A multimodal model is capable of understanding and generating content across multiple data types or ‘modalities’. These models accept multiple input types, like text, images, and sometimes audio,  and can produce various output forms. The goal is to create models that can understand and generate content that spans multiple data formats, providing a more comprehensive and versatile approach to generative tasks.

Multimodal AI systems consist of an input module processing diverse data types, a fusion module interpreting information from various modalities, and an output module generating the final output in one or more modalities. These models can be used for creative tasks, content generation, and enhancing human-computer interactions.

OpenAI’s GPT-4 is an example of a multimodal model, which can read text and images and provide concise descriptors or analysis.

References: https://www.linkedin.com/pulse/multimodal-generative-ai-tarun-sharma-zzf9c/