  We study the biological evolution of low-latency natural neural networks for
short-term survival, and its parallels in the development of low latency
high-performance Central Processing Unit in computer design and architecture.
The necessity of accurate high-quality display of motion picture led to the
special processing units known as the GPU, just as how special visual cortex
regions of animals produced such low-latency computational capacity. The human
brain, especially considered as nothing but a scaled-up version of a primate
brain evolved in response to genomic bottleneck, producing a brain that is
trainable and prunable by society, and as a further extension, invents
language, writing and storage of narratives displaced in time and space. We
conclude that this modern digital invention of social media and the archived
collective common corpus has further evolved from just simple CPU-based
low-latency fast retrieval to high-throughput parallel processing of data using
GPUs to train Attention based Deep Learning Neural Networks producing
Generative AI with aspects like toxicity, bias, memorization, hallucination,
with intriguing close parallels in humans and their society. We show how this
paves the way for constructive approaches to eliminating such drawbacks from
human society and its proxy and collective large-scale mirror, the Generative
AI of the LLMs.
