Unless I've confused myself badly (always possible!), I think either's fine here. The | version just takes out a factor that'll be common to all hypotheses: [p(e+) / p(e-)]. (since p(Tk & e+) ≡ p(Tk | e+) * p(e+))

Since we'll renormalise, common factors don't matter. Using the | version felt right to me at the time, but whatever allows clearer thinking is the way forward.

Taking your last point first: I entirely agree on that. Most of my other points were based on the implicit assumption that readers of your post don't think something like "It's directly clear that 9 OOM will almost certainly be enough, by a similar argument".

Certainly if they do conclude anything like that, then it's going to massively drop their odds on 9-12 too. However, I'd still make an argument of a similar form: for some people, I expect that argument may well increase the 5-8 range more (than proportionately) than the 1-4 range.

On (1), I agree that the same goes for pretty-much any argument: that's why it's important. If you update without factoring in (some approximation of) your best judgement of the evidence's impact on all hypotheses, you're going to get the wrong answer. This will depend highly on your underlying model.

On the information content of the post, I'd say it's something like "12 OOMs is probably enough (without things needing to scale surprisingly well)". My credence for low OOM values is mostly based on worlds where things scale surprisingly well.

But this is a bit weird; my post didn't talk about the <7 range at all, so why would it disproportionately rule out stuff in that range?

I don't think this is weird. What matters isn't what the post talks about directly - it's the impact of the evidence provided on the various hypotheses. There's nothing inherently weird about evidence increasing our credence in [TAI by +10OOM] and leaving our credence in [TAI by +3OOM] almost unaltered (quite plausibly because it's not too relevant to the +3OOM case).

Compare the 1-2-3 coins example: learning y tells you nothing about the value of x. It's only ruling out any part of the 1 outcome in the sense that it maintains [x_heads & something independent is heads], and rules out [x_heads & something independent is tails]. It doesn't need to talk about x to do this.

You can do the same thing with the TAI first at k OOM case - call that Tk. Let's say that your post is our evidence e and that e+ stands for [e gives a compelling argument against T13+].
Updating on e+ you get the following for each k:
Initial hypotheses: [Tk & e+], [Tk & e-]
Final hypothesis: [Tk & e+]

So what ends up mattering is the ratio p[Tk | e+] : p[Tk | e-]
I'm claiming that this ratio is likely to vary with k.

Specifically, I'd expect T1 to be almost precisely independent of e+, while I'd expect T8 to be correlated. My reason on the T1 is that I think something radically unexpected would need to occur for T1 to hold, and your post just doesn't seem to give any evidence for/against that.
I expect most people would change their T8 credence on seeing the post and accepting its arguments (if they've not thought similar things before). The direction would depend on whether they thought the post's arguments could apply equally well to ~8 OOM as 12.

Note that I am assuming the argument ruling out 13+ OOM is as in the post (or similar).
If it could take any form, then it could be a more or less direct argument for T1.

Overall, I'd expect most people who agree with the post's argument to update along the following lines (but smoothly):
T0 to Ta: low increase in credence
Ta to Tb: higher increase in credence
Tb+: reduced credence

with something like (0 < a < 6) and (4 < b < 13).
I'm pretty sure a is going to be non-zero for many people.

[[ETA, I'm not claiming the >12 OOM mass must all go somewhere other than the <4 OOM case: this was a hypothetical example for the sake of simplicity. I was saying that if I had such a model (with zwomples or the like), then a perfectly good update could leave me with the same posterior credence on <4 OOM.
In fact my credence on <4 OOM was increased, but only very slightly]]

First I should clarify that the only point I'm really confident on here is the "In general, you can't just throw out the >12 OOM and re-normalise, without further assumptions" argument. 

I'm making a weak claim: we're not in a position of complete ignorance w.r.t. the new evidence's impact on alternate hypotheses.

My confidence in any specific approach is much weaker: I know little relevant data.

That said, I think the main adjustment I'd make to your description is to add the possibility for sublinear scaling of compute requirements with current techniques. E.g. if beyond some threshold meta-learning efficiency benefits are linear in compute, and non-meta-learned capabilities would otherwise scale linearly, then capabilities could scale with the square root of compute (feel free to replace with a less silly example of your own).

This doesn't require "We'll soon get more ideas" - just a version of "current methods scale" with unlucky (from the safety perspective) synergies.

So while the "current methods scale" hypothesis isn't confined to 7-12 OOMs, the distribution does depend on how things scale: a higher proportion of the 1-6 region is composed of "current methods scale (very) sublinearly".

My p(>12 OOM | sublinear scaling) was already low, so my p(1-6 OOM | sublinear scaling) doesn't get much of a post-update boost (not much mass to re-assign).
My p(>12 OOM | (super)linear scaling) was higher, but my p(1-6 OOM | (super)linear scaling) was low, so there's not too much of a boost there either (small proportion of mass assigned).

I do think it makes sense to end up with a post-update credence that's somewhat higher than before for the 1-6 range - just not proportionately higher. I'm confident the right answer for the lower range lies somewhere between [just renormalise] and [don't adjust at all], but I'm not at all sure where.

Perhaps there really is a strong argument that the post-update picture should look almost exactly like immediate renormalisation. My main point is that this does require an argument: I don't think its a situation where we can claim complete ignorance over impact to other hypotheses (and so renormalise by default), and I don't think there's a good positive argument for [all hypotheses will be impacted evenly].

Yes, we're always renormalising at the end - it amounts to saying "...and the new evidence will impact all remaining hypotheses evenly". That's fine once it's true.

I think perhaps I wasn't clear with what I mean by saying "This doesn't say anything...".
I meant that it may say nothing in absolute terms - i.e. that I may put the same probability of [TAI at 4 OOM] after seeing the evidence as before.

This means that it does say something relative to other not-ruled-out hypotheses: if I'm saying the new evidence rules out >12 OOM, and I'm also saying that this evidence should leave p([TAI at 4 OOM]) fixed, I'm implicitly claiming that the >12 OOM mass must all go somewhere other than the 4 OOM case.

Again, this can be thought of as my claiming e.g.:
[TAI at 4 OOM] will happen if and only if zwomples work
There's a 20% chance zwomples work
The new 12 OOM evidence says nothing at all about zwomples

In terms of what I actually think, my sense is that the 12 OOM arguments are most significant where [there are no high-impact synergistic/amplifying/combinatorial effects I haven't thought of].
My credence for [TAI at < 4 OOM] is largely based on such effects. Perhaps it's 80% based on some such effect having transformative impact, and 20% on we-just-do-straightforward-stuff. [Caveat: this is all just ottomh; I have NOT thought for long about this, nor looked at much evidence; I think my reasoning is sound, but specific numbers may be way off]

Since the 12 OOM arguments are of the form we-just-do-straightforward-stuff, they cause me to update the 20% component, not the 80%. So the bulk of any mass transferred from >12 OOM, goes to cases where p([we-just-did-straightforward-stuff and no strange high-impact synergies occurred]|[TAI first occurred at this level]) is high.

We do gain evidence on at least some alternatives, but not on all the factors which determine the alternatives. If we know something about those factors, we can't usually just renormalise. That's a good default, but it amounts to an assumption of ignorance.

Here's a simple example:
We play a 'game' where you observe the outcome of two fair coin tosses x and y.
You score:
1 if x is heads
2 if x is tails and y is heads
3 if x is tails and y is tails

So your score predictions start out at:
1 : 50%
2 : 25%
3 : 25%

We look at y and see that it's heads. This rules out 3.
Renormalising would get us:
1 : 66.7%
2 : 33.3%
3: 0%

This is clearly silly, since we ought to end up at 50:50 - i.e. all the mass from 3 should go to 2. This happens because the evidence that falsified 3 points was insignificant to the question "did you score 1 point?".
On the other hand, if we knew nothing about the existence of x or y, and only knew that we were starting from (1: 50%, 2: 25%, 3: 25%), and that 3 had been ruled out, it'd make sense to re-normalise.

In the TAI case, we haven't only learned that 12 OOM is probably enough (if we agree on that). Rather we've seen specific evidence that leads us to think 12 OOM is probably enough. The specifics of that evidence can lead us to think things like "This doesn't say anything about TAI at +4 OOM, since my prediction for +4 is based on orthogonal variables", or perhaps "This makes me near-certain that TAI will happen by +10 OOM, since the +12 OOM argument didn't require more than that".

If you have a bunch of hypotheses (e.g. "It'll take 1 more OOM," "It'll take 2 more OOMs," etc.) and you learn that some of them are false or unlikely (only 10% chance of it taking more than 12" then you should redistribute the mass over all your remaining hypotheses, preserving their relative strengths.

This depends on the mechanism by which you assigned the mass initially - in particular, whether it's absolute or relative. If you start out with specific absolute probability estimates as the strongest evidence for some hypotheses, then you can't just renormalise when you falsify others.

E.g. consider we start out with these beliefs:
If [approach X] is viable, TAI will take at most 5 OOM; 20% chance [approach X] is viable.
If [approach X] isn't viable, 0.1% chance TAI will take at most 5 OOM.
30% chance TAI will take at least 13 OOM.

We now get this new information:
There's a 95% chance [approach Y] is viable; if [approach Y] is viable TAI will take at most 12 OOM.

We now need to reassign most of the 30% mass we have on >13 OOM, but we can't simply renormalise: we haven't (necessarily) gained any information on the viability of [approach X].
Our post-update [TAI <= 5OOM] credence should remain almost exactly 20%. Increasing it to ~26% would not make any sense.

For AI timelines, we may well have some concrete, inside-view reasons to put absolute probabilities on contributing factors to short timelines (even without new breakthroughs we may put absolute numbers on statements of the form "[this kind of thing] scales/generalises"). These probabilities shouldn't necessarily be increased when we learn something giving evidence about other scenarios. (the probability of a short timeline should change, but in general not proportionately)

Perhaps if you're getting most of your initial distribution from a more outside-view perspective, then you're right.

I don't see a good reason to exclude agenda-style posts, but I do think it'd be important to treat them differently from more here-is-a-specific-technical-result posts.

Broadly, we'd want to be improving the top-level collective AI alignment research 'algorithm'. With that in mind, I don't see an area where more feedback/clarification/critique of some kind wouldn't be helpful.
The questions seem to be:
What form should feedback/review... take in a given context?
Where is it most efficient to focus our efforts?

Productive feedback/clarification on high-level agendas seems potentially quite efficient. My worry would be to avoid excessive selection pressure towards paths that are clear and simply justified. However, where an agenda does use specific assumptions and arguments to motivate its direction, early 'review' seems useful.

I understand what you mean with the CCC (and that this seems a bit of a nit-pick!), but I think the wording could usefully be clarified.

As you suggest here, the following is what you mean:

CCC says (for non-evil goals) "if the optimal policy is catastrophic, then it's because of power-seeking"

However, that's not what the CCC currently says.
E.g. compare:
[Unaligned goals] tend to [have catastrophe-inducing optimal policies] because of [power-seeking incentives].
[People teleported to the moon] tend to [die] because of [lack of oxygen].

The latter doesn't lead to the conclusion: "If people teleported to the moon had oxygen, they wouldn't tend to die."

Your meaning will become clear to anyone who reads this sequence.
For anyone taking a more cursory look, I think it'd be clearer if your clarification were the official CCC:

CCC: (for non-evil goals) "if the optimal policy is catastrophic, then it's because of power-seeking"

Currently, I worry about people pulling an accidental motte-and-bailey on themselves, and thinking that [weak interpretation of CCC] implies [conclusions based on strong interpretation]. (or thinking that you're claiming this)

I think things are already fine for any spike outside S, e.g. paperclip maximiser, since non-obstruction doesn't say anything there.

I actually think saying "our goals aren't on a spike" amounts to a stronger version of my [assume humans know what the AI knows as the baseline]. I'm now thinking that neither of these will work, for much the same reason. (see below)

The way I'm imagining spikes within S is like this:
We define a pretty broad S, presumably implicitly, hoping to give ourselves a broad range of non-obstruction.

For all P in U we later conclude that our actual goals are in T ⊂ U ⊂ S.
We optimize for AU on T, overlooking some factors that are important for P in U \ T.
We do better on T than we would have by optimising more broadly over U (we can cut corners in U \ T).
We do worse on U \ T since we weren't directly optimising for that set (AU on U \ T varies quite a lot).
We then get an AU spike within U, peaking on T.

The reason I don't think telling the AI something like "our goals aren't on a spike" will help, is that this would not be a statement about our goals, but about our understanding and competence. It'd be to say that we never optimise for a goal set we mistakenly believe includes our true goals (and that we hit what we aim for similarly well for any target within S).

It amounts to saying something like "We don't have blind-spots", "We won't aim for the wrong target", or, in the terms above, "We will never mistake any T for any U".
In this context, this is stronger and more general than my suggestion of "assume for the baseline that we know everything you know". (lack of that knowledge is just one way to screw up the optimisation target)

In either case, this is equivalent to telling the AI to assume an unrealistically proficient/well-informed pol.
The issue is that, as far as non-obstruction is concerned, the AI can then take actions which have arbitrarily bad consequences for us if we don't perform as well as pol.
I.e. non-obstruction then doesn't provide any AU guarantee if our policy isn't actually that good.

My current intuition is that anything of the form "assume our goals aren't on a spike", "assume we know everything you know"... only avoid creating other serious problems if they're actually true - since then the AI's prediction of pol's performance isn't unrealistically high.

Even for "we know everything you know", that's a high bar if it has to apply when the AI is off.
For "our goals aren't on a spike", it's an even higher bar.

If we could actually make it true that our goals weren't on a spike in this sense, that'd be great.
I don't see any easy way to do that.
[Perhaps if the ability to successfully optimise for S already puts such high demands on our understanding, that distinguishing Ts from Us is comparatively easy.... That seems unlikely to me.]
 

Thinking of corrigibility, it's not clear to me that non-obstruction is quite what I want.
Perhaps a closer version would be something like:
A non-obstructive AI on S needs to do no worse for each P in S than pol(P | off & humans have all the AI's knowledge)

This feels a bit patchy, but in principle it'd fix the most common/obvious issue of the kind I'm raising: that the AI would often otherwise have an incentive to hide information from the users so as to avoid 'obstructing' them when they change their minds.

I think this is more in the spirit of non-obstruction, since it compares the AI's actions to a fully informed human baseline (I'm not claiming it's precise, but in the direction that makes sense to me). Perhaps the extra information does smooth out any undesirable spikes the AI might anticipate.



I do otherwise expect such issues to be common.
But perhaps it's usually about the AI knowing more than the humans.

I may well be wrong about any/all of this, but (unless I'm confused), it's not a quibble about edge cases.
If I'm wrong about default spikiness, then it's much more of an edge case.

 

(You're right about my P, -P example missing your main point; I just meant it as an example, not as a response to the point you were making with it; I should have realised that would make my overall point less clear, given that interpreting it as a direct response was natural; apologies if that seemed less-than-constructive: not my intent)