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.

Evolutionary algorithms are designed to find impressive solutions in complex search spaces. Meeting this aim requires that the heuristic guiding search aligns with the structure of the search space, i.e. the effectiveness of rewarding properties of individuals (like fitness or novelty) depends on how those properties are distributed. Interestingly, researchers can rarely access ground truth about such connectivity, especially in settings like evolutionary robotics (ER) where search spaces are large and an individual’s behavior could potentially inform search in many different ways. This paper raises the intriguing possibility of adapting or simplifying existing ER domains such that we know everything about the search space’s structure, to enable us to develop intuitions and quickly explore new search algorithms. The proposed approach is to pair an expressive (but limited) encoding with a benchmark ER domain, and precompute the behavior of all possible individuals. Such precomputation enables evaluation as a look-up table, and the further precomputation of normally-intractable quantities, like exact rarity of behaviors and a variety of evolvability metrics. Evolution can then be driven and gauged by such properties with extreme efficiency. The hope is that insights gleaned from this sandbox can inspire new and effective approaches that generalize to when everything is not known.

Natural evolution and complex adaptations often surprise scientists. However, the creativity of evolution is not limited to the natural world, transcending any particular substrate. In the context of digital evolution, artificial organisms evolving in computational environments are also able to elicit surprise and wonder. Indeed, most digital evolution researchers can relate anecdotes highlighting how common it is for their algorithms to creatively subvert their expectations or intentions, expose unrecognized bugs in their code, produce unexpectedly potent adaptations, or engage in behaviors and outcomes uncannily convergent with ones found in nature. Such stories routinely reveal the surprise and creativity of evolution in these digital worlds, but they rarely fit into the standard scientific narrative and are treated as obstacles to be overcome rather than interesting results. Bugs are fixed, experiments are refocused, and one-off surprises become stories traded among researchers through lossy, inefficient and error-prone oral tradition. Moreover, to our knowledge, no collection of such anecdotes has been published before and many natural scientists do not recognize how lifelike digital organisms are and how natural their evolution can be. We have crowd-sourced the writing of a paper and collected first-hand reports from artificial life and evolutionary computation researchers, creating a written, fact-checked collection of entertaining and important stories. It serves to show that evolutionary surprise generalizes beyond the natural world, and may indeed be a universal property of all complex evolving systems.