Emergence or emergent behavior is a concept that encapsulates the idea of complex results arising from simple rules or interactions within an AI system. It refers to a phenomenon where the collective system exhibits behaviors or characteristics that are more complex than those of the individual components. These behaviors aren’t part of the initial programming but spontaneously evolve from the interactions within the system.
For example, the concept of emergence is prominently displayed in swarm intelligence in AI, such as in a flock of independently moving drones that can signal one another to avoid collisions – like birds in a flock making a “sharp left turn” together. While each drone is programmed with a simple rule set, their collective behavior displays a complex, emergent feature of being able to move as a single, cohesive unit. This is a form of decentralized, self-organized system, where macro-level behavior emerges from the local interactions of its components.
The term “intelligence explosion” refers to a hypothetical scenario where an artificially intelligent system self-improves, triggering a series of rapid, cascading improvements leading to a superintelligent system – far surpassing human intelligence. This phenomenon of rapidly evolving intelligence, not originally programmed but emerging from the system’s ongoing self-enhancement, is a form of emergent behavior. It’s important to note that as of now, this concept remains largely theoretical with little empirical evidence in contemporary AI models. We’re still a long way from creating AI systems capable of such a level of autonomy and self-directed learning.
