Primary ?s - Descriptives
When will we get AGI?
Note: “AGI” stands in for “advanced AI systems”, and is used for brevity
- Example dialogue: “All right, now I’m going to give a spiel. So, people talk about the promise of AI, which can mean many things, but one of them is getting very general capable systems, perhaps with the cognitive capabilities to replace all current human jobs so you could have a CEO AI or a scientist AI, etcetera. And I usually think about this in the frame of the 2012: we have the deep learning revolution, we’ve got AlexNet, GPUs. 10 years later, here we are, and we’ve got systems like GPT-3 which have kind of weirdly emergent capabilities. They can do some text generation and some language translation and some code and some math. And one could imagine that if we continue pouring in all the human investment that we’re pouring into this like money, competition between nations, human talent, so much talent and training all the young people up, and if we continue to have algorithmic improvements at the rate we’ve seen and continue to have hardware improvements, so maybe we get optical computing or quantum computing, then one could imagine that eventually this scales to more of quite general systems, or maybe we hit a limit and we have to do a paradigm shift in order to get to the highly capable AI stage. Regardless of how we get there, my question is, do you think this will ever happen, and if so when?”
96/97 participants had some kind of response.
Some participants had both “will happen” and “won’t happen” tags (e.g. because they changed their response during the conversation) and are labeled as “both”.
Note: most of the graphs on this doc are not exclusive (same person can be represented in multiple bars), but the one below is. So each of the 97 participants is represented exactly once.
73 / 97 (75%) said at some point in the conversation that it will happen.
Among the 73 people who said at any point that it will happen…
Among the 30 people who said at any point that it won’t happen…
Split by Field
Visualizing AGI time horizon broken down by field is tricky, because participants could be tagged with multiple fields and with multiple time horizons. So if, say, someone in the Vision field was tagged with both ‘<50’ and ‘50-200’ time horizons, including both tags on a bar plot would give the impression that there were actually two people in Vision, one with each time horizon. This would result in an over-representation of people who had multiple tags (n = 21). Thus, for only the cases where we are examining time-horizon split by field, we simplified by assigning one time-horizon per participant: if they ever endorsed ‘wide range’, they were assigned ‘wide range’; otherwise, they were assigned whichever of their endorsed time horizons was the soonest.
The simplification above results in the following breakdown:
## whenAGIdata_simp_lowest
## None/NA <50 50-200 >200 wide range wonthappen
## 4 19 24 9 20 21An alternative solution for those with multiple time-horizon tags would have been to assign each multi-tag case its own tag. We chose not to do this for the following graphs, in part because there would have been 15 timing tags, the breakdown of which is represented in the table below.
| Var1 | Freq |
|---|---|
| wonthappen | 21 |
| 50-200 | 20 |
| <50 | 16 |
| wide range | 10 |
| >200 | 5 |
| >200 + wonthappen | 4 |
| None/NA | 4 |
| wide range + 50-200 | 4 |
| 50-200 + wonthappen | 3 |
| wide range + <50 | 3 |
| <50 + wonthappen | 2 |
| wide range + >200 | 2 |
| <50 + 50-200 | 1 |
| 50-200 + >200 | 1 |
| wide range + <50 + >200 | 1 |
Field 1 (from interview response)
The graph below shows the proportion of people (among those who had answers, so removing the “None.NA” responses from above) with each answer type within each field. So, for all the people in the ‘long.term.AI.safety’ category for whom we have an answer for the when-AGI question (which is 2 total participants), 100% of them said ‘<50’. If you are using the interactive version (rather than the static version) of this report, hover over a bar to see the total participants in that category.
Observation/summary: No one in NLP/translation, near-term safety, or interpretablity/exlainability endorsed a <50 year time horizon. Meanwhile, no one in long-term AI safety, neuro/cognitive science, and robotics just said AGI won’t happen. People in theory were somewhat more likely to give a wide range.
Field 2 (from Google Scholar)
The graph below shows the proportion of people (among those who had answers, so removing the “None.NA” responses from above) with each answer type within each field. So, for all the people in the ‘Deep.Learning’ category for whom we have an answer for the when-AGI question (which is 25 total participants), 28% of them said ‘<50’. If you are using the interactive version (rather than the static version) of this report, hover over a bar to see the total participants in that category.
Observation/summary: No one in NLP or Optimization endorsed a <50 year time horizon. Meanwhile, no one in Applications/Data Analysis or Inference just said AGI won’t happen. People in vision were somewhat more likely to say that AGI wouldn’t happen.
Split by Sector
The proportions below exclude people in research institutes. So, for all the people in the ‘wide range’ category (N=19), 79% of them are in academia and 21% of them are in industry. People in both sectors get counted for both (so if everyone in a category were in both sectors, it would show 100% academia and 100% industry) If you are using the interactive version (rather than the static version) of this report, hover over a bar to see the total participants in that category.
Observation: Very roughly/noisily: as timelines get higher, a larger proportion of the participants fall in academia and a smaller proportion fall into industry… except for ‘won’t happen’.
Split by Age
Remember, age was estimated based on college graduation year
Observation: Not much going on here.
Split by h-index
For the graphs below, the interviewee with the outlier h-index value (>200) was removed.
Observation: People with closer time horizons seem to have higher h-indices.
Alignment Problem
“What do you think of the argument ‘highly intelligent systems will fail to optimize exactly what their designers intended them to, and this is dangerous’?”
- Example dialogue: “Alright, so these next questions are about these highly intelligent systems. So imagine we have a CEO AI, and I’m like,”Alright, CEO AI, I wish for you to maximize profit, and try not to exploit people, and don’t run out of money, and try to avoid side effects.” And this might be problematic, because currently we’re finding it technically challenging to translate human values, preferences and intentions into mathematical formulations that can be optimized by systems, and this might continue to be a problem in the future. So what do you think of the argument “Highly intelligent systems will fail to optimize exactly what their designers intended them to and this is dangerous”?
95/97 participants had some kind of response. For example quotes, search the tag names in the Tagged-Quotes document.
Among the 58 people who said at any point that it is invalid…
Split by Field
I’m going to simplify by saying that if someone ever said valid, then their answer is valid. If someone gave any of the other responses but never said valid, they will be marked as invalid.
The simplification above results in the following breakdown:
## alignment_validity
## invalid.other None/NA valid
## 40 2 55Field 1 (from interview response)
The graph below shows the proportion of people (among those who had answers, so removing the “None.NA” responses from above) with each answer type within each field. So, for all the people in the ‘long.term.AI.safety’ category for whom we have an answer for the alignment problem (which is 2 total participants), 100% of them said ‘valid’. If you are using the interactive version (rather than the static version) of this report, hover over a bar to see the total participants in that category.
Observation/summary: people in vision, NLP / translation, & deep learning were more likely to think the AI alignment arguments were invalid, with a >50% chance of not saying the arguments are valid. Meanwhile, people in RL, interpretability / explainability, robotics, & safety were pretty inclined (>60%) to say at some point that the argument was valid.
Field 2 (from Google Scholar)
The graphs below shows the proportion of people (among those who had answers, so removing the “None.NA” responses from above) with each answer type within each field. So, for all the people in the ‘Deep.Learning’ category for whom we have an answer for the alignment problem (which is 26 total participants), 65% of them said ‘valid’. If you are using the interactive version (rather than the static version) of this report, hover over a bar to see the total participants in that category.
Observation/summary: People in Computing, NLP, Computer Vision, & Math or Theory were more likely to think the AI alignment arguments were invalid, with a >50% chance of not saying the arguments are valid. Meanwhile, people in Inference and Near-Term Safety and Related were very likely (>80%) to say at some point that the argument was valid.
Split by: Heard of AI alignment?
Specifically, split by the participants’ answer to the question “Heard of AI alignment?”, which is described below. (The interviewer manually went through and binarized participants’ responses for the question “Heard of AI alignment?”; we will use those binarized tags rather than the initial tags.)
Proportions…
Observation: People who had heard of AI alignment were a bit more likely to find the alignment argument valid than people who had not heard of AI alignment, but not by a huge margin.
There’s a subgroup of interest: those who had not heard of AI alignment before but thought the argument for it was valid. What fields (using field2) are these 30 people in?