By enabling systems to simultaneously comprehend and analyze text, images, audio, and video, Multimodal AI is revolutionizing the capabilities of artificial intelligence. Unlike traditional models of AI which tend to concentrate on a single domain (such as text or images), multimodal AI blends various types of input into single outputs with richer context understanding.
What Is Multimodal AI?
Multimodal AI focuses on the existence of multiple methods (or modalities) of conveying information, and it refers to dissimilar forms of data, such as written, audial, visual, or even physical. Using sophisticated neural frameworks, these systems deal with patterns out of different sources emulating the human perception.
A good example of this would be a person watching a video:
They understand what is being said (audio/text)
They recognize who is talking (visual)
They connect and understand what emotion is being expressed (tone + face)
Multimodal AI aims to achieve this kind of comprehensive perception.
How Does It Work?
Like any other fundamental form of artificial intelligence, Multimodal AI is based on fusion models hich combines information from various sources together. Typically, this includes:
1. Encoding Each Modality
Encoding is done by specialists for each type of input (text, images, sounds etc) which is processed first by an associated encoder:
Text → transformer-based NLP module
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