The new paper by Stuart Armstrong (FHI) and Kaj Sotala (SI) has now been published (PDF) as part of the Beyond AI conference proceedings. Some of these results were previously discussed here. The original predictions data are available here.
Abstract:
This paper will look at the various predictions that have been made about AI and propose decomposition schemas for analysing them. It will propose a variety of theoretical tools for analysing, judging and improving these predictions. Focusing specifically on timeline predictions (dates given by which we should expect the creation of AI), it will show that there are strong theoretical grounds to expect predictions to be quite poor in this area. Using a database of 95 AI timeline predictions, it will show that these expectations are born out in practice: expert predictions contradict each other considerably, and are indistinguishable from non-expert predictions and past failed predictions. Predictions that AI lie 15 to 25 years in the future are the most common, from experts and non-experts alike.
It's a good paper overall, and I'm glad to see it's been published - especially the Maes-Garreau material! (I wonder what Kevin Kelly made of our results? His reaction would've been neat to mention.)
But reading it all in one place, I think one part seems pretty weak: the criticizing of the 'expert' predictions. It seems to me like there ought to be more rigorous forms of assessment, and I wonder about possible explanations for the clumping at 20+ years: the full median-estimate graph seems to show a consistent expert trend post-1970s to put AI at x-2050 (I can't read the dates because the graphs are so illegible, what the heck?) and also many recent predictions. Perhaps there really is a forming expert consensus and the clump is due to the topic gaining a great deal of attention recently, and then the non-expert predictions are just taking their cue from the experts (as one would hope!)
The link now points to the fixed proceedings (better image resolution). Sorry once again. Jan