👉 Generator computing is a machine learning technique used primarily in natural language processing (NLP) to produce new, coherent text based on given prompts or contexts. It operates by learning the statistical patterns and structures of large datasets of text, such as books, articles, or websites. During training, the generator network is exposed to pairs of input and corresponding output sequences, enabling it to understand the nuances of language, including grammar, syntax, and context. Once trained, the generator can take a random input (often called a "seed") and generate a sequence of words that closely mimic the style and content of the training data, effectively creating new text that sounds natural and contextually appropriate. This process is widely used in applications like chatbots, automated content generation, and language translation systems.
