Intermediate
What is a generative adversarial network?
Generative Adversarial Network (GAN) is a machine learning architecture in which a generator and discriminator are trained in opposition. The generator learns to create realistic samples, while the discriminator learns to distinguish generated samples from real data.
What is a deepfake?
Deepfakes are AI-generated or AI-manipulated images, audio, or video that make people, objects, places, entities, or events appear authentic when they are not.
What is synthetic media?
Synthetic media is media content that is wholly or partly generated or manipulated by AI. It can include text, images, audio, video, avatars, voices, and other digital artifacts.
What is prompt engineering?
Prompt engineering is the practice of designing, testing, and refining inputs to generative AI models so they produce desired outputs more reliably. It can involve specifying roles, constraints, examples, context, format, and evaluation criteria without changing the model's underlying weights.
What is temperature in generative AI?
Temperature is a model-generation parameter that controls the randomness of an AI system’s output. Lower values tend to produce more focused and deterministic responses, while higher values increase variety, creativity, and the risk of less coherent or less reliable output.
What is AI watermarking?
AI Watermarking is the embedding of visible or hidden identifiers into AI-generated or AI-modified content to indicate origin, provenance, or generation status. Watermarks may be added during generation or after generation and may require specialized tools to detect.
Advanced
What is content provenance?
Provenance and watermarking refer to signals that help identify how content was created, where it came from, or whether it was AI-generated or modified. These mechanisms support authenticity, trust, moderation, auditability, and responsible content governance.
Why does generated content need human review?
Human evaluation is the process of having people judge the quality, safety, usefulness, or correctness of model outputs. It is especially important for tasks where there is no single correct answer, such as translation quality, summarization, dialogue, or creative generation.