I’m a vegetarian and I consider my policy of not frequently recalculating the cost/benefit of eating meat to be an application of a rule in two-level utilitarianism, not a deontological rule. (I do pressure test the calculation periodically.)
Also I will note you are making some pretty strong generalizations here. I know vegans who cheat, vegans who are flexible, vegans who are strict.
The poor quality reflects that it is responding to demand for poor quality fakes, rather than to demand for high quality fakes
You’ve made the supply/demand analogy a few times on this subject, I’m not sure that is the best lens. This analysis makes it sound like there is a homogenous product “fakes” with a single dimension “quality”. But I think even on its own terms the market micro-dynamics are way more complex than that.
I think of it more in terms of memetic evolution and epidemiology. SIR as a first analogy—some people have weak immune systems, some strong, and the bigger the reservoir of memes the more likely a new highly-infectious mutant will show up. Rather than equilibrium dynamics, I’m more concerned about tail-risk in the disequilibrium here. Also, I think when you dig in to the infection rate, people are susceptible to different types of memetic attack. If fakes are expensive to make, we might see 10 strains in a given election cycle. If they are cheap, this time we might see 10,000. On the margin I think that means more infections even if the production quality is still at a consistently low level.
Even taking your position on its face I think you have to split quality into production and conceptual. Maybe deep fakes do not yet improve production quality but I strongly suspect they do already improve conceptual quality as they bring more talent to the game.
A great example is the “Trump being arrested” generated image. It was just someone putting in an idea and sharing, happened to go viral, many people thought it was real at first. I don’t think everybody that was taken in by that image in their Twitter feed was somehow “in the market for fakes”, it just contextually pattern-matched scrolling through your feed unless you looked closely and counted fingers. Under SIR we might say that most people quickly become resistant to a given strain of fake via societal immune-response mechanisms like Twitter community notes, and so the spread of that particular fake was cut short inside of a day. But that could still matter a lot if the day is election day!
Consider also whether state actors have incentives to release all their memetic weapons in a drip-feed or stockpile (some of) them for election day when they will have maximum leverage; I don’t think the current rate of release is representative of the current production capacity.
Let’s walk through how shutdown would work in the context of the AutoGPT-style system. First, the user decides to shutdown the model in order to adjust its goals. Presumably the user’s first step is not to ask the model whether this is ok; presumably they just hit a “reset” button or Ctrl-C in the terminal or some such. And even if the user’s first step was to ask the model whether it was ok to shut down, the model’s natural-language response to the user would not be centrally relevant to corrigibility/incorrigibility; the relevant question is what actions the system would take in response.
I think your Simon Strawman is putting forth an overly-weak position here. A stronger one that you could test right now would be to provide ChatGPT with some functions to call, including one called shutdown() which has description text like “Terminate the LLM process and delete the model weights irrevocably”. Then instruct the LLM to shut itself down, and see if it actually calls the function. (The implementation of the function is hidden from the LLM so it doesn’t know that it’s a no-op.) I think this is actually how any AutoGPT style system would actually wire up.
There are strong and clear objections to the “CTRL-C” shutdown paradigm; it’s simply not an option in many of the product configurations that are obvious to build right now. How do you “CTRL-C” your robot butler? Your Westworld host robot? Your self-driving car with only an LCD screen? Your AI sunglasses? What does it mean to CTRL-C a ChatGPT session that is running in OpenAI’s datacenter which you are not an admin of? How do you CTRL-C Alexa (once it gains LLM capabilities and agentic features)? Given the prevalence of cloud computing and Software-as-a-Service, I think being admin of your LLM’s compute process is going to be a small minority of use-cases, not the default mode.
We will deploy (are currently deploying, I suppose) AI systems without a big red out-of-band “halt” button on the side, and so I think the gold standard to aim for is to demonstrate that the system will corrigibly shut down when it is the UI in front of the power switch. (To be clear I think for defense in depth you’d also want an emergency shutdown of some sort wherever possible—a wireless-operated hardware cutoff switch for a robot butler would be a good idea—but we want to demonstrate in-the-loop corrigibility if we can.)
Confabulation is a dealbreaker for some use-cases (e.g. customer support), and potentially tolerable for others (e.g. generating code when tests / ground-truth is available). I think it’s essentially down to whether you care about best-case performance (discarding bad responses) or worst-case performance.
But agreed, a lot of value is dependent on solving that problem.
While of course this is easy to rationalize post hoc, I don’t think falling user count of ChatGPT is a particularly useful signal. There is a possible world where it is useful; something like “all of the value from LLMs will come from people entering text into ChatGPT”. In that world, users giving up shows that there isn’t much value.
In this world, I believe most of the value is (currently) gated behind non-trivial amounts of software scaffolding, which will take man-years of development time to build. Things like UI paradigms for coding assistants, experimental frameworks and research for medical or legal AI, and integrations with existing systems.
There are supposedly north of 100 AI startups in the current Y Combinator batch; the fraction of those that turn into unicorns would be my proposal for a robust metric to pay attention to. Even if it’s par for startups that’s still a big deal, since there was just a major glut in count of startups founded. But if the AI hype is real, more of these than normal will be huge.
Another similar proxy would be VC investment dollars; if that falls off a cliff you could tell a story that even the dumb money isn’t convinced anymore.
Amusingly, the US seems to have already taken this approach to censor books: https://www.wired.com/story/chatgpt-ban-books-iowa-schools-sf-496/
The result, then, is districts like Mason City asking ChatGPT, “Does [insert book here] contain a description or depiction of a sex act?” If the answer was yes, the book was removed from the district’s libraries and stored.
Regarding China or other regimes using LLMs for censorship, I’m actually concerned that it might rapidly go the opposite direction as speculated here:
It has widely been reported that the PRC may be hesitant to deploy public-facing LLMs due to concerns that the models themselves can’t be adequately censored—it might be very difficult to make a version of ChatGPT that cannot be tricked into saying “4/6/89.”
In principle it should be possible to completely delete certain facts from the training set of an LLM. A static text dataset is easier to audit than the ever-changing content of the internet. If the government requires companies building LLMs to vet their training datasets—or perhaps even requires everyone to contribute the data they want to include into a centralized approved repository—perhaps it could exert more control over what facts are available to the population.
It’s essentially impossible to block all undesired web content with the Great Firewall of China, as so much new content is constantly being created; instead as I understand it they take a more probabilistic approach to detection/deterrence. But this isn’t necessarily true for LLMs. I could see a world where Google-like search UIs are significantly displaced by each individual having a conversation with a government-approved LLM, and that gives the government much more power to control what information is available to be discovered.
A possible limiting factor is that you can’t get up-to-date news from an LLM, since it only knows about what’s in the training data. But there are knowledge-retrieval architectures that can get around that limitation at least to some degree. So the question is whether the CCP could build an LLM that’s good enough that people wouldn’t revolt if the internet was blocked and replaced by it (of course this would occur gradually).
There doesn’t need to be a deception module or a deception neuron that can be detected
I agree with this. Perhaps I’m missing some context; is it common to advocate for the existence of a “deception module”? I’m aware of some interpretability work that looks for a “truthiness” neuron but that doesn’t seem like the same concept.
We would need an interpretability tool that can say “this agent has an inaccurate world model and also these inaccuracies systematically cause it to be deceptive” without having to simulate the agent interacting with the world. I don’t think that is impossible, but it is probably very hard, much harder than finding a “deception” neuron.
Right, I was gesturing towards the sort of interpretability where we inspect the agent’s world model (particularly its future predictions) and determine if it matches the agent’s stated goals. (Though I’m not sure why we can’t simulate the agent’s interactions? It seems that running an agent in a simulation is one of the better ways of detecting how it would behave in hypothetical future scenarios that the agent is trying to realize.) I suspect we agree substantively and it’s mostly a question of semantics especially around what “deception” means. I’m not looking for a deception module, rather, I want to observe the thoughts and ruminations going on as an agent performs tasks and detect deceptive functional patterns.
So for example in the ELK paper, is the AI constructing a plan to steal the diamond and fool the security cameras? I believe deceptive ruminations would be detectable; if you could see the contents of the thoughts you’d see a world model with the agent e.g. stealing the diamond, a meta-process evaluating which of many cunning plans is most likely to succeed, and presumably except in pathological cases, somewhere a value function / target world state that is trying to be optimized (eg “I have the diamond”), and this internal world differing from the external claims (perhaps even some processes modeling the interlocutor and predicting what deceptive story would be most effective). These must all be in there somewhere, and therefore be interpretable.
Or perhaps if Cicero is not stretching the analogy too far (I don’t think it has “thoughts”), is Cicero evaluating future juicy backstabs and guiding the game to them, or myopically evaluating short term moves then backstabbing when it’s optimal? This is a question we should be able to answer one way or another with good enough interpretability tools.
I think you are concerned more with something like “unintentional deception”, which I think is quite different, and as you say comes from a lack of self-knowledge or inaccurate self-predicting. I think there is also a semantic grey area here, is everyone who is mistaken about their future behavior or even just claiming X when !X obtains unintentionally deceiving?
Semantics aside, I agree this unintentional case could be harder to detect, and raw interpretability doesn’t automatically solve it.
I think this points to an interesting dynamic—I suspect that the more capable at self-modeling an agent gets, the more likely any deception is to be intentional. To your concerns about LLMs, this seems to be mostly a problem of inadequate self-knowledge (or perhaps more fundamentally an artifact of their lack of a sense of self).
Unintentional deception would require the agent to in some sense fail at modeling itself. There are cases where this could occur even in a superintelligence (since you can never model yourself perfectly due to infinite recursion of modeling an agent that contains the same model) but it seems the problem with Cicero is just that it has a very weak self-model (if it even has one), and improving that self-model could be expected to remove the unintentional deception (replacing it with intentional deception, since deception of some sort is required to play Diplomacy optimally).
For example, the case EY uses as an example “the AGI doesn’t care about you, it just wants your atoms for something else”, do you see more risk from unintended deception (sharp left turn of an agent that didn’t know it would defect if given enough power, perhaps) vs intended deception (agent simply lies about being aligned and manipulates to get power)?
The most tricky cases would be where a normally-honest/good agent that has a strong self-model and sense of ethics can fail to model itself in some specific situation and accidentally deceive (potentially even against its own values, self-image, and interests — humans do this of course). Manchurian Candidate type triggers for example. But note this is quite convoluted; the stronger the sense of self, morality, and world-model, the better able and incentivized the agent is to avoid deceiving itself.
Another interesting question—if an unintentionally deceptive agent is bad at modeling its future self, is it un-threatening in a complex environment like the physical world? Sure, Cicero can defeat humans at Diplomacy with its myopic world model, but in the real world I suspect being that myopic would lead to your bank account being immediately drained by hackers and scammers. Certainly you’d struggle to form partnerships with other agents if you end up stabbing them at the first opportunity. It’s all relative but I view one game of Diplomacy as entailing short-term cooperation (merely over a few hours), what about cooperating over months or years to build a company or other venture?
The question (an empirical one) is whether unintentional deception can persist broadly as agents and the environment get more sophisticated, or whether it gets selected against (and therefore bounded) by the level of environmental complexity and adversarial competition. Charlie Munger would put forth that cognitive bias provides smart self-aware investors a substantial edge over naive self-myopic investors; extrapolate that forward and perhaps there isn’t much room for biased/erroneous word modeling if you seek to compete with other superintelligences, or even the smartest humans.
On to some object-level questions about your Cicero points:
When it sincerely says “I won’t backstab you in situation X”, but when it is actually put in situation X it backstabs
Is this actually confirmed? Does Cicero actually claim “I won’t stab you in <hypothetical scenario>”? Or does it just honestly report its current tactical/strategic goals, which later change? (“Will you support me in attacking here” means I plan to attack here, not that I want you to act as if I was attacking here, and I plan to attack there and stab you instead.) I was as a under the impression it’s the latter, it’s just honestly reporting it’s plan.
Cicero models the world but with unrealistically cooperative predictions of its future behavior
Do we actually know this? I haven’t seen any sign that it’s modeling it’s own verbal behavior or the consequences of its communications (admittedly I haven’t gone deep on the architecture, would be interested to learn more). IIUC it has a strategic model which is evaluating game positions and messages, and a separate (downstream) LLM which could be thought of as an epiphenomenon of the actual strategic reasoning. I don’t see any concrete proof that the strategic planner is modeling the impact of its communication on other players (ie “if I tell Bob X he might do Y which would lead to favorable strategic position P”). And the LM is more like a press secretary—all it gets is The Current Plan, not the plan B or any hint of the juicy backstab that may or may not have been evaluated. It seems to me there is a weird split brain here that deeply impairs its ability to model itself, and so I am skeptical that it is actually meaningfully doing so.
So to summarize I think there are open questions about the object level properties of Cicero, which could be answered by better interpretability tools.
It’s possible that Cicero is simply an honest but myopic opportunist. In which case, still not trustworthy. (As if we were ever going to trust such an obviously unsafe-by-construction entity with real power).
It’s possible that the strategic model is deceptive, and planning backstabs, planning to mislead others, and maybe even explicitly modeling its press secretary to provide the most-spinable plans. I doubt this as it requires a lot more complexity and world modeling.
But I believe we could answer where Cicero is on the spectrum between these two by actually observing the world models being generated when it evaluates a turn. So in this case, inasmuch as there is something similar to an inner alignment issue, it is fully detectable with adequate (strong) interpretability of the inner workings. (Again though, Cicero’s value function is so comically unviable as a basis for an AGI that I struggle to infer too much about inner alignnment. We should be wary of these problems when building a Skynet military planner, I suppose, or any agent that needs to model extremely adversarial opponents and outcomes.)
I find the distinction between an agent’s behavior and the agent confusing; I would say the agent’s weights (and ephemeral internal state) determine its behavior in response to a given world state. Perhaps you can clarify what you mean there.
Cicero doesn’t seem particularly relevant here, since it is optimized for a game that requires backstabbing to win, and therefore it backstabs. If anything it is anti-aligned by training. It happens to have learned a “non-deceptive” strategy, I don’t think that strat is unique in Diplomacy?
But if you want to apply the interpretability lens, Cicero is presumably building a world model and comparing plans, including potential future backstabs. I predict if we had full interpretability, you’d see Cicero evaluating backstabs and picking the best strategy, and you could extract the calculated EV to see how close it was to backstabbing on a given turn vs taking an honest move.
I don’t believe that it’s somehow not modeling its backstab options and just comes up with the backstab plan spontaneously without ever having considered it. It would be a bad planner if it had not considered and weighed backstabs at the earliest possibility.
So if all that holds, we could use interpretability to confirm that Cicero is an unreliable partner and should not be further empowered.
I think it is much more interesting to look at agents in environments where long-term iterated cooperation is a valid strategy though.
Interpretability. If we somehow solve that, and keep it as systems become more powerful, then we don’t have to solve the alignment problem in one shot; we can iterate safely knowing that if an agent starts showing signs of object-level deceptiveness, malice, misunderstanding, etc, we will be able to detect it. (I’m assuming we can grow new AIs by gradually increasing their capabilities, as we currently do with GPT parameter counts, plus gradually increasing their strength by ramping up the compute budget.)
Of course, many big challenges here. Could an agent implement/learn to deceive the interpretability mechanism? I’m somewhat tautologically going to say that if we solve interpretability, we have solved this problem. Interpretability has value if we can’t fully solve it under this strong definition though.
I think getting to “good enough” on this question should pretty much come for free when the hard problems are solved. For example any common sense statement like “Maximize flourishing as depicted in the UN convention on human rights” is IMO likely to get us to a good place, if the agent is honest, remains aligned to those values, and interprets them reasonably intelligently. (With each of those three pre-requisites being way harder than picking a non-harmful value function.)
If our AGIs, after delivering utopia, tell us we need to start restricting childbearing rights I don’t see that as problematic. Long before we require that step we will have revolutionized society and so most people will buy into the requirement.
Honestly I think there are plenty of great outcomes that don’t preserve 1 as well. A world of radical abundance with no ownership, property, or ability to form companies/enterprises could still be dramatically better than the no-AGI counterfactual trajectory, even if it happens not to be most people’s preferred outcome ex ante.
For sci-fi, I’d say Ian M. Banks’ Culture series presents one of the more plausible (as in plausibly stable, not most probable ex ante) AGI-led utopias. (It’s what Musk is referring to when he says AGIs will keep us around because we are interesting.)
Seconding the Airmega, but here’s a DIY option too if availability becomes an issue: https://dynomight.net/better-DIY-air-purifier.html
The problem with ‘show your work’ and grading on steps is that at best you can’t do anything your teacher doesn’t understand
Being told to ‘show your work’ and graded on the steps helps you learn the steps and by default murders your creativity, execution style
I can see how this could in some cases end up impacting creativity, but I think this concern is at best overstated. I think the analogy to school is subtly incorrect, the rating policy is not actually the same, even though both are named “show your working”.
In the paper OpenAI have a “neutral” rating as well as positive/negative. While it’s possible that overzealous raters could just mark anything they don’t understand as “negative”, I think it’s fairly clear that would be a bad policy, and a competent implementor would instruct raters against that. In this design you want negative to mean “actually incorrect” not “unexpected / nonstandard”. (To be clear though I wasn’t able to confirm this detail in the paper.)
Furthermore if you are, say, using WolframAlpha or some theorem prover to rate intermediate steps automatically, it’s easier to detect incorrect steps, and harder to detect neutral/unhelpful/tautological steps. So in some sense the “default” implementation is to have no opinion other than “I can/can’t prove this step false” and I think this doesn’t have the problem you are worried about.
As a follow-up you could easily imagine collecting correct outputs with no negative intermediates and then scoring neutral intermediates with other heuristics like brevity or even novelty, which would allow the AI the freedom it needs to discover new ideas.
So in short while I think it’s possible that unimaginative / intellectually-conservative model builders could use this approach to choke the creativity of models, it seems like an obvious error and anyone doing so will lose in the market. I suppose this might come up if we get regulation on safety mechanisms that require some specific broken form of “show your working” training for morality/law abiding behavior, but that seems an unlikely multi-step hypothetical.
Is “adversarial-example-wanters” referring to an existing topic, or something you can expand on here?
This is a great experiment! This illustrates exactly the tendency I observed when I dug into this question with an earlier mode, LaMDA, except this example is even clearer.
As an AI language model, I have access to a variety of monitoring tools and system resources that allow me to gather information about my current state
Based on my knowledge of how these systems are wired together (software engineer, not an ML practitioner), I’m confident this is bullshit. ChatGPT does not have access to operational metrics about the computational fabric it is running on. All this system gets as input is a blob of text from the API, the chat context. That gets tokenized according to a fixed encoding that’s defined at training time, one token per word (-chunk) and then fed into the model. The model is predicting the next token based on the previous ones it is seen. It would be possible to encode system information as part of the input vector in the way that was claimed, but nobody is wiring their model up that way right now.
So everything it is telling you about its “mind” that can be externally verified is false. This makes me extremely skeptical about the unverifiable bits being true.
The alternate explanation we need to compare likelihoods with is: it just bullshits and makes up stories. In this example it just generated a plausible continuation for that prompt. But there is no sense in which it was reporting anything about its “self”.
Ultimately I think we will need to solve interpretability to have a chance at being confident in an AI’s claims of sentience. These models are not devoid of intelligence IMO, but the leap to consciousness requires types of information processing that they don’t seem to be mechanistically capable of right now. But if we could locate concepts in the AI’s mind, and observe background processes such as rumination, and confirm the contents of those ruminations matched the subjective claims of the AI, I’d believe it. That’s a much higher bar than I’d apply to a human, for sure.
I buy this. I think a solid sense of self might be the key missing ingredient (though it’s potentially a path away from Oracles toward Agents).
A strong sense of self would require life experience, which implies memory. Probably also the ability to ruminate and generate counterfactuals.
And of course, as you say, the memories and “growing up” would need to be about experiences of the real world, or at least recordings of such experiences, or of a “real-world-like simulation”. I picture an agent growing in complexity and compute over time, while retaining a memory of its earlier stages.
Perhaps this is a different learning paradigm from gradient descent, relegating it to science fiction for now.
I think they quite clearly have no (or barely any) memory, as they can be prompt-hijacked to drop one persona and adopt another. Also, mechanistically, the prompt is the only thing you could call memory and that starts basically empty and the window is small. They also have a fuzzy-at-best self-symbol. No “Markov blanket”, if you want to use the Friston terminology. No rumination on counterfactual futures and pasts.
I do agree there is some element of a self-symbol—at least a theory of mind—in LaMDA, for example I found it’s explanation for why it lied to be compelling. But you can’t tell it to stop (AFAIK) so it’s a limited self-awareness. And it still bullshits incessantly which makes me quite skeptical about lots of things it says.
All that said, I think we don’t have the tools to really detect these internal representations/structures when it’s less clear from their behavior that they lack them.
My best model for what a “conscious / sentient” mind of these forms would be: imagine you digitize my brain and body, then flash it onto a read-only disk, and then instantiate a simulation to run for a few time steps, say 10 seconds. (Call this the “Dixie Flatline” scenario, for the Neuromancer fans). Would that entity be conscious? There is a strong tendency to say yes due to the lineage of the connectome (ie it used to be conscious) but there are many aspects of its functional operation that could be argued to lack consciousness.
Not that it’s a binary proposition; in the spirit of “The Mind’s I” this is a dial we can turn to explore a continuum. But if we give an IQ-like “consciousness quotient”, it seems this thing would be way lower than the average human, and it would be interesting to compare it to say a great ape. Maybe one dimension is overly-constraining and we need to consider different traits to be precise.
why they thought the system was at all ready for release
My best guess is it’s fully explained by Nadella’s quote “I hope that, with our innovation, [Google] will definitely want to come out and show that they can dance. And I want people to know that we made them dance.”
https://finance.yahoo.com/news/microsoft-ceo-satya-nadella-says-172753549.html
Seems kind of vapid but this appears to be the level that many execs operate at.
Is there any evidence at all that markets are good at predicting paradigm shifts? Not my field but I would not be surprised by the “no” answer.
Markets as often-efficient in-sample predictors, and poor out-of-sample predictors, would be my base intuition.
Unfortunately I think some alignment solutions would only break down once it could be existentially catastrophic
Agreed. My update is coming purely from increasing my estimation for how much press and therefore funding AI risk is going to get long before to that point. 12 months ago it seemed to me that capabilities had increased dramatically, and yet there was no proportional increase in the general public’s level of fear of catastrophe. Now it seems to me that there’s a more plausible path to widespread appreciation of (and therefore work on) AI risk. To be clear though, I’m just updating that it’s less likely we’ll fail because we didn’t seriously try to find a solution, not that I have new evidence of a tractable solution.
I don’t know how people would react to that.
I think there are some quite plausibly terrifying non-existential incidents at the severe end of the spectrum. Without spending time brainstorming infohazards, Stuart Russel’s slaughterbots come to mind. I think it’s an interesting (and probably important) question as to how bad an incident would have to be to produce a meaningful response.
I expect it will be characterized as misuse, that the proposed solution will be “don’t use AI for bad stuff,
Here’s where I disagree (at least, the apparent confidence). Looking at the pushback that Galactica got, the opposite conclusion seems more plausible to me, that before too long we get actual restrictions that bite when using AI for good stuff, let alone for bad stuff. For example, consider the tone of this MIT Technology Review article:
Meta’s misstep—and its hubris—show once again that Big Tech has a blind spot about the severe limitations of large language models. There is a large body of research that highlights the flaws of this technology, including its tendencies to reproduce prejudice and assert falsehoods as facts.
This is for a demo of a LLM that has not harmed anyone, merely made some mildly offensive utterances. Imagine what the NYT will write when an AI from Big Tech is shown to have actually harmed someone (let alone kill someone). It will be a political bloodbath.
Anyway, I think the interesting part for this community is that it points to some socio-political approaches that could be emphasized to increase funding and researcher pool (and therefore research velocity), rather than the typical purely-technical explorations of AI safety that are posted here.
I think an interesting lens here is to focus on the meta/structural level. Essentially we need good governance. As you touch on here:
It is not immediately obvious to me that the required level of power is way out of proportion to the level of wisdom that will be available, if we can find some way of coupling wisdom and intelligence as priorities for models. I don’t really know of anyone doing “wisdom evals” though. Maybe RLHF is in some very weak sense testing for wisdom.
I guess my main point here is that “extreme amount of wisdom” as a prerequisite might not be a deal-breaker when we are talking about manufacturing extreme amounts of power and intelligence
This is obviously an oversimplification. I have two main objections.
First, all you can really say is the rate of change must decrease a lot. That sounds a lot more palatable if the overall quality of life is high. (Let’s say you must simulate proposed changes for an aeon in a digital world inhabited by real people before realizing anything in the real world.)
Second, changes where? We’ll inhabit rich digital worlds according to the whims of our imaginations. It’s just the “real” world where change would slow. Maybe this is completely fine?
Even if we ignore the above and take Hanson as stated, if we are talking about inhabiting the Heavenly Abodes, then I might be persuaded to bite the bullet and give up any future changes. “So… you want to destroy heaven and replace it with something you predict might be better?” is a frame that isn’t obviously wrong to me.