An omni model in AI, often seen in models like GPT-4o, refers to a unified, end-to-end multimodal architecture capable of processing and generating information across text, audio, vision, and other data types simultaneously, unlike previous models that combined separate specialized components.
This integrated approach allows for lower latency, naturalistic conversation, and complex tasks like understanding tone and emotion, leading to a more seamless and human-like interaction with AI.
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Another way to think about it, is the difference between a macro-kernel and a micro-kernel. The macro-kernel is equivalent to the Omni model in AI. The agents in an agent model in AI operate independently or in concert with other agents to complete complex tasks by leveraging memory, instruction sets, and external tools to interact with their surroundings and adapt their behavior based on new information. This is a micro-kernel with the kernel modules it needs to do ‘X’.
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