When Will AI Exceed Human Performance? Evidence from AI Experts

By Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, Owain Evans. 2017

703 citations recorded by the source corpus.

Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans. These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI.

Subfields: Information Systems and Management, Management Information Systems, Management Science and Operations Research, Evaluation & Benchmarks

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Citations in the corpus

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020, background): “Advanced language models may also lead to the automation of various jobs in the coming decades [16].”
Measuring the Algorithmic Efficiency of Neural Networks (2020, background): “However, there’s massive disagreement as to how much progress in capabilities we should expect in the near and long term [Grace et al., 2017].”
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, methodology): “…2017), modeling task-level automation and aggregating the results to occupation-level insights (Arntz et al., 2017), collecting expert forecasts (Grace et al., 2018), and most relevantly to this paper, devising a new rubric to assess worker activities for their suitability for machine learning…”
The alignment problem from a deep learning perspective (2022, background): “…2023b, DeepMind, 2023] and is taken seriously by leading ML researchers, who in two surveys gave median estimates of 2061 and 2059 for the year in which AI will outperform humans at all tasks—although some expect this to occur much sooner or later [Grace et al., 2018, Stein-Perlman et al., 2022].”
The Impact of Artificial Intelligence on the Labor Market (2019, background): “4Examples include Frey and Osborne (2017), Arntz, Gregory, and Zierahn (2017) , Grace et al. (2017), Manyika et al. (2017), Brynjolfsson, Mitchell, and Rock (2018), and Felten, Raj, and Seamans (2017).”
Towards Safe Artificial General Intelligence (2018, background): “Many other examples are described by Gwern (2011), Irpan (2018), and Lehman et al.”
When Combating Hype, Proceed with Caution (2021, background): “Expert forecasts suggest that this could take place within a few decades (Grace et al., 2018).”
Why AI is harder than we think (2021, background): “In surveys of AI researchers carried out in 2016 and 2018, the median prediction of those surveyed gave a 50 percent chance that human-level AI would be created by 2040–2060, though there was much variance of opinion, both for sooner and later estimates [21, 22].”
Artificial intelligence and big data in entrepreneurship: a new era has begun (2019, background): “Arguably, this seems like an unlikely utopia though (at least for the near future; Brynjolfsson and Mcafee 2017), but similar discussions are currently held with respect to a large variety of jobs and human performance domains (Grace et al. 2018).”
Six Human-Centered Artificial Intelligence Grand Challenges (2023, neutral): “Although some have envisioned a future in which AI eclipses human intelligence (Grace et al., 2018), this group argues for a future in which advances in AI augment rather than replace humans and improve their environment.”