Executive summary: Interviews with 17 AI safety experts reveal a diversity of views on key questions about the future of AI and AI safety, with some dissent from standard narratives.
Key points:
Respondents expect the first human-level AI to resemble scaled-up language models with additional capabilities, but some believe major breakthroughs are still needed.
The standard unaligned AI takeover scenario was the most common existential risk story, but some pushed back on its assumptions. Alternative risks included instability, inequality, gradual disempowerment, and institutional collapse.
Key priorities for AI safety included technical solutions, spreading safety mindsets, sensible regulation, and building AI science. Promising research directions were mechanistic interpretability, black box evaluations, and governance.
Perceived mistakes by the AI safety community included overreliance on theoretical arguments, insularity, pushing fringe views, enabling race dynamics, lack of independent thought, misguided advocacy for AI pause, and neglecting policy.
The interviews had some limitations, including potential selection bias and lack of input from certain key organizations.
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: Interviews with 17 AI safety experts reveal a diversity of views on key questions about the future of AI and AI safety, with some dissent from standard narratives.
Key points:
Respondents expect the first human-level AI to resemble scaled-up language models with additional capabilities, but some believe major breakthroughs are still needed.
The standard unaligned AI takeover scenario was the most common existential risk story, but some pushed back on its assumptions. Alternative risks included instability, inequality, gradual disempowerment, and institutional collapse.
Key priorities for AI safety included technical solutions, spreading safety mindsets, sensible regulation, and building AI science. Promising research directions were mechanistic interpretability, black box evaluations, and governance.
Perceived mistakes by the AI safety community included overreliance on theoretical arguments, insularity, pushing fringe views, enabling race dynamics, lack of independent thought, misguided advocacy for AI pause, and neglecting policy.
The interviews had some limitations, including potential selection bias and lack of input from certain key organizations.
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.