Multimodal AI

The development of multimodal AI has been a collaborative effort over time, with no single inventor. Its roots lie in disciplines like cognitive science and artificial intelligence. Influences from thinkers such as Howard Gardner, known for his theory of multiple intelligences, laid the foundation for combining different modes like speech, vision, and text in learning systems—an idea that evolved into modern multimodal AI through the work of global AI researchers.

Multimodal learning is more than just a style—it's a flexible approach that combines different modes of input, such as visual, auditory, and kinesthetic. In the context of multimodal AI, this idea translates into how machines process and respond to various types of human communication, improving their ability to interact naturally across different formats.

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Although they sound similar, multimedia and multimodal serve different purposes. Multimedia deals with presenting information using varied content types like video, images, and audio. In contrast, multimodal AI focuses on interpreting and responding to diverse forms of human input—like text, voice, and gestures—enabling more dynamic and human-like machine interactions.

Media planning today often relies on AI systems capable of interpreting multiple data sources. With multimodal AI, marketers gain deeper insight into consumer behavior by analyzing text, audio, video, and social interactions simultaneously. This leads to smarter ad placements, precise audience targeting, and better campaign performance.