This work was funded by Polaris Ventures

There is currently no consensus on how difficult the AI alignment problem is. We have yet to encounter any real-world, in the wild instances of the most concerning threat models, like deceptive misalignment. However, there are compelling theoretical arguments which suggest these failures will arise eventually.

Will current alignment methods accidentally train deceptive, power-seeking AIs that appear aligned, or not? We must make decisions about which techniques to avoid and which are safe despite not having a clear answer to this question.

To this end, a year ago, we introduced the AI alignment difficulty scale, a framework for understanding the increasing challenges of aligning artificial intelligence systems with human values.

This follow-up article revisits our original scale, exploring how our understanding of alignment difficulty has evolved and what new insights we've gained. This article will explore three main themes that have emerged [...]

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Outline:

(02:17) The Scale

(03:17) Easy, Medium and Hard Difficulty

(03:44) Levels 1-3

(07:21) Levels 4-7

(14:47) Levels 8-10

(17:23) Dynamics of the Scale

(22:23) Increasing Costs and Challenges

(24:21) Key Factors Changing Across the Scale

(25:49) Defining Alignment Difficulty

(27:38) High Impact Tasks

(29:54) Task Difficulty and Complexity of Feedback

(34:45) Influence of Architecture

(36:19) Conclusion

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