Code generators rely on algorithms trained on pre-existing code, often derived from publicly available open-source projects. These tools can create new code by drawing from those examples and, in some cases, also provide features to analyze and debug code or suggest improvements.
Examples: CodePal | GitHub Copilot
Source: CodePal
Image generators are trained by studying collections of images paired with captions or descriptive text. After learning the associations between images and concepts, they can blend these elements to produce new visuals in various styles, ranging from photorealism to abstraction.
Example: Dall-E 3 in ChatGPT | Stable Diffusion
Prompt: Create a portrait of a character inspired by classic cartoon rabbit traits, created in a style reminiscent of Van Gogh.
Source: ChatGPT
Text generators are developed by analyzing extensive datasets of written material from books, articles, and websites. This analysis helps identify patterns and connections within the text, enabling the generators to produce new content by predicting which words or sentences are most likely to come next in a sequence. They can create a diverse range of outputs, including essays, memos, brochures, poems, songs, and screenplays.
Source: ChatGPT
