Multimodal AI
Multimodal AI can understand or generate more than one type of input or output—e.g. text, images, audio—in a single model or flow.
In Simple Terms
Think of it as a colleague who can read the slide deck and the memo at the same time.
Detailed Explanation
Multimodal models (e.g. vision-language models) take images and text together, or produce both. That enables image description, visual QA, and combined interfaces. When to use it: when your task involves images, diagrams, or mixed media. Common mistakes: assuming all multimodal models support the same modalities or that image understanding is always accurate.
Related Terms
Natural Language Processing
Technology that helps computers understand, interpret, and manipulate human language.
Read moreRAG
Retrieval-Augmented Generation combines AI models with external knowledge retrieval for accurate responses.
Read moreDeep Learning
Deep learning is machine learning using neural networks with many layers. Depth allows models to learn hierarchical representations and has driven breakthroughs in vision, language, and other domains.
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