Multimodal Models in Generative AI

Consider how you experience the world. You don’t rely only on your eyes: you’re likely seeing, hearing, smelling, and feeling things all at once. Humans naturally combine all five senses to build a rich understanding of what’s happening.

AI, however, was long built to handle just one kind of input at a time: only text, or only images. That’s called unimodal AI. But the real world isn’t unimodal, so AI is now moving toward multimodal systems that can work with multiple types of information together.

Multimodal AI is like teaching AI to be more like us: to understand the world by simultaneously processing information from multiple data types. Just like we use all our senses, multimodal AI uses different senses of data to get a more complete and intelligent understanding.

What are modalities?

In AI, a modality is a specific type of data or input: a way information is represented.

For humans, modalities are our senses: sight, sound, touch, smell, and taste. For AI, modalities are data types it can process, such as:

• Visual: images, photos, drawings, videos

• Auditory: speech, environmental sounds, music

• Textual: documents, articles, web pages, social media posts, code

In other applications, you might also see:

• Sensor data: temperature, pressure, GPS, lidar, radar

• Biological signals: EEG, ECG, and other medical signals

Each modality offers a different view of the same thing. For example, a photo of a cat (visual) and the sentence “This is a cat” (text) describe the same object in different ways. Multimodal AI learns to understand and combine these different perspectives.

Why multimodal AI matters

Why not just stick with AI that handles one thing at a time (like only text or only images)? Because combining modalities makes AI much more powerful.

• Richer understanding:
  Watching a movie on mute gives you only part of the story. Add dialogue, music, and sound effects, and the meaning becomes much clearer. Similarly, multimodal AI can understand situations better by combining visual, audio, and text inputs.

• More robust:
  When one sense is unreliable (like vision in fog), you lean on others (like hearing or touch). Multimodal AI does the ...