Thank you for writing this. I’ve tried to summarize this article (missing good points made above, but might be useful to people deciding whether to read the full article):
Summary
AGI might be developed by 2027, but we lack clear plans for tackling misalignment risks. This post:
calls for better short-timeline AI alignment plans
lists promising interventions that could be stacked to reduce risks
This plan focuses on two minimum requirements:
Secure model weights and algorithmic secrets
Ensure the first AI capable of alignment research isn’t scheming
Layer 1 interventions (essential):
AI systems should maintain human-legible and faithful reasoning.
If achieved, we should monitor this reasoning, particularly for scheming, power-seeking, and broad goal-directedness (using other models or simple probes).
If not, we should fall back on control techniques that assume the model might be scheming.
Evaluations support other strategies, and give us better awareness of model alignment and capabilities.
Information and physical security protects model weights and algorithmic secrets.
Layer 2 interventions (important):
Continue improving ‘current’ alignment methods like RLHF and RLAIF.
Maintain research on interpretability, oversight, and “superalignment”, and preparing to accelerate this work once we have human-level AI R&D.
Increase transparency in AI companies’ safety planning (internally, with experts, and publicly).
Develop a safety-first culture in AI organizations.
This plan is meant as a starting point, and Marius encourages others to come up with better plans.
Thank you for writing this. I’ve tried to summarize this article (missing good points made above, but might be useful to people deciding whether to read the full article):
Summary
AGI might be developed by 2027, but we lack clear plans for tackling misalignment risks. This post:
calls for better short-timeline AI alignment plans
lists promising interventions that could be stacked to reduce risks
This plan focuses on two minimum requirements:
Secure model weights and algorithmic secrets
Ensure the first AI capable of alignment research isn’t scheming
Layer 1 interventions (essential):
AI systems should maintain human-legible and faithful reasoning.
If achieved, we should monitor this reasoning, particularly for scheming, power-seeking, and broad goal-directedness (using other models or simple probes).
If not, we should fall back on control techniques that assume the model might be scheming.
Evaluations support other strategies, and give us better awareness of model alignment and capabilities.
Information and physical security protects model weights and algorithmic secrets.
Layer 2 interventions (important):
Continue improving ‘current’ alignment methods like RLHF and RLAIF.
Maintain research on interpretability, oversight, and “superalignment”, and preparing to accelerate this work once we have human-level AI R&D.
Increase transparency in AI companies’ safety planning (internally, with experts, and publicly).
Develop a safety-first culture in AI organizations.
This plan is meant as a starting point, and Marius encourages others to come up with better plans.