Nominated Posts for the 2019 Review

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2019 Review Discussion

There has been considerable debate over whether development in AI will experience a discontinuity, or whether it will follow a more continuous growth curve. Given the lack of consensus and the confusing, diverse terminology, it is natural to hypothesize that much of the debate is due to simple misunderstandings. Here, I seek to dissolve some misconceptions about the continuous perspective, based mostly on how I have seen people misinterpret it in my own experience.

First, we need to know what I even mean by continuous takeoff. When I say it, I mean a scenario where the development of competent, powerful AI follows a trajectory that is roughly in line with what we would have expected by extrapolating from past progress. That is, there is no point at...

[Epistemic status: Strong claims vaguely stated and weakly held. I expect that writing this and digesting feedback on it will lead to a much better version in the future. EDIT: So far this has stood the test of time. EDIT: As of September 2020 I think this is one of the most important things to be thinking about.]

This post attempts to generalize and articulate a problem that people have been thinking about since at least 2016. [Edit: 2009 in fact!] In short, here is the problem:

Consequentialists can get caught in commitment races, in which they want to make commitments as soon as possible. When consequentialists make commitments too soon, disastrous outcomes can sometimes result. The situation we are in (building AGI and letting it self-modify) may be...

2JesseClifton1moIt seems like we can kind of separate the problem of equilibrium selection from the problem of “thinking more”, if “thinking more” just means refining one’s world models and credences over them. One can make conditional commitments of the form: “When I encounter future bargaining partners, we will (based on our models at that time) agree on a world-model according to some protocol and apply some solution concept (e.g. Nash or Kalai-Smorodinsky) to it in order to arrive at an agreement.” The set of solution concepts you commit to regarding as acceptable still poses an equilibrium selection problem. But, on the face of it at least, the “thinking more” part is handled by conditional commitments to act on the basis of future beliefs. I guess there’s the problem of what protocols for specifying future world-models you commit to regarding as acceptable. Maybe there are additional protocols that haven’t occurred to you, but which other agents may have committed to and which you would regard as acceptable when presented to you. Hopefully it is possible to specify sufficiently flexible methods for determining whether protocols proposed by your future counterparts are acceptable that this is not a problem.
2Daniel Kokotajlo1moIf I read you correctly, you are suggesting that some portion of the problem can be solved, basically -- that it's in some sense obviously a good idea to make a certain sort of commitment, e.g. "When I encounter future bargaining partners, we will (based on our models at that time) agree on a world-model according to some protocol and apply some solution concept (e.g. Nash or Kalai-Smorodinsky) to it in order to arrive at an agreement.” So the commitment races problem may still exist, but it's about what other commitments to make besides this one, and when. Is this a fair summary? I guess my response would be "On the object level, this seems like maybe a reasonable commitment to me, though I'd have lots of questions about the details. We want it to be vague/general/flexible enough that we can get along nicely with various future agents with somewhat different protocols, and what about agents that are otherwise reasonable and cooperative but for some reason don't want to agree on a world-model with us? On the meta level though, I'm still feeling burned from the various things that seemed like good commitments to me and turned out to be dangerous, so I'd like to have some sort of stronger reason to think this is safe."
2JesseClifton1moYeah I agree the details aren’t clear. Hopefully your conditional commitment can be made flexible enough that it leaves you open to being convinced by agents who have good reasons for refusing to do this world-model agreement thing. It’s certainly not clear to me how one could do this. If you had some trusted “deliberation module”, which engages in open-ended generation and scrutiny of arguments, then maybe you could make a commitment of the form “use this protocol, unless my counterpart provides reasons which cause my deliberation module to be convinced otherwise”. Idk. Your meta-level concern seems warranted. One would at least want to try to formalize the kinds of commitments we’re discussing and ask if they provide any guarantees, modulo equilibrium selection.

I think we are on the same page then. I like the idea of a deliberation module; it seems similar to the "moral reasoning module" I suggested a while back. The key is to make it not itself a coward or bully, reasoning about schelling points and universal principles and the like instead of about what-will-lead-to-the-best-expected-outcomes-given-my-current-credences.

Suppose that 1% of the world’s resources are controlled by unaligned AI, and 99% of the world’s resources are controlled by humans. We might hope that at least 99% of the universe’s resources end up being used for stuff-humans-like (in expectation).

Jessica Taylor argued for this conclusion in Strategies for Coalitions in Unit-Sum Games: if the humans divide into 99 groups each of which acquires influence as effectively as the unaligned AI, then by symmetry each group should end, up with as much influence as the AI, i.e. they should end up with 99% of the influence.

This argument rests on what I’ll call the strategy-stealing assumption: for any strategy an unaligned AI could use to influence the long-run future, there is an analogous strategy that a similarly-sized group...

Categorising the ways that the strategy-stealing assumption can fail:

  • Humans don't just care about acquiring flexible long-term influence, because
    • 4. They also want to stay alive.
    • 5 and 6. They want to stay in touch with the rest of the world without going insane.
    • 11. and also they just have a lot of other preferences.
    • (maybe Wei Dai's point about logical time also goes here)
  • It is intrinsically easier to gather flexible influence in pursuit of some goals, because
    • 1. It's easier to build AIs to pursue goals that are easy to check.
    • 3. It's easie
... (read more)

If you're not familiar with the double descent phenomenon, I think you should be. I consider double descent to be one of the most interesting and surprising recent results in analyzing and understanding modern machine learning. Today, Preetum et al. released a new paper, “Deep Double Descent,” which I think is a big further advancement in our understanding of this phenomenon. I'd highly recommend at least reading the summary of the paper on the OpenAI blog. However, I will also try to summarize the paper here, as well as give a history of the literature on double descent and some of my personal thoughts.

Prior work

The double descent phenomenon was first discovered by Mikhail Belkin et al., who were confused by the phenomenon wherein modern ML practitioners would


Finally got around to that one, and am also pretty into that explanation for the cases of double descent we observe. It also tentatively makes me want to say that the decrease in variance with model size is the 'real story'/primary thing we should think about.

It seems likely to me that AIs will be able to coordinate with each other much more easily (i.e., at lower cost and greater scale) than humans currently can, for example by merging into coherent unified agents by combining their utility functions. This has been discussed at least since 2009, but I'm not sure its implications have been widely recognized. In this post I talk about two such implications that occurred to me relatively recently.

I was recently reminded of this quote from Robin Hanson's Prefer Law To Values:

The later era when robots are vastly more capable than people should be much like the case of choosing a nation in which to retire. In this case we don’t expect to have much in the way of skills to


This post is excellent, in that it has a very high importance-to-word-count ratio. It'll take up only a page or so, but convey a very useful and relevant idea, and moreover ask an important question that will hopefully stimulate further thought.

"Gradient hacking" is a term I've been using recently to describe the phenomenon wherein a deceptively aligned mesa-optimizer might be able to purposefully act in ways which cause gradient descent to update it in a particular way. In Risks from Learned Optimization, we included the following footnote to reflect this possibility:

Furthermore, a deceptively aligned mesa-optimizer would be incentivized to cause there to be a systematic bias in the direction of preventing the base optimizer from modifying its mesa-objective. Thus, in the context of a local optimization process, a deceptive mesa-optimizer might try to “hack” its own gradient (by, for example, making itself more brittle in the case where its objective gets changed) to ensure that the base optimizer adjusts it in such a way that leaves its

4Adam Shimi3moAs I said elsewhere, I'm glad that my review captured points you deem important! I agree that gradient hacking isn't limited to inner optimizers; yet I don't think that defining it that way in the post was necessarily a bad idea. First, it's for coherence with Risks from Learned Optimization. Second, assuming some internal structure definitely helps with conceptualizing the kind of things that count as gradient hacking. With inner optimizer, you can say relatively unambiguously "it tries to protect it's mesa-objective", as there should be an explicit representation of it. That becomes harder without the inner optimization hypothesis. That being said, I am definitely focusing on gradient hacking as an issue with learned goal-directed systems instead of learned optimizers. This is one case where I have argued [] that a definition of goal-directedness would allow us to remove the explicit optimization hypothesis without sacrificing the clarity it brought. Two thoughts about that: * Even if some subnetwork basically captures SGD (or the relevant training process), I'm unconvinced that it would be useful in the beginning, and so it might be "written over" by the updates. * Related to the previous point, it looks crucial to understand what is needed in addition to a model of SGD in order to gradient hack. Which brings me to your next point. I'm confused about what you mean here. If the point is to make the network a local minimal, you probably just have to make it very brittle to any change. I also not sure what you mean by competing networks. I assumed it meant the neighboring models in model space, which are reachable by reasonable gradients. If that's the case, then I think my example is simpler and doesn't need the SGD modelling. If not, then I would appreciate more detailed explanations. Why is that supposed to be a good thing? Sure
4Ofer Givoli3moI think the part in bold should instead be something like "failing hard if SGD would (not) update weights in such and such way". (SGD is a local search algorithm; it gradually improves a single network.) As I already argued in another thread [] , the idea is not that SGD creates the gradient hacking logic specifically (in case this is what you had in mind here). As an analogy, consider a human that decides to 1-box in Newcomb's problem (which is related to the idea of gradient hacking, because the human decides to 1-box in order to have the property of "being a person that 1-boxs", because having that property is instrumentally useful). The specific strategy to 1-box is not selected for by human evolution, but rather general problem-solving capabilities were (and those capabilities resulted in the human coming up with the 1-box strategy).
4Adam Shimi3moAgreed. I said something similar in my comment [] . Thanks for the concrete example, I think I understand better what you meant. What you describe looks like the hypothesis "Any sufficiently intelligent model will be able to gradient hack, and thus will do it". Which might be true. But I'm actually more interested in the question of how gradient hacking could emerge without having to pass that threshold of intelligence, because I believe such examples will be easier to interpret and study. So in summary, I do think what you say makes sense for the general risk of gradient hacking, yet I don't believe it is really useful for studying gradient hacking with our current knowledge.

It does seem useful to make the distinction between thinking about how gradient hacking failures look like in worlds where they cause an existential catastrophe, and thinking about how to best pursue empirical research today about gradient hacking.

The stereotyped image of AI catastrophe is a powerful, malicious AI system that takes its creators by surprise and quickly achieves a decisive advantage over the rest of humanity.

I think this is probably not what failure will look like, and I want to try to paint a more realistic picture. I’ll tell the story in two parts:

  • Part I: machine learning will increase our ability to “get what we can measure,” which could cause a slow-rolling catastrophe. ("Going out with a whimper.")
  • Part II: ML training, like competitive economies or natural ecosystems, can give rise to “greedy” patterns that try to expand their own influence. Such patterns can ultimately dominate the behavior of a system and cause sudden breakdowns. ("Going out with a bang," an instance of optimization daemons.)


I think this post (and similarly, Evan's summary of Chris Olah's views) are essential both in their own right and as mutual foils to MIRI's research agenda. We see related concepts (mesa-optimization originally came out of Paul's talk of daemons in Solomonoff induction, if I remember right) but very different strategies for achieving both inner and outer alignment. (The crux of the disagreement seems to be the probability of success from adapting current methods.)

Strongly recommended for inclusion.

[Epistemic status: Argument by analogy to historical cases. Best case scenario it's just one argument among many. Edit: Also, thanks to feedback from others, especially Paul, I intend to write a significantly improved version of this post in the next two weeks. Edit: I never did, because in the course of writing my response I realized the original argument made a big mistake. See this review.]

I have on several occasions heard people say things like this:

The original Bostrom/Yudkowsky paradigm envisioned a single AI built by a single AI project, undergoing intelligence explosion all by itself and attaining a decisive strategic advantage as a result. However, this is very unrealistic. Discontinuous jumps in technological capability are very rare, and it is very implausible that one project

It's hard to know how to judge a post that deems itself superseded by a post from a later year, but I lean toward taking Daniel at his word and hoping we survive until the 2021 Review comes around.

2Daniel Kokotajlo3moReviewI've written up a review here [] , which I made into a separate post because it's long. Now that I read the instructions more carefully, I realize that I maybe should have just put it here and waited for mods to promote it if they wanted to. Oops, sorry, happy to undo if you like.

Note: I am not Chris Olah. This post was the result of lots of back-and-forth with Chris, but everything here is my interpretation of what Chris believes, not necessarily what he actually believes. Chris also wanted me to emphasize that his thinking is informed by all of his colleagues on the OpenAI Clarity team and at other organizations.

In thinking about AGI safety—and really any complex topic on which many smart people disagree—I’ve often found it very useful to build a collection of different viewpoints from people that I respect that I feel like I understand well enough to be able to think from their perspective. For example, I will often try to compare what an idea feels like when I put on my Paul Christiano hat to


The content here is very valuable, even if the genre of "I talked a lot with X and here's my articulation of X's model" comes across to me as a weird intellectual ghostwriting. I can't think of a way around that, though.

This post is eventually about partial agency. However, it's been a somewhat tricky point for me to convey; I take the long route. Epistemic status: slightly crazy.

I've occasionally said "Everything boils down to credit assignment problems."

What I really mean is that credit assignment pops up in a wide range of scenarios, and improvements to credit assignment algorithms have broad implications. For example:

  • Politics.
    • When politics focuses on (re-)electing candidates based on their track records, it's about credit assignment. The practice is sometimes derogatorily called "finger pointing", but the basic computation makes sense: figure out good and bad qualities via previous performance, and vote accordingly.
    • When politics instead focuses on policy, it is still (to a degree) about credit assignment. Was raising the minimum wage responsible for reduced employment? Was it

I think I have juuust enough background to follow the broad strokes of this post, but not to quite grok the parts I think Abram was most interested in. 

I definitely caused me to think about credit assignment. I actually ended up thinking about it largely through the lens of Moral Mazes (where challenges of credit assignment combine with other forces to create a really bad environment). Re-reading this post, while I don't quite follow everything, I do successfully get a taste of how credit assignment fits into a bunch of different domains.

For the "myop... (read more)

This is the first of five posts in the Risks from Learned Optimization Sequence based on the paper “Risks from Learned Optimization in Advanced Machine Learning Systems” by Evan Hubinger, Chris van Merwijk, Vladimir Mikulik, Joar Skalse, and Scott Garrabrant. Each post in the sequence corresponds to a different section of the paper.

Evan Hubinger, Chris van Merwijk, Vladimir Mikulik, and Joar Skalse contributed equally to this sequence. With special thanks to Paul Christiano, Eric Drexler, Rob Bensinger, Jan Leike, Rohin Shah, William Saunders, Buck Shlegeris, David Dalrymple, Abram Demski, Stuart Armstrong, Linda Linsefors, Carl Shulman, Toby Ord, Kate Woolverton, and everyone else who provided feedback on earlier versions of this sequence.



The goal of this sequence is to analyze the type of learned optimization that occurs when a...

For me, this is the paper where I learned to connect ideas about delegation to machine learning. The paper sets up simple ideas of mesa-optimizers, and shows a number of constraints and variables that will determine how the mesa-optimizers will be developed – in some environments you want to do a lot of thinking in advance then delegate execution of a very simple algorithm to do your work (e.g. this simple algorithm Critch developed that my group house uses to decide on the rent for each room), and in some environments you want to do a little thinking and ... (read more)

6DanielFilan3moReview[NB: this is a review of the paper, which I have recently read, not of the post series, which I have not] For a while before this paper was published, several people in AI alignment had discussed things like mesa-optimization as serious concerns. That being said, these concerns had not been published in their most convincing form in great details. The two counterexamples that I’m aware of are the posts What does the universal prior actually look like? [] by Paul Christiano, and Optimization daemons [] on Arbital. However, the first post only discussed the issue in the context of Solomonoff induction, where the dynamics are somewhat different, and the second is short and hard to discover. I see the value in this paper as taking these concerns, laying out (a) a better (altho still imperfectly precise) concretization of what the object of concern is and (b) how it could happen, and putting it in a discoverable and citable format. By doing so, it moves the discussion forward by giving people something concrete to actually reason and argue about. I am relatively convinced that mesa-optimization (somewhat more broadly construed than in the paper, see below) is a problem for AI alignment, and I think the arguments in the paper are persuasive enough to be concerning. I think the weakest argument is in the deceptive alignment section: it is not really made clear why mesa-optimizers would have objectives that extend across parameter updates. As I see it, the two biggest flaws with the paper are: Its heuristic nature. The arguments given do not reach the certainty of proofs, and no experimental evidence is provided. This means that one can have at most provisional confidence that the arguments are correct and that the concerns are real (which is not to imply that certainty is required to warrant concern and further research). Premature formalizatio

An actual debate about instrumental convergence, in a public space! Major respect to all involved, especially Yoshua Bengio for great facilitation.

For posterity (i.e. having a good historical archive) and further discussion, I've reproduced the conversation here. I'm happy to make edits at the request of anyone in the discussion who is quoted below. I've improved formatting for clarity and fixed some typos. For people who are not researchers in this area who wish to comment, see the public version of this post here. For people who do work on the relevant areas, please sign up in the top right. It will take a day or so to confirm membership.

Original Post

Yann LeCun: "don't fear the Terminator", a short opinion piece by Tony Zador and me that was just...

Note 1: This review is also a top-level post.

Note 2: I think that 'robust instrumentality' is a more apt name for 'instrumental convergence.' That said, for backwards compatibility, this comment often uses the latter. 

In the summer of 2019, I was building up a corpus of basic reinforcement learning theory. I wandered through a sun-dappled Berkeley, my head in the clouds, my mind bent on a single ambition: proving the existence of instrumental convergence. 


I needed to find the right definitions first, and I couldn't even imagine what... (read more)

This essay is an adaptation of a talk I gave at the Human-Aligned AI Summer School 2019 about our work on mesa-optimisation. My goal here is to write an informal, accessible and intuitive introduction to the worry that we describe in our full-length report.

I will skip most of the detailed analysis from our report, and encourage the curious reader to follow up this essay with our sequence or report.

The essay has six parts:

Two distinctions draws the foundational distinctions between
“optimised” and “optimising”, and between utility and reward.

What objectives? discusses the behavioral and internal approaches to understanding objectives of ML systems.

Why worry? outlines the risk posed by the utility ≠ reward gap.

Mesa-optimisers introduces our language for analysing this worry.

An alignment agenda sketches different alignment problems presented by these ideas,...

More than a year since writing this post, I would still say it represents the key ideas in the sequence on mesa-optimisation which remain central in today's conversations on mesa-optimisation. I still largely stand by what I wrote, and recommend this post as a complement to that sequence for two reasons:

First, skipping some detail allows it to focus on the important points, making it better-suited than the full sequence for obtaining an overview of the area. 

Second, unlike the sequence, it deemphasises the mechanism of optimisation, and explicitly cas... (read more)

4Oliver Habryka3moReviewI think this post and the Gradient Hacking [] post caused me to actually understand and feel able to productively engage with the idea of inner-optimizers. I think the paper and full sequence was good, but I bounced off of it a few times, and this helped me get traction on the core ideas in the space. I also think that some parts of this essay hold up better as a core abstraction than the actual mesa-optimizer paper itself, though I am not at all confident about this. But I just noticed that when I am internally thinking through alignment problems related to inner optimization, I more often think of Utility != Reward than I think of most of the content in the actual paper and sequence. Though the sequence set the groundwork for this, so of course giving attribution is hard.
2Ben Pace3moFor another datapoint, I'll mention that I didn't read this post nor Gradient Hacking at the time, I read the sequence, and I found that to be pretty enlightening and quite readable.

AI risk ideas are piling up in my head (and in my notebook) faster than I can write them down as full posts, so I'm going to condense multiple posts into one again. I may expand some or all of these into full posts in the future. References to prior art are also welcome as I haven't done an extensive search myself yet.

The "search engine" model of AGI development

The current OpenAI/DeepMind model of AGI development (i.e., fund research using only investor / parent company money, without making significant profits) isn't likely to be sustainable, assuming a soft takeoff, but the "search engine" model very well could be. In the "search engine" model, a company (and eventually the AGI itself) funds AGI research and development by selling AI


I have now linked at least 10 times to the heading on "'Generate evidence of difficulty' as a research purpose" section of this post. It was a thing that I kind of wanted to point to before this post came out, but felt confused about it, and this post finally gave me a pointer to it. 

I think that section was substantially more novel and valuable to me than the rest of this post, but it is also evidence that others might have also not had some of the other ideas on their map, and so they might found it similarly valuable because of a different section. 

This post is based on chapter 15 of Uri Alon’s book An Introduction to Systems Biology: Design Principles of Biological Circuits. See the book for more details and citations; see here for a review of most of the rest of the book.

Fun fact: biological systems are highly modular, at multiple different scales. This can be quantified and verified statistically, e.g. by mapping out protein networks and algorithmically partitioning them into parts, then comparing the connectivity of the parts. It can also be seen more qualitatively in everyday biological work: proteins have subunits which retain their function when fused to other proteins, receptor circuits can be swapped out to make bacteria follow different chemical gradients, manipulating specific genes can turn a fly’s antennae into legs, organs perform specific...

The material here is one seed of a worldview which I've updated toward a lot more over the past year. Some other posts which involve the theme include Science in a High Dimensional World, What is Abstraction?, Alignment by Default, and the companion post to this one Book Review: Design Principles of Biological Circuits.

Two ideas unify all of these:

  1. Our universe has a simplifying structure: it abstracts well, implying a particular kind of modularity.
  2. Goal-oriented systems in our universe tend to evolve a modular structure which reflects the structure of the u
... (read more)

Technical Appendix: First safeguard?

This sequence is written to be broadly accessible, although perhaps its focus on capable AI systems assumes familiarity with basic arguments for the importance of AI alignment. The technical appendices are an exception, targeting the technically inclined.

Why do I claim that an impact measure would be "the first proposed safeguard which maybe actually stops a powerful agent with an imperfect objective from ruining things – without assuming anything about the objective"?

The safeguard proposal shouldn't have to say "and here we solve this opaque, hard problem, and then it works". If we have the impact measure, we have the math, and then we have the code.

So what about:


Here are prediction questions for the predictions that TurnTrout himself provided in the concluding post of the Reframing Impact sequence

Elicit Prediction (eli
... (read more)

(Cross-posted to personal blog. Summarized in Alignment Newsletter #76. Thanks to Jan Leike and Tom Everitt for their helpful feedback on this post.)

There are a few different classifications of safety problems, including the Specification, Robustness and Assurance (SRA) taxonomy and the Goodhart's Law taxonomy. In SRA, the specification category is about defining the purpose of the system, i.e. specifying its incentives. Since incentive problems can be seen as manifestations of Goodhart's Law, we explore how the specification category of the SRA taxonomy maps to the Goodhart taxonomy. The mapping is an attempt to integrate different breakdowns of the safety problem space into a coherent whole. We hope that a consistent classification of current safety problems will help develop solutions that are effective for entire classes of problems,...

Writing this post helped clarify my understanding of the concepts in both taxonomies - the different levels of specification and types of Goodhart effects. The parts of the taxonomies that I was not sure how to match up usually corresponded to the concepts I was most confused about. For example, I initially thought that adversarial Goodhart is an emergent specification problem, but upon further reflection this didn't seem right. Looking back, I think I still endorse the mapping described in this post.

I hoped to get more comments on this post... (read more)

Since the CAIS technical report is a gargantuan 210 page document, I figured I'd write a post to summarize it. I have focused on the earlier chapters, because I found those to be more important for understanding the core model. Later chapters speculate about more concrete details of how AI might develop, as well as the implications of the CAIS model on strategy. ETA: This comment provides updates based on more discussion with Eric.

The Model

The core idea is to look at the pathway by which we will develop general intelligence, rather than assuming that at some point we will get a superintelligent AGI agent. To predict how AI will progress in the future, we can look at how AI progresses currently -- through research and development (R&D)...

I trust past-me to have summarized CAIS much better than current-me; back when this post was written I had just finished reading CAIS for the third or fourth time, and I haven't read it since. (This isn't a compliment -- I read it multiple times because I had a lot of trouble understanding it.)

I've put in two points of my own in the post. First:

(My opinion: I think this isn't engaging with the worry with RL agents -- typically, we're worried about the setting where the RL agent is learning or planning at test time, which can happen in learn-to-learn and on

... (read more)
2Oliver Habryka3moReviewI think the CAIS framing that Eric Drexler proposed gave concrete shape to a set of intuitions that many people have been relying on for their thinking about AGI. I also tend to think that those intuitions and models aren't actually very good at modeling AGI, but I nevertheless think it productively moved the discourse forward a good bit. In particular I am very grateful about the comment thread between Wei Dai and Rohin, which really helped me engage with the CAIS ideas, and I think were necessary to get me to my current understanding of CAIS and to pass the basic ITT of CAIS (which I think I have succeeded in in a few conversations I've had since the report came out). An additional reference that has not been brought up in the comments or the post is Gwern's writing on this, under the heading: "Why Tool AIs Want to Be Agent AIs" []
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