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arXiv · 2206.04132

Forecasting AI Progress: Evidence from a Survey of Machine Learning Researchers

Baobao Zhang·Noemi Dreksler·Markus Anderljung·Lauren Kahn·Charlie Giattino·Allan Dafoe·Michael C. Horowitz
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Abstract

Advances in artificial intelligence (AI) are shaping modern life, from transportation, health care, science, finance, to national defense. Forecasts of AI development could help improve policy- and decision-making. We report the results from a large survey of AI and machine learning (ML) researchers on their beliefs about progress in AI. The survey, fielded in late 2019, elicited forecasts for near-term AI development milestones and high- or human-level machine intelligence, defined as when machines are able to accomplish every or almost every task humans are able to do currently. As part of this study, we re-contacted respondents from a highly-cited study by Grace et al. (2018), in which AI/ML researchers gave forecasts about high-level machine intelligence and near-term milestones in AI development. Results from our 2019 survey show that, in aggregate, AI/ML researchers surveyed placed a 50% likelihood of human-level machine intelligence being achieved by 2060. The results show researchers newly contacted in 2019 expressed similar beliefs about the progress of advanced AI as respondents in the Grace et al. (2018) survey. For the recontacted participants from the Grace et al. (2018) study, the aggregate forecast for a 50% likelihood of high-level machine intelligence shifted from 2062 to 2076, although this change is not statistically significant, likely due to the small size of our panel sample. Forecasts of several near-term AI milestones have reduced in time, suggesting more optimism about AI progress. Finally, AI/ML researchers also exhibited significant optimism about how human-level machine intelligence will impact society.

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Baobao Zhang, Noemi Dreksler, Markus Anderljung, Lauren Kahn, Charlie Giattino, Allan Dafoe, Michael C. Horowitz. 2022-06-08. Forecasting AI Progress: Evidence from a Survey of Machine Learning Researchers. https://arxiv.org/abs/2206.04132

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