Good post, and I basically agree with this. I do think it's good to mostly focus on the experimental implications when talking about these things. When I say "many worlds", what I primarily mean is that I predict that we should never observe a spontaneous collapse, even if we do crazy things like putting conscious observers into superposition, or putting large chunks of the gravitational field into superposition. So if we ever did observe such a spontaneous collapse, that would falsify many worlds.
Amount of calculation isn't so much the concern here as the amount of bits used to implement that calculation. And there's no law that forces the amount of bits encoding the computation to be equal. Copenhagen can just waste bits on computations that MWI doesn't have to do.
In particular, I mentioned earlier that Copenhagen has to have rules for when measurements occur and what basis they occur in. How does MWI incur a similar cost? What does MWI have to compute that Copenhagen doesn't that uses up the same number of bits of source code?
Like, yes, an expected-value-maximizing agent that has a utility function similar to ours might have to do some computations that involve identifying worlds, but the complexity of the utility function doesn't count against the complexity of any particular theory. And an expected value maximizer is naturally going to try and identify its zone of influence, which is going to look like a particular subset of worlds in MWI. But this happens automatically exactly because the thing is an EV-maximizer, and not because the laws of physics incurred extra complexity in order to single out worlds.
Right, so we both agree that the randomness used to determine the result of a measurement in Copenhagen, and the information required to locate yourself in MWI is the same number of bits. But the argument for MWI was never that it had an advantage on this front, but rather that Copenhagen used up some extra bits in the machine that generates the output tape in order to implement the wavefunction collapse procedure. (Not to decide the outcome of the collapse, those random bits are already spoken for. Just the source code of the procedure that collapses the wavefunction and such.) Such code has to answer questions like: Under what circumstances does the wavefunction collapse? What determines the basis the measurement is made in? There needs to be code for actually projecting the wavefunction and then re-normalizing it. This extra complexity is what people mean when they say that collapse theories are less parsimonious/have extra assumptions.
Disagree.
If you're talking about the code complexity of "interleaving": If the Turing machine simulates quantum mechanics at all, it already has to "interleave" the representations of states for tiny things like a electrons being in a superposition of spin states or whatever. This must be done in order to agree with experimental results. And then at that point not having to put in extra rules to "collapse the wavefunction" makes things simpler.
If you're talking about the complexity of locating yourself in the computation: Inferring which world you're in is equally complex to inferring which way all the Copenhagen coin tosses came up. It's the same number of bits. (In practice, we don't have to identify our location down to a single world, just as we don't care about the outcome of all the Copenhagen coin tosses.)
This notion of faith seems like an interesting idea, but I'm not 100% sure I understand it well enough to actually apply it in an example.
Suppose Descartes were to say: "Y'know, even if there were an evil Daemon fooling every one of my senses for every hour of the day, I can still know what specific illusions the Daemon is choosing to show me. And hey, actually, it sure does seem like there are some clear regularities and patterns in those illusions, so I can sometimes predict what the Daemon will show me next. So in that sense it doesn't matter whether my predictions are about the physical laws of a material world, or just patterns in the thoughts of an evil being. My mental models seem to be useful either way."
Is that what faith is?
If a rationalist hates the idea of heat death enough that they fool themselves into thinking that there must be some way that the increase in entropy can be reversed, is that an example of not seeing the world as it is? How does this flow from a lack of the first thing?
To be clear, I'm definitely pretty sympathetic to TurnTrout's type error objection. (Namely: "If the agent gets a high reward for ingesting superdrug X, but did not ingest it during training, then we shouldn't particularly expect the agent to want to ingest superdrug X during deployment, even if it realizes this would produce high reward.") But just rereading what Zack has written, it seems quite different from what TurnTrout is saying and I still stand by my interpretation of it.
I wasn't trying to sign up to defend everything Eliezer said in that paragraph, especially not the exact phrasing, so can't reply to the rest of your comment which is pretty insightful.
It's the same thing for piecewise-linear functions defined by multi-layer parameterized graphical function approximators: the model is the dataset. It's just not meaningful to talk about what a loss function implies, independently of the training data. (Mean squared error of what? Negative log likelihood of what? Finish the sentence!)
This confusion about loss functions...
I don't think this is a confusion, but rather a mere difference in terminology. Eliezer's notion of "loss function" is equivalent to Zack's notion of "loss function" curried with the training data. Thus, when Eliezer writes about the network modelling or not modelling the loss function, this would include modelling the process that generated the training data.
Could you give an example of knowledge and skills not being value neutral?
(No need to do so if you're just talking about the value of information depending on the values one has, which is unsurprising. But it sounds like you might be making a more substantial point?)
Fair enough for the alignment comparison, I was just hoping you could maybe correct the quoted paragraph to say "performance on the hold-out data" or something similar.
(The reason to expect more spread would be that training performance can't detect overfitting but performance on the hold-out data can. I'm guessing some of the nets trained in Miller et al did indeed overfit (specifically the ones with lower performance).)
They don't actually. One could equally well say: "Fundamental theories of physics have to specify when and how increases in entropy occur. Thermal randomness isn't simple." This is wrong because once you've described the fundamental laws and they happen to be reversible, and also aren't too simple, increasing entropy from a low entropy initial state is a natural consequence of those laws. Similarly, decoherence is a natural consequence of the laws of quantum mechanics (with a not-too-simple Hamiltonian) applied to a low entropy initial state.