Thanks! This post was interesting and helped clarify my thoughts on some relevant issues. Still, I want to push in the other direction.
I think historical data poses its own sets of flaws and limitations—and there are certain questions that it cannot easily answer. Therefore, I still think one should from philosophical/conceptual analysis since there’s often no better approach to answering the questions. To be concrete, here are two main categories of questions that AI forecasting is looking to answer and where conceptual analysis must be used.
1. Timelines and capabilities forecasting Here, I think the historical data about other technologies and their diffusion is pretty good to predict AI’s diffusion in the economy.
But to answer questions like “will AI automate all human-level cognitive labor”, I think it would be dubious to only look through the history of other transformative technology and conclude “no because electricity/TV/internet/phones did not”. There is a much clearer mechanism for AI to automate these tasks than any of those past technologies, and therefore the historical data just isn’t that persuasive to me.
2. What risks does TAI pose and how should we mitigate them Here I think conceptual analysis is much more valuable. There are benefits of doing some economic modelling to see how to react to issues like job loss etc., especially for the near-term. But for questions like “how likely is human disempowerment from powerful AI”, I don’t see any good alternative to conceptual thinking. I really like Forethought’s post about what to focus on here which encompasses non-alignment problems and basically focuses on high level strategic questions since those are easier to predict:
Of course, there are lots of possible issues with conceptual thinking that you mention in the post. Where I’m at is just accepting that no approach is especially good and that we should keep some amount of epistemic humility in our arguments since it’s very easy to be deeply confused in both directions.
(Copying my comment from Substack)
Thanks! This post was interesting and helped clarify my thoughts on some relevant issues. Still, I want to push in the other direction.
I think historical data poses its own sets of flaws and limitations—and there are certain questions that it cannot easily answer. Therefore, I still think one should from philosophical/conceptual analysis since there’s often no better approach to answering the questions. To be concrete, here are two main categories of questions that AI forecasting is looking to answer and where conceptual analysis must be used.
1. Timelines and capabilities forecasting
Here, I think the historical data about other technologies and their diffusion is pretty good to predict AI’s diffusion in the economy.
But to answer questions like “will AI automate all human-level cognitive labor”, I think it would be dubious to only look through the history of other transformative technology and conclude “no because electricity/TV/internet/phones did not”. There is a much clearer mechanism for AI to automate these tasks than any of those past technologies, and therefore the historical data just isn’t that persuasive to me.
2. What risks does TAI pose and how should we mitigate them
Here I think conceptual analysis is much more valuable. There are benefits of doing some economic modelling to see how to react to issues like job loss etc., especially for the near-term. But for questions like “how likely is human disempowerment from powerful AI”, I don’t see any good alternative to conceptual thinking. I really like Forethought’s post about what to focus on here which encompasses non-alignment problems and basically focuses on high level strategic questions since those are easier to predict:
Of course, there are lots of possible issues with conceptual thinking that you mention in the post. Where I’m at is just accepting that no approach is especially good and that we should keep some amount of epistemic humility in our arguments since it’s very easy to be deeply confused in both directions.