Generative AI – Slide 11 Deep Dive
Understand what Slide 11 explains: how generative AI models learn patterns, transform inputs, and generate new content using encoded representations.
Understand what Slide 11 explains: how generative AI models learn patterns, transform inputs, and generate new content using encoded representations.
Slide 11 illustrates a key concept in generative AI: the transformation of input data into a learned representation before generating new output. It highlights how systems like Large Language Models take text, encode it into numerical vectors, process it through multiple layers, and decode it back into meaningful output.
Raw data like text or images is converted into vectors that capture semantic meaning.
The internal space where AI models understand relationships between concepts.
The model transforms latent vectors back into readable or visual output.
User enters text, prompts, or other data.
Model converts input into numerical vectors.
Transformers use attention layers to predict the next elements.
The model generates new text, images, or structured data.
Chatbots, content creation, summarization, and ideation tools.
Art generation, concept design, and visual prototyping.
Code generation, translation, and structured data conversion.
It’s the mathematical form of input data that helps the model understand meaning and relationships.
It predicts the next most likely token based on patterns learned from billions of examples.
No; it’s probabilistic and can make mistakes depending on input quality and model training.
Continue to the next slide or dive deeper into model architecture and training.
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