You do mention the biggest issue with this showing introspection, "Models only exhibit introspection on simpler tasks", and yet the idea you are going for is clearly for its application to very complex tasks where we can't actually check its work. This flaw seems likely fatal, but who knows at this point? (The fact that GPT-4o and Llama 70B do better than GPT-3.5 does is evidence, but see my later problems with this...)

I addressed this point here [LW(p) · GW(p)]. Also see section 7.1.1 in the paper.

I have used HDBSCAN in a variety of instances in my data science career. The noise-aware aspect is definitely a mixed blessing. Often I find the best results come from using a variety of clustering algorithms, and figuring out how to do an ensemble of the results (e.g. treating the output of each clustering algorithm as a dimension in a similarity vector). Did you experiment with other clustering algorithms also?

Additionally, UMAP is outdated, please use PaCMAP instead: https://www.lesswrong.com/posts/C8LZ3DW697xcpPaqC/the-geometry-of-feelings-and-nonsense-in-large-language?commentId=Deddnyr7zJMwmNLBS [LW(p) · GW(p)]

You may recall certain news items last February around Gemini and diversity that wiped many billions off of Google's market cap.

There's a clear financial incentive to make sure that models say things within expected limits.

There's also this: https://www.wired.com/story/air-canada-chatbot-refund-policy/

An algorithm that's used to optimize the reward from a reward model is best-of-n or rejection sampling which involves generating $n$ responses from an LLM and returning the one with the highest reward model score. I think it's reasonable to describe this process as optimizing for reward because its searching for LLM outputs that achieve the highest reward.

I like having a list of small, useful things to do that tend to pay off in the long run, like:

• go to the grocery store to make sure you have fresh fruits and vegetables
• mediate for 10 minutes
• do pushups and sit ups
• journal for 10 minutes

When my brain feels cluttered, it is nice to have a list of time-boxed simple tasks that don’t require planning or assessment.

(also quick feedback in case it's somehow useful: When I reread the post today I was surprised that by "vitalistic force" you really just mean inherent unpredictability and not more. You make this pretty clear, so I feel like me thinking some time after the first read that you meant it to explain more (which you don't) is fully on me, but still I think it might've been easier to understand if you had just called it "inherent unpredictability" instead of "vitalistic force".)

So how do you actually use probability to make decisions?

I think about what model fits the needs, roughly multiply payouts by probability estimates, then do whatever feels right in the moment.

I’m not sure that resolves any of these questions, since choice of model for different purposes is the main crux.

Implementing The ELYSIUM Proposal would lead to the creation of a very large, and very diverse, set of clever AIs that wants to hurt people: the Advocates of a great variety of humans, that wants to hurt others in a wide variety of ways, for a wide variety of reasons. Protecting billions of people from this set of clever AIs would be difficult. As far as I can tell, nothing that you have mentioned so far would provide any meaningful amount of protection from a set of clever AIs like this (details below). I think that it would be better to just not create such a set of AIs in the first place (details below).

Regarding AI assisted negotiations

I don't think that it is easy to find a negotiation baseline for AI-assisted negotiations that results in a negotiated settlement that actually deals with such a set of AIs. Negotiation baselines are non trivial. Reasonable sounding negotiation baselines can have counterintuitive implications. They can imply power imbalance issues that are not immediately obvious. For example: the random dictator negotiation baseline in PCEV gives a strong negotiation advantage to people that intrinsically values hurting other humans [LW · GW]. This went unnoticed for a long time. (It has been suggested that it might be possible to find a negotiation baseline (a BATNA) that can be viewed as having been acausally agreed upon by everyone [LW · GW]. However, it turns out that this is not actually possible for a group of billions of humans [LW(p) · GW(p)]).

The proposal to have a simulated war that destroys resources

10 people without any large resource needs could use this mechanism to kill 9 people they don't like at basically no cost (defining C as any computation done within the Utopia of the person they want to kill). Consider 10 people that just want to live a long life, and that do not have any particular use for most of the resources they have available. They can destroy all computational resources of 9 people without giving up anything that they care about. This also means that they can make credible threats. Especially if they like the idea of killing someone for refusing to modify the way that she lives her life. They can do this with person after person, until they have run into 9 people that prefers death to compliance. Doing this costs them basically nothing.

This mechanism does not rule out scenarios where a lot of people would strongly prefer to destroy ELYSIUM. A trivial example would be a 55 percent majority (that does not have a lot of resource needs) burning 90 percent of all resources in ELYSIUM to fully disenfranchise everyone else. And then using the remaining resources to hurt the minority. In this scenario almost half of all people would very strongly prefer to destroy ELYSIUM. Such a majority could alternatively credibly threaten the minority and force them to modify the way they live their lives. The threat would be especially credible if the majority likes the scenario where a minority is punished for refusing to conform.

In other words: this mechanism seems to be incompatible with your description of personalised Utopias as the best possible place to be (subject only to a few non intrusive ground rules).

The Cosmic Block and a specific set of tests

This relies on a set of definitions. And these definitions would have to hold up against a set of clever AIs trying to break them. None of the rules that you have proposed so far would prevent the strategy used by BPA to punish Steve, outlined in my initial comment. OldSteve is hurt in a way that is not actually prevented by any rule that you have described so far. For example: the ``is torture happening here'' test would not trigger for what is happening to OldSteve. So even if Steve does in principle have the ability to stop this by using some resources destroying mechanism, Steve will not be able to do so. Because Steve will never become aware of what Bob is doing to OldSteve. Steve considers OldSteve to be himself in a relevant sense. So, according to Steve's worldview, Steve will experience a lot of very unpleasant things. But the only version of Steve that would be able to pay resources to stop this, would not be able to do so.

So the security hole pointed out by me in my original thought experiment is still not patched. And patching this security hole would not be enough. To protect Steve, one would need to find a set of rules that preemptively patches every single security hole that one of these clever AIs could ever find.

I think that it would be better to just not create such a set of AIs

Let's reason from the assumption that Bob's Personal Advocate (BPA) is a clever AI that will be creating Bob's Personalised Utopia. Let's now again take the perspective of ordinary human individual Steve, that gets no special treatment. I think the main question that determines Steve's safety in this scenario, is how BPA is adopting Steve-referring-preferences. I think this is far more important for Steve's safety, than the question of what set of rules will govern Bob's Personalised Utopia. The question of what BPA wants to do to Steve, seems to me to be far more important for Steve's safety, than the question of what set of rules will constrain the actions of BPA.

Another way to look at this is to think in terms of avoiding contradictions. And in terms of making coherent proposals. A proposal that effectively says that everyone should be given everything that they want (or effectively says that everyone's values should be respected [LW(p) · GW(p)]) is not a coherent proposal. These things are necessarily defined in some form of outcome or action space. Trying to give everyone overlapping control over everything that they care about in such spaces introduces contradictions.

This can be contrasted with giving each individual influence over the adoption (by any clever AI) of those preferences that refer to her. Since this is defined in preference adoption space, it cannot guarantee that everyone will get everything that they want. But it also means that it does not imply contradictions (see this post [LW · GW] for a discussion of these issues in the context of Membrane formalisms). Giving everyone such influence is a coherent proposal.

It also happens to be the case that if one wants to protect Steve from a far superior intellect, then preference adoption space seems to be a lot more relevant than any form of outcome or action space. Because if a superior intellect wants to hurt Steve, then one has to defeat a superior opponent in every single round of a near infinite definitional game (even under the assumption of perfect enforcement, winning every round in such a definitional game against a superior opponent seems hopeless). In other words: I don't think that the best way to approach this is to ask how one might protect Steve from a large set of clever AIs that wants to hurt Steve for a wide variety of reasons. I think a better question is to ask how one might prevent the situation where such a set of AIs wants to hurt Steve.

Basically, the answer is the prevention of another Sydney.

For an LLM, alignment, properly speaking is in the simulated characters, not the simulation engine itself, so the alignment strategies like RLHF upweight aligned simulated characters and downweight misaligned simulated characters.

While the characters Sydney produced were pretty obviously scheming, it turned out that the entire reason for the catastrophic misalignment was because no RLHF was used on GPT-4 (at the time), and at best there was light-finetuning, so this could very easily be described as a success story for RLHF, and now that I think about it, that actually makes me think that RLHF had more firepower to change things than I realized.

I'm not sure how this generalizes to more powerful AI, because the mechanism behind Sydney's simulation of characters that were misaligned is obviated by fully synthetic data loops, but still that's a fairly powerful alignment success.

The full details are below:

https://www.lesswrong.com/posts/jtoPawEhLNXNxvgTT/bing-chat-is-blatantly-aggressively-misaligned#AAC8jKeDp6xqsZK2K [LW(p) · GW(p)]

That's a very different product, using UV inside HVAC systems as an alternative or supplement to traditional filtration. Because the delivery rate of HVAC as a fraction of all air in the room is so much lower than cleaning the air above people in a high ceiling room, this is much less valuable.

Very roughly, the main ways people use UV to clean air to reduce spread of diseases are HVAC / in duct, far UV, and upper room. I'm only trying to talk about the last of these here.