Lesson 20 of 28 · Using AI well

What is multimodal AI?

Multimodal AI is AI that can understand or create more than one kind of data, such as text, images, audio and video, in a single system. You can show it a photo and ask about it in words, or speak to it and hear a spoken reply. A "modality" is simply a type of data, much as sight and hearing are different human senses.

What "multimodal" means

People are naturally multimodal. In a restaurant you read the menu, look at photos of the dishes and listen to the waiter, then combine all three into one decision. Most early AI tools handled only one modality: a speech recogniser turned audio into text, an image classifier put labels on photos, and a chatbot read and wrote text.

A multimodal AI connects these abilities. It can take a screenshot, a voice note and a typed question together and answer with all of them in mind. Some products do this by chaining separate models behind the scenes; others use one model trained on several types of data. Both can feel the same, but a chain loses whatever one step cannot pass on: a speech-to-text step, for example, drops the tone of voice.

How it works, in plain terms

Computers only handle numbers, so every input is first turned into a list of numbers that captures its meaning, called an embedding. An image is cut into small patches, audio into short slices and text into tokens (small pieces of words), and each piece becomes numbers.

The trick is to place all these numbers in one shared space, so that a photo of a dog and the words "a dog" end up close together. Researchers taught models this by showing them huge numbers of images paired with captions; CLIP, published in 2021, is a well-known example. A language model can then reason over image pieces much as it reasons over words. Think of it as a bilingual dictionary between pictures and language.

Where you meet it in real tools

You have probably used multimodal AI already, even if nobody called it that. Notice the two directions: some tools understand several types of input (image in, text out), while others generate a different type of output (text in, image out). Product pages say "multimodal" for either, so check which inputs and outputs your plan actually includes.

Chat assistants that accept photo, screenshot or PDF uploads and answer questions about them.
Voice modes, where you talk to an assistant and it talks back.
Image and video generators that turn a written description into pictures or clips.
Meeting tools that transcribe a call, then summarise it and list action items.
Document tools that read scanned forms, receipts and invoices.

What it is not, and where it goes wrong

Multimodal AI does not see or hear the way people do. It matches patterns, so it can misread small print, handwriting or a busy chart, miscount objects, or describe details that are not in the picture: the visual version of a hallucination, a confident but wrong answer.

Photos and recordings also carry more personal information than you might expect, such as faces, locations and documents in the background. Before uploading, ask whether you would be comfortable with the provider storing that file, and check the tool's privacy settings.

Example

Try it: turn a photo into a table

Try this prompt

List every item and number you can see in this image as a table with two columns: item and number. If any text is blurry or cut off, write "unclear" in that cell instead of guessing.

First attach a clear photo of a receipt, a menu or a simple chart; the assistant must accept image uploads. Then compare the table with the original. The cells it marked unclear, and any mistakes it did not mark, show you where multimodal reading is reliable and where it is not.

Key points

Multimodal AI understands or generates more than one type of data: text, images, audio or video.
Each input is turned into numbers in a shared space, so pictures and words can be compared.
"Multimodal" can mean many inputs, many outputs or both; check what a plan includes.
It can misread small text, charts and handwriting, so verify anything you will rely on.
Photos and recordings can reveal more personal data than text; think before uploading.

Try it on Mindber

Find multimodal AI assistants

ChatGPT on Mindber

Midjourney: text-to-image on Mindber

Glossary: multimodal AI

AI fundamentals

Frequently asked questions

Is ChatGPT multimodal?

Yes, in the sense that matters to users: the ChatGPT apps accept images, files and voice as well as typed text, and can create images. Which of these features you get depends on your plan and the model you select, and features change over time, so check the current product page before relying on a specific input type.

What is the difference between multimodal AI and generative AI?

They describe different things. Generative AI is about what a model does: it creates new content. Multimodal AI is about which types of data it works with: more than one. Many modern tools are both, such as an assistant that reads your photo and writes a caption, but a multimodal system can also simply analyse, for example sorting images and sounds without generating anything.

Does multimodal AI cost more to use?

Often, yes. Images, audio and video are converted into many more tokens or processing units than a short text message, and API providers usually price them separately. In subscription tools, voice, image and video features may be limited by plan or by daily caps. Estimate your usage before you commit, especially for video.