Generative AI
GANs (Generative Adversarial Networks)
Two AI models competing against each other, one creates fakes, the other tries to catch them, until the fakes are perfect.
Definition
A generative model architecture consisting of a Generator (creates synthetic data) and a Discriminator (tries to distinguish real from fake). They train adversarially until the Generator produces realistic outputs.
Why it matters
Pioneered high-quality image generation and remains important for data augmentation and synthetic data.
Related terms in Generative AI
Diffusion Models
AI that creates images by starting with pure noise and gradually refining it into a clear picture, like watching a Polaroid develop.
Foundation Models
Massive AI models (like GPT-4 or Claude) pre-trained on enormous datasets that can be adapted for thousands of different tasks.
Generative AI
AI that creates new content, text, images, code, music, video, rather than just analyzing existing data.
Multimodal AI
AI that can understand and generate multiple types of content, text, images, audio, and video, all at once.
From vocabulary to outcomes
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