Emergence

In the context of artificial intelligence, emergence refers to complex macro-level patterns arising from simple low-level rules and interactions. Key characteristics:

Global behaviors emerge from local individual behaviors and interactions.
Higher-level complexity self-organizes from bottom-up growth.
Unexpected novelty and creativity arise from the system.
Not directly programmed but comes from decentralized behaviors.

Examples in AI include:

Flocking simulations where coordination emerges from agent rules.
Machine learning models self-organizing internal representations.
Generative art created from algorithmic rulesets.
Sophisticated strategies from simple reinforcement learning agents.

Emergence allows solving problems decentralizedly without top-down oversight. It provides a source of creativity and learning in AI systems.

Engineering emergent systems requires balancing organization and randomness. Understanding emergent phenomena remains an open scientific challenge.