I primarily mentioned it because I think people base their 'what is the S-risk outcome' on basically antialigned AGI. The post has 'AI hell' in the title and uses comparisons between extreme suffering versus extreme bliss, calls s-risks more important than alignment (which I think makes sense to a reasonable degree if antialigned s-risk is likely or a sizable portion of weaker dystopias are likely, but I don't think makes sense for antialigned being very unlikely and my considering weak dystopias to also be overall not likely) . The extrema argument is why I don't think that weak dystopias are likely, because I think that - unless we succeed at alignment to a notable degree - then the extremes of whatever values shake out are not something that keeps humans around for very long. So I don't expect weaker dystopias to occur either.
I expect that most AIs aren't going to value making a notable deliberate AI hell, whether out of the lightcone or 5% of it or 0.01% of it. If we make an aligned-AGI and then some other AGI says 'I will simulate a bunch of humans in torment unless you give me a planet' then I expect that our aligned-AGI uses a decision-theory that doesn't give into dt-Threats and doesn't give in (and thus isn't threatened, because the other AGI gains nothing from actually simulating humans in that).
So, while I do expect that weak dystopias have a noticeable chance of occurring, I think it is significantly unlikely? It grows more likely we'll end up in a weak dystopia as alignment progresses. Like if we manage to get enough of a 'caring about humans specifically' (though I expect a lot of attempts like that to fall apart and have weird extremes when they're optimized over!), then that raises the chances of a weak dystopia.
However I also believe that alignment is roughly the way to solve these. To get notable progress on making AGIs avoid specific area, I believe that requires more alignment progress than we have currently.
There is the class of problems where the unaligned AGI decides to simulate us to get more insight into humans, insight into evolved species, and insight into various other pieces of that. That would most likely be bad, but I expect it to not be a significant portion of computation and also not continually executed for (really long length of time). So I don't consider that to be a notable s-risk.
. If I imagine trading extreme suffering for extreme bliss personally, I end up with ratios of 1 to 300 million – e.g., that I would accept a second of extreme suffering for ten years of extreme bliss. The ratio is highly unstable as I vary the scenarios, but the point is that I disvalue suffering many orders of magnitude more than I value bliss.
I also disvalue suffering significantly more than I value happiness (I think bliss is the wrong term to use here), but not to that level. My gut feeling wants to dispute those numbers as being practical, but I'll just take them as gesturing at the comparative feeling.
An idea that I've seen once, but not sure where, is: you can probably improve the amount of happiness you experience in a utopia by a large amount. Not through wireheading, which at least for me is undesirable, but 'simply' redesigning the human mind in a less hedonic-treadmill manner (while also not just cutting out boredom). I think the usual way of visualizing extreme dystopias as possible-futures has the issue that it is easy to compare them to the current state of humanity rather than an actual strong utopia. I expect that there's a good amount of mind redesign work, in the vein of some of the mind-design posts in Fun Theory but ramped up to superintelligence design+consideration capabilities, that would vastly increase the amount of possible happiness/Fun and make the tradeoff more balanced. I find it plausible that suffering is just easier to cause and more impactful even relative to strong-utopia-level enhanced-minds, but I believe this does change the calculus significantly. I might not take a 50/50 coin for strong dystopia/strong utopia, but I'd maybe take a 10/90 coin. Thankfully we aren't in that scenario, and have better odds.
In the language of Superintelligent AI is necessary for an amazing future but far from sufficient, I expect that the majority of possible s-risks are weak dystopias rather than strong dystopias. We're unlikely to succeed at alignment enough and then signflip it (like, I expect strong dystopia to be dominated by 'we succeed at alignment to an extreme degree' ^ 'our architecture is not resistant to signflips' ^ 'somehow the sign flips'). So, I think literal worse-case Hell and the immediate surrounding possibilities are negligible.
I expect that the extrema of most AIs, even ones with attempted alignment patches, to be weird and unlikely to be of particular value to us. The ways values resolve has a lot of room to maneuver early on, before it becomes a coherent agent, and I don't expect those to have extrema that are best fit by humans (see various of So8res other posts). Thus, I think it is unlikely that we end up with a weak dystopia (at least for a long time, which is the s-risk) relative to x-risk.
That said, I do think there’s more overlap (in expectation) between minds produced by processes similar to biological evolution, than between evolved minds and (unaligned) ML-style minds. I expect more aliens to care about at least some things that we vaguely recognize, even if the correspondence is never exact.
On my models, it’s entirely possible that there just turns out to be ~no overlap between humans and aliens, because aliens turn out to be very alien. But “lots of overlap” is also very plausible. (Whereas I don’t think “lots of overlap” is plausible for humans and misaligned AGI.)
The Principles of Deep Learning Theory uses renormalization group flow in its analysis of deep learning, though it is applied at a 'lower level' than an AI's capabilities.
One minor thing I've noticed when thinking on interpretability is that of in-distribution versus out-of-distribution versus - what I call - out-of-representation data. I would assume this has been observed elsewhere, but I haven't seen it mentioned before.
In-distribution could be considered inputs in the same ''structure'' of what you trained the neural network on; out-of-distribution is exotic inputs, like an adversarially noisy image of a panda or a picture of a building for an animal-recognizer NN.
Out-of-representation would be when you have a neural network that takes in inputs of a certain form/encoding that restricts the representable values. However, the neural network can theoretically take anything in between, it just shouldn't ever.
The most obvious example would be if you had a NN that was trained on RGB pixels from images to classify them. Each pixel value is normalized in the range of . Out of representation here would be if you gave it a very 'fake' input of . All of the images when you give them to NN, whether noisy garbage or a typical image, would be properly normalized within that range. However, with direct access to the neural networks inputs, you give it out-of-representation values that aren't properly encoded at all.
I think this has some benefits for some types of interpretability, (though it is probably already paid attention to?), in that you can constrain the possible inputs when you consider the network. If you know the inputs to the network are always bounded in a certain range, or even just share a property like being positive, then you can constrain the intermediate neuron outputs. This would potentially help in ignoring out-of-representation behavior, such as some neurons only being a good approximation of a sine-wave for in-representation inputs.
I initially wrote a long comment discussing the post, but I rewrote it as a list-based version that tries to more efficiently parcel up the different objections/agreements/cruxes.
This list ended up basically just as long, but I feel it is better structured than my original intended comment.
(Section 1): How fast can humans develop novel technologies
(Section 2): Unstoppable intellect meets the complexity of the universe
(Section 3): What does AGI want?
(Section 4): What does it take to make a pencil?
(Section 5): YOLO AGI?
(Section 6): But what about AlphaFold?
(Section 7): What if AGI settles for a robot army?
(Section 8): Mere mortals can't comprehend AGI
(Section 9): (Not commented upon)
(If there's odd grammar/spelling, then that's primarily because I wrote this while feeling sleepy and then continued for several more hours)
While human moral values are subjective, there is a sufficiently large shared amount that you can target at aligning an AI to that. As well, values held by a majority (ex: caring for other humans, enjoying certain fun things) are also essentially shared. Values that are held by smaller groups can also be catered to.
If humans were sampled from the entire space of possible values, then yes we (maybe) couldn't build an AI aligned to humanity, but we only take up a relatively small space and have a lot of shared values.
The AI problem is easier in some ways (and significantly harder in others) because we're not taking an existing system and trying to align it. We want to design the system (and/or systems that produce that system, aka optimization) to be aligned in the first place. This can be done through formal work to provide guarantees, lots of code, and lots of testing.
However, doing that for some arbitrary agent or even just a human isn't really a focus of most alignment research. A human has the issue that they're already misaligned (in a sense), and there are many various technological/ethical/social issues with either retraining them or performing the modifications to get them aligned. If the ideas that people had for alignment were about 'converting' a misaligned intelligence to an aligned one, then humans could maybe be a test-case, but that isn't really the focus. We also are only 'slowly' advancing our ability to understand the body and how the brain works. While we have some of the same issues with neural networks, it is a lot cheaper, less unethical, we can rerun it (for non-dangerous networks), etcetera.
Though, there has been talk of things like incentives, moral mazes, inadequate equilibria and more which are somewhat related to the alignment/misalignment of humans and where they can do better.
I'm also not sure that I consider astronomical suffering outcome (by how its described in the paper) to be bad by itself.
If you have (absurd amount of people) and they have some amount of suffering (ex: it shakes out that humans prefer some degree of negative-reinforcement as possible outcomes, so it remains) then that can be more suffering in terms of magnitude, but has the benefits of being more diffuse (people aren't broken by a short-term large amount of suffering) and with less individual extremes of suffering. Obviously it would be bad to have a world that has astronomical suffering that is then concentrated on a large amount of people, but that's why I think - a naive application of - astronomical suffering is incorrect because it ignores diffuse experiences, relative experiences (like, if we have 50% of people with notably bad suffering today, then your large future civilization with only 0.01% of people with notably bad suffering can still swamp that number, though the article mentions this I believe), and more minor suffering adding up over long periods of time.
(I think some of this comes from talking about things in terms of suffering versus happiness rather than negative utility versus positive utility? Where zero is defined as 'universe filled with things we dont care about'. Like, you can have astronomical suffering that isn't that much negative utility because it is diffuse / lower in a relative sense / less extreme, but 'everyone is having a terrible time in this dystopia' has astronomical suffering and high negative utility)