Some considerations on how the human brain must be arranged in order to make its replication in a thinking machine possible
Emanuel Diamant

TL;DR
This paper explores how the human brain's structure must be arranged to enable its replication in thinking machines, challenging traditional views by drawing on Kolmogorov complexity theory to understand information processing and intelligence.
Contribution
It introduces a novel perspective on brain architecture for machine replication, based on mathematical information theory rather than biological inspiration.
Findings
Highlights the fundamental differences between human and computer vision.
Proposes a new framework for understanding brain information processing.
Connects Kolmogorov complexity to human intelligence modeling.
Abstract
For the most of my life, I have earned my living as a computer vision professional busy with image processing tasks and problems. In the computer vision community there is a widespread belief that artificial vision systems faithfully replicate human vision abilities or at least very closely mimic them. It was a great surprise to me when one day I have realized that computer and human vision have next to nothing in common. The former is occupied with extensive data processing, carrying out massive pixel-based calculations, while the latter is busy with meaningful information processing, concerned with smart objects-based manipulations. And the gap between the two is insurmountable. To resolve this confusion, I had had to return and revaluate first the vision phenomenon itself, define more carefully what visual information is and how to treat it properly. In this work I have not been, as…
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Taxonomy
TopicsComputability, Logic, AI Algorithms
