Editing Multimodal AI Models and the Architecture of Perception (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Multimodal AI''' — Artificial intelligence systems capable of processing, understanding, and generating multiple forms (modalities) of data simultaneously, such as text, images, audio, and video.
* '''Modality''' — A specific type of data or format of information. Text, images, and audio are distinct modalities.
* '''Cross-Modal Learning''' — The process by which an AI learns the complex relationships between different modalities (e.g., learning that the text word "dog" corresponds to the visual pixels of a dog in an image).
* '''Embedding Space''' — The underlying mathematical dimension where AI models map different modalities. A multimodal model maps an image of an apple and the word "apple" to the exact same location in the embedding space.
* '''Vision-Language Models (VLMs)''' — A common type of multimodal model that combines computer vision and natural language processing, allowing the AI to answer questions about an image or generate an image from text.
* '''Contrastive Language-Image Pretraining (CLIP)''' — A foundational architecture developed by OpenAI. It trains two neural networks simultaneously—one for text and one for images—to predict which images correspond to which text descriptions, creating a massive, shared multimodal embedding space.
* '''Audio-Visual Models''' — Models that process sound and video together, allowing them to understand context (like matching a speaker's lip movements to the audio track or identifying an action based on its sound).
* '''Late Fusion vs. Early Fusion''' — *Early Fusion*: Combining the raw data from different modalities immediately at the input layer. *Late Fusion*: Processing each modality in a separate neural network first, and combining their final outputs at the end.
* '''Tokenization''' — The process of breaking data down into tiny mathematical chunks (tokens). In multimodal AI, text is tokenized into word pieces, and images are tokenized into small image patches, allowing the transformer architecture to process them both using the exact same math.
* '''Generative Multimodal Models''' — AI that cannot only *understand* multiple modalities but *create* them. (e.g., Generating a video directly from a text prompt, or generating a voice speaking based on a text prompt).
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