This ends up being pretty important in practise for decision markets ("if I choose to do X, will Y?"), where by default you might e.g. only make a decision if it's a good idea (as evaluated by the market), and therefore all traders will condition on the market having a high probability which is obviously quite distortionary. 

I replied on discord that I feel there's maybe something more formalisable that's like:

• reality runs on math because, and is the same thing as, there's a generalised-state-transition function
• because reality has a notion of what happens next, realityfluid has to give you a notion of what happens next, i.e. it normalises
• the idea of a realityfluid that doesn't normalise only comes to mind at all because you learned about R^n first in elementary school instead of S^n

which I do not claim confidently because I haven't actually generated that formalisation, and am posting here because maybe there will be another Lesswronger's eyes on it that's like "ah, but...". 

Not unexpected! I think we should want AGI to, at least until it has some nice coherent CEV target, explain at each self-improvement step exactly what it's doing, to ask for permission for each part of it, to avoid doing anything in the process that's weird, to stop when asked, and to preserve these properties. 

Even more recently I bought a new laptop. This time, I made the same sheet, multiplied the score from the hard drive by $\frac{2}{3}$ because 512 GB is enough for anyone and that seemed intuitively the amount I prioritised extra hard drive space compared to RAM and processor speed, and then looked at the best laptop before sharply diminishing returns set in; this happened to be the HP ENVY 15-ep1503na 15.6" Laptop - Intel® Core™ i7, 512 GB SSD, Silver. This is because I have more money now, so I was aiming to maximise consumer surplus rather than minimise the amount I was spending.^[1]

Surprisingly, it came with a touch screen! That's just the kind of nice thing that laptops do nowadays, because as I concluded in my post, everything nice about laptops correlates with everything else so high/low end is an axis it makes sense to sort things on. Less surprisingly, it came with a graphics card, because ditto.

Unfortunately this high-end laptop is somewhat loud; probably my next one will be less loud, up to including an explicit penalty for noise.

1. ^{^}

   It would have been predictable, however, at the time that I bought that new laptop, that I would have had that much money at a later date. Which means that I should have just skipped straight to consumer surplus maxxing.

I don't think it's quite that; a more central example I think would be something like a post about extrapolating demographic trends to 2070 under the UN's assumptions, where then justifying whether or not 2070 is a real year is kind of a different field. 

$\arg maxU$, as a mathematical structure, is smarter than god and perfectly aligned to $U$; the value of $\arg maxU$ will never actually be $\arg maxV$ because $V$ is more objectively rational, or because you made a typo and it knows you meant to say $\arg maxV$; and no matter how complicated the mapping is from $a$ to $U\left(a\right)$ it will never fall short of giving the $a$ that gives the highest value of $U$.

Which is why in principle you can align a superior being, like $\arg max$, or maybe like a superintelligence.

"The AI does our alignment homework" doesn't seem so bad - I don't have much hope for it, but because it's a prosaic alignment scheme so someone trying to implement it can't constrain where Murphy shows up, rather than because it's an "incoherent path description".

A concrete way this might be implemented is 

• A language model is trained on a giant text corpus to learn a bunch of adaptations that make it good at math, and then fine-tuned for honesty. It's still being trained at a safe and low level of intelligence where honesty can be checked, so this gets a policy that produces things that are mostly honest on easy questions and sometimes wrong and sometimes gibberish and never superhumanly deceptive.^[1]
• It's set to work producing conceptually crisp pieces of alignment math, things like expected utility theory or logical inductors, slowly on inspectable scratchpads and so on, with the dumbest model that can actually factor scientific research^[1], with human research assistants to hold their hand if that lets you make the model dumber. It does this, rather than engineering, because this kind of crisp alignment math is fairly uniquely pinned down so it can be verified, and it's easier to generate compared to any strong pivotal engineering task where you're competing against humans on their own ground so you need to be smarter than humans, so while it's operating in a more dangerous domain it's using a safer level of intelligence.^[1]
• The human programmers then use this alignment math to make an corrigible thingy that has dangerous levels of intelligence that does difficult engineering and doesn't know about humans, while this time knowing what they're doing. Getting the crisp alignment math from parallelisable language models helps a lot and gives them a large lead time, because a lot of it's the alignment version of backprop where it would have took a surprising amount of time to discover otherwise.

This all happens at safe-ish low-ish levels of intelligence (such a model would probably be able to autonomously self-replicate on the internet, but probably not reverse protein folding, which means that all the ways it could be dangerous are "well don't do that"s as long as you keep the code secret^[1]), with the actual dangerous levels of optimisation being done by something made by the humans using pieces of alignment math which are constrained down to a tiny number of possibilities.

EDIT 2023-07-25: A longer debate that I think is worth reading about the model that leads it to being an incoherent path description between Holden Karnofsky (pro) and Nate Soares (against) is here; I hadn't read this as of writing this.

1. ^{^}

   Unless it isn't; it's a giant pile of tensors, how would you know? But this isn't special to this use case.

The solanine poisoning example was originally posted to Reddit here, the picture of Sydney Bing from a text description was posted on Twitter here.

The alignment, safety and interpretability is continuing at full speed, but if all the efforts of the alignment community are sufficient to get enough of this to avoid the destruction of the world in 2042, and AGI is created in 2037, then at the end you get a destroyed world.

It might not be possible in real life (List of Lethalities: "we can't just decide not to build AGI"), and even if possible it might not be tractable enough to be worth focusing any attention on, but it would be nice if there was some way to make sure that AGI happens after alignment is sufficient at full speed (EDIT: or, failing that, to happen later, so if alignment goes quickly that takes the world from bad outcomes to good outcomes, instead of bad outcomes to bad outcomes).