Generative AI
Variational Autoencoder (VAE)
An AI that can both compress data into a meaningful code AND generate new, similar data from that code.
Definition
A generative model that learns a probabilistic latent representation of data, enabling both compression and generation. Combines autoencoder architecture with probabilistic inference.
Why it matters
An important generative architecture, particularly for controlled generation and data synthesis.
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.
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.
Generative AI
AI that creates new content, text, images, code, music, video, rather than just analyzing existing data.
From vocabulary to outcomes
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