Stuart_Armstrong

Sequences

Generalised models
Concept Extrapolation
AI Safety Subprojects
Practical Guide to Anthropics
Anthropic Decision Theory
Subagents and impact measures
If I were a well-intentioned AI...

Comments

Having done a lot of work on corrigibility, I believe that it can't be implemented in a value agnostic way; it needs a subset of human values to make sense. I also believe that it requires a lot of human values, which is almost equivalent to solving all of alignment; but this second belief is much less firm, and less widely shared.

Instead, you could have a satisficer which tries to maximize the probability that the utility is above a certain value. This leads to different dynamics than maximizing expected utility. What do you think?

If U is the utility and u is the value that it needs to be above, define a new utility V, which is 1 if and only if U>u and is 0 otherwise. This is a well-defined utility function, and the design you described is exactly equivalent with being an expected V-maximiser.

Another way of saying this is that inner alignment is more important than outer alignment.

Interesting. My intuition is the inner alignment has nothing to do with this problem. It seems that different people view the inner vs outer alignment distinction in different ways.

Thanks! Yes, this is some weird behaviour.

Keep me posted on any updates!

As we discussed, I feel that the tokens were added for some reason but then not trained on; hence why they are close to the origin, and why the algorithm goes wrong on them, because it just isn't trained on them at all.

Good work on this post.

I'll be very boring and predictable and make the usual model splintering/value extrapolation point here :-)

Namely that I don't think we can talk sensibly about an AI having "beneficial goal-directedness" without situational awareness. For instance, it's of little use to have an AI with the goal of "ensuring human flourishing" if it doesn't understand the meaning of flourishing or human. And, without situational awareness, it can't understand either; at best we could have some proxy or pointer towards these key concepts.

The key challenge seems to be to get the AI to generalise properly; even initially poor goals can work if generalised well. For instance, a money-maximising trade-bot AI could be perfectly safe if it notices that money, in its initial setting, is just a proxy for humans being able to satisfy their preferences.

So I'd be focusing on "do the goals stay safe as the AI gains situational awareness?", rather than "are the goals safe before the AI gains situational awareness?"

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