Back
AI Impacts 2023 survey
webCredibility Rating
3/5
Good(3)Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: AI Impacts
This is one of the most cited empirical benchmarks for AI researcher beliefs on timelines and risk; useful for grounding arguments about how seriously the field takes near-term versus long-term AI concerns.
Metadata
Importance: 82/100wiki pagedataset
Summary
A large-scale survey of AI researchers conducted by AI Impacts in 2023, gathering expert predictions on AI timelines, transformative AI milestones, and related risks. The survey updates and expands on prior AI Impacts surveys, providing empirical data on researcher beliefs about when high-level machine intelligence will be achieved and associated concerns.
Key Points
- •Surveyed thousands of AI researchers published at top venues (NeurIPS, ICML) about timelines to human-level AI and transformative milestones.
- •Found median estimates for high-level machine intelligence (HLMI) have shifted significantly earlier compared to the 2016 survey.
- •A substantial fraction of respondents expressed concern about catastrophic or existential risks from advanced AI systems.
- •Researchers showed notable disagreement on timelines, with wide variance in probability estimates across individuals.
- •Results inform debates about AI safety urgency, resource allocation, and the credibility of near-term vs. long-term risk framings.
Cited by 4 pages
| Page | Type | Quality |
|---|---|---|
| AI Accident Risk Cruxes | Crux | 67.0 |
| The Case For AI Existential Risk | Argument | 66.0 |
| Is AI Existential Risk Real? | Crux | 12.0 |
| Should We Pause AI Development? | Crux | 47.0 |
Cached Content Preview
HTTP 200Fetched Apr 9, 202620 KB
2023 Expert Survey on Progress in AI [AI Impacts Wiki]
skip to content
AI Impacts Wiki
User Tools
Log In
Site Tools
Search
Tools Show pagesource Old revisions Backlinks Recent Changes Media Manager Sitemap Log In >
Recent Changes
Media Manager
Sitemap
You are here: Welcome to the AI Impacts Wiki! » AI timelines portal » Predictions of Human-Level AI Timelines » AI Timeline Surveys » 2023 Expert Survey on Progress in AI
ai_timelines:predictions_of_human-level_ai_timelines:ai_timeline_surveys:2023_expert_survey_on_progress_in_ai
Table of Contents
2023 Expert Survey on Progress in AI
Details
Background
Survey methods
Questions
Participants
Changes from 2016 and 2022 ESPAI surveys
Definitions
Results
Timing of human-level performance
Intelligence explosion
AI Interpretability in 2028
How concerning are 11 future AI-related scenarios?
Overall impact of HLMI
Preferred rate of progress
How soon will 39 tasks be feasible for AI?
The alignment problem
How much should society prioritize AI safety research?
Human extinction
Frequently asked questions
How does the seniority of the participants affect the results?
Contributions
2023 Expert Survey on Progress in AI
Published 17 August, 2023. Last updated 29 January, 2024.
The 2023 Expert Survey on Progress in AI is a survey of 2,778 AI researchers that AI Impacts ran in October 2023.
Details
Background
The 2023 Expert Survey on Progress in AI (2023 ESPAI) is a rerun of the 2022 ESPAI and the 2016 ESPAI , previous surveys ran by AI Impacts in collaboration with others. Almost all of the questions in the 2023 ESPAI are identical to those in both the 2022 ESPAI and 2016 ESPAI.
A preprint about the 2023 ESPAI is available here .
Survey methods
Questions
The questions in the 2023 ESPAI are nearly identical to those in the 2022 ESPAI and the 2016 ESPAI . As in those surveys, different phrasings of some questions were randomly assigned to each respondent to measure the effects of framing differences. Each participant also received a randomized subset of certain question types and questions within certain types. Because of this, most questions have a significantly smaller number of responses than the total number of responses to the survey as a whole.
Some questions were added to the 2023 ESPAI which were not in either of the two previous surveys. We refined the new questions through an iterative process involving several rounds of testing the questions in verbal interviews with computer science graduate students and others.
Full questions
Participants
We collected the names of authors who published in 2022 at a selection of top-tier machine learning conferences (NeurIPS, ICML, ICLR, AAAI, JML
... (truncated, 20 KB total)Resource ID:
b4342da2ca0d2721 | Stable ID: YzI0NDk3Zm