I think this post is really helpful and has clarified my thinking about the different levels of AI alignment difficulty. It seems like a unique post with no historical equivalent, making it a major contribution to the AI alignment literature.
As you point out in the introduction, many LessWrong posts provide detailed accounts of specific AI risk threat models or worldviews. However, since each post typically explores only one perspective, readers must piece together insights from different posts to understand the full spectrum of views.
The new alignment difficulty scale introduced in this post offers a novel framework for thinking about AI alignment difficulty. I believe it is an improvement compared to the traditional ‘P(doom)’ approach which requires individuals to spontaneously think of several different possibilities which is mentally taxing. Additionally, reducing one’s perspective to a single number may oversimplify the issue and discourage nuanced thinking.
In contrast, the ten-level taxonomy provides concrete descriptions of ten scenarios to the reader, each describing alignment problems of varying difficulties. This comprehensive framework encourages readers to consider a variety of diverse scenarios and problems when thinking about the difficulty of the AI alignment problem. By assigning probabilities to each level, readers can construct a more comprehensive and thoughtful view of alignment difficulty. This framework therefore encourages deeper engagement with the problem.
The new taxonomy may also foster common understanding within the AI alignment community and serve as a valuable tool for facilitating high-level discussions and resolving disagreements. Additionally, it proposes hypotheses about the relative effectiveness of different AI alignment techniques which could be empirically tested in future experiments.
I think this post is really helpful and has clarified my thinking about the different levels of AI alignment difficulty. It seems like a unique post with no historical equivalent, making it a major contribution to the AI alignment literature.
As you point out in the introduction, many LessWrong posts provide detailed accounts of specific AI risk threat models or worldviews. However, since each post typically explores only one perspective, readers must piece together insights from different posts to understand the full spectrum of views.
The new alignment difficulty scale introduced in this post offers a novel framework for thinking about AI alignment difficulty. I believe it is an improvement compared to the traditional ‘P(doom)’ approach which requires individuals to spontaneously think of several different possibilities which is mentally taxing. Additionally, reducing one’s perspective to a single number may oversimplify the issue and discourage nuanced thinking.
In contrast, the ten-level taxonomy provides concrete descriptions of ten scenarios to the reader, each describing alignment problems of varying difficulties. This comprehensive framework encourages readers to consider a variety of diverse scenarios and problems when thinking about the difficulty of the AI alignment problem. By assigning probabilities to each level, readers can construct a more comprehensive and thoughtful view of alignment difficulty. This framework therefore encourages deeper engagement with the problem.
The new taxonomy may also foster common understanding within the AI alignment community and serve as a valuable tool for facilitating high-level discussions and resolving disagreements. Additionally, it proposes hypotheses about the relative effectiveness of different AI alignment techniques which could be empirically tested in future experiments.