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In this article, Paul Colognese explores whether detecting and evaluating the objectives of advanced AI systems during training and deployment is sufficient to solve the alignment problem. The idealized approach presented in the article involves detecting all objectives/intentions of any system produced during the training process, evaluating whether the outcomes produced by a system pursuing a set of objectives will be good/bad/irreversibly bad, and shutting down a system if its objectives lead to irreversibly bad outcomes.
The alignment problem for optimizing systems is defined as needing a method of training/building optimizing systems such that they never successfully pursue an irreversibly bad objective during training or deployment and pursue good objectives while rarely pursuing bad objectives. The article claims that if an overseer can accurately detect and evaluate all of the objectives of optimizing systems produced during the training process and during deployment, the overseer can prevent bad outcomes caused by optimizing systems pursuing bad objectives.
Robustly detecting an optimizing system’s objectives requires strong interpretability tools. The article discusses the problem of evaluating objectives and some of the difficulties involved. The role of interpretability is crucial in this approach, as it allows the overseer to make observations that can truly distinguish between good systems and bad-but-good-looking systems.
Detecting all objectives in an optimizing system is a challenging task, and even if the overseer could detect all of the objectives, it might be difficult to accurately predict whether a powerful optimizing system pursuing those objectives would result in good outcomes or not. The article suggests that with enough understanding of the optimizing system’s internals, it might be possible to directly translate from the internal representation of the objective to a description of the relevant parts of the corresponding outcome.
The article concludes by acknowledging that the proposed solution seems difficult to implement in practice, but pursuing this direction could lead to useful insights. Further conceptual and empirical investigation is suggested to better understand the feasibility of this approach in solving the alignment problem.
Great idea, I will experiment with that—thanks!
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