The following is a quantitative analysis of 97 interviews conducted in Feb-March 2022 with machine learning researchers, who were asked about their perceptions of artificial intelligence (AI) now and in the future, with particular focus on risks from advanced AI systems (imprecisely labeled “AGI” for brevity in the rest of this document). Of the interviewees, 92 were selected from NeurIPS or ICML 2021 submissions and 5 were outside recommendations. For each interviewee, a transcript was generated, and common responses were identified and tagged to support quantitative analysis. The transcripts, as well as a qualitative walkthrough of the interviews are available at Interviews.
Some key findings from our primary questions of interest (not discussing Demographics or “Split-By” subquestions):
There are two large methodological weaknesses that should be kept in mind when interpreting the results. First, not every question was asked of every researcher. While some questions were just added later in the interview process, some questions were intentionally asked or avoided based on interviewer judgment of participant interest; questions particularly susceptible to this have an “About this variable” section below to describe the situation in more detail.
The second issue is with the tagging, which was somewhat haphazard. One person (not the interviewer) did the majority of the tagging, while another person (the interviewer) assisted and occasionally made corrections. Tagging was not blinded, and importantly, tags were not comprehensively double-checked by the interviewer. If anyone reading this document wishes to do a more systematic tagging of the raw data, we welcome this: much of the raw data is available on this website for analysis, and we’re happy to be contacted for further advice.
With these caveats in mind, we think there is much to be learned from a quantitative analysis of these interviews and present the full results below.
There are two versions of this report: one with interactive graphs, and one with static graphs. To access all of the features of this report, like hovering over graphs to see the number of participants in each category, you need to be using the interactive version. However, the static version loads significantly faster in a browser.
Proxy: Years from graduating undergrad + 22 years
Values present for 95/97 participants.
## mean: 31.3684210526316 ## median: 30 ## range: 19 - 56 ## # with value of 0: 0
Proxy: Undergrad country (Any country with only 1 participant got re-coded as ‘Other’)
Values present for 97/97 participants.
(Any country with only 1 participant got re-coded as ‘Other’)
Values present for 97/97 participants.
Area of AI was evaluated in two ways. First, by asking the participant directly in the interview (Field1) and second, by looking up participants’ websites and Google Scholar Interests (Field2). A comparison of Field1 and Field2 is located here. The comparison isn’t particularly close, so we usually include comparisons using both Field1 and Field2. We tend to think the Field2 labels (from Google Scholar and websites) are more accurate than Field1, because the data was a little more regular and the tagger was more experienced. We also tend to think Field2 has better external validity: for both field1 and field2, we ran a correlation between proportion of participants in that field who found the alignment arguments valid and those who found the instrumental arguments valid. This correlation was much higher for field2 than field1. Given that we expect these two arguments are probing a similar construct, the higher correlation suggests better external validity for the field2 grouping.
“Can you tell me about what area of AI you work on, in a few sentences?”
Values are present for 97/97 participants.
Note: “NLP” = natural language processing. “RL” = reinforcement learning. “vision” = computer vision. “neurocogsci” = neuroscience or cognitive science. “near-term AI safety” = AI safety generally and related areas (includes robustness, privacy, fairness). “long-term AI safety” = AI alignment and/or AI safety oriented at advanced AI systems.