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.

翻译：本研究通过复杂性理论的视角探讨了人工智能（AI）系统的演进轨迹。我们挑战了基于Transformer架构的AI向人工通用智能（AGI）发展的传统线性和指数级预测，并提出存在类似于复杂系统中相变的关键临界点：当AI系统超过某一临界复杂性阈值时，其性能可能进入平台期或退化至不稳定状态。我们采用基于智能体的建模（ABM）方法，在特定假设下模拟AI系统演化的假设场景，并以基准测试性能作为能力与复杂性的代理指标。模拟结果表明，增加AI系统的复杂性可能超越上临界阈值，导致不可预测的性能行为。此外，我们开发了一种利用模拟数据和随机梯度下降优化检测阈值的实用方法，用于识别这些临界阈值。本研究为AI发展提供了新的视角，尤其与大型语言模型（LLMs）密切相关，强调需要以审慎态度推断AI的增长潜力，并凸显了开发更稳健、更全面的AI性能基准的重要性。