs): Learned compressed representations of data by reconstructing inputs, useful for uncovering hidden features but limited in generating diverse samples.

• PixelCNN: Generated images ...