Open-Endedness is Essential for Artificial Superhuman Intelligence

作者：Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktaschel · 发表于：International Conference on Machine Learning · 年份：2024 · DOI：10.48550/arxiv.2406.04268 · 被引用次数：85 · 研究领域：Computer Science

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.