Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI

TL;DR

This paper argues that current neural network paradigms are fundamentally insufficient for achieving artificial general intelligence, highlighting theoretical, philosophical, and neuroscientific limitations, and proposing a new framework for genuine machine intelligence.

Contribution

It introduces a conceptual framework separating computational substrate from architectural organization and critiques current neural network approaches as inadequate for true understanding.

Findings

Neural networks act as static function approximators, lacking structural richness.

Current theories like the Universal Approximation Theorem are misinterpreted at the wrong abstraction level.

A new framework is proposed to distinguish substrate from architecture for genuine intelligence.

Abstract

Within the limited scope of this paper, we argue that artificial general intelligence cannot emerge from current neural network paradigms regardless of scale, nor is such an approach healthy for the field at present. Drawing on various notions, discussions, present-day developments and observations, current debates and critiques, experiments, and so on in between philosophy, including the Chinese Room Argument and G\"odelian argument, neuroscientific ideas, computer science, the theoretical consideration of artificial intelligence, and learning theory, we address conceptually that neural networks are architecturally insufficient for genuine understanding. They operate as static function approximators of a limited encoding framework - a 'sophisticated sponge' exhibiting complex behaviours without structural richness that constitute intelligence. We critique the theoretical foundations the field relies on and created of recent times; for example, an interesting heuristic as neural scaling law (as an example, arXiv:2001.08361 ) made prominent in a wrong way of interpretation, The Universal Approximation Theorem addresses the wrong level of abstraction and, in parts, partially, the question of current architectures lacking dynamic restructuring capabilities. We propose a framework distinguishing existential facilities (computational substrate) from architectural organization (interpretive structures), and outline principles for what genuine machine intelligence would require, and furthermore, a conceptual method of structuralizing the richer framework on which the principle of neural network system takes hold.