This post lists and explains several different “types” of AI takeoff that people talk about. Rather than summarize all the definitions (which would only be slightly shorter than the post itself), I’ll try to name the main axes that definitions vary on (but as a result this is less of a summary and more of an analysis):
1. _Locality_. It could be the case that a single AI project far outpaces the rest of the world (e.g. via recursive self-improvement), or that there will never be extreme variations amongst AI projects across all tasks, in which case the “cognitive effort” will be distributed across multiple actors. This roughly corresponds to the Yudkowsky-Hanson FOOM debate, and the latter position also seems to be that taken by <@CAIS@>(@Reframing Superintelligence: Comprehensive AI Services as General Intelligence@). 2. _Wall clock time_. In Superintelligence, takeoffs are defined based on how long it takes for a human-level AI system to become strongly superintelligent, with “slow” being decades to centuries, and “fast” being minutes to days. 3. _GDP trend extrapolation_. Here, a continuation of an exponential trend would mean there is no takeoff (even if we some day get superintelligent AI), a hyperbolic trend where the doubling time of GDP decreases in a relatively continuous / gradual manner counts as continuous / gradual / slow takeoff, and a curve which shows a discontinuity would be a discontinuous / hard takeoff.
Planned opinion:
I found this post useful for clarifying exactly which axes of takeoff people disagree about, and also for introducing me to some notions of takeoff I hadn’t seen before (though I haven’t summarized them here).
Planned summary for the Alignment Newsletter:
Planned opinion: