Multimodal AI refers to artificial intelligence systems that can understand, process, and generate information from multiple types of data inputs simultaneously, such as text, images, audio, and video. Unlike unimodal systems that specialize in a single domain (e.g., text-only LLMs), multimodal models can combine context from visual and auditory cues to perform complex tasks like image captioning, video analysis, and intuitive human-computer interaction.
The concept has roots in the early attempts to combine computer vision and natural language processing. However, the modern era of Multimodal AI began to accelerate with the advent of Transformer architectures. Notable milestones include OpenAI's CLIP (2021) and DeepMind's Flamingo, leading up to large multimodal models (LMMs) like GPT-4V and Gemini.
Multimodal AI is rapidly becoming the standard for state-of-the-art AI. It is being integrated into search engines, virtual assistants, content creation tools, and autonomous vehicles. The ability to 'see' and 'hear' allows AI to operate more effectively in the real world, driving adoption across industries from healthcare (analyzing scans and reports) to entertainment.