Generative AI (GenAI) refers to machine learning methods that allow artificial intelligence systems to generate new artifacts rather than simply analyzing existing data. The key distinction is that generative models can produce novel outputs that are original, diverse, and realistic.
Some defining characteristics of GenAI:
- Powerful generative algorithms like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models are cutting-edge generative techniques that learn to create novel, realistic samples across different data types.
- GANs, VAEs, and Diffusion Models leverage advances in deep learning and probabilistic modeling, allowing them to create high-fidelity images indistinguishable from real photos, produce natural-sounding speech and music, and mimic coherent language.