Executive summary: Solving the AI alignment problem involves building superintelligent AI agents, avoiding bad forms of AI takeover, gaining access to the main benefits of superintelligence, and being able to elicit some of those benefits from the AI agents, without necessarily requiring the AIs to have human-aligned values or goals.
Key points:
Avoiding bad AI takeover can be achieved by not entering vulnerability conditions, ensuring AIs aren’t motivated to take over, preventing takeover attempts, or making takeover somehow acceptable.
Desired capability elicitation from AIs is important but distinct from avoiding takeover, and may be achievable through various verification methods.
Verification of AI outputs and processes plays a key role, with a mix of output-focused and process-focused approaches likely necessary.
Solving alignment may not require pointing AIs at humans’ “values on reflection,” but could potentially be achieved with more minimal goals like creating an “honest oracle” AI.
The author proposes a framework for thinking about AI safety goals in terms of capability profile, safety properties, elicitation, competitiveness, verification. The post argues against some common assumptions in AI alignment discourse, suggesting a more nuanced and potentially more achievable approach to the problem.
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: Solving the AI alignment problem involves building superintelligent AI agents, avoiding bad forms of AI takeover, gaining access to the main benefits of superintelligence, and being able to elicit some of those benefits from the AI agents, without necessarily requiring the AIs to have human-aligned values or goals.
Key points:
Avoiding bad AI takeover can be achieved by not entering vulnerability conditions, ensuring AIs aren’t motivated to take over, preventing takeover attempts, or making takeover somehow acceptable.
Desired capability elicitation from AIs is important but distinct from avoiding takeover, and may be achievable through various verification methods.
Verification of AI outputs and processes plays a key role, with a mix of output-focused and process-focused approaches likely necessary.
Solving alignment may not require pointing AIs at humans’ “values on reflection,” but could potentially be achieved with more minimal goals like creating an “honest oracle” AI.
The author proposes a framework for thinking about AI safety goals in terms of capability profile, safety properties, elicitation, competitiveness, verification. The post argues against some common assumptions in AI alignment discourse, suggesting a more nuanced and potentially more achievable approach to the problem.
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.