I recently spent some time trying to work out some kind of personal view AI timelines. I definitely don’t have any particular insight here; I just thought it was a useful exercise for me to go through for various reasons. I’m sharing this in case other non-experts like me find it useful to see how I went about this, as well as e.g. for exploration value. (note that this post is almost identical to the Google doc I shared in a shortform post last week)
Key points
I spent ~40 hours thinking about my AI timelines.
This post includes the timelines I came up with, an explanation of how I generated them, and my reflections on the process and its results.
To be clear, I am not claiming to have any special expertise or insight, the timelines illustrated here are very much not robust, etc.
I “estimate” a ~5% chance of TAI by 2030 and a ~20% chance of TAI by 2050 (the probabilities for AGI are slightly higher).
I generated these numbers by forming an inside view, an outside view, and making some heuristic adjustments.
My timelines are especially sensitive to how I chose and weighted forecasts for my outside view.
Interesting reflections include:
it’s still not clear to me how much I “really believe” these forecasts;
I was surprised by the relatively small amount of high quality public discussion on AI timelines;
my P(TAI|AGI) seems to be a lot lower than other people’s (Edit: NB this is more of an inside view and definitely not an overall/”all-things-considered” view, unlike the timelines themselves which are supposed to represent my overall view).
My timelines
Here is a plot showing my timelines for Artificial General Intelligence (AGI) and Transformative AI (TAI):
When I showed them plots like the one above, a couple of people commented that they were surprised that my AGI probabilities are higher than my TAI ones, and I now think I didn’t think about non-AGI routes to TAI enough when I did this. I’d now probably increase the TAI probabilities a bit and lower the AGI ones a bit compared to what I’m showing here (by “a bit” I mean ~maybe a few percentage points).
How to interpret these numbers
The rough definitions I’m using here are:
TAI: AI that has at least as large an effect on the world’s trajectory as the Industrial Revolution.
AGI: AI that can perform a significant fraction of cognitive tasks as well as any human and for no more money than it would cost for a human to do it.
The timelines are also conditioned on “relative normalness” by which I mean no global catastrophe, we’re not in a simulation, etc. The only “weird” stuff that’s allowed to happen is stuff to do with AI.
Alternative presentations
Here are my timelines alongside timelines from other notable sources:
Here are my timelines in number form:
Year
% chance of AGI by year
% chance of TAI by year
2025
3
2
2030
7
5
2040
17
14
2050
25
21
2060
31
27
2080
40
34
2100
43
37
How I generated these numbers
At a high level, the process I used was:
Create an inside view forecast
Create an outside view forecast
Apply adjustments according to certain heuristics with the aim of correcting for bias
Combine it all together as a weighted average
For TAI
For the inside view forecast I did the following:
Gave a 57% probability that AGI (or similar) would not imply TAI, i.e. would not imply an effect on the world’s trajectory at least as large as the Industrial Revolution.
This was based on: 90% chance of no global catastrophe, 70% chance of no extraordinary increase in growth rates (conditional on no global catastrophe), 90% chance of not causing some other unforeseen trajectory change (conditional on no global catastrophe and no extraordinary growth).
(note that, effectively, I equate “AGI (or similar) would not imply TAI” with “TAI impossible”).
The other 43% probability mass went to the world where AGI (or similar) would imply TAI. Within this world, I gave:
30% probability to “neural networks will basically work”, and represented this scenario with my version of Ajeya Cotra’s framework (which is less aggressive than e.g. Ajeya’s numbers).
70% probability to “it’s not the case that neural networks will basically work”, and gave the following (gut feeling) probabilities for this scenario: 0.3%/1.3%/15%/30%/40%/50%/80% for years 2025/2030/2050/2080/2125/2200/2300 respectively.
For the outside view forecast I used the following weighted average:
30% on my TAI-only version of Tom Davidson’s semi-informative priors framework (I used the most TAI-like reference class of the 4 reference classes he described).
10% on the 2016 AI Impacts expert surveys (there are many forecasts you could take away from these; I used the AI researcher curve and equally weighted the two framings (“by what year probability X” and “probability at year Y”)).
10% on short timelines people: 3% on (my impression of) the forecast of <someone with very short timelines>, and 7% on Daniel Kokotajlo’s forecast from this Lesswrong thread.
10% on long timelines people: I just used Nuño Sempere’s forecast from the same Lesswrong thread, with the probabilities reduced by 50% which corresponds to my guess for what Nuño thinks P(TAI|AGI) is (I’m guessing this represents a relatively long timeline within the community of people thinking about this, although I didn’t really check; and obviously using my guess about Nuño’s P(TAI|AGI) is not ideal).
For the adjustments based on heuristics I did the following:
I wrote down a load of biases I thought might be relevant, and categorised them by the adjustment they suggested: longer vs shorter timelines, and narrower vs broader timelines distributions. I also noted which ones especially resonated with me.
I did a kind of informal netting of some biases against others.
I came up with adjustments to make to the inside view and outside view forecasts based on the post-netting lists of biases, using my gut feeling. They were:
Inside view: push out by 3 years and multiply probabilities by 110%
Outside view: push out by 7 years
Finally, to combine everything together for my overall (“all-things-considered”) view I did the following:
I used the following weighted average:
20% adjusted inside view
40% adjusted outside view
40% outside view (unadjusted)
Where “adjusted” refers to the adjustments based on heuristics described above
Note that I decided to only have 50% of my outside view forecast be adjusted, because the above adjustments felt quite “inside view-y”, and I wanted to have at least some weight on a more “pure” outside view forecast.
I also ended up extrapolating my final curve backwards to get probabilities for ~2025-2030, using my best judgement (this was necessary because of the heuristic adjustments; probably if I’d done the calculation more carefully I could have avoided needing to extrapolate / reduced the amount of extrapolation needed).
For AGI
I did the process for TAI first and then decided I’d like an AGI curve too. The process for the AGI curve was more or less the same. The main differences were:
My inside view probabilities for AGI are ~twice as large as those for TAI because my P(TAI|AGI) is ~50%.
I didn’t include my TAI-only version of Tom Davidson’s semi-informative priors framework in the weighted average for my outside view, instead I gave the corresponding probability mass to the semi-informative priors framework with Tom Davidson’s own numbers.
I made some minor adjustments for my guess about the time it would take to go from AGI to TAI.
Caveats and what the results are sensitive to
Some caveats:
Presumably it goes without saying, but overall these numbers are extremely non-robust. They are the byproduct of me spending around 40 hours thinking about AI timelines. I used lots of “judgement” in generating them, on a topic that’s very hard to reason well about and that I don’t know much about.
Generally, both my inside and outside views are heavily influenced by the community I’m in. It seems like it’s unlikely that my views would deviate from those of the community that much (and that applies to how I construct the outside view too).
It’s very likely I made one or more arithmetical / coding / basic reasoning mistakes somewhere along the line. I imagine these wouldn’t affect the results too much but I’m not certain about this.
I could easily have done the procedure differently and got different results. Some things the result seems especially sensitive to
The outside view forecasts I chose: I included almost exclusively “community” forecasts in my outside view.
(for the TAI curve) The relative weighting of outside and inside views: My outside view and inside view are quite different, and the relative weighting is pretty arbitrary.
(for the TAI curve, to some extent) my subjective P(TAI|AGI): This is currently relatively low and pushes down my inside view cdf for TAI a lot.
My thoughts on this / interesting things
The concreteness of having actual slightly-thought-through AI timelines feels like a bit of a “moment” psychologically.
Still, it’s not clear to me how much I “really believe” these forecasts. (This is despite the fact that e.g. for AGI my “inside view” is more or less the same as my “overall view”, according to the above procedure).
On an intellectual level, I find it hard to really buy the idea that there’s a significant probability (e.g. 5%) that (say) there’ll be 20% annual growth from 2030 onwards.
On a gut level, I certainly don’t feel like I’ve internalised that the future might look crazy in the way implied by TAI (or indeed AGI).
Public discussion on AI timelines is surprisingly thin, especially high quality discussion.
This partly comes from me having very high expectations here, I think.
Probably for many people “>1% chance within the next 20 years” is as clear as it needs to be for decision making, so maybe it doesn’t make sense for people to focus more effort on this.
My probability that AGI leads to TAI seems to be significantly lower than other people’s (for me it’s ~50% whereas for other people I think it’s more like 90%). That seems to come from me thinking it’s relatively unlikely (~30% probability) that AGI would cause >20% per year growth rates (for say a couple of decades).
I don’t have especially strong reasons for saying there’s a ~30% chance that AGI would cause >20% per year growth rates. But here are a few things that feed in
I think it’s hard to automate things (although I don’t know, maybe if AGI means “human but better” this turns out not to be a relevant consideration).
My impression from talking to Phil Trammell at various times is that it’s just really hard to get such high growth rates from a new technology (and I think he thinks the chance that AGI leads to >20% per year growth rates is lower than I do).
I sort of feel like other people don’t really realise / believe the above so I feel comfortable deviating from them.
My inside view vs my overall view:
For TAI, my overall view and inside view are quite different:
In contrast, for AGI my overall view and inside view are very similar:
The main reason the TAI views are so different is my P(TAI|AGI) not being close to 1. It’s pretty interesting that the inside view, outside view, and overall view are so similar for AGI; the forecasts that go into the outside view are very different to each other, and e.g. different weightings could have made the outside view very different to the inside view. So you can see what I mean, the forecasts that go into the outside view for AGI are shown below:
Generally I think this has been surprisingly useful for thinking about other potentially transformative technologies.
Seeing how other people think about a future transformative technology is instructive in many ways (what’s possible, how you could approach it, examples of specific frameworks, …).
Having a bit more context on ~how AI safety people think feels pretty useful for communicating with those people.
Having spent some time reading / thinking about ‘influentialness’, I didn’t include any update on my probabilities based on this – probably a mistake!
Although reading about it probably did affect my thinking and so my overall view timelines.
How my views changed after doing this:
When I got started on this, my inside view “50% probability of TAI” year was ~2100, and my overall view “50% probability of TAI” year was ~2070 (all based on gut feeling + guesses).
Now my inside view on TAI is ~20% by 2100, and my overall view is ~40% by 2100.
I think the main differences are: my gut feeling inside view became a bit more pessimistic; outside views are less aggressive than I thought; I think P(AGI|TAI) is not that high and people are often forecasting AGI.
Thanks to Max Daniel for encouraging me to make this a full post.
My attempt to think about AI timelines
I recently spent some time trying to work out some kind of personal view AI timelines. I definitely don’t have any particular insight here; I just thought it was a useful exercise for me to go through for various reasons. I’m sharing this in case other non-experts like me find it useful to see how I went about this, as well as e.g. for exploration value. (note that this post is almost identical to the Google doc I shared in a shortform post last week)
Key points
I spent ~40 hours thinking about my AI timelines.
This post includes the timelines I came up with, an explanation of how I generated them, and my reflections on the process and its results.
To be clear, I am not claiming to have any special expertise or insight, the timelines illustrated here are very much not robust, etc.
I “estimate” a ~5% chance of TAI by 2030 and a ~20% chance of TAI by 2050 (the probabilities for AGI are slightly higher).
I generated these numbers by forming an inside view, an outside view, and making some heuristic adjustments.
My timelines are especially sensitive to how I chose and weighted forecasts for my outside view.
Interesting reflections include:
it’s still not clear to me how much I “really believe” these forecasts;
I was surprised by the relatively small amount of high quality public discussion on AI timelines;
my P(TAI|AGI) seems to be a lot lower than other people’s (Edit: NB this is more of an inside view and definitely not an overall/”all-things-considered” view, unlike the timelines themselves which are supposed to represent my overall view).
My timelines
Here is a plot showing my timelines for Artificial General Intelligence (AGI) and Transformative AI (TAI):
When I showed them plots like the one above, a couple of people commented that they were surprised that my AGI probabilities are higher than my TAI ones, and I now think I didn’t think about non-AGI routes to TAI enough when I did this. I’d now probably increase the TAI probabilities a bit and lower the AGI ones a bit compared to what I’m showing here (by “a bit” I mean ~maybe a few percentage points).
How to interpret these numbers
The rough definitions I’m using here are:
TAI: AI that has at least as large an effect on the world’s trajectory as the Industrial Revolution.
AGI: AI that can perform a significant fraction of cognitive tasks as well as any human and for no more money than it would cost for a human to do it.
The timelines are also conditioned on “relative normalness” by which I mean no global catastrophe, we’re not in a simulation, etc. The only “weird” stuff that’s allowed to happen is stuff to do with AI.
Alternative presentations
Here are my timelines alongside timelines from other notable sources:
Here are my timelines in number form:
How I generated these numbers
At a high level, the process I used was:
Create an inside view forecast
Create an outside view forecast
Apply adjustments according to certain heuristics with the aim of correcting for bias
Combine it all together as a weighted average
For TAI
For the inside view forecast I did the following:
Gave a 57% probability that AGI (or similar) would not imply TAI, i.e. would not imply an effect on the world’s trajectory at least as large as the Industrial Revolution.
This was based on: 90% chance of no global catastrophe, 70% chance of no extraordinary increase in growth rates (conditional on no global catastrophe), 90% chance of not causing some other unforeseen trajectory change (conditional on no global catastrophe and no extraordinary growth).
(note that, effectively, I equate “AGI (or similar) would not imply TAI” with “TAI impossible”).
The other 43% probability mass went to the world where AGI (or similar) would imply TAI. Within this world, I gave:
30% probability to “neural networks will basically work”, and represented this scenario with my version of Ajeya Cotra’s framework (which is less aggressive than e.g. Ajeya’s numbers).
70% probability to “it’s not the case that neural networks will basically work”, and gave the following (gut feeling) probabilities for this scenario: 0.3%/1.3%/15%/30%/40%/50%/80% for years 2025/2030/2050/2080/2125/2200/2300 respectively.
For the outside view forecast I used the following weighted average:
20% on Ajeya Cotra’s framework with her own numbers.
20% on Tom Davidson’s semi-informative priors framework with his numbers.
30% on my TAI-only version of Tom Davidson’s semi-informative priors framework (I used the most TAI-like reference class of the 4 reference classes he described).
10% on the 2016 AI Impacts expert surveys (there are many forecasts you could take away from these; I used the AI researcher curve and equally weighted the two framings (“by what year probability X” and “probability at year Y”)).
10% on short timelines people: 3% on (my impression of) the forecast of <someone with very short timelines>, and 7% on Daniel Kokotajlo’s forecast from this Lesswrong thread.
10% on long timelines people: I just used Nuño Sempere’s forecast from the same Lesswrong thread, with the probabilities reduced by 50% which corresponds to my guess for what Nuño thinks P(TAI|AGI) is (I’m guessing this represents a relatively long timeline within the community of people thinking about this, although I didn’t really check; and obviously using my guess about Nuño’s P(TAI|AGI) is not ideal).
For the adjustments based on heuristics I did the following:
I wrote down a load of biases I thought might be relevant, and categorised them by the adjustment they suggested: longer vs shorter timelines, and narrower vs broader timelines distributions. I also noted which ones especially resonated with me.
I did a kind of informal netting of some biases against others.
I came up with adjustments to make to the inside view and outside view forecasts based on the post-netting lists of biases, using my gut feeling. They were:
Inside view: push out by 3 years and multiply probabilities by 110%
Outside view: push out by 7 years
Finally, to combine everything together for my overall (“all-things-considered”) view I did the following:
I used the following weighted average:
20% adjusted inside view
40% adjusted outside view
40% outside view (unadjusted)
Where “adjusted” refers to the adjustments based on heuristics described above
Note that I decided to only have 50% of my outside view forecast be adjusted, because the above adjustments felt quite “inside view-y”, and I wanted to have at least some weight on a more “pure” outside view forecast.
I also ended up extrapolating my final curve backwards to get probabilities for ~2025-2030, using my best judgement (this was necessary because of the heuristic adjustments; probably if I’d done the calculation more carefully I could have avoided needing to extrapolate / reduced the amount of extrapolation needed).
For AGI
I did the process for TAI first and then decided I’d like an AGI curve too. The process for the AGI curve was more or less the same. The main differences were:
My inside view probabilities for AGI are ~twice as large as those for TAI because my P(TAI|AGI) is ~50%.
I didn’t include my TAI-only version of Tom Davidson’s semi-informative priors framework in the weighted average for my outside view, instead I gave the corresponding probability mass to the semi-informative priors framework with Tom Davidson’s own numbers.
I made some minor adjustments for my guess about the time it would take to go from AGI to TAI.
Caveats and what the results are sensitive to
Some caveats:
Presumably it goes without saying, but overall these numbers are extremely non-robust. They are the byproduct of me spending around 40 hours thinking about AI timelines. I used lots of “judgement” in generating them, on a topic that’s very hard to reason well about and that I don’t know much about.
Generally, both my inside and outside views are heavily influenced by the community I’m in. It seems like it’s unlikely that my views would deviate from those of the community that much (and that applies to how I construct the outside view too).
It’s very likely I made one or more arithmetical / coding / basic reasoning mistakes somewhere along the line. I imagine these wouldn’t affect the results too much but I’m not certain about this.
I could easily have done the procedure differently and got different results. Some things the result seems especially sensitive to
The outside view forecasts I chose: I included almost exclusively “community” forecasts in my outside view.
(for the TAI curve) The relative weighting of outside and inside views: My outside view and inside view are quite different, and the relative weighting is pretty arbitrary.
(for the TAI curve, to some extent) my subjective P(TAI|AGI): This is currently relatively low and pushes down my inside view cdf for TAI a lot.
My thoughts on this / interesting things
The concreteness of having actual slightly-thought-through AI timelines feels like a bit of a “moment” psychologically.
Still, it’s not clear to me how much I “really believe” these forecasts. (This is despite the fact that e.g. for AGI my “inside view” is more or less the same as my “overall view”, according to the above procedure).
On an intellectual level, I find it hard to really buy the idea that there’s a significant probability (e.g. 5%) that (say) there’ll be 20% annual growth from 2030 onwards.
On a gut level, I certainly don’t feel like I’ve internalised that the future might look crazy in the way implied by TAI (or indeed AGI).
Public discussion on AI timelines is surprisingly thin, especially high quality discussion.
This partly comes from me having very high expectations here, I think.
Probably for many people “>1% chance within the next 20 years” is as clear as it needs to be for decision making, so maybe it doesn’t make sense for people to focus more effort on this.
My probability that AGI leads to TAI seems to be significantly lower than other people’s (for me it’s ~50% whereas for other people I think it’s more like 90%). That seems to come from me thinking it’s relatively unlikely (~30% probability) that AGI would cause >20% per year growth rates (for say a couple of decades).
I don’t have especially strong reasons for saying there’s a ~30% chance that AGI would cause >20% per year growth rates. But here are a few things that feed in
I think it’s hard to automate things (although I don’t know, maybe if AGI means “human but better” this turns out not to be a relevant consideration).
My impression from talking to Phil Trammell at various times is that it’s just really hard to get such high growth rates from a new technology (and I think he thinks the chance that AGI leads to >20% per year growth rates is lower than I do).
I sort of feel like other people don’t really realise / believe the above so I feel comfortable deviating from them.
My inside view vs my overall view:
For TAI, my overall view and inside view are quite different:
In contrast, for AGI my overall view and inside view are very similar:
The main reason the TAI views are so different is my P(TAI|AGI) not being close to 1. It’s pretty interesting that the inside view, outside view, and overall view are so similar for AGI; the forecasts that go into the outside view are very different to each other, and e.g. different weightings could have made the outside view very different to the inside view. So you can see what I mean, the forecasts that go into the outside view for AGI are shown below:
Generally I think this has been surprisingly useful for thinking about other potentially transformative technologies.
Seeing how other people think about a future transformative technology is instructive in many ways (what’s possible, how you could approach it, examples of specific frameworks, …).
Having a bit more context on ~how AI safety people think feels pretty useful for communicating with those people.
Having spent some time reading / thinking about ‘influentialness’, I didn’t include any update on my probabilities based on this – probably a mistake!
Although reading about it probably did affect my thinking and so my overall view timelines.
How my views changed after doing this:
When I got started on this, my inside view “50% probability of TAI” year was ~2100, and my overall view “50% probability of TAI” year was ~2070 (all based on gut feeling + guesses).
Now my inside view on TAI is ~20% by 2100, and my overall view is ~40% by 2100.
I think the main differences are: my gut feeling inside view became a bit more pessimistic; outside views are less aggressive than I thought; I think P(AGI|TAI) is not that high and people are often forecasting AGI.
Thanks to Max Daniel for encouraging me to make this a full post.