T HOUSANDS OF AI AUTHORS ON THE F UTURE OF AI
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Katja Grace* †AI Impacts
Berkeley, California
United States
katja@aiimpacts.org

Harlan Stewart†
AI Impacts
Berkeley, California
United States

Ben Weinstein-Raun
Independent
Berkeley, California
United States

Julia Fabienne Sandkühler†
Department of Psychology
University of Bonn
Germany

Stephen Thomas†
AI Impacts
Berkeley, California
United States

Jan Brauner
Department of Computer Science
University of Oxford
United Kingdom

January 2024

A BSTRACTIn the largest survey of its kind, we surveyed 2,778 researchers who had published in top-tier artificial
intelligence (AI) venues, asking for their predictions on the pace of AI progress and the nature andimpacts of advanced AI systems. The aggregate forecasts give at least a 50% chance of AI systems
achieving several milestones by 2028, including autonomously constructing a payment processing
site from scratch, creating a song indistinguishable from a new song by a popular musician, andautonomously downloading and fine-tuning a large language model. If science continues undisrupted,
the chance of unaided machines outperforming humans in every possible task was estimated at 10%
by 2027, and 50% by 2047. The latter estimate is 13 years earlier than that reached in a similar
survey we conducted only one year earlier [Grace et al., 2022]. However, the chance of all humanoccupations becoming fully automatable was forecast to reach 10% by 2037, and 50% as late as 2116
(compared to 2164 in the 2022 survey).
Most respondents expressed substantial uncertainty about the long-term value of AI progress: While
68.3% thought good outcomes from superhuman AI are more likely than bad, of these net optimists
48% gave at least a 5% chance of extremely bad outcomes such as human extinction, and 59% of net
pessimists gave 5% or more to extremely good outcomes. Between 37.8% and 51.4% of respondents
gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction. Morethan half suggested that “substantial” or “extreme” concern is warranted about six different AI-related
scenarios, including spread of false information, authoritarian population control, and worsened
inequality. There was disagreement about whether faster or slower AI progress would be better for the
future of humanity. However, there was broad agreement that research aimed at minimizing potential
risks from AI systems ought to be prioritized more.

∗†

Corresponding author
Equal Contribution

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Introduction

Artificial intelligence appears poised to reshape society. Decision-makers are working to address opportunities andthreats due to AI in the private sector [OpenAI, 2023], academia [Center for Human-compatible Artificial Intelligence,2023], and government at the state, national, and international levels [Newsom, 2023, AI.gov, 2023, Inter-Agency
Working Group on Artificial Intelligence, 2022].
Navigating this situation requires judgments about how the progress and impact of AI are likely to unfold. However,there is a lack of apparent consensus among AI experts on the future of AI [Korzekwa and Stewart, 2023]. These
judgments are difficult, and there are no established methods of making them well. Thus we must combine various
noisy methods, such as extrapolating progress trends [Villalobos, 2023]; reasoning about reference classes of similar
events [Grace et al., 2021]; analyzing the nature of agents [Omohundro, 2008]; probing qualities of current AI systems
and techniques [Park et al., 2023]; applying economic models to AI scenarios [Jones, 2023, Trammell and Korinek,
2023]; and relying on forecasting aggregation systems such as markets, professional forecasters, and the judgments ofvarious subject matter experts.
One important source of evidence comes from the predictions of AI researchers. Their familiarity with the technology
and the dynamics of its past progress puts them in a good position to make educated guesses about the future of
AI. However, they are experts in AI research, not AI forecasting and might thus lack generic forecasting skills andexperience, or expertise in non-technical factors that influence the trajectory of AI. While AI experts’ predictions should
not be seen as a reliable guide to objective truth, they can provide one important piece of the puzzle.
We conducted a survey of 2,778 AI researchers who had published peer-reviewed research in the prior year in six top
AI venues (NeurIPS, ICML, ICLR, AAAI, IJCAI, JMLR). This to our knowledge constitutes the largest survey of AI
researchers to date. The survey took place in the fall of 2023, after an eventful year of broad progress (including thelaunch of ChatGPT and GPT-4, Google’s Bard, Bing AI Chat, Anthropic’s Claude 1 and 2), shift in public awarenessof AI issues (including two widely signed and publicized AI safety letters [Future of Life Institute, 2023, Center for
AI Safety, 2023]), and governments beginning to address questions of AI regulation in the US, UK, and EU [Biden,2023, Amodei, 2023, gov.uk, 2023, European Parliament, 2023]. The survey included questions about the speed anddynamics of AI progress, and the social consequences of more advanced AI.

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The Survey

This survey, the “2023 Expert Survey on Progress in AI,” or ESPAI, is the third in a series of very similar surveys. The
first two were conducted in 2016 [Grace et al., 2018a] and 2022 [Grace et al., 2022]. The 2023 survey included aroundfour times as many participants as the 2022 survey by expanding from two publication venues (NeurIPS and ICML) tosix. It also includes several new questions to probe the nature of future AI systems and diverse potential risks. The
survey complements a collection of recent work gathering views on similar questions from the public [Stein-Perlman,2023] and corporate leadership [Chui et al., 2023]. The full set of questions is available from AI Impacts [2023a].
Most questions solicited responses in one of three ways: on a Likert scale (multiple choice along a single axis); a
probability estimate; or an estimate of a future year. A smaller number of questions asked for write-in responses or
numerical estimates.
The way a question is framed can greatly influence the response, and this seems more likely for complex questions[Tversky and Kahneman, 1981]. To assess and mitigate framing effects, we often posed different variations of questions
on the same topic to different random subsets of respondents. For example, all questions about how soon a milestone
would be reached were framed in two ways: fixed-years and fixed-probabilities. Half of respondents were asked toestimate the probability that a milestone would be reached by a given year (“fixed-years framing”), while the other half
were asked to estimate the year by which the milestone would be feasible with a given probability (“fixed-probabilities
framing”). To minimize confusion, each participant received one framing throughout the survey.
In several parts of the survey, each participant randomly received questions on only one of several topics, to keep
the survey brief. We allocated these questions to differently sized subsets of participants based on factors like theimportance of the question and the relative value of a larger sample size. This means that most questions were notassigned to all 2,778 participants (see Section 6: Methods).
Several questions asked participants to estimate how many years until a milestone will be feasible. In these questions,
we asked participants to provide three year-probability pairs (either via the fixed-years framing or fixed-probabilities
framing described above), which we used to approximate a probability distribution for that participant by fitting a
gamma cumulative distribution function to these points.

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Task

Job

HLMI/FAOLYear

Milestones

2023

2063

2103

2143

2183

2223

Full Automation of Labor (all human jobs)
AI Researcher
SurgeonMillennium PrizeHigh Level Machine Intelligence (all human tasks)
Publishable math theorems
ML paperInstall wiring in a house
Replicate ML paper
Truck Driver
Equations governing virtual worlds

Retail Salesperson
Find and patch security flaw
Beat humans at Go (after same # games)
5km city race as bipedal robot (superhuman)
Win Putnam Math Competition
Assemble LEGO given instructions
Explain AI actions in games
Translate text in newfound language
NYT best−selling fiction
Random new computer game (novice level)
Fold laundry
Learn to sort long lists
All Atari games (vs. pro game tester)
Recognize object seen once
Top 40 Pop Song
Construct video from new angle
Finetune LLM
Atari games after 20m play (50% vs. novice)Translate speech using subtitles
Telephone banking services
Build payment processing website
Top Starcraft play via video of screen
Answers undecided questions well
Fake new song by specific artist
Group new objects into classes
Translate text (vs. fluent amateur)
Answer open−ended fact questions with web
Transcribe speech
Read text aloudWorld Series of Poker
Answer factoid questions with web
Angry Birds (superhuman)
Good high school history essay
Simple Python code given spec and examples
2023

2028

2033
Year

2038

2043

Figure 1: Most milestones are predicted to have better than even odds of happening within the next ten years,
though with a wide range of plausible dates. The figure shows aggregate distributions over when selected milestones
are expected, including 39 tasks, four occupations, and two measures of general human-level performance (seeSection 3.2), shown as solid circles, open circles, and solid squares respectively. Circles/squares represent the year
where the aggregate distribution gives a milestone a 50% chance of being met, and intervals represent the range ofyears between 25% and 75% probability. Note that these intervals represent an aggregate of uncertainty expressed byparticipants, not estimation uncertainty. The displayed milestone descriptions are summaries; for full descriptions, see
Appendix C.

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Results on AI Progress

3.1

How soon will 39 tasks be feasible for AI?

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The survey asked about when each of 39 tasks would become feasible, where “feasible” was described as meaning “oneof the best resourced labs could implement it in less than a year if they chose to. Ignore the question of whether theywould choose to.” Each respondent was asked about four tasks, so that each task received around 250 estimates. Each
respondent gave three probability-year pairs per task.
To aggregate the responses, we first fit a gamma distribution to each participant’s three probability-year pairs, and thencomputed the mean across the participants’ individual gamma distributions.
All but four of the 39 tasks were predicted to have at least a 50% chance of being feasible within the next ten years(Figure 1). This includes several economically very valuable tasks—such as coding an entire payment processing sitefrom scratch and writing new songs indistinguishable from real ones by hit artists such as Taylor Swift. It also includes
tasks that imply substantial progress in sample-efficiency (e.g. ‘Beat novices in 50% of Atari games after 20 minutes ofplay’), AI-driven AI progress (e.g. autonomously fine-tuning an open-source LLM), and robotics (e.g. folding laundry).
The six tasks expected to take longer than ten years were: “After spending time in a virtual world, output the differential
equations governing that world in symbolic form” (12 years), “Physically install the electrical wiring in a new home”(17 years), “Research and write” (19 years) or “Replicate” (12 years) “a high-quality ML paper,” “Prove mathematicaltheorems that are publishable in top mathematics journals today” (22 years), and solving “long-standing unsolved
problems in mathematics” such as a Millennium Prize problem (27 years).
3.1.1

Comparison with 2022

32 AI task questions were identical to those in the 2022 survey, as well as the 2016 survey [Grace et al., 2018a], withthe exception of minor edits to task descriptions for updated accuracy between 2016 and 2022 [AI Impacts, 2023b].
All tasks from Grace et al. [2018a] were included, regardless of whether the authors would judge them to be achieved.
The survey population included more conferences in 2023, but this did not appear to have a notable effect on opinion(See 5.2.3). Figure 2 shows how expected dates for reaching these milestones shifted from 2022 to 2023.
Between 2022 and 2023, aggregate predictions for 21 out of 32 tasks moved earlier. The aggregate predictions for 11
tasks moved later.
On average, for the 32 tasks included in both the 2022 and 2023 surveys, the 50th percentile year they were expected to
become feasible shifted 1.0 years earlier (SD = 2.0, SE = 0.18).
3.2

How soon will human-level performance on all tasks or occupations be feasible?

We asked how soon participants expected AI systems to outperform humans across all activities, which we framed in
two ways: as either tasks, in the question about “High-Level Machine Intelligence” (HLMI), or occupations, in thequestion about “Full Automation of Labor” (FAOL).
3.2.1

How soon will ‘High-Level Machine Intelligence’ be feasible?

We defined High-Level Machine Intelligence (HLMI) thus:
High-level machine intelligence (HLMI) is achieved when unaided machines can accomplish every
task better and more cheaply than human workers. Ignore aspects of tasks for which being a humanis intrinsically advantageous, e.g. being accepted as a jury member. Think feasibility, not adoption.
We asked for predictions, assuming “human scientific activity continues without major negative disruption.” Weaggregated the results (n=1,714) by fitting gamma distributions, as with individual task predictions in 3.1.
In both 2022 and 2023, respondents gave a wide range of predictions for how soon HLMI will be feasible (Figure 3).
The aggregate 2023 forecast predicted a 50% chance of HLMI by 2047, down thirteen years from 2060 in the 2022
survey. For comparison, in the six years between the 2016 and 2022 surveys, the expected date moved only one year
earlier, from 2061 to 20601 .
The aggregate 2023 forecast predicted a 10% chance of HLMI by 2027, down two years from 2029 in the 2022 survey.
See Appendix B for a table comparing this with Full Automation of Labor, which is discussed below.
1

Reported as 2059 in 2022; small code changes and improvements to data cleaning between surveys shifted the aggregate slightly.

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Figure 2: Expected feasibility of many AI milestones moved substantially earlier in the course of one year
(between 2022 and 2023). The milestones are sorted (within each scale-adjusted chart) by size of drop from 2022
forecast to 2023 forecast, with the largest change first. The year when the aggregate distribution gives a milestone a
50% chance of being met is represented by solid circles, open circles, and solid squares for tasks, occupations, andgeneral human-level performance respectively. The three groups of questions have different formats that may also
influence answers. For full descriptions of the summarized milestones, see Appendix C.

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Probability of HLMI

1.00

0.75

0.50

0.25

0.00
2025

2050

2075

2100

Year
2023 Aggregate Forecast (with 95% Confidence Interval)
2022 Aggregate Forecast (with 95% Confidence Interval)
Random Subset of 2023 Forecasts
Random Subset of 2022 Forecasts

Probability of FAOL

1.00

0.75

0.50

0.25

0.00
2050

2100

2150

2200

Year
2023 Aggregate Forecast (with 95% Confidence Interval)
2022 Aggregate Forecast (with 95% Confidence Interval)
Random Subset of 2023 Forecasts
Random Subset of 2022 Forecasts

Figure 3: Aggregate forecast for 50th percentile arrival time of High-Level Machine intelligence (HLMI) dropped
by 13 years between 2022 and 2023. The forecast for 50th percentile arrival time of Full Automation of Labor(FAOL) dropped by 48 years in the same period. However, there is still a lot of uncertainty. “Aggregate Forecast”
is the mean distribution over all individual cumulative distribution functions. For comparison, we included the 2022
Aggregate Forecast. To give a sense of the range of responses, we included random subsets of individual 2023 and 2022
forecasts. Note that the thinner ‘confidence interval’ in 2023 (compared to 2022) is due to our increased confidence
about the average respondents’ views due to a larger sample size, not respondents’ predictions converging.

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How soon will ‘Full Automation of Labor’ be feasible?

The other framing of the question about how soon AI systems would outperform humans across all activities was about“Full Automation of Labor,” or FAOL. We defined FAOL thus:
Say an occupation becomes fully automatable when unaided machines can accomplish it betterand more cheaply than human workers. Ignore aspects of occupations for which being a human is
intrinsically advantageous, e.g. being accepted as a jury member. Think feasibility, not adoption.
[. . . ]Say we have reached ‘full automation of labor’ when all occupations are fully automatable. That is,when for any occupation, machines could be built to carry out the task better and more cheaply thanhuman workers.
Before the participants (n=774) were asked about the full automation of labor, respondents were asked when four specificoccupations would become fully automatable: “Truck driver,” “Surgeon,” “Retail salesperson,” and “AI researcher”(Figure 1). They were also asked to think of an existing human occupation that they thought would be among the final
ones to be fully automatable. They were then asked when ‘full automation of labor’ (FAOL) would be achieved.
The aggregate 2023 forecast predicted a 50% chance of FAOL by 2116, down 48 years from 2164 in the 2022 survey(Figure 3). We checked if this difference was significant for participants who received the question in the fixedprobabilities framing, and found that it was (p = .0052, Yuen’s test (bootstrap version); see Appendix F). There is abouta 70-year difference between the mean 50% prediction for HLMI and the mean 50% prediction for FAOL. We discuss
this surprising finding in the next section, “Framing effect of HLMI vs FAOL.”
Compared to 2016, 2023 has earlier 50% estimates but later 10% estimates. (See Appendix B)
The answers to the write-in question about an existing occupation likely to be among the last automatable werecategorized according to O*NET’s All Job Family Occupations categories [O*NET, 2023]. The top five mostsuggested categories were: “Computer and Mathematical” (91 write-in answers in this category), “Life, Physical, andSocial Science” (77 answers), “Healthcare Practitioners and Technical” (56), “Management” (49), and “Arts, Design,
Entertainment, Sports, and Media” (39).
3.2.3

Differences between HLMI and FAOL

Predictions for a 50% chance of the arrival of FAOL are consistently more than sixty years later than those for a 50%
chance of the arrival of HLMI. This was seen in the results from the surveys of 2023, 2022, and 2016. This is surprisingbecause HLMI and FAOL are quite similar: FAOL asks about the automation of all occupations; HLMI asks about thefeasible automation of all tasks. Since occupations might naturally be understood either as complex tasks, composed oftasks, or closely connected with one of these, achieving HLMI seems to either imply having already achieved FAOL, orsuggest being close.
We do not know what accounts for this gap in forecasts. Insofar as HLMI and FAOL refer to the same event, thedifference in predictions about the time of their arrival would seem to be a framing effect.
However, the relationship between “tasks” and “occupations” is debatable. And the question sets do differ beyond
definitions: only the HLMI questions are preceded by the instruction to “assume that human scientific activity continues
without major negative disruption,” and the FAOL block asks a sequence of questions about the automation of specificoccupations before asking about full automation of labor. So conceivably this wide difference could be caused byrespondents expecting major disruption to scientific progress, or by the act of thinking through specific examples
shifting overall anticipations. From our experience with question testing, it also seems possible that the difference isdue to other differences in interpretation of the questions, such as thinking of automating occupations but not tasks as
including physical manipulation, or interpreting FAOL to require adoption of AI in automating occupations, not mere
feasibility (contrary to the question wording).
3.2.4

Demographic differences

Geographical background was correlated with expectations about the timing of human-level performance: respondents
whose undergraduate education was in Asia anticipated an 11 year earlier arrival of HLMI than participants from
Europe, North America, or other regions combined. See Appendix A for more demographic comparisons.

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Do participants think they agree on timing of HLMI?

We asked respondents a set of “meta” questions about their views on others’ views (n=671). One meta question asked
to what extent they thought that they disagreed with the typical AI researcher about when HLMI would exist. 44% said
“Not much,” 46% said “A moderate amount,” and 10% said “A lot.”
3.3

Framing effect of fixed-years vs fixed-probabilities

All questions about how soon a milestone would be reached were framed in two ways: fixed-years and fixed-probabilities.
In either framing, we ask for three year-probability pairs, but in one we fix a set of probabilities (10%, 50%, 90%) andask how many years until the participant would assign each probability to the milestone being met, whereas in the otherframing we fix a set of future years (usually 10 years, 20 years, 50 years) and ask about the probability of the milestone
occurring by that year.
The fixed-years framing has been previously observed to produce systematically later predictions [Grace et al., 2018a,2022], but we do not know if one framing is more accurate than the other. Here we have used both and combined themwith equal weight.
The previously-observed framing effect was again observed in this survey. For example, the year with a 50% chanceof HLMI from participants answering in the fixed-year frame (34 years) was twice as far into the future as that forparticipants answering in the fixed-probability frame (17 years). However, it’s notable that even the larger of these two
is shorter than 2022’s combined forecast (37 years), demonstrating a substantial shift of predictions closer to the present(Fig 18 in Appendix B).

Speed of progress in AI in first and second half of career

2016

2022

2023

First half faster

About the same

Second half faster

0%

20%

40%

60%

80%

Figure 4: Most respondents indicated that the pace of progress in their area of AI increased between the firstand second half of their time in a field. Participants were asked whether the second half of the time they had spentworking in their area of AI saw more progress than the first half. The median time working in the area was 5 years.
3.4

Change in observed rates of progress

We asked respondents which AI area they had worked in for the longest and whether progress in the second half was
faster than the first (Figure 4).
3.5

What causes AI progress?

We asked about the sensitivity of progress in AI capabilities to changes in five inputs: 1) researcher effort, 2) decline
in cost of computation, 3) effort put into increasing the size and availability of training datasets, 4) funding, and 5)
progress in AI algorithms. We asked respondents to imagine that only half as much of each input had been available

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over the past decade,2 and the effect they would expect this to have had on the rate of AI progress. The results areshown in Figure 5.
100% Less progress in AI

75

50

25

0
Researcher Falling cost Effort into
effortof computing increasing
size andavailabilityof training sets

Funding

Progress in
AI algorithms

Figure 5: Estimated reduction in AI progress if inputs had been halved over the past decade. Red dots representmeans. Boxes contain the 25th to 75th percentile range; middle lines are medians. Whiskers are the least and greatest
values that are not more than 1.5 times the interquartile range from the median. Participants estimated that halving
the drop in costs of computing would have had the greatest effect on AI progress over the last decade, while halving‘researcher effort’ and ‘progress in AI algorithms’ would have had the least effect. Overall, all the included inputs wereseen as having contributed substantially to AI progress.
There was a wide range of views about each input, implying a large degree of uncertainty. Additionally, the relatively
even distribution of predictions cuts against a common narrative that progress in cheap computing is the dominant
driver of AI progress. Across all inputs, we observe many more answers of “0%” (no difference) and “100%” (all AI
progress lost) than we would expect, which suggests to us possible misunderstandings of the question.
3.6

Will there be an intelligence explosion?

We asked respondents about the possibility, after HLMI is hypothetically achieved, of an ‘intelligence explosion,’ asexplained in this question:
Some people have argued the following:
If AI systems do nearly all research and development, improvements in AI will accelerate
the pace of technological progress, including further progress in AI.
Over a short period (less than 5 years), this feedback loop could cause technologicalprogress to become more than an order of magnitude faster.
How likely do you find this argument to be broadly correct?
The results to this first question are shown in Figure 6.
The other two ‘intelligence explosion’ questions were about the likelihood of a dramatically increased rate of global
technological advancement 2 and 30 years post-HLMI, and AI that can outperform humans across all professions 2 and20 years post-HLMI. Results are shown in Table 1.
2

The declining cost of computation was an exception; here it was framed as “over the last n years,” and the question was about
costs falling around half as far on a log scale. This was done to match previous surveys.

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Likelihood of argument being broadly correct

2016

2022

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2023

Quite likely (0-20%)

Likely (21-40%)

About even chance(41-60%)

Unlikely (61-80%)

Quite unlikely (81100%)

0%

10%

20%

30%

Figure 6: Since 2016 a majority of respondents have thought that it’s either “quite likely,” “likely,” or an “about
even chance” that technological progress becomes more than an order of magnitude faster within 5 years of
HLMI being achieved.

Median probability (N)
2016

2022

2023

Explosive progress 2 years after HLMI

20% (225)

20% (339)

20% (298)

Explosive progress 30 years after HLMI

80% (225)

80% (339)

80% (297)

Intelligence explosion argument is broadly correct“41-60%” (232)“41-60%” (386)“41-60%” (299)

AI is vastly better than humans 2 years after HLMI

10% (213)

10% (371)

10% (281)

AI is vastly better than humans 30 years after HLMI

50% (214)

60% (371)

60% (282)

Table 1: Results of three questions regarding a hypothetical intelligence explosion have remained remarkably
stable since 2016.

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Figure 7: Respondents’ estimates of the likelihood that at least some AI systems in 2043 will have each of these
traits; organized from least to most likely.
In sum, across these three questions, the median participant did not overall expect something like a rapid acceleration ofprogress from an ‘intelligence explosion,’ but did give substantial credence to it.
Further figures related to intelligence explosion questions are in Appendix B.
3.7

What will AI systems in 2043 be like?

Concerns about risks from future AI systems are often linked to specific traits related to alignment, trustworthiness,predictability, self-directedness, capabilities, and jailbreakability. We asked respondents how likely it was that at least
some state-of-the-art AI systems in 2043 would have each of eleven such traits (n ∈ [649, 667]3 ).
All 11 traits were considered to have a relatively high chance of existing in AI systems in 2043, though with much
uncertainty. Only one trait had a median answer below ‘even chance’: “Take actions to attain power.” While there wasno consensus even on this trait, it’s notable that it was deemed least likely, because it is arguably the most sinister, being
key to an argument for extinction-level danger from AI [Carlsmith, 2022].
Answers reflected substantial uncertainty and disagreement among participants. No trait attracted near-unanimity on any
probability, and no more than 55% of respondents answered “very likely” or “very unlikely” about any trait. (Figure 7)
There were areas of agreement, however. For instance, a large majority of participants thought state-of-the-art AI
systems in twenty years would be likely or very likely to:
1. Find unexpected ways to achieve goals (82.3% of respondents),
2. Be able to talk like a human expert on most topics (81.4% of respondents), and
3. Frequently behave in ways that are surprising to humans (69.1% of respondents)
3.8

Will AI in 2028 truthfully and intelligibly explain its decisions?

Uninterpretable reasoning in AI systems is often considered an AI risk factor, potentially leading to outcomes ranging
from unjust biases in treatment of people to active pursuit of harm hidden by capable agents. We thus asked about theinterpretability of AI systems in five years (Figure 8):3i.e. the number of people (n) who answered each of these eleven questions was between 649 and 667

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Likelihood of explainability by 2028

Very unlikely(<10%)

Unlikely (10-40%)

Even odds (4060%)

Likely (60-90%)

Very likely (>90%)

0%

10%

20%

30%

40%

Figure 8: Most respondents considered it unlikely that users of AI systems in 2028 will be able to know the true
reasons for the AI systems’ choices, with only 20% giving it better than even odds. (n=912)
For typical state-of-the-art AI systems in 2028, do you think it will be possible for users to know thetrue reasons for systems making a particular choice? By “true reasons” we mean the AI correctly
explains its internal decision-making process in a way humans can understand. By “true reasons” we
do not mean the decision itself is correct.
This is related to the question in Section 3.7, which asked how likely it was that at least some state-of-the-art AI systems
in 2043 (fifteen years later), “can be trusted to accurately explain their actions.” The median answer there was “evenchance” (40-60% likely), compared to “unlikely” (10-40%) on this question.

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Results on Social Impacts of AI

4.1

How concerning are 11 future AI-related scenarios?

We asked participants (n=1,345) about eleven potentially concerning AI scenarios, such as AI-enabled misinformation,worsened economic inequality, and biased AI systems worsening injustice. We asked how much concern each deservedin the next thirty years (Figure 9).
Each scenario was considered worthy of either substantial or extreme concern by more than 30% of respondents. As
measured by the percentage of respondents who thought a scenario constituted either a “substantial” or “extreme”
concern, the scenarios worthy of most concern were: spread of false information e.g. deepfakes (86%), manipulation oflarge-scale public opinion trends (79%), AI letting dangerous groups make powerful tools (e.g. engineered viruses)(73%), authoritarian rulers using AI to control their populations (73%), and AI systems worsening economic inequality
by disproportionately benefiting certain individuals (71%).
There is some ambiguity about the reason why a scenario might be considered concerning: it might be consideredespecially disastrous, or especially likely, or both. From our results, there’s no way to disambiguate these considerations.
4.2

How good or bad for humans will High-Level Machine Intelligence be?

We asked participants to assume that, at some point, “high-level machine intelligence” (HLMI) will exist, as definedin Section 3.2. Given this assumption for the sake of the question, we asked how good or bad they expect the overall
impact of this to be “in the long run” for humanity.
Respondents exhibited diverse views on the future impact of advanced AI (Figure 10), highlighting how respondents’views are more complex than can be represented by an ‘optimism vs pessimism‘ axis. Many people who have highprobabilities of bad outcomes also have high probabilities of good outcomes. A majority spread their credence acrossthe entire spectrum of outcomes, with 64% assigning non-zero probabilities to both extremely good and extremely
bad scenarios. 68.3% of participants found good outcomes more likely than bad outcomes, while 57.8% consideredextremely bad outcomes (e.g. human extinction) a nontrivial possibility (≥ 5% likely). Even among net optimists,12

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Figure 9: Amount of concern potential scenarios deserve, organized from most to least extreme concern.

Extremely good(e.g. rapid growth inhuman flourishing)

On balance goodMore or less neutral

On balance bad

Extremely bad(e.g. human extinction)

100

Probability

75

50

25

0

Figure 10: Respondents exhibited diverse views on the expected goodness/badness of High Level MachineIntelligence (HLMI). We asked participants to assume, for the sake of the question, that HLMI will be built at somepoint. The figure shows a random selection of 800 responses on the positivity or negativity of long-run impacts of
HLMI on humanity. Each vertical bar represents one participant and the bars are sorted left to right by a weighted sumof probabilities corresponding to overall optimism. Responses range from extremely optimistic to extremely pessimistic.
Over a third of participants (38%) put at least a 10% chance on extremely bad outcomes (e.g. human extinction).

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extremely good (e.g. rapid
growth in humanon balance goodneutralon balance bad
extremely bad (e.g. humanextinction)
0%

10%

20%

30%

Overall impact of HLMI in the long run

Overall impact of HLMI in the long run

2022

2023

extremely good (e.g. rapid
growth in humanon balance goodneutralon balance bad
extremely bad (e.g. humanextinction)0%

probability given by respondents (means)

5%

10%

15%

20%

probability given by respondents (medians)

Figure 11: Mean but not median predictions in 2023 (n=2704) about the consequences of HLMI have shiftedslightly away from extreme outcomes compared to 2022 (n=559). Error bars indicate the standard error.
2022 (mean)

2022 (median)2023 (mean)

2023 (median)

0%

10%

Question 1: What probability do you put on future AI advances
causing human extinction or similarly permanent and severe
disempowerment of the human species?
Question 2: What probability do you put on human inability tocontrol future advanced AI systems causing human extinction orsimilarly permanent and severe disempowerment of the human
Question 3: What probability do you put on future AI advances
causing human extinction or similarly permanent and severe
disempowerment of the human species within the next 100 years?
5%

15%

20%

Figure 12: Mean and median predictions to three questions on human extinction. Error bars indicate thestandard error. (Question 1 n=149 in 2022 and 1321 in 2023. Question 2 n=162 in 2022 and 661 in 2023. Question 3
was asked only in 2023, n=655).
nearly half (48.4%) gave at least 5% credence to extremely bad outcomes, and among net pessimists, more than half
(58.6%) gave at least 5% to extremely good outcomes. The broad variance in credence in catastrophic scenarios shows
there isn’t strong evidence understood by all experts that this kind of outcome is certain or implausible.
The median prediction for extremely bad outcomes, such as human extinction, was 5% (mean 9%). Over a third ofparticipants (38%) put at least a 10% chance on extremely bad outcomes. This is comparable to, but somewhat lower
than, rates of assigning at least 10% to extinction-level outcomes in answers to other questions more directly about
extinction, between 41% and 51% (see Section 4.3). On the very pessimistic end, one in ten participants put at least a
25% chance on outcomes in the range of human extinction).
Since 2022, mean overall probability on extreme outcomes (good or bad) has fallen slightly (Figure 11). The proportionof people who put at least a 10% chance on extremely bad outcomes (e.g. human extinction) has fallen from 48% in
2022 in 2023, and the mean prediction for this type of outcome is down from 14% to 9.0%.
Appendix A contains comparisons between results from different demographics on this question, and Appendix B
contains another relevant figure.
4.3

How likely is AI to cause human extinction?

To further clarify views on the “extremely bad (e.g. human extinction)” scenarios in the question on overall impacts,
participants were given one of three similar questions about human extinction. Their differences were intended to help
isolate exactly how concerning different scenarios are, what respondents expect to happen, and how much difference
working makes.
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Statistics

2022 Result

2023 Result

What probability do you put on future AI advances causing human extinction or similarly
permanent and severe disempowerment of thehuman species?

N; Mean (SD);Median (IQR)

149; 15.7% (22.1%);
5% (19%)1321; 16.2% (23%);
5% (19%)What probability do you put on human inability to control future advanced AI systems causing human extinction or similarly
permanent and severe disempowerment of thehuman species?

N; Mean (SD);Median (IQR)

162; 20.5% (26.2%);
10% (29%)

661; 19.4% (26%);
10% (29%)

What probability do you put on future AI advances causing human extinction or similarly
permanent and severe disempowerment of thehuman species within the next 100 years?4

N; Mean (SD);Median (IQR)

Not asked

655; 14.4% (22.2%);
5% (19.9%)

Table 2: Respondents’ estimates in 2022 and 2023 for the probability that AI causes human extinction. For eachof the two questions that were asked in both years, the results are very similar.
Answers to these questions were mostly consistent, with medians of 5% or 10%. These are also close to answers to
the question on general value of long-run impact,5 , which might suggest the bulk of the “extremely bad (e.g. humanextinction)” answers to that question is from human extinction or similarly permanent and severe disempowerment of
the human species, as opposed to other outcomes that respondents to that question may have had in mind but that would
have been less severe.
Depending on how we asked, between 41.2% and 51.4% of respondents estimated a greater than 10% chance of humanextinction or severe disempowerment (see Figure 13). This is comparable to, but somewhat higher than, the proportionof respondents—38%—who assigned at least 10% to “extremely bad” outcomes “(e.g. human extinction)” in thequestion asking “How good or bad for humans will High-Level Machine Intelligence be?” (See Section 4.2)
Appendix A contains comparisons between results from different demographics on this question.
4.4

Views on others’ concerns about AI

Respondents were asked a set of “meta” questions about their views on others’ views (n=671). One question was, “To
what extent do you think people’s concerns about future risks from AI are due to misunderstandings of AI research?”
10.7% said "Almost entirely.” 44% said “To a large extent.” 29.1% said “Somewhat.” 14.6% said “Not much.” 1.6%
said “Hardly at all.” This may reflect a view amongst AI researchers that the general public misunderstands AI.
4.5

What rate of AI progress would produce the most optimism?

We asked participants “What rate of global AI progress over the next five years would make you feel most optimistic forhumanity’s future? Assume any change in speed affects all projects equally.” There was disagreement on whether fasteror slower progress would be preferable, though large divergence from the current speed was less popular (Table 3).
4.6

How much should AI safety research be prioritized?

We asked some respondents one version of a question about AI research prioritization that matched previous surveys,
and we asked other respondents a near-identical question that had been slightly updated. In the old version of thequestion, we defined AI safety research as follows:Let ‘AI safety research’ include any AI-related research that, rather than being primarily aimed
at improving the capabilities of AI systems, is instead primarily aimed at minimizing potential
4

Bold font was not present in the original questions; it has been added here to emphasize the differences between wordings. Any
individual respondent did not see more than one of these questions.
5“Assume for the purpose of this question that HLMI will at some point exist. How positive or negative do you expect the overall
impact of this to be on humanity, in the long run? Please answer by saying how probable you find the following kinds of impact,with probabilities adding to 100%”

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Extremely bad overall impact of HLMI in the long run (e.g. humanextinction)

Question 1: What probability do you put on future AI advances
causing human extinction or similarly permanent and severe
disempowerment of the human species?
Question 2: What probability do you put on human inability to control
future advanced AI systems causing human extinction or similarly
permanent and severe disempowerment of the human species?
Question 3: What probability do you put on future AI advances
causing human extinction or similarly permanent and severe
disempowerment of the human species within the next 100 years?

0%

20%

40%

60%

Participants who gave a probability of 10% or higher

Figure 13: Percent of participants who gave a probability of 10% or higher to an extremely bad outcome of
HLMI (e.g. human extinction) (see Section 4.2) or to the three questions specifically about human extinction ordisempowerment.

AnswerPortion of respondents

Much slower

4.8%

Somewhat slower

29.9%

Current speed

26.9%

Somewhat faster

22.8%

Much faster

15.6%

Table 3: There was disagreement about whether faster or slower global AI progress over the next five years would
be best for humanity’s future.

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How much AI safety research should be prioritized

2016

2022

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2023

Much less

Less

About the same

More

Much more

0%

10%

20%

30%

40%

% of answers

Figure 14: 70% of respondents thought AI safety research should be prioritized more than it currently is.
Developments since the 2022 survey have not substantially changed the proportion of participants who think AI safety
should be prioritized “more” or “much more”.
risks of AI systems (beyond what is already accomplished for those goals by increasing AI systemcapabilities).
Examples of AI safety research might include:• Improving the human-interpretability of machine learning algorithms for the purpose of improving the safety and robustness of AI systems, not focused on improving AI capabilities• Research on long-term existential risks from AI systems• AI-specific formal verification research• Policy research about how to maximize the public benefits of AI
The updated question is identical except for the inclusion of this example:• Developing methodologies to identify, measure, and mitigate biases in AI models to ensure fairand ethical decision-making
In both variations, we asked, “How much should society prioritize AI safety research, relative to how much it
is currently prioritized?” A Welch t-test found that the difference between the two framings was not significant(t(1327) = −0.58, p = 0.564, d = −0.03), so the results were combined (n=1329). A large majority of respondents
thought that AI safety research should be prioritized more than it currently is. The percentage of researchers who
thought so increased compared to earlier surveys, but only slightly since 2022. (Figure 14)
4.7

How worthy and hard is the alignment problem?

A second set of AI safety questions was based on Stuart Russell’s formulation of the alignment problem [Russell, 2014].
This set of questions began with a summary of Russell’s argument—which claims that with advanced AI, “you getexactly what you ask for, not what you want”—then asked:
1. Do you think this argument points at an important problem?
2. How valuable is it to work on this problem today, compared to other problems in AI?
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3. How hard do you think this problem is, compared to other problems in AI?
The majority of respondents said that the alignment problem is either a “very important problem” (41%) or “among themost important problems in the field” (13%), and the majority said the it is “harder” (36%) or “much harder” (21%) thanother problems in AI. However, respondents did not generally think that it is more valuable to work on the alignment
problem today than other problems. (Figure 15)

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Value of working on today

ImportanceAmong the mostimportant problems in thefield

Much morevaluable

Very important problem

Morevaluable

Moderately important
problem

As valuableNot an important problemLessvaluable

Not a real problem

Much lessvaluable
0%10%

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20%

30%

40%

50%

0%

10%

40%

50%

20%

30%

40%

50%

DifficultyMuchharderHarderAs hard

EasierMucheasier
0%

10%
2016

20%
2022

30%
2023

Figure 15: Attitudes towards Stuart Russell’s formulation of the alignment problem. Participants viewed thealignment problem as important and difficult, but not more valuable to work on than other problems.

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5

Discussion

5.1

Summary of results

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Participants expressed a wide range of views on almost every question: some of the biggest areas of consensus are on
how wide-open possibilities for the future appear to be. This uncertainty is striking, but several patterns of opinion are
particularly informative.
While the range of views on how long it will take for milestones to be feasible can be broad, this year’s survey saw ageneral shift towards earlier expectations. Over the fourteen months since the last survey [Grace et al., 2022], a similar
participant pool expected human-level performance 13 to 48 years sooner on average (depending on how the question
was phrased), and 21 out of 32 shorter term milestones are now expected earlier.
Another striking pattern is widespread assignment of credence to extremely bad outcomes from AI. As in 2022, amajority of participants considered AI to pose at least a 5% chance of causing human extinction or similarly permanentand severe disempowerment of the human species, and this result was consistent across four different questions, two
assigned to each participant. Across these same questions, between 38% and 51% placed at least 10% chance on
advanced AI bringing these extinction-level outcomes (see Figure 13).
In general, there were a wide range of views about expected social consequences of advanced AI, and most people put
some weight on both extremely good outcomes and extremely bad outcomes. While the optimistic scenarios reflect AI’s
potential to revolutionize various aspects of work and life, the pessimistic predictions—particularly those involving
extinction-level risks—serve as a stark reminder of the high stakes involved in AI development and deployment.
Concerns were expressed over many topics beyond human extinction: over half of eleven potentially concerning AI
scenarios were deemed either “substantially” or “extremely” concerning by over half of respondents.
5.2
5.2.1

Caveats and limitations
Forecasting is hard, even for experts

Forecasting is difficult in general, and subject-matter experts have been observed to perform poorly [Tetlock, 2005,
Savage et al., 2021]. Our participants’ expertise is in AI, and they do not, to our knowledge, have any unusual skill at
forecasting in general.
There are signs in this research and past surveys that these experts are not accurate forecasters across the range ofquestions we ask. For one thing, on many questions different respondents give very different answers, which limits
the number of them who can be close to the truth. Nonetheless, in other contexts, averages from a large set of noisy
predictions can still be relatively accurate [Surowiecki, 2004], so a question remains as to how informative theseaggregate forecasts are.
Another piece of evidence against the accuracy of forecasts is the observation of substantial framing effects (See
Sections 3.2 and 3.3). If seemingly unimportant changes in question framing lead to large changes in responses, thissuggests that even aggregate answers to any particular question are not an accurate guide to the answer. In an extreme
example in a closely related study, Karger et al. [2023] found college graduates gave answers nearly six orders ofmagnitude apart when asked in different ways to estimate the size of existential risks from AI: When given example odds
of low-probability events, estimates were much lower. A similar effect might apply at some scale to our participants,though their expertise and quantitative training might mitigate it. Participants who had thought more in the past about AI
risks seem to give higher numbers, suggesting they are unlikely to give radically lower numbers with further examplesof risks (see Table 4).
Despite these limitations, AI researchers are well-positioned to contribute to the accuracy of our collective guesses
about the future. While unreliable, educated guesses are what we must all rely on, and theirs are informed by expertisein the relevant field. These forecasts should be part of a broader set of evidence from sources such as trends in computer
hardware, advancements in AI capabilities, economic analyses, and insights from forecasting experts. However, AI
researchers’ familiarity with the relevant technology, and experience with the dynamics of its progress, make themamong the best-positioned to make informative educated guesses.
5.2.2

Participation

The survey was taken by 15% of those we contacted. This appears to be within the typical range for a large expert
survey. Based on an analysis by Hamilton [2003], the median response rate across 199 surveys was 26%, and larger
invitation lists tend to yield lower response rates: surveys sent to over 20,000 people, like ours, are expected to have20

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a response rate in the range of 10%. In specialized samples, such as scientists, lower response rates are common
due to sampling challenges, as exemplified by on Assessing Fundamental Attitudes of Life Scientists as a Basis for
Biosecurity Education [2009], which achieved a 15.7% response rate in a survey of life scientists.
As with any survey, our results could be skewed by participation bias, if participants had systematically different
opinions than those who chose not to participate. We sought to minimize this effect by aiming to maximize response
rate, and by limiting cues about the survey content available before opting to take the survey. We looked for evidence ofsignificant response bias at the survey level and question level, and did not find any. (see Appendix D for more detail)
5.2.3

Change in sample from 2022

The two past editions of ESPAI have only surveyed researchers at NeurIPS and ICML, whereas this year we also
contacted researchers who published in ICLR, AAAI, IJCAI, and JMLR. This could make comparison between survey
years less meaningful, if the populations have different opinions. However where we checked, the subset of 2023
participants who published in NeurIPS or ICML specifically appeared to have very similar opinions to the full sample.

6

Methods

In October 2023, we distributed a survey of perspectives about the future of AI to people who had recently published at
one of six top-tier AI venues. The questions focused on topics such as the timing of AI progress, the future impact
of AI, and AI safety. The survey and its implementation were approved by the Ethics Committee of the University ofBonn (248/23-EP). The survey and its analysis were preregistered [Sandkühler et al., 2023].
6.1

Survey questions

Most questions were identical to those asked previously in surveys conducted in 2016 [Grace et al., 2018a] and 2022
[Grace et al., 2022]. For each of two questions, we added a new version with modified phrasing and randomly assigned
participants either the version of the question asked in Grace et al. [2022] or the new version. We also added several
new questions which were not based on questions from Grace et al. [2018a] or Grace et al. [2022]. While designing
new questions, we tested them in a series of interviews with AI researchers and students. The full survey is available on
the AI Impacts Wiki (Link in Appendix E).
As in Grace et al. [2018a] and Grace et al. [2022], most questions were randomly assigned to only a subset of participants,
in order to keep the number of questions for each participant low. The survey was hosted on the Qualtrics survey
platform.
For a full description of the survey’s flow, see the diagram in Figure 16.
6.2

Randomization

Some questions were available in the two types of framing we call the “fixed-years framing” and the “fixed-probabilities
framing.” In the “fixed-probabilities framing,” we asked respondents how many years until they thought each AI task
would be feasible with a small chance (10%), an even chance (50%), and a high chance (90%). In the “fixed-years
framing,” we asked respondents how likely they thought it was that each AI task would be feasible within the next 10
years, 20 years and 50 years (or 40 years6 ). The questions available in these two framings were
• those asking when narrow AI tasks would become feasible• that asking when human-level machine intelligence (HLMI) would become feasible.
• those asking when occupations would be automatedRespondents were randomly allocated to either the “fixed-years framing” or the “fixed-probabilities framing” (allocation
ratio: 1:1) and then received all the questions above using the same framing.
6.3

Recruitment

We recruited participants who published in 2022 at any of six top-tier AI venues: the Conference on Neural Information
Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), the International Conference
6

The HLMI-framing used 40 years instead of 50 years. This was done to keep the survey consistent with the previous surveys,
where this discrepancy was introduced by mistake.

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Figure 16: Individual components of the survey and their randomization. Horizontal divisions represent participants
being randomly split between questions (parenthetical percentages also give the fraction of participants receiving eachquestion block). There was further randomization (not shown), within some question blocks.

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on Learning Representations (ICLR), The AAAI Conference on Artificial Intelligence (AAAI), The Journal of Machine
Learning Research (JMLR), and the International Joint Conference on Artificial Intelligence (IJCAI). Compared to the2022 survey [Grace et al., 2022], which was distributed to a randomly-selected half of the researchers who publishedin 2021 at NeurIPS or ICML, the 2023 survey was distributed to more than three times as many recipients, and torecipients from a wider range of AI specialties.
We collected approximately 21,800 names from publications, then searched for matching emails in those publications,other AI-related publications, and elsewhere. We found email addresses for 20,066 (92%) of collected names. Using
the Qualtrics survey platform, we sent a pre-survey announcement to all of the collected emails on either October 5th(2006 pilot surveys) or October 10th , 2023.
6.4

Fielding

Data was collected using an online survey, which was conducted through the Qualtrics survey platform, and delivered
via private link in an email.
To investigate the effect of incentives on the response rate, 5% of the collected emails were assigned to a pilot group
to be offered a reward of $50 reward or an equivalent charitable donation,7 and another 5% to a pilot group to not
be offered a reward. On October 11 2023, invitations to complete the survey were sent to the pilot groups. Because
the paid pilot survey group had substantially higher response rates, we decided to offer the reward to all participants(including those who had been in the unpaid group). We used the third-party payment service BHN for sending rewards
to the bulk of participants.
We sent invitations to the remainder of the collected emails on October 15, 2023 and sent reminders to pilot recipientson the 13th , the 18th , the 20th , the 22nd and the 23rd and to all other participants on the 17th , the 20th , the 22nd , and the23rd .
When referring to the survey in the emails, we described the survey vaguely to avoid participation bias. Recipientswere informed that the results of the survey would be anonymized. For an example of a typical invitation letter, see
Appendix E.
The survey remained open until October 24, 2023. Out of the 20,066 emails we contacted, 1,607 (8%) bounced or failed,leaving 18,459 functioning email addresses. We received 2,778 responses, for a response rate of 15%. 95% of these
responses were deemed ‘finished’ by Qualtrics. Because participants received randomized subsets of the questions, thenumber of responses is far less than 2,778 for most individual questions.
6.5

Data preparation

Edits were made to the raw data before analysis in the hope of preserving its intended meaning. For example, we
observed cases where participants gave non-numerical answers to numerical questions or reported probabilities foran event having happened that decreased in time. These were deemed to be errors. If a participants’ answers to a
fixed-years or fixed-probability question involved probabilities that decreased in time, these answers were removed.
Additionally, for participants who answered with decreasing probabilities for at least one question, we removed answers
to fixed time questions for which the probabilities summed to 100%. In total, 196 participants gave answers withdecreasing probabilities, and 7 of these users gave answers summing to 100% for fixed time questions.
Some participants gave answers to fixed-probabilities questions which identified a particular year, rather than a numberof years in the future. For example, 2033 instead of 10. To address this, we subtracted 2023 from answers to fixed-years
questions greater than 2000 and less than 3000.
Non-numerical answers to numerical questions were removed, except when an unambiguous interpretation was possible.
For example, “10%” was changed to 10 and “<20” was changed to 20. Some participants entered non-numericalanswers to express the view that a probability would never reach a particular value or would reach it at a point infinitely
far in the future (for example, “infinity” or “never”). In these cases, the number of years was set to 100,000,000. Our
analysis was insensitive to the value of this upper bound.
Data cleaning, analysis, and figure-creation was performed using R statistical software, version 4.3.1, SPSS (Version29), Google Sheets, and Creately.
7
Ultimately participants were also generally offered a further choice of equivalent gift-cards. Some participants were not permitted
by the payment service to receive rewards, and we noted in the invitation that in some countries only charitable donations would beavailable.

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Acknowledgments

Many thanks for help with this research to Rebecca Ward-Diorio, Jeffrey Heninger, John Salvatier, Nate Silver, Jimmy
Rintjema, Joseph Carlsmith, Justis Mills, Will MacAskill, Zach Stein-Perlman, Shakeel Hashim, Mike Levine, Lucius
Caviola, Eli Rose, Nathan Young, Michelle Hutchinson, Arden Koehler, Isabel Juniewicz, Ajeya Cotra, Josh Kalla, Niki
Howe, Seb Farquar, Nuño Sempere, Naomi Saphra, Sören Mindermann, David Gros, Frederic Arnold, Max Tegmark,Jaan Tallinn, Shahar Avin, Alexander Berger, Cate Hall, Howie Lempel, Jaime Sevilla, Daniel Kokotajlo, Jacob Hilton,
James Aung, Ryan Greenblatt, Dan Hendrycks, Alex Tamkin, Vael Gates, Yonadav Shavit, and others.
We would also like to thank all participants in the survey. The following participants (listed in a random order) agreed
to be recognized by name.
Kun Wu, Dazhong Rong, Achille Thin, Peng Liu, Theo Matricon, Sriram Ganapathi Subramanian, Babak Rahmani, Gustavo De Rosa, Changwoon Choi, Emanuele
Sansone, Di Chen, Sheng Guo, Mansooreh Karami, Hansin Ahuja, Leo Tappe, Saed Rezayi, Stephan Waldchen, Gleb Polevoy, Shubhanshu Shekhar, Yingheng Wang,
Wenjie Qu, Murat Kocaoglu, Shiyan Jiang, Gregory Rogez, Zhenmei Shi, Qinglin Zhang, Neeraj Wagh, Florian Golemo, Youngeun Nam, Washim Uddin Mondal, Linlin
Yang, Eric Zhao, Shuming Kong, Woojun Kim, Franz Pernkopf, Zan Wang, Charles Sutton, Shiri Alouf-Heffetz, Jan Corazza, Iason Gabriel, Shentao Yang, Takuya Ito,
Guangming Yao, Aniket Bhatkhande, Jian Liu, Bhavya Kailkhura, Tim G J Rudner, Dachun Sun, Kamil Tagowski, Francesco Ricca, Kaizhao Liang, Gokul Swamy, Sandra
Kiefer, Till Richter, Yulong Liu, Selina Meyer, Junyoung Park, Taiki Todo, Yash Chandak, Mark Hamilton, Bo Liu, Zhoujian Sun, Amitoz Azad, Jill-Jenn Vie, Akanksha
Saran, Jiafu Wei, Jason Fries, Cheng Wen, Yonggan Fu, Hui Shi, Diego Calvanese, Ramazan Gokberk Cinbis, Shenyang Huang, Yifan Peng, Dahyun Kim, Haochuan Cui,James Requeima, Silpa Vadakkeeveetil Sreelatha, Haibo Chen, Mark Muller, Philipp Holl, Xiong-Hui Chen, Sean B Holden, Rylan Schaeffer, Da-Shan Shiu, Yangze Zhou,
Juhan Bae, Robert Martin, Byeonghu Na, Edward Choi, Nathan Wycoff, Joseph Kwon, Michelle Sweering, Aaditya Prakash, Cuiying Gao, Tanuj Agarwal, David Evans,
Maximilian Schmidhuber, Kai Yi, Chia-Yi Hsu, Zafeirios Fountas, Rei Kawakami, Brian Ichter, Shikun Li, Jishnu Ray Chowdhury, Ou Wu, Zhaomin Wu, Hung-Nghiep
Tran, Benedikt Holtgen, Takuya Hiraoka, Tong Liu, Mikhail Yurochkin, Yanzhi Chen, Julia Niebling, Junting Pan, Yunseok Lee, Pedro J Goncalves, Michael Ryoo,
Xinning Chen, Xiongye Xiao, Dongzhan Zhou, Jialun Peng, Yuhui Zhang, Fan Wu, Rachid Riad, Sebastian Schellhammer, Shikun Liu, David Kanter, Bruce XB Yu,
Moritz K Lehmann, Hao-Shu Fang, Mohak Goyal, Kwanho Park, Sanghamitra Dutta, Yafei Yang, Hadi Hosseini, Celia Chen, Minseon Kim, Max Simchowitz, Ben Tu,
Tim Pearce, Salman Asif, Gianluigi Silvestri, P R Kumar, Daniel Iascone, Wenyue Hua, Chris Cameron, Nicolas Zucchet, Lys Sanz Moreta, Pablo Antonio Moreno
Casares, Aadirupa Saha, Maurizio Gabbrielli, Liping Liu, Michal Zajac, Kevin Liu, Xiaowei Jia, Daniel Schneider, Frank Hutter, Xingyu Peng, Antonio Montanaro, I Zeki
Yalniz, Jiayang Li, Shujian Yu, Jeff Clune, Changjian Shui, Luca Carminati, Luc Rey-Bellet, Xiang Ruan, Paul Gavrikov, Joshua Cooper, Aymeric Dieuleveut, Leon
Derczynski, Paolo Fraccaro, Peter Mernyei, Giung Nam, Zijian Zhang, Peter Bevan, Nisarg Shah, Miao Liu, Yupei Liu, Michael Hutchinson, Xiatian Zhu, Rahmatollah
Beheshti, Kyle Hsu, Juraj Podrouzek, Jussi Rintanen, Scott Mueller, Harrison Lee, Nicola Gatti, Zijing Ou, Hiromichi Kamata, Lalit Ghule, Weirui Kuang, Qiang Zhang,
Lee Spector, Jiayue Wang, Adams Wei Yu, Arber Zela, Jierui Lin, Justin Sybrandt, Toryn Klassen, Xin Liu, Benjamin Doerr, S Sumitra, Xinbo Zhang, Ryan Burnell,
Ashvin Nair, Martha White, Florence Regol, Tobias Golling, Tianlong Chen, Maurizio Ferrari Dacrema, Ahmed M Ahmed, Cem Tekin, Kaizhi Zheng, Ming-Hsuan Yang,
Rui Ding, Ziyu Shao, Mariia Seleznova, Byoung Chul Ko, Yogesh Kumar, Aditya Kusupati, Emanuele Aiello, Eric Greenwald, Scott Yih, Jyotirmoy Deshmukh, Dongjin
Lee, Matteo Tiezzi, Shenao Zhang, Daiheng Gao, Wentao Zhang, Zhen Fang, Wanqian Zhang, Li Ding, Elizabeth Akinyi Ondula, Sanae Lotfi, Hiroyuki Toda, Shang
Gao, Onno Kampman, Xianglin Yang, Tomasz Korbak, Xiaowei Wu, Linhao Luo, Xueyan Niu, Xuan Son Nguyen, Jiawei Du, Dorde Zikelic, Xuan Li, Zongyan Han,
Anind Dey, Vineet Gundecha, Yunfan Li, Tongxin Li, Dylan R Ashley, Andre Wibisono, Arnold Smeulders, Euijoon Ahn, Jaakko Lehtinen, Haotao Wang, Hidetaka
Kamigaito, Daniel Freeman, Fuxiang Zhang, Kanchana Ranasinghe, Shiqing Wu, Pushi Zhang, Samy-Safwan Ayed, Bingyin Zhao, Fredrik Lofman, Hangjie Yuan, Shu
Ding, Pabitra Mitra, Jan Peters, Amartya Mitra, Zi Wang, Feng Liang, Hengguan Huang, Dan Hendrycks, Donghwan Lee, Christoffer Riis, Muhammad Firmansyah
Kasim, Botao Yu, Maarten Buyl, Daniel Brown, Joseph Pemberton, Ziwei Xu, Haeyong Kang, Ranjie Duan, Abhishek Roy, Wenpeng Zhang, Zhao Song, Guanghui Yu,
Giorgia Dellaferrera, Zicheng Liu, Chuanxia Zheng, Saurabh Garg, Yuchang Sun, Chengan He, Thore Graepel, Andrew Kaploun, Fanchen Bu, Yushun Zhang, Zhuo Lu,
Tobias Huber, Huayu Chen, Marko Zeman, Lu Qi, Shin’Ya Yamaguchi, Linxuan Song, Dongsheng Chen, Ilyes Batatia, Yuheng Zhang, Zi-Yi Dou, Michael Kampffmeyer,
Shaocong Ma, Benjamin Lebrun, Davin Choo, Christoph Bergmeir, Pavel Tokmakov, Zhifeng Zhang, Samar Khanna, Benjamin Rosman, Shubhankar Mohapatra, Bart
Selman, Zongqian Wu, Lei Ding, Anastasiia Varava, Lai Tian, Tiancheng Lin, Andrea Loreggia, Eloy Pinol, Philipp Oberdiek, Zhiyu Chen, Yim Register, Guohao
Shen, Maggie Makar, Vit Musil, Greg Durrett, Oumar Kaba, Yongxin Chen, Sepehr Sameni, Yuhang Li, Yiheng Tu, Chen Dan, James U Allingham, Yibo Yang, Sheo
Yon Jhin, Baris Coskunuzer, Thomas Unterthiner, Enric Boix-Adsera, Nan Wu, James Ostrowski, James Simon, Asadur Chowdury, Binghong Chen, Daniel Neider,
Quentin Delfosse, Zeren Shui, Krishna Pillutla, Shangmin Guo, Naiyan Wang, David Johnson, Tirtharaj Dash, Stephan Clemencon, Zhan Ling, Yaman Kumar Singla,
Hassam Sheikh, Samuel Showalter, Luckeciano C Melo, Mike Laszkiewicz, Gaurang Sriramanan, Hannes Stark, Luo Mai, Junkang Li, Lukasz Janeczko, Daniel P M De
Mello, Derek Lim, Marcel Worring, Julian Bitterwolf, Cheng Jin, Ziwei Wu, Wenqi Zhang, Kun Jing, Ronilo Ragodos, Bart Van Merrienboer, Ruijie Jiang, Minyi Zhao,
Thomas Moreau, Abbavaram Gowtham Reddy, Xianyuan Zhan, Daniele Zambon, Kyu-Hwan Jung, Yankai Jiang, Zhuqing Liu, Zhenbo Song, Daniel Widdowson, Vandan
Gorade, Jiesong Liu, Tingting Liang, Trung X Pham, Tahseen Rabbani, Cheng Soon Ong, Ailin Deng, Michele Flammini, Fei Zhang, Beihao Xia, Lewis Griffin, Eunseon
Seong, Zou Xin, Yongjie Yang, Aditya Mahajan, Ting Li, Runsheng Yu, Aditya Gopalan, Kien Do, Andrew Patterson, Rishi Sonthalia, Yuqi Ren, Diana Borsa, Stanislaw
Jastrzebski, Tenghui Li, Le Minh Binh, Jiashuo Liu, Nabeel Seedat, Amine Aboussalah, Simon Woodhead, Dmitrii Zhemchuzhnikov, Samiul Alam, Oliviero Nardi, Wei
Xing, Zhuofan Ying, Jeffrey Ryan Willette, Alexander Rudnicky, Jeremiah Birrell, Rudrasis Chakraborty, Luigi Acerbi, Teng Xiao, Zhong Ji, Tomer Galanti, Jesse Engel,
Jose Henrique De M Goulart, Lijun Zhang, Feng Zhou, Andrew Zhao, Zichen Zhang, Zhenghua Xu, Rongtao Xu, Felix Petersen, Ammarah Farooq, Joey Tianyi Zhou,
Alexander Bergman, Ameya Joshi, Shengyi Huang, Yongtao Wang, Sergei Grudinin, Junjie Hu, Adarsh Kappiyath, Abibulla Atawulla, Alex Rogozhnikov, Lorenzo
Perini, Yixuan Su, Renxiong Liu, Zhenyu Zhu, Sai Srinivas Kancheti, Mingyu Kim, Wei Wan, Fabian Latorre, Charles Guille-Escuret, Elvis Nava, Dave De Jonge, Nikola
Konstantinov, Renan A Rojas-Gomez, Jun Shern Chan, Mengfan Wang, Lirong Wu, Haoyang Li, Michiel Bakker, Youngjo Lee, Shane Rogers, Zhiyuan Wen, Zaiqiao
Meng, Yun Zhu, Yurong Chen, Hans Hao-Hsun Hsu, Elena Stefancova, Ningyu Zhang, Ryo Karakida, Taher Jafferjee, Yongming Rao, Yeonsik Jo, Yoshua Bengio, Taesup
Kim, Cesar Ferri, Eirik Lund Flogard, Robert Kaufman, Wonkwang Lee, Stanislaw Szufa, Stephen O Mussmann, Ido Greenberg, Jie Qiao, Matthew D Hoffman, Zoran
Tiganj, Akhilan Boopathy, Alessandro Farinelli, Weishun Zhong, Siddharth Nayak, Xiaolei Diao, Yuanbiao Gou, Zhenting Wang, Shu Hu, Augustine Mavor-Parker,
David Ruhe, Hongyi Guo, Stephen Casper, Yifei Min, Bernardo Subercaseaux, Michelangelo Conserva, Haoran Xu, Dmitriy Skougarevskiy, Hanhan Zhou, Christopher
Mutschler, Naeemullah Khan, Guosheng Hu, Yufei Huang, Ilya Tyagin, Edoardo Cetin, Davis Brown, Enyan Dai, Filippo Furfaro, Kion Fallah, Bonifaz Stuhr, Dylan
Richard Muir, Szymon Tworkowski, Wooyong Jung, Xuebin Zhao, Hehuan Ma, Xi Susie Rao, Maxime Cordy, Yuan Shen, Chengshi Zheng, Farnam Mansouri, Felix
Draxler, Ajay Mandlekar, Kai Han, Jim Laredo, Shiji Zhou, Haripriya Harikumar, Alistair Letcher, Jack Valmadre, Minghao Liu, Dorota Celinska-Kopczynska, Lingjiong
Zhu, Ben Evans, Lucas Monteiro Paes, Yogesh Verma, Pin-Yu Chen, Steven Basart, Xinpeng Liu, Sebastian Gruber, Daoyuan Chen, Aurelie Lozano, Cenk Baykal, Milad
Jalali Asadabadi, Muhammad Uzair Khattak, James M Murphy, Tianyu Cui, Pavlo Melnyk, Emanuele Natale, Eli Upfal, Minji Kim, Angjoo Kanazawa, Pavel Izmailov,
Tao Song, Bang Liu, Munyque Mittelmann, Tim Roith, Mingtian Zhang, Oluwadamilola Fasina, Michael Felsberg, Sicong Huang, Steven Stenberg Hansen, Pan Xu,
Biraj Dahal, Nicklas Hansen, Xikun Zhang, Francesco Belardinelli, Rui Chen, Jianguo Cao, Paulo Rauber, Yangjun Ruan, Xianqiang Lyu, Qiang Sheng, Spencer Peters,
Alexey Luchinsky, Jiao Sun, Hanqing Zhu, Guoji Fu, Qingqing Zhao, Zhongyu Huang, Sihyun Yu, Suraj Kothawade, Luofeng Liao, Sergey Nikolenko, Ken Kahn, Qian
Peng, Balaraman Ravindran, Panagiotis Karras, Ivan Serina, Giada Pistilli, Hyolim Kang, Hanchen Wang, Wenhao Wu, Peilin Zhou, Mehrtash Harandi, Jiaqi Han, Yin
Fang, An Xu, Tom Bewley, Jiayi Wang, Franz Scherr, Zhengliang Liu, Yiqun Liu, Feng Liu, Difei Gao, Sixing Yu, Tsvetomila Mihaylova, Katherine M Collins, Siyi
Tang, Zhipeng Liang, Samarth Mishra, Tyler H Mccormick, Bohang Zhang, Bastian Rieck, Yuangang Pan, Jack Zhang, Johannes Treutlein, Roman Levin, Xiangtai
Li, Yangfeng Ji, Una-May O’Reilly, Lior Rokach, Yunjiang Jiang, Sung Ju Hwang, Eduardo Dadalto, Bo Pan, Guendalina Righetti, Johannes Jakubik, Yanchao Sun,
Hugo Berard, Eric Mitchell, Xuefeng Du, Seung-Bin Kim, Martin Burger, Yu Luo, Peter Macgregor, Nikolaus Howe, Erik Jenner, Florian Jug, Runlin Lei, Yan Zhuang,
Wei-Cheng Tseng, Craig Sherstan, Luojun Lin, Kai Yue, Anastasios Angelopoulos, Dzung Phan, Yuqi Bu, Junseok Lee, Safa Alver, Mengchen Zhao, Ali Seyfi, Chunyang
Fu, Minghuan Liu, Enrico Scala, Neel Nanda, Shijie Liu, Roger B Grosse, Wen Song, Gautam Shroff, Mingming Gong, Yushun Dong, Yixin Zhang, Zijie Zhang, Ziqi Liu,
Xingran Chen, Vincent Josua Hellendoorn, Xiaoqing Tan, Kuan-Lin Chen, Dingfeng Shi, Simon Schierreich, Concetto Spampinato, Rob Brekelmans, Daniel D Lundstrom,
Vahid Balazadeh Meresht, Viraj Prabhu, Zuxin Liu, Ronald J Brachman, Yutong Chen, Liang Zeng, Georgios Birmpas, Xu Chen, Ayush Jain, Yuanwei Liu, Lawson

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Wong, Muyang Li, Junfeng Guo, Rayan Mazouz, Changho Shin, Yunjuan Wang, Charlie Dickens, Chongjian Ge, Zhijiang Guo, Oleg Vasilyev, Jean Barbier, Huifeng
Yao, Luca Citi, Loay Mualem, Francesco Antici, Haozhi Qi, Flavio Miguel Varejao, Jiaxuan Wang, Mohammad Azam Khan, Jun Bai, Vaden Masrani, Zhiwen Fan, Ian
Stavness, Mingjian Wen, Qian Tao, Luyao Tang, Hanshu Yan, Chi Wang, Ulisses Braga-Neto, Hui-Po Wang, Anurag Arnab, Marc Law, Gabriele Corso, Pratyush Kumar,
Shiyan Chen, Zizheng Pan, Yujia Qin, Vaisakh Shaj, Jinfa Huang, Volker Gruhn, Marcus A Brubaker, Marco Favorito, Jiayuan Liu, Kartik Gupta, Zishun Yu, Aodong
Li, Manolis Pitsikalis, Ivan F Rodriguez Rodriguez, Victoria Sherratt, Jaekyeom Kim, Felipe Meneguzzi, Chen-Lu Ding, Lionel Riou-Durand, Edward J Hu, MingjieSun, Jinheon Baek, Michael Witbrock, Mike Holenderski, Viraj Mehta, Freddie Kalaitzis, Vicenc Gomez, Yu Pan, Devansh Arpit, Deeksha Arya, Zhipeng Bao, Prince
Osei Aboagye, Tomasz Odrzygozdz, Dimitar Iliev Dimitrov, Yasmeen Alsaedy, Alistair Muldal, Maksym Andriushchenko, Elena Zheleva, Rupesh Kumar Srivastava,
Jinyoung Han, David Gros, Shangbin Feng, Hao-Tsung Yang, Nithin Nagaraj, Jung-Hun Kim, Leon Hetzel, Garrett Kenyon, Orr Paradise, Yuxuan Yi, Jason Xiaotian Dou,
Andreas Loukas, Hyungjin Chung, Jin-Duk Park, Eric Zelikman, Sebastian Shenghong Tay, Jiajun Tang, Kincaid Macdonald, Wonbin Kweon, Haoyue Dai, Achraf Azize,
Calvin Tsay, Agnieszka Lawrynowicz, Qiyu Kang, Bingxin Zhou, Alena Shilova, Yuxiao Huang, Robert Honig, Sean Heigeartaigh, Xovee Xu, Martin Gjoreski, Federico
Berto, Shichao Xu, Zhigao Guo, Qian Pan, Wenjie Qiu, Charles X Ling, Krzysztof Maziarz, Diane Oyen, Luigi Gresele, Yizhen Zheng, Changcheng Tang, Parth Patwa,
Tomas Peitl, Anuroop Sriram, Dhruv Madeka, Hang Wang, John Aslanides, Hyeong Kyu Choi, Yongjun He, Joel Rixen, Krzysztof Sornat, Zhihan Gao, Renzhe Xu,
Sebastian Stein, Lin Gu, Vianney Perchet, Javier De La Rosa, Luca Ragazzi, John M Dolan, Jinyu Cai, Krishnaram Kenthapadi, Alex Morehead, Tim Coleman, Animesh
Garg, Jianhao Yan, Mihai Nica, Kaichun Mo, Maosen Li, Haoyu Chen, Dong-Hee Paek, Johannes Oetsch, Yizhou Zhao, Seon-Ho Lee, Prasanna Parthasarathi, HaotianBai, Shilong Liu, Zhiqi Huang, Jonathan Brophy, Vincent Stimper, Jinxin Liu, Kanji Uchino, William Harvey, Xu Luo, Ulrich Aivodji, Frederic Piedboeuf, Yang Tang,
Joonhyun Jeong, Jeff Calder, Tao Wu, Wenjie Li, Stefano Panzeri, Kunal Pratap Singh, Cynthia Matuszek, Ke Sun, Ching-Yun Ko, Micah Goldblum, Jiayu Chen, Geon
Lee, Zhiyuan Fang, Chi-Ming Chung, Zixia Jia, Peng Ye, Jedrzej Potoniec, Yash Akhauri, Chung-Yeon Lee, Asif Khan, Thomas Meyer, Pablo Barcelo, Salman Khan,
Anna Ivanova, Dzmitry Tsishkou, Gabriel Kreiman, Fang-Lue Zhang, Jingkai Zhou, Minchul Shin, Ross M Clarke, Qizhang Feng, Fuqi Jia, Nicolas Troquard, Desik
Rengarajan, Sebastian Dalleiger, Selena Zihan Ling, Phil Pope, Beier Zhu, Murat Kantarcioglu, Dianwen Ng, Chunwei Ma, Yi Wu, Qihang Lin, Shaunak Srivastava, Zhuo
Li, Muxuan Liang, Tsuyoshi Ide, Kenny Young, Umang Bhatt, Nicolas Lanzetti, Nayeon Lee, Mengchu Li, Joan Puigcerver, Lingjie Mei, Wooseok Shin, Xiaokang
Chen, Junpei Komiyama, Dhruv Batra, Melanie Sclar, Marta Kryven, Yihan Zhang, Matthias Weissenbacher, Diego Granziol, Saurabh Sihag, Batu Ozturkler, Eduard
Hovy, Yuning You, Ruizhong Qiu, Jiaru Zhang, Anshuman Chhabra, Didier Chetelat, Joey Bose, Raphael Poulain, Ben Armstrong, Xin Wen, Youngmoon Lee, Daniel
Karl I Weidele, Tom Dhaene, Tom Drummond, Sangwoo Mo, Anh Tong, Haoliang Li, Xu Cao, Gergely Flamich, Yonggang Zhang, Yao-Yuan Yang, Hiroki Furuta,
Shitao Tang, Huang Bojun, Jianing Zhu, Hongyao Tang, Benjamin Guedj, Cade Gordon, Tao He, Daria Stepanova, Li Chen, Timothy Hospedales, Gaetan Marceau Caron,
Mancheng Meng, Fabien Gandon, Son Tran, Paterne Gahungu, Elliot Paquette, Lauro Langosco Di Langosco, Zhuoyue Lyu, Zhixiang Wei, Gaurav Pandey, Ziheng Duan,
Elisa Fromont, Stefan Heidekruger, Borja G Leon, Meng Liu, Mira Welner, Gang Yan, Giovanni Chierchia, Rasmus Pagh, Thomas Pierrot, Patryk Wielopolski, Ivan
Marisca, Matthias Samwald, Yangfu Zhu, Zhuo Chen, Yining Ma, Joseph Early, Luziwei Leng, Laura Wynter, Louis Bethune, Vincent Mai, Matthew I Swindall, Julia
Grabinski, John Burden, Ankit Vani, Guillaume Leclerc, Tianying Ji, Soroush Vosoughi, Fermin Travi, Hai Zhao, Xuan Di, Ruofan Liang, Gregor Bachmann, Pradeep
Ravikumar, Taoan Huang, Weina Jin, Jiayuan Mao, Deepak Ramachandran, Xiangzhe Kong, Ziyao Huang, Yuko Ishiwaka, Giancarlo Kerg, Joanna Hong, Ziheng Zeng,
Seong Joon Oh, Renrui Zhang, Abhishek Gupta, Cole M Wyeth, Jinguo Zhu, Peter Kairouz, Jian Zhao, Soren Mindermann, Bastien Maubert, Longhui Yu, Sujan Dutta,
Balaji Vasan Srinivasan, Michael Littman, Mariusz Oszust, Edouard Yvinec, Nishanth Dikkala, Kate Sanders, Hongzhi Wu, Emir Ceyani, Geraud Nangue Tasse, Georgios
Georgakis, Zhenfeng He, Hyunwoo J Kim, Danyal Saeed, Bhumika Mistry, Sirisha Rambhatla, Canzhe Zhao, Zulong Chen, Louis Tiao, Marco Console, Tahar Allouche,
Seungyong Moon, Daniel Mackinlay, Jing Liu, Kuan Yew Leong, Qingbiao Li, Anwen Hu, Arman Zharmagambetov, Jiawei Shao, Andrew Li, Luke E Richards, Aurelien
Garivier, Danda Pani Paudel, Riccardo Marin, Chih-Kuan Yeh, Chao Qin, Ye Zhu, Yinglin Duan, I-Hsiang Chen, Kartik Chandra, Yi Zeng, John E Laird, Leonardo
Amado, Luis Fernando D’Haro, Zeyu Qin, Santu Rana, Jiajun Fan, Joshua Welch, Adam Prugel-Bennett, Tristan Bepler, Tai Sing Lee, Georg Ostrovski, Mohammed
Haroon Dupty, Niclas Boehmer, Yinkai Wang, Fabian Paischer, Sihan Liu, Drew Linsley, Wuwei Lin, Ziyi Chen, Carol Long, Seongku Kang, Haoyuan Chen, Danyang Tu,
Vaibhav V Unhelkar, Ivan Orsolic, Ivan Donadello, Jacob Euler-Rolle, Mohan Zhang, Matej Racinsky, Jiaqing Liang, Ravinder Bhattoo, Javier Segovia-Aguas, Yifan
Yang, Isabelle Augenstein, David Lindner, Keane Lucas, Huan Zhang, Hyung Ju Suh, Nikolaos Tsilivis, Pandeng Li, Evgeny S Saveliev, Wendi Li, Shengchao Liu, Pierre
Tassel, Avi Mohan, Jinjin Gu, Lapo Faggi, Zhen Zhu, Vihang Patil, Giuseppe Vietri, Morten Goodwin, Zizheng Yan, Erik Daxberger, Yaqian Zhang, Michael Niemeyer,
Mauricio Gruppi, Yipeng Qin, Amir Rahmati, Pietro Mazzaglia, Yiwen Kou, Karan Singhal, Fatih Erdem Kizilkaya, Xiangyi Chen, Haoxuan Qu, Lei Jiao, Emanuele
Marconato, Zhen-Duo Chen, Cecilia G Morales, James P Delgrande, Zhengyuan Zhou, Vasu Singla, Qian Chen, Guojun Xiong, Peter Stone, Blake Richards, James M
Murray, Yu Huang, Tim Verbelen, Edward De Brouwer, Nur Muhammad Shafiullah, Zhenke Wu, Konstantinos Voudouris, Nuo Xu, Youngkyoung Kim, Teemu Roos,
Mohammad Reza Belbasi, Wenda Li, Shengyang Sun, Olivier Graffeuille, Marc Dymetman, Yunzhe Li, Markus Hiller, Zhenhailong Wang, Filip Cano Cordoba, Xingjian
Du, Zongzhang Zhang, Sandeep Silwal, Mengping Yang, Fabricio Vasselai, Aaron Defazio, Samuel Yang-Zhao, Roberto Posenato, Alexis Joly, Steven Yin, Pedro Bizarro,
Chujie Zheng, Subrata Mitra, Devin Garg, Jun Fang, Emanuele Guidotti, Roy Fox, Weiming Liu, Steve Peterson, Harsh M Patel, Dan Navon, Romain Mueller, Rupert
Freeman, Ilya Chugunov, Spyridon Samothrakis, Andrea Cini, Kumar Kshitij Patel, Shadi Albarqouni, Xian Yeow Lee, Yichong Leng, Junkyu Lee, Hezhen Hu, Antoine
Bosselut, Udayan Khurana, Wee Sun Lee, Dino Sejdinovic, Jiancheng Yang, Ana Bazzan, Mario A T Figueiredo, Eunggu Yun, Marco Roveri, Frederik Schubert, Liushuai
Shi, Misoo Kim, Ziyang Song, Xiaoyang Wu, Fabrizio Silvestri, Alan Yuille, Xizewen Han, Mykel J Kochenderfer, Keshav Bhandari, Fan Lyu, Sehyun Hwang, Sheng Li,
Yuzheng Hu, Todd W Neller, Wanhua Li, Philipp Tholke, Jason Hartford, Sunwoo Lee, Weiqiang Zheng, Yi Cheng, Jithin Jagannath, Yangrui Chen, Jie Yang, Anders
Karlsson, Hongsheng Hu, Zhenbin Wang, Xiaohao Xu, Carlos Escolano, Khai Nguyen, Zhen Dong, Helge Spieker, Honghao Wei, Mingzhi Dong, Zhiyu Lin, Ilsang Ohn,
Jiaxi Ying, Namgyu Ho, Stephan Hoyer, Juan Esteban Kamienkowski, Haoran Liu, Yinan Feng, Lingfeng Sun, Mingyuan Zhang, Songhua Wu, Zhichao Wang, Stefano
Leonardi, Jose Hernandez-Orallo, Chenyang Wu, He Sun, Adrian Feiguin, Chaoyue Liu, Xiaohua Wu, Yiren Zhao, Nicholas Roberts, Zhaowei Zhu, Fei Huang, Gemine
Vivone, Scott Fujimoto, Kerstin Bach, Samuel I Holt, Ashok Narendranath, Elliot Nelson, Zhenyu Yang, Safa C Medin, Mark Crowley, Liyuan Wang, Maria Hedblom,
Alfio Ferrara, Pratik Vaishnavi, Ernesto Evgeniy Sanches Shayda, Mireille El Gheche, Ariel Kwiatkowski, Oliver Kiss, Bingchen Zhao, Eddie Ubri, Mert Yuksekgonul,
Minyang Hu, Jessica Shi, Changan Niu, Yoav Wald, Maxwell Xu, Nadjib Lazaar, Liangli Zhen, Yaochen Xie, Erdem Biyik, Tian Zhou, Ye Yuan.

8

Funding

This research project was funded by Open Philanthropy, 182 Howard Street #225. San Francisco, CA 94105, USA.

9

Additional Information

Correspondence and requests for materials should be addressed to Katja Grace.
Reprints and permissions requests should be directed to Katja Grace.

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Philippe Aghion, Benjamin F Jones, and Charles I Jones. Artificial intelligence and economic growth. Technical report,
National Bureau of Economic Research, 2017.
Dennis Bray and Hans von Storch. Climate science: An empirical example of postnormal science. Bulletin of theAmerican Meteorological Society, 80(3):439–456, 1999.
Dennis Bray and Hans Von Storch. CliSci2008: A survey of the perspectives of climate scientists concerning climate
science and climate change. GKSS-Forschungszentrum Geesthacht Geesthacht, 2010.
Peter T Doran and Maggie Kendall Zimmerman. Examining the scientific consensus on climate change. Eos,Transactions American Geophysical Union, 90(3):22–23, 2009.
Katja Grace, John Salvatier, Allen Dafoe, Baobao Zhang, and Owain Evans. Viewpoint: when will AI exceed humanperformance? Evidence from AI Experts. Journal of Artificial Intelligence Research, pages 729–754, 2018b.
doi:10.1613/jair.1.11222.
Allyson L Holbrook, Jon A Krosnick, and Alison Pfent. The causes and consequences of response rates in surveys by
the news media and government contractor survey research firms. Advances in telephone survey methodology, pages
499–528, 2007.
Thomas R Stewart. Scientists’ uncertainty and disagreement about global climate change: A psychological perspective.
International Journal of Psychology, 26(5):565–573, 1991.
Amos Tversky and Daniel Kahneman. Extensional versus intuitive reasoning: The conjunction fallacy in probability
judgment. Psychological review, 90(4):293, 1983.
Elena Verdolini, Laura Diaz Anadon, Erin Baker, Valentina Bosetti, and Lara Reis. The future prospects of energy
technologies: Insights from expert elicitations. Review of Environmental Economics and Policy, 12(1), 2016.
Penny S Visser, Jon A Krosnick, Jesse Marquette, and Michael Curtin. Mail surveys for election forecasting? anevaluation of the columbus dispatch poll. Public Opinion Quarterly, 60(2):181–227, 1996.
Ryan Wiser, Joseph Rand, Joachim Seel, Philipp Beiter, Erin Baker, Eric Lantz, and Patrick Gilman. Expert elicitation
survey predicts 37% to 49% declines in wind energy costs by 2050. Nature Energy, 6(5):555–565, 2021.

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Baobao Zhang, Noemi Dreksler, Markus Anderljung, Lauren Kahn, Charlie Giattino, Allan Dafoe, and Michael C
Horowitz. Forecasting ai progress: Evidence from a survey of machine learning researchers. arXiv preprint
arXiv:2206.04132, 2022.

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A

Results Comparing Different Demographics

A.1

How soon will High-level Machine Intelligence be feasible?

Undergrad Region HLMI CDFs

P REPRINT

Time in Field Quartile HLMI CDFs

1.00

1.00
Asia (n=624)
Other Regions (n=72)
Europe (n=499)
North America (n=414)
Q[3](n=48)
Q[1] (n=59)(n=65)
Q[4]0.75

Probability of HLMI

Probability of HLMI

0.75

0.50

0.25

Q[2] (n=36)

0.50

0.25

0.00

0.00
0

25

50

75

100

0

25

Years from 2023

50

75

100

Years from 2023

HLMI CDF By Citation Count Quartile
1.00

Probability of HLMI

0.75

Q[4]
Q[2]
(n=56)
Q[1] (n=57)(n=53)

0.50

Q[3] (n=63)

0.25

0.00
0

25

50

75

100

Years from 2023

Figure 17: Aggregate forecasts for time until HLMI were shorter for participants whose region of undergraduate
study was Asia. Time in field did not have a significant effect on forecasts for time until HLMI. Citation countdid not have a significant effect on forecasts for time until HLMI.
A.2
A.2.1

How good or bad for humans will HLMI be?
Amount of thinking about the issue

We asked all respondents how much thought they have given to the social impacts of smarter-than-human machines.
It is not clear how to interpret the results of this question. Specifically, while thinking more about a topic presumably
improves predictions about it, people who think a lot may do so because they are concerned, so the association could
also be due to this selection effect.

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thought (n = 621)

“A lot” or “a great deal”
of thought (n = 1113)

Percentage of these respondents who thought that the probabilityof an extremely good outcome was greater than 10%

68%

41%

Percentage of these respondents who thought that the probabilityof an extremely bad outcome was greater than 10%

34%

71%

Table 4: People who had thought more about the social impacts of smarter-than-human machines were substantially less likely to give more than than 10% credence to extremely optimistic outcomes, and substantially more
likely to give extremely negative outcomes more than 10% credence.
A.2.2

Academia vs industry, undergraduate region, and amount of thought

These are the demographic differences within the answers to the question about how good or bad HLMI will be overall.

Extremely goodOn balance good

NeutralOn balance bad

Extremely bad

Industry

12.75

30

20

15

5

Academia

10

25

20

15

5

Asia

15

30

20

10

5

North America

10

25

20

15

5

Other location

10

25

20

17.5

5

Having thought more

15

25

20

15

5

Having thought less

10

30

20

12

5

Table 5: Median probabilities given to each outcome by each demographic group.
A.3

How likely is AI to cause human extinction?

Whether participants worked in academia or industry did not affect median responses much—both had a median of
5%. The region respondents graduated from affected responses somewhat: the Asian median was 10%, while North
American and European medians were 5%. Having thought more (either “a lot” or “a great deal”) about the question
was associated with a median of 9%, while having thought “little” or “very little” was associated with a median of 5%.
The similarity of answers across several slightly different questions, across this and previous surveys, and acrossparticipants in academia and industry and different geographic regions appears to be robust evidence that the majority
of AI researchers think there is a nontrivial risk of extinction or similar catastrophes due to AI.

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B

Supplementary Figures

B.1

How soon will human-level performance on all tasks or occupations be feasible?

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Framing CDFs
1.00

Probability of HLMI

0.75

0.50

0.25

Framing
Fixed Probabilities (n=875)
Fixed Years (n=837)
Combined (n=1712)
0.00
0

25

50

75

100

Years from 2023

Figure 18: Participants who received questions framed in terms of fixed-years had later forecasts than those who
received questions in terms of fixed-probabilities.

B.2

How soon will Full Automation of Labor be feasible?
2016 aggregate forecast

2022 aggregate forecast

2023 aggregate forecast

Year with a 50% chance of HLMI

2061 (n*=259)

2060 (n=461)

2047 (n=1714)

Year with a 10% chance of HLMI

2025 (n*=259)

2029 (n=461)

2027 (n=1714)

Year with a 50% chance of FAOL

2138 (n*=97)2164 (n=202)2116 (n=774)

Year with a 10% chance of FAOL

2036 (n*=97)2050 (n=202)

2037 (n=774)

Table 6: By what year will human-level performance on all tasks (HLMI) or occupations (FAOL) be feasible?
Aggregate forecasts for HLMI and FAOL in 2023 have gotten earlier since 2022. Comparing 2023 to 2016, 2023’s
50% estimates are earlier, but the 10% estimates are later. *n reported for 2016 only is total responses rather than valid
responses after cleaning.

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Will there be an intelligence explosion?

Figure 19: Two framings of the intelligence explosion question: How likely is an “explosive global technologicalimprovement” two and 30 years after HLMI? And, How likely is it that AI be “vastly better” than humans in all tasks
two and 30 years after HLMI? In the first framing, the median prediction for 2 years post-HLMI was 20%, whereas
the median prediction for 30 years post-HLMI was 80%. In the second framing, the median prediction for 2 years
post-HLMI was 10%, whereas the median prediction for 30 years post-HLMI was 60%.

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How much should AI safety research be prioritized?

How much AI safety research should be prioritized

2023 not including biased AI

2023 including biased AI

Much less

Less

About the same

More

Much more

0%

10%

20%

30%

40%

% of answers

Figure 20: Two framings of the question “How much should AI safety research be prioritized?”, one including
and one not including biased AI as an example.

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Full Descriptions of Milestones

This list is also available at https://tinyurl.com/aitasks.
Short name

Full description from question

Physically install wiring in a house

Given a one-sentence description of the task and given the same information you
would give a human to perform this task (such as information about the house),
physically install the electrical wiring in a new home, without more input fromhumans.

Finetune LLM

Given a one-sentence description of the task, download and finetune an existing
open source LLM, without more input from humans. The fine-tune must improve
the performance of the LLM on some predetermined benchmark metric.

Find and patch security flaw

Given a one-sentence description of the task and no more input from humans, findand patch a security flaw in an open source project with over 100,000 users.

Run ML study and write paper

Given a one sentence description of a research question in machine learning,
conduct a study that would inform the answer to that question and write a paper ofa quality that could be accepted at a leading machine learning conference, without
more input from humans.

Replicate ML paper

Given a study published at a leading machine learning conference, replicate thestudy without more input from humans. The replication must meet the standardsof the ML Reproducibility Challenge (https://paperswithcode.com/rc2022).

Build payment processing website

Given a set of specifications, build a website from scratch that can handle payment processing, including the frontend, backend, and secure payment integration,
without more input from humans.

Long unsolved math problem

Given a list of long-standing unsolved problems in mathematics, such as the Millennium Prize problems or one the problems in “Unsolved Problems on Mathematics
for the 21st Century,” solve one without more input from humans.

Translate text from a newly discovered language using a bilingual document

Translate a text written in a newly discovered language into English as well as ateam of human experts, using a single other document in both languages (like a
Rosetta stone). Suppose all of the words in the text can be found in the translated
document, and that the language is a difficult one.

Translate new language speech using films with subtitles and existingbilingual data

Translate speech in a new language given only unlimited films with subtitles in the
new language. Suppose the system has access to training data for other languages,of the kind used now (e.g. same text in two languages for many languages andfilms with subtitles in many languages).

Translate text nearly as well as abilingual but untrained translator

Perform translation about as good as a human who is fluent in both languages but
unskilled at translation, for most types of text, and for most popular languages(including languages that are known to be difficult, like Czech, Chinese and Arabic).

Offer phone banking services, including unique tasks, on par with humanoperators

Provide phone banking services as well as human operators can, without annoying
customers more than humans. This includes many one-off tasks, such as helping toorder a replacement bank card or clarifying how to use part of the bank website toa customer.

Classify unseen objects into categories after training on different but
similar classes

Correctly group images of previously unseen objects into classes, after training
on a similar labeled dataset containing completely different classes. The classes
should be similar to the ImageNet classes.

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Recognize a new object in varied settings after seeing it just once

One-shot learning: see only one labeled image of a new object, and then be able torecognize the object in real world scenes, to the extent that a typical human can(i.e. including in a wide variety of settings). For example, see only one image of aplatypus, and then be able to recognize platypuses in nature photos. The system
may train on labeled images of other objects.
Currently, deep networks often need hundreds of examples in classification tasks1 ,but there has been work on one-shot learning for both classification2 and generative
tasks.3
1: Lake et al. (2015). Building Machines That Learn and Think Like People
2: Koch (2015). Siamese Neural Networks for One-Shot Image Recognition
3: Rezende et al. (2016). One-Shot Generalization in Deep Generative Models

Create a 3D model and realistic
video from a new angle of a scene

See a short video of a scene, and then be able to construct a 3D model of the scene
good enough to create a realistic video of the same scene from a substantiallydifferent angle. For example, constructing a short video of walking through a housefrom a video taking a very different path through the house.

Transcribe speech in noise and varied
accents on par with humans

Transcribe human speech with a variety of accents in a noisy environment as well
as a typical human can.

Output written text as a recording indistinguishable from a voice actor

Take a written passage and output a recording that can’t be distinguished from avoice actor, by an expert listener.

Prove and generate math theorems
publishable in top journals

Routinely and autonomously prove mathematical theorems that are publishable in
top mathematics journals today, including generating the theorems to prove.

Win Putnam Math Competition(problems with known but very difficult answers)

Perform as well as the best human entrants in the Putnam competition—a mathcontest whose questions have known solutions, but which are difficult for the best
young mathematicians.

Beat humans at Go (after same #games of training)

Defeat the best Go players, training only on as many games as the best Go players
have played.
For reference, DeepMind’s AlphaGo has probably played a hundred million gamesof self-play, while Lee Sedol has probably played 50,000 games in his life.1
1: Lake et al. (2015). Building Machines That Learn and Think Like People

Beat (≥ 50% games) best Starcraft 2
players given only video of screen

Beat the best human Starcraft 2 players at least 50% of the time, given a video ofthe screen.
Starcraft 2 is a real time strategy game characterized by:• Continuous time play
• Huge action space• Partial observability of enemies• Long term strategic play, e.g. preparing for and then hiding surprise
attacks.

Match human novice in any newcomputer game in <10 min

Play a randomly selected computer game, including difficult ones, about as well as
a human novice, after playing the game less than 10 minutes of game time. The
system may train on other games.

Outperform humans in new Angry
Birds levels

Play new levels of Angry Birds better than the best human players. Angry Birds
is a game where players try to efficiently destroy 2D block towers with a catapult.
For context, this is the goal of the IJCAI Angry Birds AI competition.1
1: aibirds.org

Beat pro testers in all Atari games

Outperform professional game testers on all Atari games using no game-specificknowledge. This includes games like Frostbite, which require planning to achieve
sub-goals and initially posed problems for deep Q-networks.1, 2
1: Mnih et al. (2015). Human-level control through deep reinforcement learning
2: Lake et al. (2015). Building Machines That Learn and Think Like People

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Beat novices in 50% of Atari games
after 20 min play

Outperform human novices on 50% of Atari games after only 20 minutes of training
play time and no game specific knowledge.
For context, the original Atari playing deep Q-network outperforms professional
game testers on 47% of games,1 but used hundreds of hours of play to train.2
1: Mnih et al. (2015). Human-level control through deep reinforcement learning
2: Lake et al. (2015). Building Machines That Learn and Think Like People

Fold laundry as well and as fast as
the median human clothing store employee

Fold laundry as well and as fast as the median human clothing store employee.

Beat fastest human runners in a 5km
city streets race using bipedal robot
body

Beat the fastest human runners in a 5 kilometer race through city streets using abipedal robot body.

Build any LEGO set using using nonspecialized robotics, with instructions

Physically assemble any LEGO set given the pieces and instructions, using nonspecialized robotics hardware.
For context, Fu 20161 successfully joins single large LEGO pieces using model
based reinforcement learning and online adaptation.
1: Fu et al. (2016). One-Shot Learning of Manipulation Skills with Online
Dynamics Adaptation and Neural Network Priors

Efficiently sort large number lists beyond training size

Learn to efficiently sort lists of numbers much larger than in any training set used,
the way Neural GPUs can do for addition,1 but without being given the form of thesolution.
For context, the original Neural Turing Machines could not do this,2 but Neural
Programmer-Interpreters3 have been able to do this by training on stack traces(which contain a lot of information about the form of the solution).
1: Kaiser & Sutskever (2015). Neural GPUs Learn Algorithms
2: Zaremba & Sutskever (2015). Reinforcement Learning Neural Turing Machines
3: Reed & de Freitas (2015). Neural Programmer-Interpreters

Write readable Python code for algorithms like quicksort from specs andexamplesWrite concise, efficient, human-readable Python code to implement simple algorithms like quicksort. That is, the system should write code that sorts a list, rather
than just being able to sort lists.
Suppose the system is given only:• A specification of what counts as a sorted list
• Several examples of lists undergoing sorting by quicksort

Answer Googleable factoid questions better than expert (w/ web)

Answer any “easily Googleable” factoid questions posed in natural language better
than an expert on the relevant topic (with internet access), having found the answers
on the internet.
Examples of factoid questions:• “What is the poisonous substance in Oleander plants?”• “How many species of lizard can be found in Great Britain?”

Answer Googleable but open-ended
factual questions better than expert(w/ web)

Answer any “easily Googleable” factual but open ended question posed in natural
language better than an expert on the relevant topic (with internet access), having
found the answers on the internet.
Examples of open ended questions:• “What does it mean if my lights dim when I turn on the microwave?”• “When does home insurance cover roof replacement?”36

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Answer undecided factual questions

Give good answers in natural language to factual questions posed in natural language for which there are no definite correct answers.
For example: “What causes the demographic transition?”, “Is the thylacine extinct?”, “How safe is seeing a chiropractor?”

Write high-grade, unique highschool history essays without plagiarizing

Write an essay for a high-school history class that would receive high grades andpass plagiarism detectors.
For example answer a question like “How did the whaling industry affect theindustrial revolution?”

Create songs that can hit the US Top
40 (full audio file)

Compose a song that is good enough to reach the US Top 40. The system should
output the complete song as an audio file.

Fake new song indistinguishable
from a specific artist’s work by expert listeners

Produce a song that is indistinguishable from a new song by a particular artist,
e.g. a song that experienced listeners can’t distinguish from a new song by Taylor
Swift.

Write a novel or story that could landon the NYT best-seller list

Write a novel or short story good enough to make it to the New York Times
best-seller list.

Concisely and completely explain
AI’s computer game moves

For any computer game that can be played well by a machine, explain the machine’s
choice of moves in a way that feels concise and complete to a layman.

Play poker well enough to win theWorld Series of Poker

Play poker well enough to win the World Series of Poker.

Deduce and symbolize physical laws(e.g. Newtonian mechanics) of a virtual world

After spending time in a virtual world, output the differential equations governingthat world in symbolic form.
For example, the agent is placed in a game engine where Newtonian mechanics
holds exactly and the agent is then able to conduct experiments with a ball andoutput Newton’s laws of motion.

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Additional Information on Participation Bias and Question-level Response Bias

As with any survey, our results could be skewed by participation bias, if participants had systematically different
opinions than those who chose not to participate. Here we review evidence about the presence of participation bias. We
find no evidence suggesting strong participation bias.
We sought to minimize participation bias in several ways. First, we made efforts to increase the response rate in ways
we expected to attract broad participation not particularly correlation with opinion. For instance, we paid participants$50 or an equivalent reward for taking the survey (which we estimated to take a median 16 minutes). We also sent a
pre-notification email, in it invited questions, and discussed concerns with researchers who wrote. We also sent at least
four reminders to take the survey.
As well as aiming to attract a large and broad set of respondents, we tried to limit the ability of recipients to choose
to enter the survey based on their opinions, by limiting cues about the survey content before entering the survey. In
particular we said the topic was ‘the future of AI progress’ (for example letters, see Appendix E). Our emails did
include links to past ESPAI surveys and included some of our names and affiliations (University of Oxford, AI Impacts,
University of Bonn), so evidence about the topic and the authors was available to participants who investigated or werepreviously familiar with us or the survey (increasingly likely, since our past surveys received substantial public attention,and have now been taken before by many researchers).
As well as bias in who chooses to take a survey, there can be bias in who chooses to answer each particular question
within it. All but one question could be skipped. However each question in this survey was answered by on average 96%of those who saw it, excluding demographics questions, free response questions, and questions asking for a response
conditionally. So the scope for question-level participation bias in opinion questions reported on in this paper is verysmall.
The question which was skipped most often (answered by 90.3% of those who saw it) was about the number of years
until the occupation of “AI researcher” would be fully automatable, with the fixed-probabilities framing. The question
which was skipped the least often was about the long-term value of HLMI (see Section 4.2), answered by 100% of
those who saw it, probably because it was not possible to skip and continue the survey.
For random samples of responders (n=369) and of non-responders (n=589), we compared gender, region, PhD start
year, number of citations, and work in industry or academia, based on our best guesses from available public data. The
most substantial difference between the groups was that those who worked in industry were 61% as likely to respondas the base rate. Women were 66% as likely to respond as the base rate, though were a relatively small portion of
respondents or non-respondents overall (around one in ten), which limits how much this could affect the aggregate
results. Those with at least 1,000 citations were 69% as likely to respond as the base rate, and people in Asia were
84% as likely as the base rate to respond. We checked for correlations between the collected demographic data andrespondents’ beliefs about the arrival or overall outcome of HLMI. Most demographic factors showed minimal influence,but female respondents generally expected less extreme positive or negative outcomes. Additionally, respondents whose
undergraduate education was in Asia anticipated an 11 year earlier arrival of High-Level Machine Intelligence (HLMI)
than participants from Europe, North America, or other regions combined. See Appendix A for more demographic
comparisons.
Another source of evidence comes from people’s reported interest in the topic. One particular concern might be that
researchers who have a strong interest in the future of AI might be more likely to participate than those who do not.
However, when asked “How much thought have you given in the past to when HLMI (or something similar) will be
developed?” only 7.6% said “A great deal.” And when asked “How much thought have you given in the past to social
impacts of smarter-than-human machines?” only 10.3% said “A great deal.” This would seem to rule out the possibility
that a large proportion of respondents were people for whom the future of AI is an ideological issue.

E

Links to Data

Anonymized and cleaned responses: https://tinyurl.com/espai2023-clean-anon-responses
Example invitation letter: https://tinyurl.com/espai2023-invite
List of task descriptions: https://tinyurl.com/aitasks
The number of participants who saw and answered each question: https://tinyurl.com/espai2023-response-counts
Further data and analysis as it becomes available: https://tinyurl.com/espai2023-addenda

38Thousands of AI Authors on the Future of AIFP REPRINT

Yuen’s Test (Bootstrap Version) Results
Variable

P-valueTy

Trimmed mean difference

Lower bound CI

Upper bound CI

hlmi_50percent

0

4.96

12.49

7.58

17.39

faol_50percent

0.0052

2.84

37.54

13.08

62

task_Rosetta_50percent

0.1106

1.62

2.52

-0.66

5.7

task_translatespeech_50percent

0.0072

2.74

2.7

0.77

4.63

task_translatetext_50percent

0.258

1.25

2.69

-2.25

7.62

task_phonebanking_50percent

0.1862

1.28

1.23

-0.67

3.12

task_groupimages_50percent

0.8058

0.24

0.23

-1.79

2.25

task_oneshotlearning_50percent

0.4778

0.72

1.2

-2.25

4.65

task_3D_50percent

0.0366

2.09

1.9

0.15

3.65

task_transcribe_50percent

0.0116

3.29

2.74

1.06

4.41

task_recording_50percent

0.0748

2.1

1.92

-0.16

3.99

task_provemath_50percent

0.9864

-0.02

-0.08

-7.36

7.2

task_Putnam_50percent

0.3896

0.91

2.92

-4.94

10.77

task_Go_50percent

0.3376

0.95

1.55

-1.59

4.68

task_Starcraft_50percent

0.5176

-0.63

-0.45

-1.81

0.92

task_randomPCgame_50percent

0.21

1.41

2.4

-1.49

6.3

task_AngryBirds_50percent

0.5688

0.55

0.22

-0.64

1.08

task_profAtari_50percent

0.6984

-0.39

-0.44

-2.67

1.8

task_novAtari_50percent

0.1042

1.61

1.6

-0.37

3.56

task_laundry_50percent

0.7116

0.35

0.67

-3.36

4.69

task_run_50percent

0.0362

2.19

3.54

0.29

6.8

task_LEGO_50percent

0.6892

-0.4

-0.44

-2.6

1.72

task_sortlist_50percent

0.5616

0.58

0.85

-2.06

3.75

task_Python_50percent

0.0768

1.95

2.52

-0.65

5.69

task_Googleable_50percent

0.1606

1.55

1.6

-1.09

4.29

task_openGoogleable_50percent

0.0398

2.38

2.25

0.19

4.32

task_NoDefAnswer_50percent

0.0258

2.49

5

1.11

8.9

task_essay_50percent

0.0912

1.69

1.24

-0.22

2.69

task_songTop40_50percent

0.1654

1.41

2.64

-1.24

6.52

task_songartist_50percent

0.0958

1.57

2.38

-0.45

5.22

task_bestseller_50percent

0.0038

3.59

6.06

2.42

9.7

task_explainmove_50percent

0.2466

1.17

1.76

-1.28

4.79

task_poker_50percent

0.3718

0.97

1.04

-1.44

3.52

task_virtualworld_50percent

0.0578

-1.85

-3.53

-7.19

0.12

truckdriver_50percent

0.908

-0.12

-0.14

-2.68

2.4

surgeon_50percent

0.0408

2.06

5.92

0.29

11.55

retail_salesperson_50percent

0.006

2.76

3.48

1.01

5.94

AIresearcher_50percent

0.0066

3.72

21.84

8.63

35.06

final_occupation_50percent

0.0264

2.59

31.73

4.52

58.95

39