When Will AI Exceed Human Performance? Evidence from AI Experts
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Abstract (arXiv)
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.
Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2017.
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Citing papers per year: 2018: 1; 2019: 0; 2020: 1; 2021: 9; 2022: 7; 2023: 9; 2024: 8; 2025: 4; 2026: 2.
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- 2024AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publications14 Mar 2024
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- 2023Toward enriched Cognitive Learning with XAI19 Dec 2023
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- 2023ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models29 Sep 2023
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- 2023Quantifying the Benefit of Artificial Intelligence for Scientific Research17 Apr 2023
- 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models17 Mar 2023
- 2023Algorithmic Ghost in the Research Shell: Large Language Models and Academic Knowledge Creation in Management Research10 Mar 2023
- 2023AI Risk Skepticism, A Comprehensive Survey16 Feb 2023
- 2023Fair Decision-making Under Uncertainty29 Jan 2023
- 2022Examining the Differential Risk from High-level Artificial Intelligence and the Question of Control6 Nov 2022
- 2022The Alignment Problem from a Deep Learning Perspective30 Aug 2022
- 2022Text to Image Generation: Leaving no Language Behind19 Aug 2022
- 2022Is Power-Seeking AI an Existential Risk?16 Jun 2022
- 2022X-Risk Analysis for AI Research13 Jun 2022
- 2022Researching Alignment Research: Unsupervised Analysis6 Jun 2022
- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Model14 Apr 2022
- 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI11 Nov 2021
- 2021The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail15 Oct 2021
- 2021Phy-Q as a measure for physical reasoning intelligence31 Aug 2021
- 2021Online Fairness-Aware Learning with Imbalanced Data Streams13 Aug 2021
- 2021AI Risk Skepticism2 May 2021
- 2021Axes for Sociotechnical Inquiry in AI Research26 Apr 2021
- 2021Why AI is Harder Than We Think26 Apr 2021
- 2021Artificial Intelligence Narratives: An Objective Perspective on Current Developments18 Mar 2021
- 2021AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks4 Feb 2021
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks22 May 2020
- 2018A Roadmap for Robust End-to-End Alignment4 Sep 2018
- 2018A Roadmap for Robust End-to-End Alignmentcited by 0
- 2018A Roadmap for Robust End-to-End Alignment4 Sep 2018
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 711
- 2022The Alignment Problem from a Deep Learning Perspectivecited by 246
- 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelscited by 174
- 2022Is Power-Seeking AI an Existential Risk?cited by 101
- 2022X-Risk Analysis for AI Researchcited by 60
- 2024Thousands of AI Authors on the Future of AIcited by 48
- 2021Why AI is Harder Than We Thinkcited by 40
- 2021The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Failcited by 38
- 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
- 2024Complementarity in Human-AI Collaboration: Concept, Sources, and Evidencecited by 35
- 2023ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Modelscited by 17
- 2025How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupationscited by 17
12 most cited of 39 All 39 (JSON)
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- 2025A Criminology of Machines4 Nov 2025
- 2025How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupations26 Oct 2025
- 2025On the Same Page: Dimensions of Perceived Shared Understanding in Human-AI Interaction26 May 2025
- 2025Serious Games: Human-AI Interaction, Evolution, and Coevolution22 May 2025
- 2024MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models19 Aug 2024
- 2024Misrepresented Technological Solutions in Imagined Futures: The Origins and Dangers of AI Hype in the Research Community8 Aug 2024
- 2024Investigating the Role of Explainability and AI Literacy in User Compliance18 Jun 2024
- 2024Mining Potentially Explanatory Patterns via Partial Solutions5 Apr 2024
- 2024Cooperative Evolutionary Pressure and Diminishing Returns Might Explain the Fermi Paradox: On What Super-AIs Are Like1 Apr 2024
- 2024Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence21 Mar 2024
12 newest of 39 All 39 (JSON)