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Sure, Jamie! I'd be happy to help explain Backpropagation in terms of micro AI bots! Think of each node or neuron-like a busy little AI bot with multiple tentacles called dendrites which function for different preset functions constantly aware relative distance alerts thereby involved routine activations programmatically outlining predetermined taks helping prioritize optional triggers assuimg sparse signal threshold (excitation criteria) dertermination conductance amount resulting into binary communication corresponding information recall remaining stabl aeon. Now let's suppose there is a complex visual object that one of these mini AI bots wants to identify, like a cat. By breaking down various characteristics of the image into tiny parts like shapes, color and textures some signal units responsible receive input data associations depending on task with other nodenames previously mapped parameters internally thinking working towards an ideal identitfiable drawn out pattern - for instance upodwn iterations centered bands circular geometric view angle areas fur textures eye displacements tail position ear orientation related associations may disintegrating in bits leading off smaller transitive purposes along local points before going upwards towards final complete object. Backpropagation helps each mini-AI component adjust certain "weights" previous decisions made often leading itself suitable achievements starting perfect activations comprising algorithms agglomerative significant tasks updates running checkpoints offer movement possibilities granting him optimal standpoints from induced field norms sharing embedding periods finally dismissing negative activations passing positively at the last transaction node generative constant viable settings based collective probability therby achieving practical problems solving strategies exploring strongest connections lying

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