Open-Endedness is Essential for Artificial Superhuman Intelligence
This addresses the challenge of creating self-improving AI for advancing towards superhuman intelligence, but it is a position paper with no empirical results, making it incremental in proposing a conceptual framework.
The paper argues that open-endedness, defined through novelty and learnability, is essential for achieving artificial superhuman intelligence (ASI) and outlines a path using foundation models to make novel, human-relevant discoveries, while also discussing safety implications.
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.