Multimodal Model

A multimodal model is one AI model that handles more than one kind of input and output, text, images, audio, video, reasoning across them in a shared internal representation instead of requiring a separate tool per format.

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§ 01DefinitionMultimodal Model

No jargon, no math

In plain words, what is it?

You have text, photos and audio, and instead of three separate tools you use one. Each modality gets its own encoder: text becomes tokens, an image becomes patches, audio becomes segments, and all of them are projected into one shared numeric space, where comparable words, shapes and sounds end up close together. Inside, a transformer runs attention across modalities: ask “what value is this gauge showing?” and attention links your question to the patches of the image that matter. On the way out, a decoder writes in whichever modality you asked for: text, or instructions to generate an image or a voice. The boundary: the weaknesses add up. If the model's eye misreads the dial, the answer comes out confident and wrong; and it remains a predictor of plausibility, not a database.

A real example

The scene. A field technician finds an old pump with a nameplate corroded by years of salt air. They photograph the worn type code and ask: “read the code and find the equivalent spare part in the manual”.Behind the scenes. An encoder turns the photo into numeric patches, the question into tokens, and attention mixes them in one space: the corroded letter-shapes meet the vocabulary of pump codes.The answer. “The code reads KL-40, third character uncertain due to corrosion. The AT-88 manual lists the equivalent impeller as KL-40-B, page 212.” With the manual reference attached.The boundary. If the corrosion covers too much, the model says so: “the third character cannot be read with confidence”. When it cannot see, it must admit it instead of completing the pattern.

§ 02Glossary3

Terms that live nearby.

LLMS & LANGUAGE

Large Language Model

A large language model (LLM) is an AI system trained on massive collections of text to predict the next token, which gives it a general ability to write, summarise, translate and reason without being programmed for each task.

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DATA & RETRIEVAL

Embedding

An embedding is a list of numbers that captures the meaning of a piece of text, image or audio, so that content with similar meaning gets similar numbers and the computer can measure closeness with plain arithmetic.

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LLMS & LANGUAGE

Transformer

The transformer is the neural network architecture, published by Google researchers in 2017, behind every modern language model: it processes all tokens of a text at once and uses attention to let each token weigh the relevance of every other token.

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