Open-Endedness Is Essential for Artificial Superhuman Intelligence
Open-Endedness is Essential for Artificial Superhuman Intelligence

    
    
                
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            Abstract: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.
    

    
    
  
      Submission history From: Michael Dennis [view email]          [v1]
        Thu, 6 Jun 2024 17:15:02 UTC (618 KB)
