Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm

TL;DR

This paper introduces a novel artificial life framework where intelligent organisms emerge from atomic elements interacting through neural and physical rules, forming a basis for a general AI generating algorithm.

Contribution

It presents a new environment-based framework for emergent intelligence without predefined agents, proposing a scalable AI generation method.

Findings

Emergence of complex organisms from simple atomic elements.

Potential for scalable AI development through evolutionary processes.

Framework lays groundwork for future advancements in artificial life and AI generation.

Abstract

We propose an artificial life framework aimed at facilitating the emergence of intelligent organisms. In this framework there is no explicit notion of an agent: instead there is an environment made of atomic elements. These elements contain neural operations and interact through exchanges of information and through physics-like rules contained in the environment. We discuss how an evolutionary process can lead to the emergence of different organisms made of many such atomic elements which can coexist and thrive in the environment. We discuss how this forms the basis of a general AI generating algorithm. We provide a simplified implementation of such system and discuss what advances need to be made to scale it up further.