It would be awesome to easily ensemble Elicit distributions (e.g., take a weighted average). If ensembling were easy, I would have definitely updated my distribution more aggressively, e.g., averaging my inside view / prosaic AGI scenario distribution with datscilly’s outside view distribution (instead of a uniform distribution as an outside view), and/or other distributions which weighed different considerations more heavily (e.g., hardware constraints). It’d be quite informative to see each commenter’s independent/original/prior distribution (before to viewing everyone else’s), and then each commenter’s ensembled/posterior distribution, incorporating or averaging with the distributions of others. I suspect in many cases these two distributions would look quite different, so it would be easy for people to quickly update their views based on the arguments/distributions of others (and see how much they updated).
This has an earlier median (2040) than your original distribution (2046).
(Note for the colab: You can use this to run your own aggregations by plugging in Elicit snapshots of the distributions you want to aggregate. We’re actively working on the Elicit API, so if the notebook breaks lmk so we can update it).
Your “Ethan computed” distribution matches the intended/described distribution from my original prediction comment. The tail now looks uniform, while my distribution had an unintentional decay that came from me using Elicit’s smoothing.
Now that I see how uniform looks visually/accurately, it does look slightly odd (without any decay towards zero), and a bit arbitrary that the uniform distribution ends at 2100. So I think it makes a lot of sense to use Datscilly’s outside view as my outside view prior as you did! So overall, I think the ensembled distribution more accurately represents my beliefs, after updating on the other distributions in the LessWrong AGI timelines post.
The above ensemble distribution looks pretty optimistic, which makes me wonder if there is some “double counting” of scenarios-that-lead-to-AGI between the inside and outside view distributions. I.e., Datscilly’s outside view arguably does incorporate the possibility that we get AGI via “Prosaic AGI” as I described it.
It would be awesome to easily ensemble Elicit distributions (e.g., take a weighted average). If ensembling were easy, I would have definitely updated my distribution more aggressively, e.g., averaging my inside view / prosaic AGI scenario distribution with datscilly’s outside view distribution (instead of a uniform distribution as an outside view), and/or other distributions which weighed different considerations more heavily (e.g., hardware constraints). It’d be quite informative to see each commenter’s independent/original/prior distribution (before to viewing everyone else’s), and then each commenter’s ensembled/posterior distribution, incorporating or averaging with the distributions of others. I suspect in many cases these two distributions would look quite different, so it would be easy for people to quickly update their views based on the arguments/distributions of others (and see how much they updated).
Here’s a colab you can use to do this! I used it to make these aggregations:
The Ethan + Datscilly distribution is a calculation of:
- 25% * Your inside view of prosaic AGI
- 60% * Datscilly’s prediction (renormalized so that all the probability < 2100)
- 15% * We get AGI > 2100 or never
This has an earlier median (2040) than your original distribution (2046).
(Note for the colab: You can use this to run your own aggregations by plugging in Elicit snapshots of the distributions you want to aggregate. We’re actively working on the Elicit API, so if the notebook breaks lmk so we can update it).
Wow thanks for doing this! My takeaways:
Your “Ethan computed” distribution matches the intended/described distribution from my original prediction comment. The tail now looks uniform, while my distribution had an unintentional decay that came from me using Elicit’s smoothing.
Now that I see how uniform looks visually/accurately, it does look slightly odd (without any decay towards zero), and a bit arbitrary that the uniform distribution ends at 2100. So I think it makes a lot of sense to use Datscilly’s outside view as my outside view prior as you did! So overall, I think the ensembled distribution more accurately represents my beliefs, after updating on the other distributions in the LessWrong AGI timelines post.
The above ensemble distribution looks pretty optimistic, which makes me wonder if there is some “double counting” of scenarios-that-lead-to-AGI between the inside and outside view distributions. I.e., Datscilly’s outside view arguably does incorporate the possibility that we get AGI via “Prosaic AGI” as I described it.