MIRI research associate Benja Fallenstein and UC Berkeley student Alex Mennen have released a new working paper titled “Predicting AGI: What can we say when we know so little?”
From the introduction:
This analysis does not attempt to predict when AGI will actually be achieved, but instead, to predict when this epistemic state with respect to AGI will change, such that we will have a clear idea of how much further progress is needed before we reach AGI. Metaphorically speaking, instead of predicting when AI takes off, we predict when it will start taxiing to the runway.
The paper argues for a Pareto distribution for “time to taxi,” and concludes:
in general, a Pareto distribution suggests that we should put a much greater emphasis on short-term strategies than a less skewed distribution (e.g. a normal distribution) with the same median would.
The post New Paper: “Predicting AGI: What can we say when we know so little?” appeared first on Machine Intelligence Research Institute.