Generative AI – Slide 99
A clear technical explanation of the concept illustrated, with practical applications and simple examples.
A clear technical explanation of the concept illustrated, with practical applications and simple examples.
Slide 99 focuses on how generative models use internal latent representations to transform inputs into new, meaningful outputs. It highlights how models learn patterns rather than simply storing data.
A compressed internal space where patterns and relationships are stored.
The model identifies structure in training data, not exact memorization.
Outputs are produced by sampling and decoding latent representations.
1. Input
Text, image, or prompt enters the model.
2. Encoding
Converted into latent-space vectors.
3. Transformation
Model applies learned patterns.
4. Output
New text, image, or structure is generated.
Text Generation
Summaries, chatbots, content creation.
Image Synthesis
Concept art, product visualization.
Code Generation
Automation and developer assistance.
Classifies, predicts, or detects patterns but does not create new data.
Produces new text, images, or structures based on learned patterns.
Does the model memorize?
No. It generalizes patterns into latent space.
Why is latent space important?
It enables flexible and creative generation.
What enables creativity?
Sampling variations from learned distributions.