Open-Endedness is Essential for Artificial Superhuman Intelligence
<|reference_start|>Open-Endedness is Essential for Artificial Superhuman Intelligence: In recent years there has been a tremendous surge in the general capabilities of AI systems, mainly fuelled by training foundation models on internetscale data. Nevertheless, the creation of openended, ever self-improving AI remains elusive. In this position paper, we argue that the ingredients are now in place to achieve openendedness in AI systems with respect to a human observer. Furthermore, we claim that such open-endedness is an essential property of any artificial superhuman intelligence (ASI). We begin by providing a concrete formal definition of open-endedness through the lens of novelty and learnability. We then illustrate a path towards ASI via open-ended systems built on top of foundation models, capable of making novel, humanrelevant discoveries. We conclude by examining the safety implications of generally-capable openended AI. We expect that open-ended foundation models will prove to be an increasingly fertile and safety-critical area of research in the near future.<|reference_end|>
@article{hughes2024open-endedness,
    title={Open-Endedness is Essential for Artificial Superhuman Intelligence},
    author={Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal Behbahani,
  Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktaschel},
    journal={arXiv preprint arXiv:2406.04268},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04268},
    primaryClass={cs.LG cs.AI}
}