Even if tool AI is controllable, tool AI can be used to assist in building non-tool AI. A benign superassistant is one query away from outputting world-ending code.

In my opinion the hard part would not be figuring out where to donate to {decrease P(doom) a lot} rather than {decrease P(doom) a little}, but figuring out where to donate to {decrease P(doom)} rather than {increase P(doom)}.

(oops, this ended up being fairly long-winded! hope you don't mind. feel free to ask for further clarifications.)

There's a bunch of things wrong with your description, so I'll first try to rewrite it in my own words, but still as close to the way you wrote it (so as to try to bridge the gap to your ontology) as possible. Note that I might post QACI 2 somewhat soon, which simplifies a bunch of QACI by locating the user as {whatever is interacting with the computer the AI is running on} rather than by using a beacon.

A first pass is to correct your description to the following:

1. We find a competent honourable human at a particular point in time $H$, like Joe Carlsmith or Wei Dai, and give them a rock engraved with a 1GB secret key, large enough that in counterfactuals it could replace with an entire snapshot of . We also give them the ability to express a 1GB output, eg by writing a 1GB key somewhere which is somehow "signed" as the only . This is part of $H$ — $H$ is not just the human being queried at a particular point in time, it's also the human producing an answer in some way. So $H$ is a function from 1GB bitstring to 1GB bitstring. We define $H^{+}$ as $H$, followed by whichever new process $H$ describes in its output — typically another instance of $H$ except with a different 1GB payload.

2. We want a model $M$ of the agent $H^{+}$. In QACI, we get $M$ by asking a Solomonoff-like ideal reasoner for their best guess about $H^{+}$ after feeding them a bunch of data about the world and the secret key.

3. We then ask $M$ the question $q$, "What's the best utility-function-over-policies to maximise?" to get a utility function $U$$:(O\times A{)}^{\ast }\to R$. We then **ask our solomonoff-like ideal reasoner for their best guess about which action $A$ maximizes $U$.

Indeed, as you ask in question 3, in this description there's not really a reason to make step 3 an extra thing. The important thing to notice here is that model $M$ might get pretty good, but it'll still have uncertainty.

When you say "we get $M$ by asking a Solomonoff-like ideal reasoner for their best guess about $H^{+}$", you're implying that — positing U(M,A) to be the function that says how much utility the utility function returned by model M attributes to action A (in the current history-so-far) — we do something like:

  let M ← oracle(argmax { for model M } 𝔼 { over uncertainty } P(M))
  let A ← oracle(argmax { for action A } U(M, A))
  perform(A)

Indeed, in this scenario, the second line is fairly redundant.

The reason we ask for a utility function is because we want to get a utility function within the counterfactual — we don't want to collapse the uncertainty with an argmax before extracting a utility function, but after. That way, we can do expected-given-uncertainty utility maximization over the full distribution of model-hypotheses, rather than over our best guess about $M$. We do:

  let A ← oracle(argmax { for A } 𝔼 { for M, over uncertainty } P(M) · U(M, A))
  perform(A)

That is, we ask our ideal reasoner (oracle) for the action with the best utility given uncertainty — not just logical uncertainty, but also uncertainty about which $M$. This contrasts with what you describe, in which we first pick the most probable $M$ and then calculate the action with the best utility according only to that most-probable pick.

---

To answer the rest of your questions:

Is this basically IDA, where Step 1 is serial amplification, Step 2 is imitative distillation, and Step 3 is reward modelling?

Unclear! I'm not familiar enough with IDA, and I've bounced off explanations for it I've seen in the past. QACI doesn't feel to me like it particularly involves the concepts of distillation or amplification, but I guess it does involve the concept of iteration, sure. But I don't get the thing called IDA.

Why not replace Step 1 with Strong HCH or some other amplification scheme?

It's unclear to me how one would design an amplification scheme — see concerns of the general shape expressed here. The thing I like about my step 1 is that the QACI loop (well, really, graph (well, really, arbitrary computation, but most of the time the user will probably just call themself in sequence)) is that its setup doesn't involve any AI at all — you could go back in time before the industrial revolution and explain the core QACI idea and it would make sense assuming time-travelling-messages magic, and the magic wouldn't have to do any extrapolating. Just tell someone the idea is that they could send a message to {their past self at a particular fixed point in time}. If there's any amplification scheme, it'll be one designed by the user, inside QACI, with arbitrarily long to figure it out.

What does "bajillion" actually mean in Step 1?

As described above, we don't actually pre-determine the length of the sequence, or in fact the shape of the graph at all. Each iteration decides whether to spawn one or several next iteration, or indeed to spawn an arbitrarily different long-reflection process.

Why are we doing Step 3? Wouldn't it be better to just use M directly as our superintelligence? It seems sufficient to achieve radical abundance, life extension, existential security, etc.

Why not ask M for the policy π directly? Or some instruction for constructing π? The instruction could be "Build the policy using our super-duper RL algo with the following reward function..." but it could be anything.

Hopefully my correction above answers these.

What if there's no reward function that should be maximised? Presumably the reward function would need to be "small", i.e. less than a Exabyte, which imposes a maybe-unsatisfiable constraint.

(Again, untractable-to-naively-compute utility function*, not easily-trained-on reward function. If you have an ideal reasoner, why bother with reward functions when you can just straightforwardly do untractable-to-naively-compute utility functions?)

I guess this is kinda philosophical? I have some short thoughts on here. If an exabyte is enough to describe to describe {a communication channel with a human-on-earth} to an AI-on-earth, which I think seems likely, then it's enough to build "just have a nice corrigible assistant ask the humans what they want"-type channels.

Put another way: if there are actions which are preferable to other actions, then it seems to me like utility function are a fully lossless way for counterfactual QACI users to express which kinds of actions they want the AI to perform, which is all we need. If there's something wrong with utility function over worlds, then counterfactual QACI users can output a utility function which favors actions which lead to something other than utility maximization over worlds, for example actions which lead to the construction of a superintelligent corrigible assistant which will help the humans come up with a better scheme.

Why is there no iteration, like in IDA? For example, after Step 2, we could loop back to Step 1 but reassign $H$ as $H$ with oracle access to $M$.

Again, I don't get IDA. Iteration doesn't seem particularly needed? Note that inside QACI, the user does have access to an oracle and to all relevant pieces of hypothesis about which hypothesis it is inhabiting in — this is what, in the QACI math, this line does:

${\text{QACI}}_0$'s distribution over answers demands that the answer payload ${\pi }_r$, when interpreted as math and with all required contextual variables passed as input ($q,\mu 1,\mu 2,\alpha ,{\gamma }_q,\xi$).

Notably, $\alpha$ is the hypothesis for which world the user is being considered in, and ${\gamma }_q,\xi$ for their location within that world. Those are sufficient to fully characterize the hypothesis-for-$H$ that describes them. And because the user doesn't really return just a string but a math function which takes $q,\mu 1,\mu 2,\alpha ,{\gamma }_q,\xi$ as input and returns a string, they can have that math function do arbitrary work — including rederive $H$. In fact, rediriving $H$ is how they call a next iteration: they say (except in math) "call $H$ again (rederived using $q,\mu 1,\mu 2,\alpha ,{\gamma }_q,\xi$), but with this string, and return the result of that." See also this illustration, which is kinda wrong in places but gets the recursion call graph thing right.

Another reason to do "iteration" like this inside the counterfactual rather than in the actual factual world (if that's what IDA does, which I'm only guessing here) is that we don't have as many iteration steps as we want in the factual world — eventually OpenAI or someone else kills everyone, whereas in the counterfactual, the QACI users are the only ones who can make progress, so the QACI users essentially have as long as they want, so long as they don't take too long in each individual counterfactual step or other somewhat easily avoided actions like that.

Why isn't Step 3 recursive reward modelling? i.e. we could collect a bunch of trajectories from $\pi$ and ask $M$ to use those trajectories to improve the reward function.

Unclear if this still means anything given the rest of this post. Ask me again if it does.

Hi !

ATA is extremely neglected. The field of ATA is at a very early stage, and currently there does not exist any research project dedicated to ATA. The present post argues that this lack of progress is dangerous and that this neglect is a serious mistake.

I agree it's neglected, but there is in fact at least one researh project dedicated to at least designing alignment targets: the part of the formal alignment agenda dedicated to formal outer alignment, which is the design of math problems to which solutions would be world-saving. Our notable attempts at this are QACI and ESP (there was also some work on a QACI2, but it predates (and in-my-opinion is superceded by) ESP).

Those try to implement CEV in math. They only work for doing CEV of a single person or small group, but that's fine: just do CEV of {a single person or small group} which values all of humanity/moral-patients/whatever getting their values satisfied instead of just that group's values. If you want humanity's values to be satisfied, then "satisfying humanity's values" is not opposite to "satisfy your own values", it's merely the outcome of "satisfy your own values".

1. I wonder how much of those seemingly idealistic people retained power when it was available because they were indeed only pretending to be idealistic. Assuming one is actually initially idealistic but then gets corrupted by having power in some way, one thing someone can do in CEV that you can't do in real life is reuse the CEV process to come up with even better CEV processes which will be even more likely to retain/recover their just-before-launching-CEV values. Yes, many people would mess this up or fail in some other way in CEV; but we only need one person or group who we'd be somewhat confident would do alright in CEV. Plausibly there are at least a few eg MIRIers who would satisfy this. Importantly, to me, this reduces outer alignment to "find someone smart and reasonable and likely to have good goal-content integrity", which is a matter of social & psychology that seems to be much smaller than the initial full problem of formal outer alignment / alignment target design.

2. One of the main reasons to do CEV is because we're gonna die of AI soon, and CEV is a way to have infinite time to solve the necessary problems. Another is that even if we don't die of AI, we get eaten by various moloch instead of being able to safely solve the necessary problems at whatever pace is necessary.

the main arguments for the programmers including all of [current?] humanity in the CEV "extrapolation base" […] apply symmetrically to AIs-we're-sharing-the-world-with at the time

I think timeless values might possibly help resolve this; if some {AIs that are around at the time} are moral patients, then sure, just like other moral patients around they should get a fair share of the future.

If an AI grabs more resources than is fair, you do the exact same thing as if a human grabs more resources than is fair: satisfy the values of moral patients (including ones who are no longer around) not weighed by how much leverage they current have over the future, but how much leverage they would have over the future if things had gone more fairly/if abuse/powergrab/etc wasn't the kind of thing that gets your more control of the future.

"Sorry clippy, we do want you to get some paperclips, we just don't want you to get as many paperclips as you could if you could murder/brainhack/etc all humans, because that doesn't seem to be a very fair way to allocate the future." — and in the same breath, "Sorry Putin, we do want you to get some of whatever-intrinsic-values-you're-trying-to-satisfy, we just don't want you to get as much as ruthlessly ruling Russia can get you, because that doesn't seem to be a very fair way to allocate the future."

And this can apply regardless of how much of clippy already exists by the time you're doing CEV.

trying to solve morality by themselves

It doesn't have to be by themselves; they can defer to others inside CEV, or come up with better schemes that their initial CEV inside CEV and then defer to that. Whatever other solutions than "solve everything on your own inside CEV" might exist, they can figure those out and defer to them from inside CEV. At least that's the case in my own attempts at implementing CEV in math (eg QACI).

Seems really wonky and like there could be a lot of things that could go wrong in hard-to-predict ways, but I guess I sorta get the idea.

I guess one of the main things I'm worried about is that it seems to require that we either:

• Be really good at timing when we pause it to look at its internals, such that we look at the internals after it's had long enough to think about things that there are indeed such representations, but not long enough that it started optimizing really hard such that we either {die before we get to look at the internals} or {the internals are deceptively engineered to brainhack whoever would look at them}. If such a time interval even occurs for any amount of time at all.
• Have an AI that is powerful enough to have powerful internals-about-QACI to look at, but corrigible enough that this power is not being used to do instrumentally convergent stuff like eat the world in order to have more resources with which to reason.

Current AIs are not representative of what dealing with powerful optimizers is like; when we'll start getting powerful optimizers, they won't sit around long enough for us to look at them and ponder, they'll just quickly eat us.

So the formalized concept is Get_Simplest_Concept_Which_Can_Be_Informally_Described_As("QACI is an outer alignment scheme consisting of…") ? Is an informal definition written in english?

It seems like "natural latent" here just means "simple (in some simplicity prior)". If I read the first line of your post as:

Has anyone thought about QACI could be located in some simplicity prior, by searching the prior for concepts matching(??in some way??) some informal description in english?

It sure sounds like I should read the two posts you linked (perhaps especially this one), despite how hard I keep bouncing off of the natural latents idea. I'll give that a try.

To me kinda the whole point of QACI is that it tries to actually be fully formalized. Informal definitions seem very much not robust to when superintelligences think about them; fully formalized definitions are the only thing I know of that keep meaning the same thing regardless of what kind of AI looks at it or with what kind of ontology.

I don't really get the whole natural latents ontology at all, and mostly expect it to be too weak for us to be able to get reflectively stable goal-content integrity even as the AI becomes vastly superintelligent. If definitions are informal, that feels to me like degrees of freedom in which an ASI can just pick whichever values make its job easiest.

Perhaps something like this allows use to use current, non-vastly-superintelligent AIs to help design a formalized version of QACI or ESP which itself is robust enough to be passed to superintelligent optimizers; but my response to this is usually "have you tried first formalizing CEV/QACI/ESP by hand?" because it feels like we've barely tried and like reasonable progress can be made on it that way.

Perhaps there are some cleverer schemes where the superintelligent optimizer is pointed at the weaker current-tech-level AI, itself pointed informally at QACI, and we tell the superintelligent optimizer "do what this guy says"; but that seems like it either leaves too many degrees of freedom to the superintelligent optimizer again, or it requires solving corrigibility (the superintelligent optimizer is corrigibly assisting the weaker AI) at which point why not just point the corrigibility at the human directly and ignore QACI altogether, at least to begin with.