Definition
Generative AI refers to artificial intelligence systems that create new content such as text, images, video, or music by learning patterns from existing data. Unlike traditional AI, it produces novel outputs rather than only analyzing or classifying inputs.
Purpose
The purpose is to assist in creative tasks, automate content generation, and augment human productivity. It is widely used in design, writing, entertainment, and scientific discovery.
Importance
- Enables rapid prototyping and creativity across domains.
- Reduces manual effort in content generation.
- Raises concerns about misinformation, copyright, and misuse.
- Closely related to models like GANs, VAEs, and large language models.
How It Works
- Collect and preprocess large training datasets.
- Train generative models (e.g., GANs, transformers, diffusion models).
- Learn probability distributions of training data.
- Sample or prompt the model to generate new outputs.
- Refine outputs with user feedback or post-processing.
Examples (Real World)
- DALL·E (OpenAI): generates images from text prompts.
- Stable Diffusion: open-source text-to-image generation.
- ChatGPT: generates human-like text responses.
References / Further Reading
- “Attention Is All You Need” — Vaswani et al., NeurIPS 2017.
- Generative AI Landscape — Stanford Human-Centered AI.
- Goodfellow et al. Deep Learning. MIT Press.