Executive summary: The author argues that conventional research will not solve AI “non-alignment problems”—such as misuse, AI welfare, and moral error—before transformative AI arrives, and instead recommends focusing on strategies that raise the odds these problems get solved, especially pausing AI development.
Key points:
Non-alignment problems are distinct from technical alignment and include misuse, S-risks, AI welfare, and moral error.
Given short timelines, traditional research is unlikely to make enough progress on these problems.
The author proposes four alternative strategies: meta-research, pausing AI, developing human-level assistants first, and steering ASI toward solving non-alignment issues.
Meta-research helps clarify approaches but yields diminishing returns if not followed by action.
Pausing AI is considered the strongest option since companies ignore non-alignment issues, though it may not increase humanity’s capacity to solve them.
Developing human-level AI could help with philosophical and ethical preparation but risks rapid escalation to superintelligence before readiness.
Steering ASI to “solve philosophy” faces major obstacles: unclear training signals, lack of researchers, and low likelihood of company adoption.
Overall, the author favors a pause despite doubts about its feasibility or effectiveness.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: The author argues that conventional research will not solve AI “non-alignment problems”—such as misuse, AI welfare, and moral error—before transformative AI arrives, and instead recommends focusing on strategies that raise the odds these problems get solved, especially pausing AI development.
Key points:
Non-alignment problems are distinct from technical alignment and include misuse, S-risks, AI welfare, and moral error.
Given short timelines, traditional research is unlikely to make enough progress on these problems.
The author proposes four alternative strategies: meta-research, pausing AI, developing human-level assistants first, and steering ASI toward solving non-alignment issues.
Meta-research helps clarify approaches but yields diminishing returns if not followed by action.
Pausing AI is considered the strongest option since companies ignore non-alignment issues, though it may not increase humanity’s capacity to solve them.
Developing human-level AI could help with philosophical and ethical preparation but risks rapid escalation to superintelligence before readiness.
Steering ASI to “solve philosophy” faces major obstacles: unclear training signals, lack of researchers, and low likelihood of company adoption.
Overall, the author favors a pause despite doubts about its feasibility or effectiveness.
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.