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: XPT forecasts on (some) Direct Approach model inputs, published by Forecasting Research Institute on August 21, 2023 on The Effective Altruism Forum.

This post was co-authored by the Forecasting Research Institute and Rose Hadshar. Thanks to Josh Rosenberg for managing this work, Zachary Jacobs and Molly Hickman for the underlying data analysis, Kayla Gamin for fact-checking and copy-editing, and the whole FRI XPT team for all their work on this project. Special thanks to staff at Epoch for their feedback and advice.

Summary

Superforecaster and expert forecasts from the Existential Risk Persuasion Tournament (XPT) differ substantially from Epoch's default Direct Approach model inputs on algorithmic progress and investment:

InputEpoch (default)XPT superforecasterXPT expertNotesBaseline growth rate in algorithmic progress (OOM/year)0.21-0.650.09-0.20.15-0.23Current spending ($, millions)$60$35$60Yearly growth in spending (%)34%-91.4%6.40%-11%5.7%-19.5%

Epoch: 80% confidence interval (CI)

XPT: 90% CI, based on 2024-2030 forecasts

Epoch: 2023 estimate

XPT: 2024 median forecast

Epoch: 80% CI

XPT: 90% CI, based on 2024-2050 forecasts

Note that there are no XPT forecasts relating to other inputs to the Direct Approach model, most notably the compute requirements parameters.

Taking the Direct Approach model as given and using relevant XPT forecasts as inputs where possible leads to substantial differences in model output:

OutputEpoch default inputsXPT superforecaster inputsXPT expert inputsMedian TAI arrival yearProbability of TAI by 2050Probability of TAI by 2070Probability of TAI by 2100

2036

2065

2052

70%

38%

49%

76%

53%

65%

80%

66%

74%

Note that regeneration affects model outputs, so these results can't be replicated directly, and the TAI probabilities presented here differ slightly from those in Epoch's blog post. Figures given here are the average of 5 regenerations.

Epoch is drawing on recent research which was not available at the time the XPT forecasters made their forecasts (the XPT closed in October 2022).

Most of the difference in outputs comes down to differences in forecasts on baseline growth rate in algorithmic progress and yearly growth in spending, where XPT forecasts differ radically from the Epoch default inputs (which extrapolate historical trends).

XPT forecasters' all-things-considered transformative artificial intelligence (TAI) timelines are much longer than those which the Direct Approach model outputs using XPT inputs:

Source of 2070 forecastXPT superforecasterXPT expertDirect Approach model53%65%XPT postmortem survey question on probability of TAI by 20703.75%16%

If you buy the assumptions of the Direct Approach model, and XPT forecasts on relevant inputs, this pushes timelines out by two to three decades compared with the default Epoch inputs.

However, it still implies TAI by 2070.

It seems very likely that XPT forecasters would not buy the assumptions of the Direct Approach model: their explicitly stated probabilities on TAI by 2070 are <20%.

Introduction

This post:

Compares Direct Approach inputs with XPT forecasts on algorithmic progress and investment, and shows how the differences in forecasts impact the outputs of the Direct Approach model.

Discusses why Epoch's inputs and XPT forecasts differ.

Notes that XPT forecasters' all-things-considered TAI timelines are longer than those which the Direct Approach model outputs using XPT inputs.

Includes an appendix on the arguments given by Epoch and in the XPT for their respective forecasts.

Background on the Direct Approach model

In May 2023, researchers at Epoch released an interactive Direct Approach model, which models the probability that TAI arrives in a given year. The model relies on:

An estimate of the compute required for TAI, based on extrapolating neural scaling laws.

Various inputs rel...