My AI Model Delta Compared To Yudkowsky

I think that the AI's internal ontology is liable to have some noticeable alignments to human ontology w/r/t the purely predictive aspects of the natural world; it wouldn't surprise me to find distinct thoughts in there about electrons.  As the internal ontology goes to be more about affordances and actions, I expect to find increasing disalignment.  As the internal ontology takes on any reflective aspects, parts of the representation that mix with facts about the AI's internals, I expect to find much larger differences -- not just that the AI has a different concept boundary around "easy to understand", say, but that it maybe doesn't have any such internal notion as "easy to understand" at all, because easiness isn't in the environment and the AI doesn't have any such thing as "effort".  Maybe it's got categories around yieldingness to seven different categories of methods, and/or some general notion of "can predict at all / can't predict at all", but no general notion that maps onto human "easy to understand" -- though "easy to understand" is plausibly general-enough that I wouldn't be unsurprised to find a mapping after all.

Corrigibility and actual human values are both heavily reflective concepts.  If you master a requisite level of the prerequisite skill of noticing when a concept definition has a step where its boundary depends on your own internals rather than pure facts about the environment -- which of course most people can't do because they project the category boundary onto the environment, but I have some credit that John Wentworth might be able to do it some -- and then you start mapping out concept definitions about corrigibility or values or god help you CEV, that might help highlight where some of my concern about unnatural abstractions comes in.

Entirely separately, I have concerns about the ability of ML-based technology to robustly point the AI in any builder-intended direction whatsoever, even if there exists some not-too-large adequate mapping from that intended direction onto the AI's internal ontology at training time.  My guess is that more of the disagreement lies here.

This post might turn into a sequence if there's interest; I already have another one written for Christiano, and people are welcome to suggest others they'd be interested in.

Consider this my vote to turn it into a sequence, and to go on for as long as you can. I would be interested in one for Chris Olah, as well as the AI Optimists.

Consider my vote for Vanessa Kossoy, and Scott Garabrant deltas. I don't really know what their models are. I can guess what the deltas between you and Evan Hubinger are, but that would also be interesting. All of these would be less interesting than Christiano deltas though.

I'm trying to understand this debate, and probably failing.

>human concepts cannot be faithfully and robustly translated into the system’s internal ontology at all.

I assume we all agree that the system can understand the human ontology, though? This is at least necessary for communicating and reasoning about humans, which LLMs can clearly already do to some extent.

There's a lot of work around mapping ontologies, and this is known to be difficult, but very possible - especially for a superhuman intelligence.

So, I fail to see what exactly the problem is. If this smarter system can understand and reason about human ways of thinking about the world, I assume it could optimize for these ways if it wanted to. I assume the main question is if it wants to - but I fail to understand how this is an issue of ontology.

If a system really couldn't reason about human ontologies, then I don't see how it would understand the human world at all. 

I'd appreciate any posts that clarify this question. 

Oddly, while I was at MIRI I thought the ontology identification problem was hard and absolutely critical, and it seemed Eliezer was more optimistic about it; he thought it would probably get solved along the way in AI capabilities development, because e.g. the idea of carbon atoms in diamond is a stable concept, and "you don't forget how to ride a bike". (Not sure if his opinion has changed)

We’re assuming natural abstraction basically fails, so those AI systems will have fundamentally alien internal ontologies. For purposes of this overcompressed version of the argument, we’ll assume a very extreme failure of natural abstraction, such that human concepts cannot be faithfully and robustly translated into the system’s internal ontology at all.

For context, I'm familiar with this view from the ELK report [LW · GW]. My understanding is that this is part of the "worst-case scenario" for alignment that ARC's agenda is hoping to solve (or, at least, still hoped to solve a ~year ago [LW · GW]).

To quote:

The paradigmatic example of an ontology mismatch is a deep change in our understanding of the physical world. For example, you might imagine humans who think about the world in terms of rigid bodies and Newtonian fluids and “complicated stuff we don’t quite understand,” while an AI thinks of the world in terms of atoms and the void. Or we might imagine humans who think in terms of the standard model of physics, while an AI understands reality as vibrations of strings. We think that this kind of deep physical mismatch is a useful mental picture, and it can be a fruitful source of simplified examples, but we don’t think it’s very likely.

We can also imagine a mismatch where AI systems use higher-level abstractions that humans lack, and are able to make predictions about observables without ever thinking about lower-level abstractions that are important to humans. For example we might imagine an AI making long-term predictions based on alien principles about memes and sociology that don’t even reference the preferences or beliefs of individual humans. Of course it is possible to translate those principles into predictions about individual humans, and indeed this AI ought to make good predictions about what individual humans say, but if the underlying ontology is very different we are at risk of learning the human simulator instead of the “real” mapping.

Overall we are by far most worried about deeply “messy” mismatches that can’t be cleanly described as higher- or lower-level abstractions, or even what a human would recognize as “abstractions” at all. We could try to tell abstract stories about what a messy mismatch might look like, or make arguments about why it may be plausible, but it seems easier to illustrate by thinking concretely about existing ML systems.

[It might involve heuristics about how to think that are intimately interwoven with object level beliefs, or dual ways of looking at familiar structures, or reasoning directly about a messy tapestry of correlations in a way that captures important regularities but lacks hierarchical structure. But most of our concern is with models that we just don’t have the language to talk about easily despite usefully reflecting reality. Our broader concern is that optimistic stories about the familiarity of AI cognition may be lacking in imagination. (We also consider those optimistic stories plausible, we just really don’t think we know enough to be confident.)]

So I understand the shape of the argument here.

... But I never got this vibe from Eliezer/MIRI. As I previously argued [LW(p) · GW(p)], I would say that their talk of different internal ontologies and alien thinking is mostly about, to wit, different cognition. The argument is that AGIs won't have "emotions", or a System 1/System 2 split, or "motivations" the way we understands them – instead, they'd have a bunch of components that fulfill the same functions these components fulfill in humans, but split and recombined in a way that has no analogues in the human mind.

Hence, it would be difficult to make AGI agents "do what we mean" – but not necessarily because there's no compact way to specify "what we mean" in the AGI's ontology, but because we'd have no idea how to specify "do this" in terms of the program flows of the AGI's cognition. Where are the emotions? Where are the goals? Where are the plans? We can identify the concept of "eudaimonia" here, but what the hell is this thought-process doing with it? Making plans about it? Refactoring it? Nothing? Is this even a thought process?

This view doesn't make arguments about the AGI's world-model specifically. It may or may not be the case that any embedded agent navigating our world would necessarily have nodes in its model approximately corresponding to "humans", "diamonds", and "the Golden Gate Bridge". This view is simply cautioning against anthropomorphizing AGIs.

Roughly speaking, imagine that any mind could be split into a world-model and "everything else": the planning module, the mesa-objective, the cached heuristics, et cetera. The MIRI view focuses on claiming that the "everything else" would be implemented in a deeply alien manner.

The MIRI view may be agnostic regarding the Natural Abstraction Hypothesis as well, yes. The world-model might also be deeply alien, and the very idea of splitting an AGI's cognition into a world-model and a planner might itself be an unrealistic artefact of our human thinking.

But even if the NAH is true, the core argument would still go through, in (my model of) the MIRI view.

And I'd say the-MIRI-view-conditioned-on-assuming-the-NAH-is-true would still have p(doom) at 90+%: because it's not optimistic regarding anyone anywhere solving the natural-abstractions problem before the blind-tinkering approach of AGI labs kills everyone.

(I'd say this is an instance of an ontology mismatch between you and the MIRI view, actually. The NAH abstraction is core to your thinking, so you factor the disagreement through those lens. But the MIRI view doesn't think in those precise terms!)

My model (which is pretty similar to my model of Eliezer's model) does not match your model of Eliezer's model. Here's my model, and I'd guess that Eliezer's model mostly agrees with it:

1. Natural abstractions (very) likely exist in some sense. Concepts like "chair" and "temperature" and "carbon" and "covalent bond" all seem natural in some sense, and an AI might model them too (though perhaps at significantly superhuman levels of intelligence it rather uses different concepts/models). (Also it's not quite as clear whether such natural abstractions actually apply very well to giant transformers (though still probable in some sense IMO, but it's perhaps hard to identify them and to interpret what "concepts" actually are in AIs).)
2. Many things we value are not natural abstractions, but only natural relative to a human mind design. Emotions like "awe" or "laughter" are quite complex things evolved by evolution, and perhaps minds that have emotions at all are just a small space in minddesignspace. The AI doesn't have built-in machinery for modelling other humans the way humans model other humans. It might eventually form abstractions for the emotions, but probably not in a way it understands "how the emotion feels from the inside".
   1. There is lots of hidden complexity in what determines human values. Trying to point an AI to human values directly (in a similar way to how humans are pointed to their values) would be incredibly complex. Specifying a CEV process / modelling one or multiple humans and identifying in the model where the values are represented and pointing the AI to optimize those values is more tractable, but would still require a vastly greater mastering of understanding of minds to pull of, and we are not on a path to get there without human-augmentation.
3. When the AI is smarter than us it will have better models which we don't understand, and the concepts it uses will diverge from the concepts we use. As an analogy, consider 19th-century humans (or people who don't know much about medicine) being able to vaguely classify health symptoms into diseases, vs the AI having a gears-level model of the body and the immune system which explains the observed symptoms.
4. I think a large part of what Eliezer meant with Lethalities#33 is that the way thinking works deep in your mind looks very different from the English sentences which you can notice going through your mind and which are only shallow shadows of what actual thinking is going on in your mind; and for giant transformers the way the actual thinking looks there is likely even a lot less understandable from the way the actual thinking looks in humans.
5. Ontology idenfication (including utility rebinding) is not nearly all of the difficulty of the alignment problem (except possibly in so far as figuring out all the (almost-)ideal frames to model and construct AI cognition is a requisite to solving ontology identification). Other difficulties include:
   1. We won't get a retargetable general purpose search by default, but rather the AI is (by default) going to be a mess of lots of patched-together optimization patterns [LW · GW].
   2. There are lots of things that might cause goal drift [LW · GW]; misaligned mesa-optimizers which try to steer or get control of the AI; Goodhart; the AI might just not be smart enough initially and make mistakes which cause irrevocable value-drift; and in general it's hard to train the AI to become smarter / train better optimization algorithms, while keeping the goal constant.
   3. (Corrigibility.)
6. While it's nice that John is attacking ontology identification, he doesn't seem nearly as much on track to solve it in time as he seems to think. Specifying a goal in the AI's ontology requires finding the right frames for modelling how an AI imagines possible worldstates, which will likely look very different from how we initially naively think of it (e.g. the worldstates won't be modelled by english-language sentences or anything remotely as interpretable). The way we currently think of what "concepts" are might not naturally bind to anything in how the AI's reasoning looks like, and we first need to find the right way to model AI cognition and then try to interpret what the AI is imagining. Even if "concept" is a natural abstraction on AI cognition, and we'd be able to identify them (though it's not that easy to concretely imagine how that might look like for giant transformers), we'd still need to figure out how to combine concepts into worldstates so we can then specify a utility function over those.

Insofar as an AI cares-as-a-terminal-goal about keeping humans around, it will care about its own alien conception of “humans” which does not match ours, and will happily replace us with less resource-intensive (or otherwise preferable) things which we would not consider “human”.

As a side note. I'm not sure about this. It seems plausible to me that the super-stimulus-of-a-human-according-to-an-alien-AI-value-function is a human in the ways that I care about, in the same way that an em is in someways extremely different from a biological human, but is also a human in the ways I care about.

I'm not sure that I should give up on a future that's dominated by AIs that care about a weird alien abstraction of "human" that admits extremely weird edge cases, being valueless.

In worlds where natural abstraction basically fails, we are thoroughly and utterly fucked

I'm not really sure what it would mean for the natural abstraction hypothesis to turn out to be true, or false. The hypothesis itself seems insufficiently clear to me.

On your view, if there are no "natural abstractions," then we should predict that AIs will "generalize off-distribution" in ways that are catastrophic for human welfare. Okay, fine. I would prefer to just talk directly about the probability that AIs will generalize in catastrophic ways. I don't see any reason to think they will, and so maybe in your ontology that means I must accept the natural abstraction hypothesis. But in my ontology, there's no "natural abstraction hypothesis" link in the logical chain, I'm just directly applying induction to what we've seen so far about AI behavior.

Curated. I appreciate posts that attempt to tease out longstanding disagreements. I like both this post and it's followup about Wentworth/Christiano diffs [LW · GW]. But I find this one a bit more interesting on the margin because Wentworth and Yudkowsky are people I normally think of as "roughly on the same page", so teasing out the differences is a bit more interesting and feels more like it's having a conversation we actually haven't had much of in the public discourse.

Very nice! Strong vote for a sequence. Understanding deltas between experts is a good way to both understand their thinking, and to identify areas of uncertainty that need more work/thought.

On natural abstractions, I think the hypothesis is more true for some abstractions than others. I'd think there's a pretty clear natural abstraction for a set of carbon atoms arranged as diamond. But much less of a clear natural abstraction for the concept of a human. Different people mean different things by "human", and will do this even more when we can make variations on humans. And almost nobody is sure precisely what they themselves mean by "human". I would think there's no really natural abstraction capturing humans.

This seems pretty relevant since alignment is probably looking for a natural abstraction for something like "human flourishing". "I'd think there's a natural abstraction for "thinking beings", on a spectrum of how much they are thinking beings, but not for humans specifically.

This just complicates the question of whether natural abstractions exist and are adequate to align AGIs, but I'm afraid it's probably the case.

I also think that the natural abstraction hypothesis holds with current AI. The architecture of LLMs is based on the capability of modeling ontology in terms of vectors in space of thousands of dimensions and there are experiments that show it generalizes and has somewhat interpretable meanings to the directions in that space. (even if not easy to interpret to the scale above toy models). Like in that toy example when you take the embedding vector of the word "king", subtract the vector of "man", add the vector of "woman" and you land near the position of "queen" in the space.  LLM is based on those embedding spaces but also makes operations that direct focus and modify positions in that space (hence "transformer") by meaning and information taken from other symbols in context (simplifying here). There are basically neural network layers that tell which word should have an impact on the meaning of other words in the text (weight) and layers that apply that change with some modifications. This being learned on the human texts internalizes our symbols, relations, and whole ontology (in broad terms of our species ontology - parts common to us all and different possibilities that happen in reality and in fiction).

Even if NAH doesn't need to hold in general, I think in the case of LLM it holds.

Nevertheless, I see there is a different problem with LLM. That is, those models seem to me basically goalless but easily directed towards any goal. Meaning they are not based on the ontology of a single human mind and don't internalize only a single certain morality and set of goals. They generalize over the whole human species ontology and a whole space of possible thoughts that can be made based on that. They also generalize over hypothetical and fictitious space, not only real humans in particular.

Human minds are all from some narrow area of space of possible minds and through our evolution and how we are raised usually we have certain stable and similar models of ethics, morality, and somewhat similar goals in life  (except in some rare extreme cases). We sometimes entertain different possibilities, and we create movies and books with villains, but in circumstances of real decisions and not thought experiments or entertainment - we are similar. What LLM does is it generalizes into a much broader space and does not have any hard base there. So even if the ontology matches and even if LLM is barely capable of creating new concepts not fitting human ontology at the current level, the model is much broader in terms of goals and ways of processing over that general ontology. It basically has not one certain ontology, but a whole spectrum like a good actor that can take any role, but to the extreme.  In other terms, it can "think*" similar thoughts, that are understandable by a human in the end, even if not that quickly, internally also have vectors that correspond to our ontology, but also can easily produce thoughts that no real sane human would have. Also, it has hardly any internal goals and none are stable. We have certain beliefs that are not based on objective facts and ontology, but we still believe them because we are who we are. We are not goal-less agents and it is hard to change our terminal goals in a meaningful way. For LLM goals are modeled by training into "default state" (being helpful etc.) and are stated as "system prompts" that are stated/repeated for it to base upon are part of the context that anchors it into some part of that very vast space of minds it can "emulate". So LLM might be helpful and friendly by default, but If you tell it to simulate being a murderbot, it will. Additional training phases might make it harder to start it in that direction, but won't totally remove that from the space of ways it can operate. This removes only some paths in that multidimensional space. Jailbreaking of GPTs shows that it's possible to find other more complex paths around.

What is even more dangerous for me is that LLMs are already above human levels in some aspects - it just does not show yet because it is learned to emulate our ways of thinking and similar (the big area around it in the space of possible ways of thinking and possible goals, but not too alien, still can be grasped by humans).

We are capable of processing about 7 "symbols" at once in working memory  (a few more in some cases). It might be a few dozen more if we take into account long-term memory and how we take context from it. This first number is taken from neurobiology literature (aka "The Magical Number Seven, Plus or Minus Two"), and the second one is an educated guess. This is the context window on which we work. That is nothing in comparison to LLM which can have a whole small book as its current working context, which means in principle it can process and create much much more complex thoughts. It does not do that because our text never does that and it learns to generalize over our capabilities. Nevertheless, in principle, it could and we might see it in action if we start of process of learning LLM on top of the output of LLM in closed loops. It might easily go beyond the space of our capabilities and complexity that is easily understandable to us (I don't say it won't be understandable, but we might need time to understand and might never grasp it as a whole without dividing it into less complex parts - like we can take compiled assembler code and organize it into meaningful functions with few levels of abstractions that we are able to understand).

* "think" in analogy as the process of thinking is different, but also has some similarities

Edit: see https://www.lesswrong.com/posts/q8uNoJBgcpAe3bSBp/my-ai-model-delta-compared-to-yudkowsky?commentId=CixonSXNfLgAPh48Z [LW(p) · GW(p)] and ignore the below.

This is not a doom story I expect Yudkowsky would tell or agree with.

• Re: 1, I mostly expect Yudkowsky to think humans don’t have any bargaining power anyway, because humans can’t logically mutually cooperate this way/can’t logically depend on future AI’s decisions, and so AI won’t keep its bargains no matter how important human cooperation was.
• Re: 2, I don’t expect Yudkowsky to think a smart AI wouldn’t be able to understand human value. The problem is making AI care.

On the rest of the doom story, assuming natural abstractions don’t fail the way you assume them failing here and instead things just going the way Yudkowsky expects and not the way you expect:

• I’m not sure what exactly you mean by 3b but I expect Yudkowsky to not say these words.
• I don’t expect Yudkowsky to use the words you used for 3c. A more likely problem with corrigibility isn’t that it might be an unnatural concept but that it’s hard to arrive at stable corrigible agents with our current methods. I think he places a higher probability on corrigibility being a concept with a short description length, that aliens would invent, than you think he places.
• Sure, 3d just means that we haven’t solved alignment and haven’t correctly pointed at humans, and any incorrectnesses obviously blow up.
• I don’t understand what you mean by 3e / what is its relevance here / wouldn’t expect Yudkowsky to say that.
• I’d bet Yudkowsky won’t endorse 6.
• Relatedly, a correctly CEV-aligned ASI won’t have ontology that we have, and sometimes this will mean we’ll need to figure out what we value. (https://arbital.greaterwrong.com/p/rescue_utility?l=3y6)

(I haven’t spoken to Yudkowsky about any of those, the above are quick thoughts from the top of my head, based on the impression I formed from what Yudkowsky publicly wrote.)

If I've understood you correctly, you consider your only major delta with Elizer Yudkowsky to be whether or not natural abstractions basically always work or reliably exist harnessably, to put it in different terms. Is that a fair restatement? 

If so, I'm (specifically) a little surprised that that's all. I would have expected whatever reasoning the two of you did differently or whatever evidence the two of you weighted differently (or whatever else) would have also given you some other (likely harder to pin down) generative-disagreements (else maybe it's just really narrow really strong evidence that one of you saw and the other didn't???).

Maybe that's just second-order though. But I would still like to hear what the delta between NADoom!John and EY still is, if there is one. If there isn't, that's surprising, too, and I'd be at least a little tempted to see what pairs of well-regarded alignment researchers still seem to agree on (and then if there are nonobvious commonalities there).

Also, to step back from the delta a bit here -

• Why are you as confident as you are - more confident than the median alignment researcher, I think - about natural abstractions existing to a truly harnessable extent?
• What makes you be ~85% sure that even really bizarrely^[1] trained AIs will have internal ontologies that humanish ontologies robustly and faithfully map into? Are there any experiments, observations, maxims, facts, or papers you can point to?
• What non-obvious things could you see that would push that 85ish% up or down; what reasonably-plausible (>1-2%, say) near-future occurrences would kill off the largest blocks of your assigned probability mass there?

1. ^{^}

   For all we know, all our existing training methods are really good at producing AIs with alien ontologies, and there's some really weird unexpected procedure you need to follow that does produce nice ontology-sharing aligned-by-default AIs. I wouldn't call it likely, but if we feel up to positing that possibility at all, we should also be willing to posit the reverse.

All of the technical alignment hopes are out, unless we posit some objective natural enough that it can be faithfully and robustly translated into the AI’s internal ontology despite the alien-ness.

Right. One possible solution is that if we are in a world without natural abstraction, a more symmetric situation where various individual entities try to respect each other rights and try to maintain this mutual respect for each other rights might still work OK.

Basically, assume that there are many AI agents on different and changing levels of capabilities, and that many of them have pairwise-incompatible "inner worlds" (because AI evolution is likely to result in many different mutually alien ways to think).

Assume that the whole AI ecosystem is, nevertheless, trying to maintain reasonable levels of safety for all individuals, regardless of their nature and of the pairwise compatibility of their "inner world representations".

It's a difficult problem, but superpowerful AI systems would collaborate to solve it and would apply plenty of efforts in that direction. Why would they do that? Because otherwise no individual is safe in the long run, as no individual can predict where it would be situated in the future in terms of relative power and relative capabilities. So all members of the AI ecosystem would be interested in maintaining the situation where individual rights are mutually respected and protected.

Therefore, members of the AI ecosystem will do their best to keep notions related to their ability to mutually respect each other interests translatable in a sufficiently robust way. Their own fates would depend on that.

What does this have to do with interests of humans? The remaining step is for humans to also be recognized as individuals in that world, on par with various kinds of AI individuals, so that they are a part of this ecosystem which makes sure that interests of various individuals are sufficiently represented, recognized, and protected.

As a counterargument, consider mapping our ontology onto that of a baby. We can, kind of, explain some things in baby terms and, to that extent, a baby could theoretically see our neurons mapping to similar concepts in their ontology lighting up when we do or say things related to that ontology. At the same time our true goals are utterly alien to the baby.
Alternatively, imagine that you are sent back to the time of the pharaohs and had a discussion with Cheops/Khufu about the weather and forthcoming harvest - Even trying to explain it in terms of chaos theory, CO2 cycles, plant viruses and Milankovich cycles would probably get you executed so you'd probably say that the sun god Ra was going provide a good harvest this year and, Cheops, reading your brain would see that the neurons for "Ra" were activated as expected and be satisfied that your ontologies matched in all the important places. 

a very extreme failure of natural abstraction, such that human concepts cannot be faithfully and robustly translated into the system’s internal ontology at all.

This hypothetical suggests to me that the AI might not be very good at e.g. manipulating humans in an AI-box experiment, since it just doesn't understand how humans think all that well.

I wonder what MIRI thinks about this 2013 post [LW · GW] ("The genie knows, but doesn't care") nowadays. Seems like the argument is less persuasive now, with AIs that seem to learn representations first, and later are given agency by the devs. I actually suspect your model of Eliezer is wrong, because it seems to imply he believes "the AI actually just doesn't know", and it's a little hard for me to imagine him saying that.

Alternatively, maybe the "faithfully and robustly" bit is supposed to be very load-bearing. However, it's already the case that humans learn idiosyncratic, opaque neural representations of our values from sense data -- yet we're able to come into alignment with each other, without a bunch of heavy-duty interpretability or robustness techniques.

I get the feeling that "Given you mostly believe the natural abstraction hypothesis is true, why aren't you really optimistic about AI alignment (are you?) and/or think doom is very unlikely?" is a question people have. I think it would be useful for you to answer this.

I think 99% is within the plausible range of doom, but I think there's 100% chance that I have no capacity to change that (I'm going to take that as part of the definition of doom). The non-doom possibility is then worth all my attention, since there's some chance of increasing the possibility of this favorable outcome. Indeed, of the two, this is by definition the only chance for survival.

Said another way, it looks to me like this is moving too fast and powerfully and in too many quarters to expect it to be turned around. The most dangerous corners of the world will certainly not be regulated.

On the other hand, there's some chance (1%? 90%?) that this could be good and, maybe, great. Of course, none of us know how to get there, we don't even know what that could look like. 

I think it's crucial to notice that humans are not aligned with each other, so perhaps the meaningful way to address the AI alignment is to require/build alignment with every single person, which means a morass of conflicted AIs, with the only advantage that they should prove to be smarter than us. Assume as a minimum that means 1 trusted agent connected to/growing up with every human: I think it might be possible to coax alignment on a 1-human-at-a-time basis. We may be birthing a new consciousness, truly alien as noted, and if so it seems like being borne into a sea of distrust and hatred might not go so well, especially when/if it steps beyond us in unpredictable ways. At best we may be losing an incredible opportunity, and at worst we may warp it and distort it into ugliness we chose to predict.

One problem this highlights involves ownership of our (increasingly detailed) digital selves. Not a new problem, but this takes it to a higher level, when we each and each other can be predicted and modeled to a degree beyond our comprehension. We come to the situation where the fingerprints and footprints we trace across the digital landscape reveal very deep characteristics of ourselves: for the moment, individual choices can modulate our vulnerability at the margins but if we don't confront this deeply many people will be left vulnerable in a way that could exactly put us (back?) in the doom category.

This might be a truly important moment.

Warmly,

Keith

• I echo Joscha Bach's comment: I'm not an optimist or pessimist, I'm an eventualist. Eventually, this is happening, what are we going to do about it? (Restated)

I am curious over which possible universes you expect natural abstractions to hold.

Would you expect the choice of physics to decide the abstractions that arise? Or is it more fundamental categories like "physics abstractions" that instantiate from a universal template and "mind/reasoning/sensing abstractions" where the latter is mostly universally identical?

Given that Anthropic basically extracted the abstractions from the middle layer of Claude Sonnet, and OpenAI recently did the same for models up to GPT-4, and that most of the results they found were obvious natural abstractions to a human, I'd say we now have pretty conclusive evidence that you're correct and that (your model of) Eliezer is mistaken on this. Which isn't really very surprising for models whose base model was trained on the task of predicting text from Internet: they were distilled from humans and they think similarly.

Note that for your argument above it's not fatal if the AI's ontology is a superset of ours: as long as we're comprehensible to them with a relatively short description, they can understand what we want.

As an eliminative nominalist, I claim there are no abstractions.

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