I’m Rob Long. I’m a philosophy fellow at the Center for AI Safety.
80k podcast episode on sentience in AI systems
To clarify, what question were you thinking that is more interesting than? I see that as one of the questions that is raised in the post. But perhaps you are contrasting “realize it is conscious by itself” with the methods discussed in “Could we build language models whose reports about sentience we can trust?”
I think I’d need to hear more about what you mean by sapience (the link didn’t make it entirely clear to me) and why that would ground moral patienthood. It is true in my opinion that there are other plausible grounds for moral patienthood besides sentience (which, its ambiguity notwithstanding, I think can be used about as precisely as sapience, see my note on usage), most notably desires, preferences, and goals. Perhaps those are part of what you mean by ‘sapience’?
What to think when a language model tells you it’s sentient
Great, thanks for the explanation. Just curious to hear your framework, no need to reply:
-If you do have some notion of moral patienthood, what properties do you think are important for moral patienthood? Do you think we face uncertainty about whether animals or AIs have these properties? -If you don’t, are there questions in the vicinity of “which systems are moral patients” that you do recognize as meaningful?
Very interesting! Thanks for your reply, and I like your distinction between questions:
Positive valence involves attention concentration whereas negative valence involves diffusion of attention / searching for ways to end this experience.
Can you elaborate on this? What is do attention concentration v. diffusion mean? Pain seems to draw attention to itself (and to motivate action to alleviate it). On my normal understanding of “concentration”, pain involves concentration. But I think I’m just unfamiliar with how you / ‘the literature’ use these terms.
I’m trying to get a better idea of your position. Suppose that, as TAG also replied, “realism about phenomenal consciousness” does not imply that consciousness is somehow fundamentally different from other forms of organization of matter. Suppose I’m a physicalist and a functionalist, so I think phenomenal consciousness just is a certain organization of matter. Do we still then need to replace “theory” with “ideology” etc?
to say that [consciousness] is the only way to process information
I don’t think anyone was claiming that. My post certainly doesn’t. If one thought consciousness were the only way to process information, wouldn’t there not even be an open question about which (if any) information-processing systems can be conscious?
A few questions:
Can you elaborate on this?
Suffering seems to need a lot of complexity
and also seems deeply connected to biological systems.
I think I agree. Of course, all of the suffering that we know about so far is instantiated in biological systems. Depends on what you mean by “deeply connected.” Do you mean that you think that the biological substrate is necessary? i.e. you have a biological theory of consciousness?
AI/computers are just a “picture” of these biological systems.
What does this mean?
Now, we could someday crack consciousness in electronic systems, but I think it would be winning the lottery to get there not on purpose.
Can you elaborate? Are you saying that, unless we deliberately try to build in some complex stuff that is necessary for suffering, AI systems won’t ‘naturally’ have the capacity for suffering? (i.e. you’ve ruled out the possibility that Steven Byrnes raised in his comment)
Thanks for this great comment! Will reply to the substantive stuff later, but first—I hadn’t heard of the The Welfare Footprint Project! Super interesting and relevant, thanks for bringing to my attention
A third (disconcerting) possibility is that the list of demands amounts to saying “don’t ever build AGIs”
That would indeed be disconcerting. I would hope that, in this world, it’s possible and profitable to have AGIs that are sentient, but which don’t suffer in quite the same way / as badly as humans and animals do. It would be nice—but is by no means guaranteed—if the really bad mental states we can get are in a kinda arbitrary and non-natural point in mind-space. This is all very hard to think about though, and I’m not sure what I think.
I’m hopeful (and hoping!) that one can soften the “we are rejecting strong illusionism” claim in #3 without everything else falling apart.
I hope so too. I was more optimistic about that until I read Kammerer’s paper, then I found myself getting worried. I need to understand that paper more deeply and figure out what I think. Fortunately, I think one thing that Kammerer worries about is that, on illusionism (or even just good old fashioned materialism), “moral patienthood” will have vague boundaries. I’m not as worried about that, and I’m guessing you aren’t either. So maybe if we’re fine with fuzzy boundaries around moral patienthood, things aren’t so bad.
But I think there’s other more worrying stuff in that paper—I should write up a summary some time soon!
Thanks, I’ll check it out! I agree that the meta-problem is a super promising way forward
The whole field seems like an extreme case of anthropomorphizing to me.
Which field? Some of these fields and findings are explicitly about humans; I take it you mean the field of AI sentience, such as it is?
Of course, we can’t assume that what holds for us holds for animals and AIs, and have to be wary of anthropomorphizing. That issue also comes up in studying, e.g., animal sentience and animal behavior. But what were you thinking is anthropomorphizing exactly? To be clear, I think we have to think carefully about what will and will not carry over from what we know about humans and animals.
The “valence” thing in humans is an artifact of evolution
I agree. Are you thinking that this means that valenced experiences couldn’t happen in AI systems? Are unlikely to? Would be curious to hear why.
where most of the brain is not available to introspection because we used to be lizards and amoebas
I also agree.with that. What was the upshot of this supposed to be?
That’s not at all how the AI systems work
What’s not how the AI systems work? (I’m guessing this will be covered by my other questions)
Key questions about artificial sentience: an opinionated guide
would read a review!
This author is: https://fantasticanachronism.com/2021/03/23/two-paths-to-the-future/
“I believe the best choice is cloning. More specifically, cloning John von Neumann one million times”
I guess even though I don’t disagree that knowledge accumulation has been a bottleneck for humans dominating all other species, I don’t see any strong reason to think that knowledge accumulation will be a bottleneck for an AGI dominating humans, since the limits to human knowledge accumulation seem mostly biological. Humans seem to get less plastic with age, mortality among other things forces us to specialize our labor, we have to sleep, we lack serial depth, we don’t even approach the physical limits on speed, we can’t run multiple instances of our own source, we have no previous example of an industrial civilization to observe, I could go on: a list of biological fetters that either wouldn’t apply to an AGI or that an AGI could emulate inside of a single mind instead of across a civilization.
I agree with this, and I think that you are hitting on a key a reason that these debates don’t hinge on what the true story of the human intelligence explosion ends up being. Whichever of these is closer to the truth
a) the evolution of individually smarter humans using general reasoning ability was the key factor
b) the evolution of better social learners and the accumulation of cultural knowledge was the key factor
...either way, there’s no reason to think that AGI has to follow the same kind of path that humans did. I found an earlier post on the Henrich model of the evolution of intelligence, Musings on Cumulative Cultural Evolution and AI. I agree with Rohin Shah’s takeaway on that post :
I actually don’t think that this suggests that AI development will need both social and asocial learning: it seems to me that in this model, the need for social learning arises because of the constraints on brain size and the limited lifetimes. Neither of these constraints apply to AI—costs grow linearly with “brain size” (model capacity, maybe also training time) as opposed to superlinearly for human brains, and the AI need not age and die. So, with AI I expect that it would be better to optimize just for asocial learning, since you don’t need to mimic the transmission across lifetimes that was needed for humans.
The core part of Ajeya’s model is a probability distribution over how many OOMs of compute we’d need with today’s ideas to get to TAI / AGI / APS-AI / AI-PONR / etc.
I didn’t know the last two acronyms despite reading a decent amount of this literature, so thought I’d leave this note for other readers. Listing all of them for completeness (readers will of course know the first two):
TAI: transformative AI
AGI: artificial general intelligence
APS-AI: Advanced, Planning, Strategically aware AI 
AI-PONR: AI point of no return 
 from Carlsmith, which Daniel does link to
 from Daniel, which he also linked
- 23 Nov 2021 17:44 UTC; 31 points)'s comment on Yudkowsky and Christiano discuss “Takeoff Speeds” by (
In general, I don’t yet see a strong reason to think that our general brain architecture is the sole, or potentially even primary reason why we’ve developed civilization, discontinuous with the rest of the animal kingdom. A strong requirement for civilization is the development of cultural accumulation via language, and more specifically, the ability to accumulate knowledge and technology over generations.
In The Secrets of Our Success, Joe Henrich argues that without our stock of cultural knowledge, individual humans are not particularly more generally intelligent than apes. (Neanderthals may very well have been more generally intelligent than humans—and indeed, their brains are bigger than ours.)
And, he claims, to the extent that individual humans are now especially intelligent, this was because of culture-driven natural selection. For Henrich, the story of human uniqueness is a story of a feedback loop: increased cultural know-how, which drives genetic selection for bigger brains and better social learning, which leads to increased cultural know-how, which drives genetic selection for bigger brains….and so forth, until you have a very weird great ape that is weak, hairless, and has put a flag on the moon.
Note: this evolution + culture feedback loop is still a huge discontinuity that led to massive changes in relatively short evolutionary time!
Just having a generalist brain doesn’t seem like enough; for example, could there have been a dolphin civilization?
Heinrich speculates that a bunch of idiosyncratic features came together to launch us into the feedback loop that led to us being cultural species. Most species, including dolphins, do not get onto this feedback loop because of a “startup” problem: bigger brains will give a fitness advantage only up to a certain point, because individual learning can only be so useful. For there to be further selection for bigger brains, you need a stock of cultural know-how (cooking, hunting, special tools) that makes individual learning very important for fitness. But, to have a stock of cultural know-how, you need big brains.
Heinrich speculates that humans overcame the startup problem due to a variety of factors that came together when we descended from the trees and started living on the ground. The important consequences of a species being on the ground (as opposed to in the trees):
It frees up your hands for tool use. Captive chimps, which are more “grounded” than wild chimps, make more tools.
It’s easier for you to find tools left by other people.
It’s easier for you to see what other people are doing and hang out with them. (“Hang out” being inapt, since that’s precisely not what you’re doing).
You need to group up with people to survive, since there are terrifying predators on the ground. Larger groups offer protection; these larger groups will accelerate the process of people messing around with tools and imitating each other.
Larger groups also produce new forms of social organization. Apparently, in smaller groups of chimps, the reproductive strategy that every male tries to follow is “fight as many males as you can for mating opportunities.” But in a larger group, it becomes better for some males to try to pair bond – to get multiple reproductive opportunities with one female, by hanging around her and taking care of her.
Pair bonding in turn allows for more kinship relationships. Kinship relationships mean you grow up around more people; this accelerates learning. Kinship also allows for more genetic selection for big-brained, slow-developing learners: it becomes less prohibitively costly to give birth to big-brained, slow-growing children, because more people are around to help out and pool food resources.
This story is, by Henrich’s own account, quite speculative. You can find it in Chapter 16 of the book.
and some links therein