+1 on this, and also I think Anthropic should get some credit for not hyping things like Claude when they definitely could have (and I think received some tangible benefit from doing so).
See: https://www.lesswrong.com/posts/xhKr5KtvdJRssMeJ3/anthropic-s-core-views-on-ai-safety?commentId=9xe2j2Edy6zuzHvP9, and also some discussion between me and Oli about whether this was good / what parts of it were good.
Jeffrey Ladish
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@Daniel_Eth asked me why I choose 1:1 offsets. The answer is that I did not have a principled reason for doing so, and do not think there’s anything special about 1:1 offsets except that they’re a decent schelling point. I think any offsets are better than no offsets here. I don’t feel like BOTECs of harm caused as a way to calculate offsets are likely to be particularly useful here but I’d be interested in arguments to this effect if people had them.
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an agent will aim its capabilities towards its current goals including by reshaping itself and its context to make itself better-targeted at those goals, creating a virtuous cycle wherein increased capabilities lock in & robustify initial alignment, so long as that initial alignment was in a “basin of attraction”, so to speak
Yeah, I think if you nail initial alignment and have a system that has developed the instrumental drive for goal-content integrity, you’re in a really good position. That’s what I mean by “getting alignment to generalize in a robust manner”, getting your AI system to the point where it “really *wants* to help you help them stay aligned with you in a deep way”.
I think a key question of inner alignment difficulty is to what extent there is a “basin of attraction”, where Yudkowsky is arguing there’s no easy basin to find, and you basically have to precariously balance on some hill on the value landscape.
I wrote a little about my confusions about when goal-content integrity might develop here.
It seems nice to have these in one place but I’d love it if someone highlighted a top 10 or something.
To determine alignment difficulty, we need to know the absolute difficulty of alignment generalization
Yeah, I agree with all of this, seems worth saying. Now to figure out the object level… 🤔
Yeah that last quote is pretty worrying. If the alignment team doesn’t have the political capital / support of leadership within the org to have people stop doing particular projects or development pathways, I am even more pessimistic about OpenAI’s trajectory. I hope that changes!
Yeah I think we should all be scared of the incentives here.
Yeah I think it can both be true that OpenAI felt more pressure to release products faster due to perceived competition risk from Anthropic, and also that Anthropic showed restraint in not trying to race them to get public demos or a product out. In terms of speeding up AI development, not building anything > building something and keeping it completely secret > building something that your competitors learn about > building something and generating public hype about it via demos > building something with hype and publicly releasing it to users & customers. I just want to make sure people are tracking the differences.
so that it’s pretty unclear that not releasing actually had much of an effect on preventing racing
It seems like if OpenAI didn’t publicly release ChatGPT then that huge hype wave wouldn’t have happened, at least for a while, since Anthropic sitting on Claude rather than release. I think it’s legit to question whether any group scaling SOTA models is net positive but I want to be clear about credit assignment, and the ChatGPT release was an action taken by OpenAI.
I both agree that the race dynamic is concerning (and would like to see Anthropic address them explicitly), and also think that Anthropic should get a fair bit of credit for not releasing Claude before ChatGPT, a thing they could have done and probably gained a lot of investment / hype over. I think Anthropic’s “let’s not contribute to AI hype” is good in the same way that OpenAI’s “let’s generate massive” hype strategy is bad.
Like definitely I’m worried about the incentive to stay competitive, especially in the product space. But I think it’s worth highlighting that Anthropic (and Deepmind and Google AI fwiw) have not rushed to product when they could have. There’s still the relevant question “is building SOTA systems net positive given this strategy”, and it’s not clear to me what the answer is, but I want to acknowledge that “building SOTA systems and generating hype / rushing to market” is the default for startups and “build SOTA systems and resist the juicy incentive” is what Anthropic has done so far & that’s significant.
Thoughts on the OpenAI alignment plan: will AI research assistants be net-positive for AI existential risk?
Thanks Buck, btw the second link was broken for me but this link works: https://cepr.org/voxeu/columns/ai-and-paperclip-problem Relevant section:
Computer scientists, however, believe that self-improvement will be recursive. In effect, to improve, and AI has to rewrite its code to become a new AI. That AI retains its single-minded goal but it will also need, to work efficiently, sub-goals. If the sub-goal is finding better ways to make paperclips, that is one matter. If, on the other hand, the goal is to acquire power, that is another.
The insight from economics is that while it may be hard, or even impossible, for a human to control a super-intelligent AI, it is equally hard for a super-intelligent AI to control another AI. Our modest super-intelligent paperclip maximiser, by switching on an AI devoted to obtaining power, unleashes a beast that will have power over it. Our control problem is the AI’s control problem too. If the AI is seeking power to protect itself from humans, doing this by creating a super-intelligent AI with more power than its parent would surely seem too risky.
Claim seems much too strong here, since it seems possible this won’t turn out to that difficult for AGI systems to solve (copies seem easier than big changes imo, but not sure), but it also seems plausible it could be hard.
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