Multimodal AI applications
AI systems have evolved beyond text-only processing to work with diverse data types. In the current landscape, we can distinguish between two key capabilities that are often confused but represent different technological approaches.
Multimodal understanding represents the ability of models to process multiple types of inputs simultaneously to perform reasoning and generate responses. These advanced systems can understand the relationships between different modalities, accepting inputs like text, images, PDFs, audio, video, and structured data. Their processing capabilities include cross-modal reasoning, context awareness, and sophisticated information extraction. Models like Gemini 2.5, GPT-4V, Sonnet 3.7, and Llama 4 exemplify this capability. For instance, a multimodal model can analyze a chart image along with a text question to provide insights about the data trend, combining visual and textual understanding in a single processing flow.
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