Model Architecture
Latent Space
The internal mathematical space where a model stores its 'understanding' of concepts.
Definition
When an AI model processes text or images, it converts them into points in a high-dimensional mathematical space — the latent space. In this space, related concepts cluster together and unrelated ones are far apart. The model doesn't 'know' things the way a human does; it has learned a mathematical representation of relationships between concepts. All the model's capabilities — translation, summarisation, reasoning — emerge from operations in this space.
Related Terms
Embedding
A numerical representation of a word or concept that captures its meaning and relationships.
Vector
A list of numbers used to represent a piece of text mathematically — the language of AI under the hood.
Neural Network
The underlying computational structure that most modern AI is built on — layers of interconnected mathematical nodes.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026
Knowledge Base·Model Architecture·Latent Space