Executive summary: Joe Carlsmith sketches his current best-guess framework for giving advanced AI systems safe motivations, emphasizing that while no detailed plan exists, progress likely requires a four-step process (robust instruction-following, avoiding alignment faking, developing a science of reliable generalization, and crafting good instructions), each of which faces deep technical and conceptual challenges.
Key points:
Central challenge – “generalization without room for mistakes”: We must ensure AIs behave safely on novel, high-stakes inputs where failure could be catastrophic, without being able to test directly or iterate after errors.
Five sub-challenges: Accurate evaluation of AI behavior, reliably causing desired behavior, limited access to relevant training data, adversarial dynamics (e.g. scheming), and opacity of current ML models.
Two main tool categories:
Behavioral science – large-scale, systematic study of AI behavior on safe inputs.
Transparency tools – ranging from interpretability to “open agency” designs to speculative new AI paradigms.
Four-step picture of success:
(1) Cause instruction-following on safe inputs with accurate evaluation.
(2) Prevent alignment faking or scheming.
(3) Build a science of non-adversarial generalization to dangerous inputs.
(4) Ensure instructions themselves rule out rogue behavior. Carlsmith sees (2) as the hardest step, (4) as comparatively easier.
Uncertainties and open questions: Whether we can achieve robust evaluation of superhuman AIs, prevent scheming under adversarial pressure, and anticipate all novel dynamics introduced when AIs face new dangerous options or gain new capabilities.
Extension to capability elicitation: If motivation control succeeds, eliciting full beneficial capabilities becomes easier and safer, though it reintroduces evaluation challenges and deeper philosophical questions about what behaviors we want from AIs.
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Executive summary: Joe Carlsmith sketches his current best-guess framework for giving advanced AI systems safe motivations, emphasizing that while no detailed plan exists, progress likely requires a four-step process (robust instruction-following, avoiding alignment faking, developing a science of reliable generalization, and crafting good instructions), each of which faces deep technical and conceptual challenges.
Key points:
Central challenge – “generalization without room for mistakes”: We must ensure AIs behave safely on novel, high-stakes inputs where failure could be catastrophic, without being able to test directly or iterate after errors.
Five sub-challenges: Accurate evaluation of AI behavior, reliably causing desired behavior, limited access to relevant training data, adversarial dynamics (e.g. scheming), and opacity of current ML models.
Two main tool categories:
Behavioral science – large-scale, systematic study of AI behavior on safe inputs.
Transparency tools – ranging from interpretability to “open agency” designs to speculative new AI paradigms.
Four-step picture of success:
(1) Cause instruction-following on safe inputs with accurate evaluation.
(2) Prevent alignment faking or scheming.
(3) Build a science of non-adversarial generalization to dangerous inputs.
(4) Ensure instructions themselves rule out rogue behavior. Carlsmith sees (2) as the hardest step, (4) as comparatively easier.
Uncertainties and open questions: Whether we can achieve robust evaluation of superhuman AIs, prevent scheming under adversarial pressure, and anticipate all novel dynamics introduced when AIs face new dangerous options or gain new capabilities.
Extension to capability elicitation: If motivation control succeeds, eliciting full beneficial capabilities becomes easier and safer, though it reintroduces evaluation challenges and deeper philosophical questions about what behaviors we want from AIs.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.