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What is native multimodality? 🍿 Just a moment

#GoogleGemini #GenerativeAI #GoogleCloud

🔗 Gemini models → https://g.dev/cloud/gemini-models

⚡ What is native multimodality? 🍿 Just a moment #01 Did you know there are three tokenizers involved when Gemini processes multimodal inputs? In this episode, we peek under the hood to see how text, vision, and audio tokenizers work together.

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The video explains that Gemini uses three specialized tokenizers to process different types of input data ( – ):

Text tokenizer: Encodes text files and the text extracted from PDFs.
Vision tokenizer: Encodes image files, frames from videos, and individual pages from PDFs.
Audio tokenizer: Encodes audio files and audio tracks from videos.

Because these tokenizers allow all inputs to live in the same semantic space, the model is able to develop a comprehensive understanding of the data provided to it ( – ).

What is native multimodality? 🍿 Just a moment #01

Native multimodality is an AI architecture where a single model is trained from the ground up to understand, process, and generate multiple types of data—such as text, images, audio, and video—all at the same time.

Unlike older systems that stitch separate specialized models together, a natively multimodal model learns to connect different formats within one shared system from the very beginning.

How It Differs from Traditional AI

Traditional multimodal systems (often called modular or stitched architectures) rely on separate models acting as translators for a central text-based brain.

Feature	Native Multimodality	Traditional (Modular) Multimodality
Training	Trained on all data types simultaneously from day one.	Separate models trained individually, then connected later.
Processing	Converts all inputs into a single shared representation.	Translates audio or images into text before the main model reads it.
Nuance & Context	Preserves tone of voice, visual details, and timing.	Often loses subtle details during the translation step.
Speed	Faster, with lower latency for real-time interaction.	Slower, as data passes through multiple separate pipelines.

Key Benefits

Deeper Cross-Modal Reasoning: Because the model understands how concepts look, sound, and read together, it can solve complex problems that require combining clues from different formats. For example, it can watch a video of a broken machine while listening to the sound it makes to diagnose a mechanical issue.
Preserved Nuance: When an audio clip is converted into plain text by a separate transcriber, things like sarcasm, emotion, or background noise get lost. A native model “hears” the raw audio directly, picking up on tone and pacing.
Seamless Generation: A single native model can take a mixed prompt—like an image and a spoken question—and respond directly with generated audio, an edited image, or text without handing the task off to another tool.

Real-World Examples

Modern foundation models like Google’s Gemini and OpenAI’s GPT-4o are built using native multimodality. This architecture powers live voice assistants that can interrupt, detect emotion, and “see” through your phone’s camera in real time.

Would you like to explore how tokenization works for non-text data in these models, or look into specific use cases for native multimodal AI?