Against the singularity hypothesis
David Thorstad (Global Priorities Institute, University of Oxford)
GPI Working Paper No. 19-2022; published in Philosophical Studies
The singularity hypothesis is a radical hypothesis about the future of artificial intelligence on which self-improving artificial agents will quickly become orders of magnitude more intelligent than the average human. Despite the ambitiousness of its claims, the singularity hypothesis has been defended at length by leading philosophers and artificial intelligence researchers. In this paper, I argue that the singularity hypothesis rests on scientifically implausible growth assumptions. I show how leading philosophical defenses of the singularity hypothesis (Chalmers 2010, Bostrom 2014) fail to overcome the case for skepticism. I conclude by drawing out philosophical implications of this discussion for our understanding of consciousness, personal identity, digital minds, existential risk, and ethical longtermism.
Other working papers
When should an effective altruist donate? – William MacAskill (Global Priorities Institute, Oxford University)
Effective altruism is the use of evidence and careful reasoning to work out how to maximize positive impact on others with a given unit of resources, and the taking of action on that basis. It’s a philosophy and a social movement that is gaining considerable steam in the philanthropic world. For example,…
The asymmetry, uncertainty, and the long term – Teruji Thomas (Global Priorities Institute, Oxford University)
The Asymmetry is the view in population ethics that, while we ought to avoid creating additional bad lives, there is no requirement to create additional good ones. The question is how to embed this view in a complete normative theory, and in particular one that treats uncertainty in a plausible way. After reviewing…
Imperfect Recall and AI Delegation – Eric Olav Chen (Global Priorities Institute, University of Oxford), Alexis Ghersengorin (Global Priorities Institute, University of Oxford) and Sami Petersen (Department of Economics, University of Oxford)
A principal wants to deploy an artificial intelligence (AI) system to perform some task. But the AI may be misaligned and aim to pursue a conflicting objective. The principal cannot restrict its options or deliver punishments. Instead, the principal is endowed with the ability to impose imperfect recall on the agent. The principal can then simulate the task and obscure whether it is real or part of a test. This allows the principal to screen misaligned AIs during testing and discipline their behaviour in deployment. By increasing the…