As someone in the P(doom) > 90% category, I think in general making overly precise predictions are a really bad way to highlight AI risks (assuming that was the goal of AI 2027).
Making predictions that are too specific just opens you up to pushback from people who are more interested in critiquing the exact details of your softer predictions (such as those around timelines) rather than your hard predictions about likely outcomes. And while I think articles like this are valuable to refine timeline predictions, I find a lot of people use them as evidence to dismiss the stronger predictions made about the risks of ASI.
I think people like Nick Bostrom make much more convincing arguments about AI risk because they don't depend on overly detailed predictions which can be easily nit-picked at, but are instead much more general and focus more on the unique nature of the risks AI presents.
For me the risk of timelines is that they're unknowable due to the unpredictable nature of ASI. The fact we are rapidly developing a technology which most people would accept comes with at least some existential risk, that we can't predict the progress curve of, and where solutions would come with significant coordination problems should concern people without having to say it will happen in x number of years.
I think AI 2027 is interesting as a science fiction about potential futures we could be heading towards, but that's really it.
The problem with being an AI doomer is that you can't say "I told you so" if you're right so any personal predictions you make have no close to no expected pay-out, either socially or economically. This is different to other risks which if you predict accurately when others don't you can still benefit from.
I have no meaningful voice in this space so I'll just keep saying we're fucked because what does it matter what I think, but I wish there were more people with influence out there who were seriously thinking about how they can best influence rather than stroking their own own egos with future predictions, which even if I happen agree with do next to nothing to improve the distribution of outcomes.
Making predictions that are too specific just opens you up to pushback from people who are more interested in critiquing the exact details of your softer predictions (such as those around timelines) rather than your hard predictions about likely outcomes. And while I think articles like this are valuable to refine timeline predictions, I find a lot of people use them as evidence to dismiss the stronger predictions made about the risks of ASI.
I think people like Nick Bostrom make much more convincing arguments about AI risk because they don't depend on overly detailed predictions which can be easily nit-picked at, but are instead much more general and focus more on the unique nature of the risks AI presents.
For me the risk of timelines is that they're unknowable due to the unpredictable nature of ASI. The fact we are rapidly developing a technology which most people would accept comes with at least some existential risk, that we can't predict the progress curve of, and where solutions would come with significant coordination problems should concern people without having to say it will happen in x number of years.
I think AI 2027 is interesting as a science fiction about potential futures we could be heading towards, but that's really it.
The problem with being an AI doomer is that you can't say "I told you so" if you're right so any personal predictions you make have no close to no expected pay-out, either socially or economically. This is different to other risks which if you predict accurately when others don't you can still benefit from.
I have no meaningful voice in this space so I'll just keep saying we're fucked because what does it matter what I think, but I wish there were more people with influence out there who were seriously thinking about how they can best influence rather than stroking their own own egos with future predictions, which even if I happen agree with do next to nothing to improve the distribution of outcomes.