Genetic Bottleneck and the Emergence of High Intelligence by Scaling-out and High Throughput
Arifa Khan, Saravanan P, and Venkatesan S.K.

TL;DR
This paper explores the evolution of neural networks and computing architectures, drawing parallels between biological evolution and technological development, highlighting how societal and biological factors influence intelligence and AI capabilities.
Contribution
It introduces a novel perspective linking biological evolution, especially genomic bottlenecks, to the development of high-performance AI systems and societal impacts.
Findings
Biological evolution parallels in AI development
Role of societal factors in shaping intelligence
Implications for reducing AI biases and toxicity
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGenetics, Bioinformatics, and Biomedical Research
