  Artificial life originated and has long studied the topic of open-ended
evolution, which seeks the principles underlying artificial systems that
innovate continually, inspired by biological evolution. Recently, interest has
grown within the broader field of AI in a generalization of open-ended
evolution, here called open-ended search, wherein such questions of
open-endedness are explored for advancing AI, whatever the nature of the
underlying search algorithm (e.g. evolutionary or gradient-based). For example,
open-ended search might design new architectures for neural networks, new
reinforcement learning algorithms, or most ambitiously, aim at designing
artificial general intelligence. This paper proposes that open-ended evolution
and artificial life have much to contribute towards the understanding of
open-ended AI, focusing here in particular on the safety of open-ended search.
The idea is that AI systems are increasingly applied in the real world, often
producing unintended harms in the process, which motivates the growing field of
AI safety. This paper argues that open-ended AI has its own safety challenges,
in particular, whether the creativity of open-ended systems can be productively
and predictably controlled. This paper explains how unique safety problems
manifest in open-ended search, and suggests concrete contributions and research
questions to explore them. The hope is to inspire progress towards creative,
useful, and safe open-ended search algorithms.
