I am strongly in favor of our very best content going on arXiv. Both communities should engage more with each other.
As follows are suggestions for posting to arXiv. As a rule of thumb, if the content of a blogpost didn’t take >300 hours of labor to create, then it probably should not go on arXiv. Maintaining a basic quality bar prevents arXiv from being overriden by people who like writing up many of their inchoate thoughts; publication standards are different for LW/AF than for arXiv. Even if a researcher spent many hours on the project, arXiv moderators do not want research that’s below a certain bar. arXiv moderators have reminded some professors that they will likely reject papers at the quality level of a Stanford undergraduate team project (e.g., http://cs231n.stanford.edu/2017/reports.html); consequently labor, topicality, and conforming to formatting standards is not sufficient for arXiv approval. Usually one’s first research project won’t be good enough for arXiv. Furthermore, conceptual/philosophical pieces probably should be primarily posted on arXiv’s .CY section. For more technical deep learning content, do not make the mistake of only putting it on .AI; these should probably go on .LG (machine learning) or .CV (computer vision) or .CL (NLP). arXiv’s .ML section is for more statistical/theoretical machine learning audiences. For content to be approved without complications, it should likely conform to standard (ICLR, ICML, NeurIPS, CVPR, ECCV, ICCV, ACL, EMNLP) formatting. This means automatic blogpost exporting is likely not viable. In trying to diffuse ideas to the broader ML community, we should avoid making the arXiv moderators mad at us.
Here’s a continual stream of related arXiv papers available through reddit and twitter.
I should say formatting is likely a large contributing factor for this outcome. Tom Dietterich, an arXiv moderator, apparently had a positive impression of the content of your grokking analysis. However, research on arXiv will be more likely to go live if it conforms to standard (ICLR, NeurIPS, ICML) formatting and isn’t a blogpost automatically exported into a TeX file.
This is why we introduced X-Risk Sheets, a questionnaire that researchers should include in their paper if they’re claiming that their paper reduces AI x-risk. This way researchers need to explain their thinking and collect evidence that they’re not just advancing capabilities.
We now include these x-risk sheets in our papers. For example, here is an example x-risk sheet included in an arXiv paper we put up yesterday.