We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (ECA), simple yet powerful one-dimensional systems that generate behaviors ranging from trivial to highly complex. By training distinct Large Language Models (LLMs) on different ECAs, we evaluated the relationship between the complexity of the rules' behavior and the intelligence exhibited by the LLMs, as reflected in their performance on downstream tasks. Our findings reveal that rules with higher complexity lead to models exhibiting greater intelligence, as demonstrated by their performance on reasoning and chess move prediction tasks. Both uniform and periodic systems, and often also highly chaotic systems, resulted in poorer downstream performance, highlighting a sweet spot of complexity conducive to intelligence. We conjecture that intelligence arises from the ability to predict complexity and that creating intelligence may require only exposure to complexity.

Today's IT systems heavily rely on IT support for smoother and faster operations. Any application or infrastructure issue is reported by tools or end-users in the form of a trouble ticket. The information about actual issue is hidden inside these ticket descriptions and is provided in different ways. In this paper, we address the problem of extracting the issues from these ticket descriptions. We break the problem into different sub-problems each presenting different challenges and hence requiring different solutions! We present our experience of applying theory into practice by presenting a real-world case-study.