In the context of complex systems, including artificial intelligence, emergence describes how complex patterns and behaviors can originate from the interactions of simpler system components without any single component being responsible for the overall behavior. This concept is crucial in fields such as robotics, neural networks, and evolutionary algorithms, where simple rules or interactions at a low level can lead to high-level complexity. For example, in cellular automata like Conway's Game of Life, intricate structures emerge from simple cell interaction rules, providing insights into how similar emergent behaviors can be replicated in AI and computational systems.
Historical overview: The concept of emergence has been recognized in philosophy and science since at least the 19th century, but it gained significant traction in the late 20th and early 21st centuries with the advent of complexity science and systems theory. It became a central theme in discussions about the behavior of complex systems, from ant colonies to brain functions and organizational behavior.
Key contributors: While the idea of emergence is discussed across various disciplines, philosopher G.H. Lewes (1817-1878) is credited with first using the term "emergent" in this context. In the realm of computer science and AI, researchers like Stephen Wolfram have further explored emergent behavior in computational systems, particularly with cellular automata, helping to bridge the gap between theoretical emergence and practical computational applications.