Unpredictability of AI

Abstract

The young field of AI Safety is still in the process of identifying its challenges and limitations. In this paper, we formally describe one such impossibility result, namely Unpredictability of AI. We prove that it is impossible to precisely and consistently predict what specific actions a smarter-than-human intelligent system will take to achieve its objectives, even if we know terminal goals of the system. In conclusion, impact of Unpredictability on AI Safety is discussed.

References in corpus (2)

• Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function)
• Building Safer AGI by introducing Artificial Stupidity

Cited by in corpus (4)

• Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations. Part II
• Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations. Part I
• Unexplainability and Incomprehensibility of Artificial Intelligence
• Position Paper: Towards Transparent Machine Learning