Multi-modal AI – using text, images, voice, and video together

by Stélio Inácio, Founder at Jon AI and AI Specialist

Definition: Multi-modal AI

Multi-modal AI is a type of artificial intelligence that can understand and process information from multiple "modes" or types of data at the same time. Instead of just understanding text, or just seeing an image, it can combine text, images, voice, and video together to get a richer, more complete picture of the world, much like a human does.

From a World in Black and White to a World in Color

For a long time, most AI models were specialists. A language model understood text. A computer vision model understood images. They were powerful, but they lived in a "uni-modal" world—like a person who could only hear, or only see. This created a gap between how AI saw the world and how we experience it.

Multi-modal AI is the bridge across that gap. By training models on vast datasets that connect different types of data (like images with captions or videos with subtitles), these new AIs learn the relationships between what we see, say, and write. This allows for a much deeper, more contextual understanding. An AI can now understand that the word "dog," the sound of a bark, and a picture of a golden retriever are all related to the same concept. This leap is transforming AI from a simple tool you type at into a collaborative partner you can talk to and show things to.

Multi-modal AI in the Real World

This isn't just a futuristic concept; you've likely already used multi-modal AI without realizing it. Here are some key examples:

Visual Search (Google Lens): When you point your phone's camera at a landmark and ask, "What is this building?", you are using multi-modal AI. It combines the image from your camera with the text (or voice) of your question to give you an answer.
Advanced Voice Assistants (ChatGPT-4o, Google's Gemini): The latest generation of AI assistants can have a fluid, real-time conversation. You can show them a live video of your surroundings, ask questions about what they're seeing, and get a spoken response. They process your voice, the video feed, and their vast knowledge base all at once.
Creative Tools (DALL-E, Midjourney): Text-to-image generators are a classic example of multi-modality. They take a text prompt ("a photorealistic astronaut riding a horse on the moon") and generate a completely new image, demonstrating a deep connection between language and visual concepts.
Smarter Self-Driving Cars: Autonomous vehicles are multi-modal to their core. They must simultaneously process data from cameras (video), LiDAR (depth), radar (motion), and GPS (location) to navigate the world safely.

How Does It Work? The Idea of a "Shared Language"

So how can a computer understand both a picture and a sentence? The magic is in creating a shared mathematical language, or what engineers call a "joint embedding space."

Imagine you have a giant dictionary that can translate not just words, but pixels and soundwaves, all into a special set of numbers (vectors). In this dictionary, the concept of a "cat" is represented by a specific number sequence. The AI learns that the English word "cat," a photograph of a cat, and a drawing of a cat should all be translated into very similar number sequences.

By converting all different modes of data into this common numerical format, the AI can begin to understand the relationships and context between them. This is how it can see a picture of a birthday cake and know how to generate text for a "Happy Birthday" song, or watch a video of a basketball game and answer the question, "Who just scored?"

Quick Check

Which of the following is the best description of multi-modal AI?

A) A very large AI that is only trained on text data.

B) An AI system that can process and integrate multiple types of data, like images, audio, and text, at the same time.

C) A type of AI that is only used for generating images.

Recap: Multi-modal AI

What we covered:

What multi-modal AI is: AI that can process text, images, voice, and video together.
How it represents a major leap forward, allowing for a more human-like, contextual understanding of the world.
Real-world examples like visual search, advanced voice assistants, and creative generation tools.
The core idea of how it works: by translating different data types into a shared mathematical "language."

Why it matters:

Multi-modal AI is breaking down the barriers between us and computers. It's making our interactions more natural, intuitive, and powerful, paving the way for truly helpful digital assistants and more accessible technology for everyone.

Next up:

We'll wrap up this chapter by reviewing the key concepts we've covered about the advanced use of AI.

Jon AI Services