Multimodal
AI systems that can understand and generate multiple types of data like text, images, and audio
What is Multimodal AI?
Multimodal AI refers to systems that can process and generate more than one type of data, such as text, images, audio, and video. Think of how humans naturally combine senses: you can look at a photo, read a caption, and listen to a voiceover all at once to understand a story. Multimodal AI aims to do the same thing.
A traditional text-only chatbot can only read and write words. A multimodal model like GPT-4 with Vision can look at an image you upload and answer questions about it, combining visual understanding with language ability.
How Does It Work?
Multimodal models are typically trained on large datasets that pair different types of data together, such as images with their text descriptions. The model learns to build a shared internal representation where a photo of a dog and the word "dog" are understood as the same concept. Different encoder modules handle each data type (a vision encoder for images, an audio encoder for sound), and their outputs are fused into a unified space the model can reason over.
Why Does It Matter?
Multimodal AI unlocks applications that were previously impossible. Doctors can upload medical scans and get AI-assisted analysis. Developers can sketch a wireframe and have AI generate working code. Accessibility tools can describe images for visually impaired users. As AI moves beyond text-only interactions, multimodal capabilities are becoming the standard expectation for next-generation AI products.