Multimodality: Quietly Transforming How AI Products "Understand the World"

What exactly is multimodality?

Multi-modal AI is evolving from a technological concept into a core battlefield for product decision-making. When models start to integrate visual, auditory, and linguistic information like humans, what we're facing is not just a technological breakthrough but also a product philosophy on how to enable AI to understand the real world. This article will dissect how multi-modal technology reconstructs the boundaries between AI and human cognition, from red light recognition to voice emotion perception.

If you've been looking at AI-related projects, products, or job recruitment information recently, you've probably come across a term: multi-modal. It's appearing more and more frequently, but strangely enough, few people really explain it clearly.

Some people understand multi-modal as "ChatGPT that can view images." Others think it's the business of algorithm engineers. Some people vaguely feel it's important but can't explain exactly why.

I want to explain multi-modal in a different way, not starting from the model structure but from a more daily-life perspective.

Humans are inherently "multi-modal"

We never understand the world solely through text.

When you're walking on the road and see a red light, you stop not because the text rule "red light = no entry" pops up in your mind, but because your vision directly triggers the judgment. When you hear the other person's tone turn cold, you subconsciously realize that the atmosphere is off, not because you analyze the sentence structure, but because the emotional information in the voice is at work.

Visual, auditory, linguistic, spatial, and experiential information occur simultaneously and complement each other.

For a long time in the past, the way AI understood the world was extremely single - minded - almost only through text.

The ceiling of single-modal AI was reached a long time ago

Early large models were essentially doing one thing:

Translating the world into text and then learning patterns from the text.

This works in many scenarios, such as Q&A, summarization, writing, and searching. But once the questions become -

"What's happening in this picture?"
"What's the emotion of this video?"
"Does this voice sound happy or nervous?"

Just relying on text, the model starts to become sluggish.

Because a lot of information isn't in the text at all.

Composition, lighting, expressions, tones, and rhythms - these are things humans can perceive at a glance. If not directly "fed" to the model, it can't learn them.

The background for the emergence of multi-modal isn't about showing off technology but a very real problem: if AI is to enter the real world, it can't just exist in the text.

So - called multi-modal is essentially teaching the model to "view the world with multiple senses"

Technically defined, multi-modal means:

Simultaneously processing and integrating multiple information forms such as text, images, videos, and audio.

But in plain language, it's actually doing something more intuitive: enabling the model to not only "read" but also "see" and "hear."

For example -

Text-to-image generation isn't just "drawing" but the model understanding "the pictures in the text."
Image understanding isn't just identifying objects but understanding the relationships, emotions, and context in the picture.
Video understanding focuses not just on frames but on time, actions, and changes.
Voice-related tasks are about processing the superposition of "information + emotion + rhythm."

This is why multi-modal models often seem "smarter" right from the start. It's not that they really understand, but that the information they receive is closer to the way humans perceive the world.

Multi-modal isn't a single function but a whole set of ability structures

In real projects, multi-modal usually doesn't appear in the form of "a single button."

It's more like a network of abilities:

One end is generation: text-to-image, text-to-video, and speech synthesis.
One end is understanding: answering questions based on images, judging video content, and voice recognition.
In the middle are a large amount of data, labels, descriptions, and alignment rules.

You'll find that multi-modal projects often don't start from the "model" but from a seemingly basic question:

How should the model understand a picture, a video, or a piece of sound?

And the answer to this question often lies not in the algorithm but in how the data is organized, described, and filtered.

Why multi-modal is becoming more like a "product problem" rather than just a technical problem

When multi-modal enters real products, the question it faces isn't "whether it can work" but -

What information do users care about?
What should the model ignore?
Which perceptions are valuable and which are noise?

These judgments essentially have very strong product decision-making attributes.

For example, if a picture has a cluttered background but a clear subject, is it a plus or a minus for the generation task? If a piece of voice has rich emotions but slightly unclear pronunciation, is it an advantage or a risk for TTS training?

There are no standard answers to these questions, but someone has to make the judgments.

And multi-modal is where AI really starts to need "human perspective participation."

The real value of multi-modal is to make AI more like it's living in the world

Going back to the original question: What exactly is multi-modal?

It's not the name of a certain model, nor is it a trendy term. It's more like a bridge for AI to move from the "text world" to the "real world."

When the model starts to receive pictures, sounds, and language simultaneously, and when it no longer relies on a single input form, it can truly enter real-life scenarios instead of just staying in the dialog box.

This is why multi-modal isn't a short-term trend but a long-term direction.

This article is from the WeChat official account "Everyone Is a Product Manager" (ID: woshipm), written by Qinglanse Dehai, and is published by 36Kr with authorization.