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
Economic inequality and the long-term future – Andreas T. Schmidt (University of Groningen) and Daan Juijn (CE Delft)
Why, if at all, should we object to economic inequality? Some central arguments – the argument from decreasing marginal utility for example – invoke instrumental reasons and object to inequality because of its effects…
Philosophical considerations relevant to valuing continued human survival: Conceptual Analysis, Population Axiology, and Decision Theory – Andreas Mogensen (Global Priorities Institute, University of Oxford)
Many think that human extinction would be a catastrophic tragedy, and that we ought to do more to reduce extinction risk. There is less agreement on exactly why. If some catastrophe were to kill everyone, that would obviously be horrific. Still, many think the deaths of billions of people don’t exhaust what would be so terrible about extinction. After all, we can be confident that billions of people are going to die – many horribly and before their time – if humanity does not go extinct. …
Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Paris School of Economics)
Estimating long-term impacts of actions is important in many areas but the key difficulty is that long-term outcomes are only observed with a long delay. One alternative approach is to measure the effect on an intermediate outcome or a statistical surrogate and then use this to estimate the long-term effect. …