We have a set of introductory slides we can share, but many of the rules are still under development and known only to the developers. Hope this helps!
arXiv doesn't support pdfs that are composed of different pdfs. Below is a link to full resolution versions of the tables, but I wasn't able to splice these into the arXiv submission. I will reformat the tables for a revised version.
http://www.rossgritz.com/wp-content/uploads/2020/08/tables.pdf
AI forecasting is an important research area but lacking in a general direction. To address this issue we present a research agenda for AI forecasting that has been generated using the Delphi technique to elicit opinion from 15 leading researchers on the topic (the majority of whom are members of this community). The research agenda can be found on arXiv through this link:
Forecasting AI Progress: A Research Agenda (link to arXiv)
The agenda was framed so that it can be useful to both members of this community as well as the technological forecasting community more broadly. To these ends we plan to submit the arXiv manuscript to Technological Forecasting and Social Change, however, we will wait for roughly a month to receive comments. Please feel free to give us your thoughts here.
I think your timeline is on point regarding capabilities. However, I do not entirely follow the jump from expert-level programming and brute-force search to an "explosive feedback loop of AI progress". You point out that there is a "clear-cut search space" in machine learning, which is true, and I agree that brute-force search could be expected to yield some progress, likely substantial progress, whereas in other scientific disciplines similar progress would be unlikely. I will even concede that explosive progress is possible, but I fail to grasp why it is likely. I think that the "clear-cut search space" is limited to low-hanging fruit, such as "different small tweaks to architectures, loss functions,... (read more)