Segment sequencing artificial intelligence topology applications

Abstract

Optional segmented generative AI (Artificial Intelligence) topologies with multiple AI type outputs participate to serve overall AI generation objectives. Multiple remote and local topology nodes within overall topology segments utilize pattern based influence along with both inner and inter segment influence to adapt as new generations occur, to maintain context and to address changing circumstances. Outside influence delivers such change notifications. AI output and input interface elements provide some such change notifications and provide alternatives to conventional driver based circuit architectures. Support processing nodes, discriminative AI elements, generative AI elements along with outside influence from input, output and communication circuitry along with other outside interactions are arranged in sub-segments that are carried out to deliver generated pieces of a multimedia based objective. Segment breaks allow for user review interaction along a segmented generation flow for editing and regeneration interactions. Personalized and random elements of influence, e.g., via objective, episodic and content patterns constrain AI generation via influence to hold to expected and desired output flow across segments. Inner and inter segment influence is delivered in a feed forward, feedback and cyclical manner, wherein correlation evaluations play part. Too much or too little correlation also drives regeneration cycling. Random influence via inherent and random tags requiring population before application within patterns. Random tags may comprise tree structures of randomness generated in lists from private and public data.

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