https://www.lesswrong.com/posts/xFotXGEotcKouifky/worlds-where-iterative-design-fails
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.
In most technical fields, we try designs, see what goes wrong, and iterate until it works. That’s the core iterative design loop. Humans are good at iterative design, and it works well in most fields in practice.
In worlds where AI alignment can be handled by iterative design, we probably survive. So long as we can see the problems and iterate on them, we can probably fix them, or at least avoid making them worse.
By the same reasoning: worlds where AI kills us are generally worlds where, for one reason or another, the iterative design loop fails. So, if we want to reduce X-risk, we generally need to focus on worlds where the iterative design loop fails for some reason; in worlds where it doesn’t fail, we probably don’t die anyway.
Why might the iterative design loop fail? Most readers probably know of two widely-discussed reasons:
- Fast takeoff: there will be a sudden phase shift in capabilities, and the design of whatever system first undergoes that phase shift needs to be right on the first try.
- Deceptive inner misalignment: an inner agent behaves well in order to deceive us, so we can’t tell there’s a problem just by trying stuff and looking at the system’s behavior.
… but these certainly aren’t the only reasons the iterative design loop potentially fails. This post will mostly talk about some particularly simple and robust failure modes, but I’d encourage you to think on your own about others. These are the things which kill us; they’re worth thinking about.Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.
In most technical fields, we try designs, see what goes wrong, and iterate until it works. That’s the core iterative design loop. Humans are good at iterative design, and it works well in most fields in practice.
In worlds where AI alignment can be handled by iterative design, we probably survive. So long as we can see the problems and iterate on them, we can probably fix them, or at least avoid making them worse.
By the same reasoning: worlds where AI kills us are generally worlds where, for one reason or another, the iterative design loop fails. So, if we want to reduce X-risk, we generally need to focus on worlds where the iterative design loop fails for some reason; in worlds where it doesn’t fail, we probably don’t die anyway.
Why might the iterative design loop fail? Most readers probably know of two widely-discussed reasons:
- Fast takeoff: there will be a sudden phase shift in capabilities, and the design of whatever system first undergoes that phase shift needs to be right on the first try.
- Deceptive inner misalignment: an inner agent behaves well in order to deceive us, so we can’t tell there’s a problem just by trying stuff and looking at the system’s behavior.
… but these certainly aren’t the only reasons the iterative design loop potentially fails. This post will mostly talk about some particularly simple and robust failure modes, but I’d encourage you to think on your own about others. These are the things which kill us; they’re worth thinking about.
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