LessWrong 2.0 Reader

Also:

• Knowing the importance of the advantages people have makes you better able to judge how well people are likely to do, which lets you make better decisions when e.g. investing in someone's company or deciding who to hire for an important role (or marry).
• Also orients you towards figuring out the difficult-to-see advantages people must have (or must lack), given the level of success that they've achieved and their visible advantages.
• If you're in a position to influence what advantages people end up with—for example, by affecting the genes your children get, or what education and training you arrange for them—then you can estimate how much each of those is worth and prioritize accordingly.

One of the things you probably notice is that having some advantages tends to make other advantages more valuable.  Certainly career-wise, several of those things are like, "If you're doing badly on this dimension, then you may be unable to work at all, or be limited to far less valuable roles".  For example, if one person's crippling anxiety takes them from 'law firm partner making $1 million' to 'law analyst making $200k', and another person's crippling anxiety takes them from 'bank teller making $50k' to 'unemployed', then, well, from a utilitarian perspective, fixing one person's problems is worth a lot more than the other's.  That is probably already acted upon today—the former law partner is more able to pay for therapy/whatever—but it could inform people who are deciding how to allocate scarce resources to young people, such as the student versions of the potential law partner and bank teller.

(Of course, the people who originally wrote about "privilege" would probably disagree in the strongest possible terms with the conclusions of the above lines of reasoning.)

This seems interesting, but I've seen no plausible case that there's a version of (1) that's both sufficient and achievable. I've seen Davidad mention e.g. approaches using boundaries formalization. This seems achievable, but clearly not sufficient. (boundaries don't help with e.g. [allow the mental influences that are desirable, but not those that are undesirable])

The [act sufficiently conservatively for safety, relative to some distribution of safety specifications] constraint seems likely to lead to paralysis (either of the form [AI system does nothing], or [AI system keeps the world locked into some least-harmful path], depending on the setup - and here of course "least harmful" isn't a utopia, since it's a distribution of safety specifications, not desirability specifications).
Am I mistaken about this?

I'm very pleased that people are thinking about this, but I fail to understand the optimism - hopefully I'm confused somewhere!
Is anyone working on toy examples as proof of concept?

I worry that there's so much deeply technical work here that not enough time is being spent to check that the concept is workable (is anyone focusing on this?). I'd suggest focusing on mental influences: what kind of specification would allow me to radically change my ideas, but not to be driven insane? What's the basis to think we can find such a specification?

It seems to me that finding a fit-for-purpose safety/acceptability specification won't be significantly easier than finding a specification for ambitious value alignment.

Good points well made. I'm not sure what you mean by "my expected log score is maximized" (and would like to know), but in any case it's probably your average world rather than your median world that does it?

Thanks.

I'm glad to see that the non-compliance reporting policy has been implemented and includes anonymous reporting. I'm still hoping to see more details. (And I'm generally confused about why Anthropic doesn't share more details on policies like this — I fail to imagine a story about how sharing details could be bad, except that the details would be seen as weak and this would make Anthropic look bad.)

What details are you imagining would be helpful for you? Sharing the PDF of the formal policy document doesn't mean much compared to whether it's actually implemented and upheld and treated as a live option that we expect staff to consider (fwiw: it is, and I don't have a non-disparage agreement). On the other hand, sharing internal docs eats a bunch of time in reviewing it before release, chance that someone seizes on a misinterpretation and leaps to conclusions, and other costs.

Not sure. I can generally imagine a company publishing what Anthropic has published but having a weak/fake system in reality. Policy details do seem less important for non-compliance reporting than some other policies — Anthropic says it has an infohazard review policy [LW(p) · GW(p)], and I expect it's good, but I'm not confident, and for other companies I wouldn't necessarily expect that their policy is good (even if they say a formal policy exists), and seeing details (with sensitive bits redacted) would help.

I mostly take back my secret policy is strong evidence of bad policy insinuation — that's ~true on my home planet, but on Earth you don't get sufficient credit for sharing good policies and there's substantial negative EV from misunderstandings and adversarial interpretations, so I guess it's often correct to not share :(

As an 80/20 of publishing, maybe you could share a policy with an external auditor who would then publish whether they think it's good or have concerns. I would feel better if that happened all the time.

Hi,

How did the event go?

Any plans to organize a meetup this year?

I'm planning to host a meetup in Sibiu this summer, because I haven't seen an event scheduled here. Any advice? I'm also planning to host a meetup in Cluj-Napoca this year, if it's not announced by someone else

Kind regards, Marius Nicoară

This does not feel super cruxy as the the power incentive still remains. 

"red line" vs "yellow line"

Passing a red-line eval indicates that the model requires ASL-n mitigations. Yellow-line evals are designed to be easier to implement and/or run, while maintaining the property that if you fail them you would also fail the red-line evals. If a model passes the yellow-line evals, we have to pause training and deployment until we put a higher standard of security and safety measures in place, or design and run new tests which demonstrate that the model is below the red line. For example, leaving out the "register a typo'd domain" step from an ARA eval, because there are only so many good typos for our domain.

assurance mechanisms

Our White House committments mean that we're already reporting safety evals to the US Government, for example. I think the natural reading of "validated" is some combination of those, though obviously it's very hard to validate that whatever you're doing is 'sufficient' security against serious cyberattacks or safety interventions on future AI systems. We do our best.

I'm glad to see that the non-compliance reporting policy has been implemented and includes anonymous reporting. I'm still hoping to see more details. (And I'm generally confused about why Anthropic doesn't share more details on policies like this — I fail to imagine a story about how sharing details could be bad, except that the details would be seen as weak and this would make Anthropic look bad.)

What details are you imagining would be helpful for you? Sharing the PDF of the formal policy document doesn't mean much compared to whether it's actually implemented and upheld and treated as a live option that we expect staff to consider (fwiw: it is, and I don't have a non-disparage agreement). On the other hand, sharing internal docs eats a bunch of time in reviewing it before release, chance that someone seizes on a misinterpretation and leaps to conclusions, and other costs.

I believe that meeting our ASL-2 deployment commitments - e.g. enforcing our acceptable use policy, and data-filtering plus harmlessness evals for any fine-tuned models - with widely available model weights is presently beyond the state of the art. If a project or organization makes RSP-like commitments, evaluations and mitigates risks, and can uphold that while releasing model weights... I think that would be pretty cool.

(also note that e.g. LLama is not open source [LW(p) · GW(p)] - I think you're talking about releasing weights; the license doesn't affect safety but as an open-source maintainer the distinction matters to me)

That's the exact thing I'm worried about, that people will equate deploying a model via API with releasing open-weights when the latter has significantly more risk due to the potential for future modification and the inability for it to be withdrawn.

Frontier Red Team, Alignment Science, Finetuning, and Alignment Stress Testing

What's the difference between a frontier red team and alignment stress-testing? Is the red team focused on the current models you're releasing and the alignment stress testing focused on the future?