EA - Who's right about inputs to the biological anchors model? by rosehadshar

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Who's right about inputs to the biological anchors model?, published by rosehadshar on July 24, 2023 on The Effective Altruism Forum.In this post, I compared forecasts from Ajeya Cotra and from forecasters in the Existential Risk Persuasion Tournament (XPT) relating to some of the inputs to Cotra's biological anchors model.Here, I give my personal take on which of those forecasts seem more plausible.Note that:I'm only considering the inputs to the bio anchors model which we have XPT forecasts for. This notably excludes the 2020 training requirements distribution, which is a very important driver of model outputs.My take is based on considering the explicit arguments that Cotra and the XPT forecasters gave, rather than on independent research.My take is subjective.I've been working with the Forecasting Research Institute (who ran the XPT) since November 2022, and this is a potential source of bias.I'm publishing this post in a personal capacity and it hasn't gone through FRI's review process.I originally wrote this early in 2023. I've tried to update it as new information came out, but I likely haven't done a comprehensive job of this.To recap, here are the relevant forecasts:See workings here and here. The 'most aggressive' and 'most conservative' forecasts can be considered equivalent to 90% confidence intervals for the median estimate.HardwareFor FLOP/$ in 2025, I think both Cotra and the XPGT forecasters are wrong, but Cotra will prove more right.Epoch's current estimate of highest GPU price-performance is 4.2e18 FLOP per $.They also find a trend in GPU price-performance of 0.1 OOM/year for state of the art GPUs. So I'll extrapolate 4.2e18 to 5.97E+18.For compute price halving time to 2100, I think it depends how likely you think it is that novel technologies like optical computing will reduce compute prices in future.This is the main argument Cotra puts forward for expecting such low prices.It's an argument made in XPT too, but less weight is put on it.Counterarguments given in XPT: fundamental physical limits, progress getting harder, rare materials capping how much prices can drop, catastrophe/extinction, optimisation shifting to memory architectures.Cotra mentions some but not all of these (she doesn't mention rare materials or memory architectures).Cotra flags that she thinks after 2040 her forecasts on this are pretty unreliable.But, because of how wrong their 2024 and 2030 forecasts seem to be, I'm not inclined to put much weight on XPT forecasts here either.I'll go with the most aggressive XPT figure, which is close to Cotra's.I don't have an inside view on the likelihood of novel technologies causing further price drops.Note that the disagreement about compute price halving times drives a lot of the difference in model output.Willingness to spendOn the most expensive training run by 2025, I think Cotra is a bit too aggressive and XPT forecasters are much too conservative.In 2022, Cotra updated downwards a bit on the likelihood of a $1bn training run by 2025.There isn't much time left for Cotra to be right.Cotra was predicting $20m by the end of 2020, and $80m by the end of 2021.GPT-3 was $4.6m in 2020. If you buy that unreleased proprietary models are likely to be 2-8x more expensive than public ones (which Cotra argues), that XPT forecasters missed this consideration, and that GPT-3 isn't proprietary and/or unreleased (flagging because I'm unsure what Cotra actually means by proprietary/unreleased), then this could be consistent with Cotra's forecasts.Epoch estimates that GPT-4 cost $50m to train at some point in 2022.Again, this could be in line with Cotra's predictions.More importantly, GPT-4 costs make XPT forecasters look quite wrong already - their 2024 prediction was surpassed in 2022. This is especially striking i...