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Context windows could make the claim from the post correct. Since the simulator can only consider a bounded amount of evidence at once, its P[Waluigi] has a lower bound. Meanwhile, it takes much less evidence than fits in the context window to bring its P[Luigi] down to effectively 0.

Imagine that, in your example, once Waluigi outputs B it will always continue outputting B (if he's already revealed to be Waluigi, there's no point in acting like Luigi). If there's a context window of 10, then the simulator's probability of Waluigi never goes below 1/1025, while Luigi's probability permanently goes to 0 once B is outputted, and so the simulator is guaranteed to eventually get stuck at Waluigi.

I expect this is true for most imperfections that simulators can have; its harder to keep track of a bunch of small updates for X over Y than it is for one big update for Y over X.

The Constitutional AI paper, in a sense, shows that a smart alien with access to an RLHFed helpful language model can figure out how to write text according to a set of human-defined rules. It scares me a bit that this works well, and I worry that this sort of self-improvement is going to be a major source of capabilities progress going forward.

Talking about what a language model "knows" feels confused. There's a big distinction between what a language model can tell you if you ask it directly, what it can tell you if you ask it with some clever prompting, and what a smart alien could tell you after only interacting with that model. A moderately smart alien that could interact with GPT-3 could correctly answer far more questions than GPT-3 can even with any amount of clever prompting.

As a sort-of normative realist wagerer (I used to describe myself that way, and still have mostly the same views, but now longer consider it a good way to describe myself), I really enjoyed this post, but I think it misses the reasons the wager seems attractive to me.

To start, I don't think of the wager as being "if normative realism is true, things matter more, so I should act as if I'm a normative realist", but as being "unless normative realism is true, I don't see how I could possibly determine what matters, and so I should act as if I'm a normative realist". 

I'm also strongly opposed to using Martha-type dilemmas to reason about meta-ethics. To start, this assumes a sort of meta-normative realism where there is an objective truth to meta-ethics, which is highly non-obvious to me. Secondly, I don't buy the validity of thought experiments that go "assume X is true" rather than "assume you observe X", especially when the metaphysical possibility of X is questionable. Finally, I think that my actual answer to any deal of the form "if X: I get $100; else: a hundred children burned alive" is to reject it, even if X is "2 + 2 = 4", and so my response to such deals has little bearing on my evaluation of X.

I'll admit that I don't think I understand ethical nihilists. The analogy of the Galumphians was very helpful, but I still expect that nihilists have something in their ontology which I would label as a should. I'll note that I don't associate ethical nihilism with any sort of gloomy connotations, I'm just confused by it. 

I really love this idea! Thanks for sharing this, I'm excited to try Calibrate.

How can list sorting be O(n)? There are n! ways to sort a list, which means that it's impossible to have a list sorting algorithm faster than O(log(n!)) = O(n*log(n)).

That's for linking the post! I quite liked it, and I agree that computational complexity doesn't pose a challenge to general intelligence. I do want to dispute your notion that "if you hear that a problem is in a certain complexity class, that is approximately zero evidence of any conclusion drawn from it". The world is filled with evidence, and it's unlikely that closely related concepts give approximately zero evidence for each other unless they are uncorrelated or there are adversarial processes present. Hearing that list-sorting is O(n*log(n)) is pretty strong evidence that it's easy to do, and hearing that simulating quantum mechanics is not in P is pretty strong evidence that it's hard to do. Sure, there are lots of exceptions, but computational complexity is in fact a decent heuristic, especially if you go with average-case complexity of an approximation, rather than worst-case complexity of an exact answer.

I'm definitely only talking about probabilities in the range of >90%. >50% is justifiable without a strong argument for the disjunctivity of doom.

I like the self-driving car analogy, and I do think the probability in 2015 that a self-driving car would ever kill someone was between 50% and 95% (mostly because of a >5% chance that AGI comes before self-driving cars).

There's still the problem of successor agents and self-modifying agents, where you need to set up incentives to create successor agents with the same utility functions and to not strategically self-modify, and I think a solution to that would probably also work as a solution to normal dishonesty.

I do expect that in a case where agents can also see each other's histories, we can make bargaining go well with the bargaining theory we know (given that the agents try to bargain well, there are of course possible agents which don't try to cooperate well).

I'm really glad that this post is addressing the disjunctivity of AI doom, as my impression is that it is more of a crux than any of the reasons in

Still, I feel like this post doesn't give a good argument for disjunctivity. To show that the arguments for a scenario with no outside view are likely, it takes more than just describing a model which is internally disjunctive. There needs to be some reason why we should strongly expect there to not be some external variables that could cause the model not to apply. 

Some examples of these, in addition to the competence of humanity, are that deep learning could hit a wall for decades, Moore's Law could come to a halt, some anti-tech regulation could cripple AI research, or alignment could turn out to be easy (which itself contains several disjunctive possibilities). I haven't thought about these, and don't claim that any of them are likely, but the possibility of these or other unknown factors invalidating the model prevents me from updating to a very high P(doom). Some of this comes from it just being a toy model, but adding more detail to the model isn't enough to notably reduce the possibility of the model being wrong from unconsidered factors.

A statement I'm very confident in is that no perpetual motion machines will be developed in the next century. I could make some disjunctive list of potential failure modes a perpetual motion machine could encounter, and thus conclude that their development is unlikely, but this wouldn't describe the actual reason a perpetual motion machine is unlikely. The actual reason is that I'm aware of certain laws of physics which prevent any perpetual motion machines from working, including ones with mechanisms wildly beyond my imagination. The outside view is another tool I can use to be very confident: I’m very confident that the next flight I take won’t crash, not because of my model of planes, but because any crash scenario which non-negligible probability would have caused some of the millions of commercial flights every year to crash, and that hasn’t happened.  Avoiding AGI doom is not physically impossible and there is no outside view against it, and without some similarly compelling reason I can’t see how very high P(doom) can be justified.

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