For reference, the 0th percentile assumed increase in computational efficiency by the authors of the forecasts is about 143x since the Chinchilla release while I am accepting values of just 60x as an immediate loss of my entire principal. By the time the bet turns to a positive return for me (around April 2026), their 0th percentile model assumes increases in computational efficiency of nearly 500x while I accept even a 150x demonstrated increase as a loss.
Where is 143x coming from? It’s been barely over 3 years, 4.6^3.1=114x.
I’m not sure what you mean by this. I’m guessing that you mean that without superexponentiality (i.e. our 0th percentile) we assume that the rate of software progress continues at approximately the same as the rate of compute progress over the past few years.
I agree that it would be nice to add uncertainty regarding how much of progress has been software progress, but I don’t think it’s crucial to the model.
Because the bet starts below the 0th percentile implied computational efficiency improvements modeled and only gets further away from such a level, this should be an obvious win for the forecasters.
This is obviously not my actual 0th percentile, the model is a simplification as all models are, I’d appreciate if you don’t treat every aspect of the model as representing my all-things-considered views. As mentioned above, it would be ideal if more uncertainty were incorporated on this variable, but we had limited time to create the model.
You’re right on the 143 being closer to 114! (I took March 1 2022 → July 1 2022 instead of March 22 2022 → June 1 2022 which is accurate).
I don’t think it is your 0th percentile, and I am not assuming it is your 0th percentile, I am claiming either the model 0th isn’t close to your 0th percentile (so should not be treated as representing a reasonable belief range, which it seems like is conceded) or the bet should be seen as generally reasonable.
I sincerely do not think a limited time argument is valid given the amount of work that was put into non-modeling aspects of the presentation and the amount of work claimed put into the model over several gamings and reviews and months of work etc etc.
If the burden of proof is on critics to do work you are not willing to do in order to show the model is flawed (for a bounty between 4-10% of the bounty you offer someone writing a supporting piece to advertise your position further), then the defense of limited time raises some hackles.
First, my argument is not: we had limited time to do this, therefore it’s fine for us to not include whatever factors we want.
My argument is: we had limited time despite putting lots of work into this, because it’s a very ambitiously scoped endeavor. Adding uncertainty to the percent of progress that is software wouldn’t have changed the qualitative takeaways, therefore it’s not ideal but okay for us to present the model without that uncertainty (shifting the median estimate a lower number my have have, I’ll separately reply to your comment on that; we should clearly distinguish these, and your 0th percentile assertions are aimed more at the lack of uncertainty in the model than the median estimate).
That being said, I agree with you that it would be nice and I will likely add uncertainty to our model because it seems like good ROI, I appreciate you pushing me to do this.
Where is 143x coming from? It’s been barely over 3 years, 4.6^3.1=114x.
I’m not sure what you mean by this. I’m guessing that you mean that without superexponentiality (i.e. our 0th percentile) we assume that the rate of software progress continues at approximately the same as the rate of compute progress over the past few years.
I agree that it would be nice to add uncertainty regarding how much of progress has been software progress, but I don’t think it’s crucial to the model.
This is obviously not my actual 0th percentile, the model is a simplification as all models are, I’d appreciate if you don’t treat every aspect of the model as representing my all-things-considered views. As mentioned above, it would be ideal if more uncertainty were incorporated on this variable, but we had limited time to create the model.
You’re right on the 143 being closer to 114! (I took March 1 2022 → July 1 2022 instead of March 22 2022 → June 1 2022 which is accurate).
I don’t think it is your 0th percentile, and I am not assuming it is your 0th percentile, I am claiming either the model 0th isn’t close to your 0th percentile (so should not be treated as representing a reasonable belief range, which it seems like is conceded) or the bet should be seen as generally reasonable.
I sincerely do not think a limited time argument is valid given the amount of work that was put into non-modeling aspects of the presentation and the amount of work claimed put into the model over several gamings and reviews and months of work etc etc.
If the burden of proof is on critics to do work you are not willing to do in order to show the model is flawed (for a bounty between 4-10% of the bounty you offer someone writing a supporting piece to advertise your position further), then the defense of limited time raises some hackles.
First, my argument is not: we had limited time to do this, therefore it’s fine for us to not include whatever factors we want.
My argument is: we had limited time despite putting lots of work into this, because it’s a very ambitiously scoped endeavor. Adding uncertainty to the percent of progress that is software wouldn’t have changed the qualitative takeaways, therefore it’s not ideal but okay for us to present the model without that uncertainty (shifting the median estimate a lower number my have have, I’ll separately reply to your comment on that; we should clearly distinguish these, and your 0th percentile assertions are aimed more at the lack of uncertainty in the model than the median estimate).
That being said, I agree with you that it would be nice and I will likely add uncertainty to our model because it seems like good ROI, I appreciate you pushing me to do this.