Modeling and Interpreting Expert Disagreement About Artificial Superintelligence
Artificial superintelligence (ASI) is artificial intelligence (AI) with capabilities that are significantly greater than human capabilities across a wide range of domains. A hallmark of the ASI issue is disagreement among experts. This paper demonstrates and discusses methodological options for modeling and interpreting expert disagreement about the risk of ASI catastrophe. Using a new model called ASI-PATH, the paper models a well-documented recent disagreement between Nick Bostrom and Ben Goertzel, two distinguished ASI experts. Three points of disagreement are considered: (1) the potential for humans to evaluate the values held by an AI, (2) the potential for humans to create an AI with values that humans would consider desirable, and (3) the potential for an AI to create for itself values that humans would consider desirable. An initial quantitative analysis shows that accounting for variation in expert judgment can have a large effect on estimates of the risk of ASI catastrophe. The risk estimates can in turn inform ASI risk management strategies, which the paper demonstrates via an analysis of the strategy of AI confinement. The paper find the optimal strength of AI confinement to depend on the balance of risk parameters (1) and (2).
Armstrong S, Sotala K (2012). How we’re predicting AI—or failing to. In Romportl J, Ircing P, Zackova E, Polak M, Schuster R (eds), Beyond AI: Artificial Dreams. Pilsen, Czech Republic: University of West Bohemia, pp. 52-75.
Armstrong S, Sotala K, Ó hÉigeartaigh SS (2014). The errors, insights and lessons of famous AI predictions – and what they mean for the future. Journal of Experimental & Theoretical Artificial Intelligence 26(3), 317-342.
Barrett AM, Baum SD (2017a). A model of pathways to artificial superintelligence catastrophe for risk and decision analysis. Journal of Experimental & Theoretical Artificial Intelligence 29(2), 397-414.
Barrett AM, Baum SD (2017b). Risk analysis and risk management for the artificial superintelligence research and development process. In Callaghan V, Miller J, Yampolskiy R, Armstrong S (eds), The Technological Singularity: Managing the Journey. Berlin: Springer, pp. 127-140.
Baum SD, B Goertzel, TG Goertzel (2011). How long until human-level AI? Results from an expert assessment. Technological Forecasting & Social Change 78(1), 185-195.
Bostrom N (2014). Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press.
Goertzel B (2015). Superintelligence: Fears, promises and potentials. Journal of Evolution and Technology 25(2), 55-87.
Goertzel B (2016). Infusing advanced AGIs with human-like value systems: Two theses. Journal of Evolution and Technology 26(1), 50-72.
Goertzel B, Pitt J (2012). Nine ways to bias open-source AGI toward friendliness. Journal of Evolution and Technology 22(1), 116-131.
Müller VC, Bostrom N (2014). Future progress in artificial intelligence: A survey of expert opinion. In Müller VC (ed), Fundamental Issues of Artificial Intelligence. Berlin: Springer, pp. 555-572.
Oreskes N (2004). The scientific consensus on climate change. Science 306(5702), 1686.
Yampolskiy R (2012). Leakproofing the Singularity: Artificial intelligence confinement problem. Journal of Consciousness Studies 19(1-2), 194-214.
This work is licensed under a Creative Commons Attribution 3.0 License.