Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligence

TL;DR

This paper investigates how surpassing certain complexity thresholds in AI systems, especially LLMs, can lead to performance instability, challenging the assumption of continuous, exponential AI progress towards AGI.

Contribution

It introduces a novel complexity-theory framework and agent-based modeling approach to identify critical points where AI performance may plateau or regress.

Findings

Increasing AI complexity can cause performance instability beyond critical thresholds

Simulation-based methodology effectively detects phase transition points in AI development

Highlights the importance of robust benchmarks to monitor AI system complexity

Abstract

In this study, we explored the progression trajectories of artificial intelligence (AI) systems through the lens of complexity theory. We challenged the conventional linear and exponential projections of AI advancement toward Artificial General Intelligence (AGI) underpinned by transformer-based architectures, and posited the existence of critical points, akin to phase transitions in complex systems, where AI performance might plateau or regress into instability upon exceeding a critical complexity threshold. We employed agent-based modelling (ABM) to simulate hypothetical scenarios of AI systems' evolution under specific assumptions, using benchmark performance as a proxy for capability and complexity. Our simulations demonstrated how increasing the complexity of the AI system could exceed an upper criticality threshold, leading to unpredictable performance behaviours. Additionally, we developed a practical methodology for detecting these critical thresholds using simulation data and stochastic gradient descent to fine-tune detection thresholds. This research offers a novel perspective on AI advancement that has a particular relevance to Large Language Models (LLMs), emphasising the need for a tempered approach to extrapolating AI's growth potential and underscoring the importance of developing more robust and comprehensive AI performance benchmarks.