• the approaches that have been attracting the most attention and funding are dead ends

adding another possible explanation to the list:

• people may feel intimidated or discouraged from sharing ideas because of ~'high standards', or something like: a tendency to require strong evidence that a new idea is not another non-solution proposal, in order to put effort into understanding it.

i have experienced this, but i don't know how common it is.

i just also recalled that janus has said they weren't sure simulators would be received well on LW. simulators was cited in another reply to this as an instance of novel ideas.

It's not just a lesswrong thing (wikipedia).

My feeling is that (like most jargon) it's to avoid ambiguity arising from the fact that "commitment" has multiple meanings. When I google commitment I get the following two definitions:

1. the state or quality of being dedicated to a cause, activity, etc.
2. an engagement or obligation that restricts freedom of action

Precommitment is a synonym for the second meaning, but not the first. When you say, "the agent commits to 1-boxing," there's no ambiguity as to which type of commitment you mean, so it seems pointless. But if you were to say, "commitment can get agents more utility," it might sound like you were saying, "dedication can get agents more utility," which is also true.

Why should I include any non-sentient systems in my moral circle? I haven't seen a case for that before.

It seems a bit weird to call these "obligations" if the considerations they are based upon are not necessarily dispositive. In common parlance, obligation is generally thought of as "something one is bound to do", i.e., something you must do either because you are force to by law or a contract, etc., or because of a social or moral requirement. But that's a mere linguistic point that others can reasonably disagree on and ultimately doesn't matter all that much anyway. 

On the object level, I suspect there will be a large amount of disagreement on what it means for an AI to "deserve" punishment or credit. I am very uncertain about such matters myself even when thinking about "deservingness" with respect to humans, who not only have a very similar psychological make-up to mine (which allows me to predict with reasonable certainty what their intent was in a given spot) but also exist in the same society as me and are thus expected to follow certain norms and rules that are reasonably clear and well-established. I don't think I know of a canonical way of extrapolating my (often confused and in any case generally intuition-based) principles and thinking about this to the case of AIs, which will likely appear quite alien to me in many respects.

This will probably make the task of "ensur[ing] that others also follow their obligations to AIs" rather tricky, even setting aside the practical enforcement problems. 

I think it pretty much only matters as a trivial refutation of (not-object-level) claims that no "serious" people in the field take AI x-risk concerns seriously, and has no bearing on object-level arguments.  My guess is that Hinton is somewhat less confused than Yann but I don't think he's talked about his models in very much depth; I'm mostly just going off the high-level arguments I've seen him make (which round off to "if we make something much smarter than us that we don't know how to control, that might go badly for us").

I think it's mostly because he's well known and have (especially after the Nobel prize) credentials recognized by the public and elites. Hinton legitimizes the AI safety movement, maybe more than anyone else. 

If you watch his Q&A at METR, he says something along the lines of "I want to retire and don't plan on doing AI safety research. I do outreach and media appearances because I think it's the best way I can help (and because I like seeing myself on TV)." 

And he's continuing to do that. The only real topic he discussed in first phone interview after receiving the prize was AI risk.

(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.