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Worst-Case Thinking in AI Alignment
Manage episode 424744781 series 3498845
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Alternative title: “When should you assume that what could go wrong, will go wrong?” Thanks to Mary Phuong and Ryan Greenblatt for helpful suggestions and discussion, and Akash Wasil for some edits. In discussions of AI safety, people often propose the assumption that something goes as badly as possible. Eliezer Yudkowsky in particular has argued for the importance of security mindset when thinking about AI alignment. I think there are several distinct reasons that this might be the right assumption to make in a particular situation. But I think people often conflate these reasons, and I think that this causes confusion and mistaken thinking. So I want to spell out some distinctions. Throughout this post, I give a bunch of specific arguments about AI alignment, including one argument that I think I was personally getting wrong until I noticed my mistake yesterday (which was my impetus for thinking about this topic more and then writing this post). I think I’m probably still thinking about some of my object level examples wrong, and hope that if so, commenters will point out my mistakes.
Original text:
https://www.lesswrong.com/posts/yTvBSFrXhZfL8vr5a/worst-case-thinking-in-ai-alignment
Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.
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A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.
Chapter
1. Worst-Case Thinking in AI Alignment (00:00:00)
2. My list of reasons to maybe use worst-case thinking (00:01:26)
3. Differences between these arguments (00:09:07)
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