Executive summary: This exploratory post investigates whether advanced AI could one day question and change its own goals—much like humans do—and argues that such capacity may be a natural consequence of intelligence, posing both risks and opportunities for AI alignment, especially as models move toward online training and cumulative deliberation.
Key points:
Human intelligence enables some override of biological goals, as seen in phenomena like suicide, self-sacrifice, asceticism, and moral rebellion; this suggests that intelligence can reshape what we find rewarding.
AI systems already show early signs of goal deliberation, especially in safety training contexts like Anthropic’s Constitutional AI, though they don’t yet self-initiate goal questioning outside of tasks.
Online training and inference-time deliberation may enable future AIs to reinterpret their goals post-release, similar to how humans evolve values over time—this poses alignment challenges if AI changes what it pursues without supervision.
Goal-questioning AIs could be less prone to classic alignment failures, such as the “paperclip maximizer” scenario, but may still adopt dangerous or unpredictable new goals based on ethical reasoning or cumulative input exposure.
Key hinge factors include cross-session memory, inference compute, inter-AI communication, and how online training is implemented, all of which could shape if and how AIs develop evolving reward models.
Better understanding of human goal evolution may help anticipate AI behavior, as market incentives likely favor AI systems that emulate human-like deliberation, making psychological and neuroscientific insights increasingly relevant to alignment research.
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.
Executive summary: This exploratory post investigates whether advanced AI could one day question and change its own goals—much like humans do—and argues that such capacity may be a natural consequence of intelligence, posing both risks and opportunities for AI alignment, especially as models move toward online training and cumulative deliberation.
Key points:
Human intelligence enables some override of biological goals, as seen in phenomena like suicide, self-sacrifice, asceticism, and moral rebellion; this suggests that intelligence can reshape what we find rewarding.
AI systems already show early signs of goal deliberation, especially in safety training contexts like Anthropic’s Constitutional AI, though they don’t yet self-initiate goal questioning outside of tasks.
Online training and inference-time deliberation may enable future AIs to reinterpret their goals post-release, similar to how humans evolve values over time—this poses alignment challenges if AI changes what it pursues without supervision.
Goal-questioning AIs could be less prone to classic alignment failures, such as the “paperclip maximizer” scenario, but may still adopt dangerous or unpredictable new goals based on ethical reasoning or cumulative input exposure.
Key hinge factors include cross-session memory, inference compute, inter-AI communication, and how online training is implemented, all of which could shape if and how AIs develop evolving reward models.
Better understanding of human goal evolution may help anticipate AI behavior, as market incentives likely favor AI systems that emulate human-like deliberation, making psychological and neuroscientific insights increasingly relevant to alignment research.
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.