Advances in deep learning algorithms and the availability of large datasets of natural language text have driven the evolution of NLG to generative AI. Early NLG systems relied on rule-based or template-based approaches, which were limited in their ability to generate diverse and creative content. However, with the rise of deep learning techniques such as recurrent neural networks (RNNs) and transformers, it has become possible to build models that can learn from large datasets of natural language text and generate new text that is more diverse and creative.
An important milestone in the evolution of generative AI was the development of the Generative Pretrained Transformer (GPT) series of models by OpenAI. The original GPT model, released in 2018, was a transformer-based model trained on a large corpus of text data. The model was able to generate coherent and fluent text that was similar in style to the original data. Subsequent versions of the model, including GPT-2 and GPT-3, have pushed the boundaries of what is possible with NLG, generating text that is increasingly diverse, creative, and even human-like in some cases.
Today, generative AI techniques are used in a wide range of applications, including content generation, chatbots, language translation, and more. As the field continues to evolve, we can expect to see more sophisticated generative AI models that can generate even more creative and diverse content.