This was a good catch! I did actually mean world GDP, not world GDP growth. Because people have already predicted on this, I added the correct questions above as new questions, and am leaving the previous questions here for reference:
You can search for the question on elicit.org/binary and see the history of all predictions made! E.G. If you copy the question title in this post, and search by clicking Filter then pasting the title into "Question title contains," you can find the question here.
Thanks!! It's primarily intended for prediction, but I feel excited about people experimenting with different ways of using this and seeing which are most useful & how they change discussions, so am interested to see what happens if you use it for other purposes too.
A rough distribution (on a log scale) based on the two points you estimated for wars (95% < 1B people die in wars, 85% < 10M people die in wars) gives a median of ~2,600 people dying. Does that seem right?
I noticed that your prediction and jmh's prediction are almost the exact opposite:
Teerth: 80%: No human being would be living on another celestial object (Moon, another planet or asteroid) (by 2030)
jmh: 90%: Humans living on the moon (by 2030)
(I plotted this here to show the difference, although this makes the assumption that you think the probability is ~uniformly distributed from 2030 – 2100). Curious why you think these differ so much? Especially jmh, since 90% by 2030 is more surprising - the Metaculus prediction for when the next human being will walk on the moon has a median of 2031.
This is a really great conditional question! I'm curious what probability everyone puts on the assumption (GPT-N gets us to TAI) being true (i.e. do these predictions have a lot of weight in your overall TAI timelines)?
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).
This links to a uniform distribution, guessing you didn't mean that! To link to your distribution, take a snapshot of your distribution, and then copy the snapshot url (which appears as a timestamp at the bottom of the page) and link that.
Daniel and SDM, what do you think of a bet with 78:22 odds (roughly 4:1) based on the differences in your distributions, i.e: If AGI happens before 2030, SDM owes Daniel $78. If AGI doesn't happen before 2030, Daniel owes SDM $22.
This was calculated by:
Identifying the earliest possible date with substantial disagreement (in this case, 2030)
Finding the probability each person assigns to the date range of now to 2030:
According to this post, a bet based on the arithmetic mean of 2 differing probability estimates yields the same expected value for each participant. In this case, the mean is (5%+39%)/2=22% chance of AGI before 2030, equivalent to 22:78 odds.
$78 and $22 can be scaled appropriately for whatever size bet you're comfortable with
Oh yeah that makes sense, I was slightly confused about the pod setup. The approach would've been different in that case (still would've estimated how many people in each pod were currently infected, but would've spent more time on the transmission rate for 30 feet outdoors). Curious what your current prediction for this is? (here is a blank distribution for the question if you want to use that)
Here's my prediction, and here's a spreadsheet with more details (I predicted expected # of people who would get COVID). Some caveats/assumptions:
There's a lot of uncertainty in each of the variables that I didn't have time to research in-depth
I didn't adjust for this being outdoors, you can add a row and adjust for that if you have a good sense of how it would affect it.
I wasn't sure how to account for the time being 3 hours. My sense is that if you're singing loudly at people < 1m for 3 hours, this is going to be a pretty high infection rate. Also, I assumed they weren't wearing masks because of the singing. I'm most uncertain about this though
You didn't mention how big the pods are. I assumed 10 people in a pod, but it would change it if this were much smaller.
Either expected number of people who get covid or number of microcovids generated by the event works as a question! My instinctive sense is that # of people who get covid will be easier to quickly reason about, but I'll see as I'm forecasting it.
In a similar vein to this, I found several resources that make me think it should be higher than 1% currently and in the next 1.5 years:
This 2012/3 paper by Vincent Müller and Nick Bostrom surveyed AI experts, in particular, 72 people who attended AGI workshops (most of whom do technical work). Of these 72, 36% thought that assuming HLMI would at some point exist, it would be either ‘on balance bad’ or ‘extremely bad’ for humanity. Obviously this isn't an indication that they understand or agree with safety concerns, but directionally suggests people are concerned and thinking about this.
This 2017 paper by Seth Baum identified 45 projects on AGI and their stance on safety (page 25). Of these, 12 were active on safety (dedicated efforts to address AGI safety issues), 3 were moderate (acknowledge safety issues, but don’t have dedicated efforts to address them), and 2 were dismissive (argue that AGI safety concerns are incorrect). The remaining 28 did not specify their stance.
We're ok with people posting multiple snapshots, if you want to update it based on later comments! You can edit your comment with a new snapshot link, or add a new comment with the latest snapshot (we'll consider the latest one, or whichever one you identify as your final submission)