Open-Endedness is Essential for Artificial Superhuman Intelligence
Authors: name: Edward Hughes, name: Michael Dennis, name: Jack Parker-Holder, name: Feryal Behbahani, name: Aditi Mavalankar, name: Yuge Shi, name: Tom Schaul, name: Tim Rocktaschel

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.