Whether “doing something once” is “extremely helpful for doing it later times” depends on whether people agree—while it is done and afterwards—on whether the measure is/was good, effective, fair, and whether people think the benefits exceeded the costs. It seems relevant whether people believe that it worked well and whether people more or less understood what worked and what didn’t.
In the abstract, I assume that people agreed before 2020 that anti-pandemic measures can be necessary. When the pandemic came and they were enacted, there was less consensus. Additionally, more people seem to evaluate the measures negatively in retrospect. (Personal impression, not a data-based statement.)
During Covid, there was political opposition to the measures, but some years later, this opposition has probably had more time to coordinate and develop.
I’m not sure I’d agree with your definition of success in trial runs. First, because the average response to how we handled COVID won’t generalize to viruses that are substantially more dangerous. If AI is paused and development continues, there will almost necessarily be larger risk in the larger capacity. If it’s substantially stronger than it was when it was initially paused, then it’s like comparing the COVID response to a hypothetical virus that’s visually much more dangerous—if it looked like the black death and there were videos of people passing out on the street because of it, I’m certain there would be a more prompt response.
Second, Operation Warp Speed was incredibly effective to my understanding in creating effective vaccines extremely quickly and then mass producing and distributing hundreds of millions of them. Using OWS as a model to improve from for the next pandemic seems like it could only help us.
I think it’s hard to make a list of when to tell that doing something once is helpful for doing them again, but some broad aspects would be A, the ability to learn from the first event, B lasting infrastructure or means of doing something as a result of the first event, C how well we’re able to test/understand A and B, the list goes on but I don’t think expanding it would necessarily be helpful
With your original comment, are you just saying that trial runs being generally helpful doesn’t always apply or are you saying that it wouldn’t apply in this instance and COVID is a strong indicator of that? If it’s the latter, can you give more reasons that the analogy works?
With my original comment, I am saying that doing something once is not always extremely helpful for doing it later times. I am not sure whether Covid helped learning/preparing for a next pandemic, but I think it had some negative effects for that with respect to the attitudes of the population, and positive effects with respect to scientific insights. I am much less sure whether it applies to an AI pause!
I am skeptical whether the word “trial run” is a good word for a real-world AI pause or pandemic measures starting in 2020. A trial run is a preliminary test, but with real-life anti-pandemic measures there are many things you cannot control, so the learning is limited.
Whether “doing something once” is “extremely helpful for doing it later times” depends on whether people agree—while it is done and afterwards—on whether the measure is/was good, effective, fair, and whether people think the benefits exceeded the costs. It seems relevant whether people believe that it worked well and whether people more or less understood what worked and what didn’t.
In the abstract, I assume that people agreed before 2020 that anti-pandemic measures can be necessary. When the pandemic came and they were enacted, there was less consensus. Additionally, more people seem to evaluate the measures negatively in retrospect. (Personal impression, not a data-based statement.)
During Covid, there was political opposition to the measures, but some years later, this opposition has probably had more time to coordinate and develop.
I’m not sure I’d agree with your definition of success in trial runs. First, because the average response to how we handled COVID won’t generalize to viruses that are substantially more dangerous. If AI is paused and development continues, there will almost necessarily be larger risk in the larger capacity. If it’s substantially stronger than it was when it was initially paused, then it’s like comparing the COVID response to a hypothetical virus that’s visually much more dangerous—if it looked like the black death and there were videos of people passing out on the street because of it, I’m certain there would be a more prompt response.
Second, Operation Warp Speed was incredibly effective to my understanding in creating effective vaccines extremely quickly and then mass producing and distributing hundreds of millions of them. Using OWS as a model to improve from for the next pandemic seems like it could only help us.
I think it’s hard to make a list of when to tell that doing something once is helpful for doing them again, but some broad aspects would be A, the ability to learn from the first event, B lasting infrastructure or means of doing something as a result of the first event, C how well we’re able to test/understand A and B, the list goes on but I don’t think expanding it would necessarily be helpful
With your original comment, are you just saying that trial runs being generally helpful doesn’t always apply or are you saying that it wouldn’t apply in this instance and COVID is a strong indicator of that? If it’s the latter, can you give more reasons that the analogy works?
With my original comment, I am saying that doing something once is not always extremely helpful for doing it later times. I am not sure whether Covid helped learning/preparing for a next pandemic, but I think it had some negative effects for that with respect to the attitudes of the population, and positive effects with respect to scientific insights. I am much less sure whether it applies to an AI pause!
I am skeptical whether the word “trial run” is a good word for a real-world AI pause or pandemic measures starting in 2020. A trial run is a preliminary test, but with real-life anti-pandemic measures there are many things you cannot control, so the learning is limited.