When Katja Grace and co-authors first released this paper in June 2017, it provoked worldwide headlines. Their findings showed AI experts believe there is a 50% probability AI will ‘outperform humans in all tasks’ by 2062.
The paper presents a timeline of dates by which several human functions may be performed by machines with equal or better ability. It projects the likelihood of ‘automating all human jobs in 120 years’.
The study questionnaire used a sample of 352 leading AI scientists, selected by their attendance at two leading machine learning conferences.
AUTHORS
- Katja Grace, Future of Humanity Institute, Oxford University, AI Impacts
- John Salvatier, AI Impacts
- Allan Dafoe, Future of Humanity Institute, Oxford University, Department of Political Science, Yale University
- Baobao Zhang, Department of Political Science, Yale University
- Owain Evans,Future of Humanity Institute, Oxford University
ABSTRACT
‘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.’
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Grace, K. et al (2018), ‘Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts,’ Journal of Artificial Intelligence Research, Vol 62, DOIhttps://doi.org/10.1613/jair.1.11222
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