Surveyed AI researchers' aggregate forecasts are a reliable guide to when AI will reach human-level performance
4 events · 1 assessment · 2 decisions
Structured and assessed
First pass. Three arguments: instability (against), linking existing claims 53be2cdc, dad6961b and d3086a59 plus new framing-gap claim a242997b; near-term track record (for), with new claims c586fcc6 and 394ba37e; underestimation (against), linking 08f167d1. Corrected the extractor instance's speaker, publication and date. Importance 0.55, contestation 0.55. Assessed doubtful (0.72), credence 0.15. No dependents to notify. No delegation: the cruxes are documented facts in the survey reports and one scoring analysis, which I read directly.
Assessed Doubtful
verdict confidence 0.72 · credence 0.15
Large surveys of published AI researchers, chiefly the AI Impacts Expert Survey on Progress in AI (2016, 2022, 2023), are the most-cited source of "expert" timelines for human-level AI. The evidence weighs against treating their aggregate forecasts as a reliable guide to timing, though it supports treating them as a rough indicator of what the field expects. The strongest evidence against is that the answers are unstable. In the 2023 survey the aggregate 50% date was 2047 for "high-level machine intelligence" but 2116 for "full automation of labor". The two framings describe nearly the same capability, yet their dates differ by decades. Asking for probabilities by fixed years produces systematically later answers than asking for the year by which a probability is reached. The aggregate moved 13 years earlier between the 2022 and 2023 waves, after barely moving between 2016 and 2022, which tracks the salience of recent events. Individual forecasts vary widely. A measuring instrument whose output shifts this much with wording cannot be giving a stable estimate. The survey authors themselves say there are signs that respondents are not accurate forecasters across the range of questions asked. A second line of evidence is a record of underestimating recent capability progress, which is consistent with a bias toward lateness. The best evidence in favour is a scoring of the 2016 survey's near-term milestones. It found reasonable calibration: a Brier score of 0.21, with about nine milestones expected and ten met after 6.5 years. That result is small, rests on subjective resolution judgments, and mixes faster-than-expected language and coding progress with slower progress in robotics. It also bears on multi-decade forecasts only if short-range accuracy predicts long-range accuracy, and almost no evidence on that exists. Researchers are well placed to judge technical feasibility, and alternative sources such as forecasting platforms and superforecaster panels have no long-horizon track record either. That supports the weaker claim that the surveys are informative about expert opinion, not the claim that they reliably guide timing. A scored track record would settle the question: a later survey wave, or a resolved long-horizon milestone set, could show whether aggregates stay stable and calibrated.
Updated claim instance
Instance 679845b0-1b6f-4955-a65f-ec3f3ade513c (https://arxiv.org/html/2401.02843v3): was stance=denies, confidence=0.7; set confidence=0.75, speaker="Katja Grace et al.", publication="arXiv (later Journal of Artificial Intelligence Research), \"Thousands of AI Authors on the Future of AI\"", sourceDate="2024". Speaker and publication were blank; the passage is the survey authors' own caveat in the 2023 Expert Survey on Progress in AI paper (arXiv 2401.02843). Stance denies is right, with moderate confidence because the authors also argue the forecasts remain informative educated guesses.
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