“ALIFE = Life as it could be” – Langton
Link to originalThe fields of artificial intelligence ( AI) and artificial life (ALIFE) (Langton, 1997) are inspired by nature and biology in their attempt to create intelligence and forms of life from human-designed computation: the main idea is to abstract the principles from the medium, i.e., biology, and utilize such principles to devise algorithms and devices that reproduce properties of their biological counterparts.
Link to originalA related vision for producing general intelligence stems from the open-ended evolution community within the field of artificial life (ALife), which is concerned with developing programs that replicate the emergent complexity characteristic of living systems. Kickstarting a process that exhibits open-endedness—that is, the endless generation of novel complexity—is seen as a key requirement for achieving this goal.
Such an open-ended process may then become an AI generating algorithm (AI-GA), by producing an ecosystem of increasingly complex problems and agents co-evolved to solve them.
However, such a system may constitute a large population of agents specialized to specific challenges. Moreover, exactly how such a co-evolving system might be implemented in practice remains an open question. We propose open-ended learning as a path toward not only an open-ended process, but one resulting in a single, generalist agent capable of dominating (or matching) any other agent in relative general intelligence over time. In this way, open-ended learning bridges the search for open-ended emergent complexity in ALife with the quest for general intelligence in AI.