All of HoldenKarnofsky's Comments + Replies

With apologies for the belated response: I think greghb makes a lot of good points here, and I agree with him on most of the specific disagreements with Daniel. In particular:

  • I agree that "Bio Anchors doesn't presume we have a brain, it presumes we have transformers. And transformers don't know what to do with a lifetime of experience, at least nowhere near as well as an infant brain does." My guess is that we should not expect human-like sample efficiency from a simple randomly initialized network; instead, we should expect to extensively train a network
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I don't think I am following the argument here. You seem focused on the comparison with evolution, which is only a minor part of Bio Anchors, and used primarily as an upper bound. (You say "the number is so vastly large (and actually unknown due to the 'level of details' problem) that it's not really relevant for timelines calculations," but actually Bio Anchors still estimates that the evolution anchor implies a ~50% chance of transformative AI this century.)

Generally, I don't see "A and B are very different" as a knockdown counterargument to "If A requir... (read more)

3Adam Shimi6mo
Thanks for the answer! Unfortunately, I don't have the time at the moment to answer in detail and have more of a conversation, as I'm fully focused on writing a long sequence about pushing for pluralism in alignment and extracting the core problem out of all the implementation details and additional assumption. I plan on going back to analyzing timeline research in the future, and will probably give better answers then. That being said, here are quick fire thoughts: * I used the evolution case because I consider it the most obvious/straightforward case, in that it sounds so large that everyone instantly assumes that it gives you an upper bound. * My general impression about this report (and one I expect Yudkowsky to share) is that it didn't made me update at all. I already updated from GPT and GPT3, and I didn't find new bits of evidence in the report and the discussions around it, despite the length of it. My current impression (please bear in mind that I haven't taken the time to study the report from that angle, so I might change my stance) is that this report, much like a lot of timeline work, seems like it takes as input a lot of assumption, and gives as output far less than was assumed. It's the opposite of compression — a lot of assumptions are needed to conclude things that aren't that strong and constraining.
1Matthew Barnett6mo
Thanks for the thoughtful reply. Here's my counter-reply. You frame my response as indicating "disagreements". But my tweet said "I broadly agree" with you, and merely pointed out ways that I thought your statements were misleading. I do just straight up disagree with you about two specific non-central claims you made, which I'll get to later. But I'd caution against interpreting me as disagreeing with you by any degree greater than what is literally implied by what I wrote. Before I get to the specific disagreements, I'll just bicker about some points you made in response to me. I think this sort of quibbling could last forever and it would serve little purpose to continue past this point, so I release you from any obligation you might think you have to reply to these points. However, you might still enjoy reading my response here, just to understand my perspective in a long-form non-Twitter format. Note: I continued to edit my response after I clicked "submit", after realizing a few errors of mine. Apologies if you read an erroneous version. MY QUIBBLES WITH WHAT YOU WROTE You said, The fact that the median for the conservative analysis is right at 2100 — which indeed is part of the 21st century [https://en.wikipedia.org/wiki/21st_century] — means that when you said, "You can run the bio anchors analysis in a lot of different ways, but they all point to transformative AI this century", you were technically correct, by the slimmest of margins. I had the sense that many people might interpret your statement as indicating a higher degree of confidence; that is, maybe something like "even the conservative analysis produces a median prediction well before 2100." Maybe no one misinterpreted you like that! It's very reasonable for to think that no one would have misinterpreted you. But this incorrect interpretation of your statement was, at least to me, the thinking that I remember having at the time I read the sentence. I intend to produce fuller thoughts on t

The Bio Anchors report is intended as a tool for making debates about AI timelines more concrete, for those who find some bio-anchor-related bound helpful (e.g., some think we should lower bound P(AGI) at some reasonably high number for any year in which we expect to hit a particular kind of "biological anchor"). Ajeya's work lengthened my own timelines, because it helped me understand that some bio-anchor-inspired arguments for shorter timelines didn't have as much going for them as I'd thought; but I think it may have shortened some other folks'.

(The pre... (read more)

I agree with this. I often default to acting as though we have ~10-15 years, partly because I think leverage is especially high conditional on timelines in that rough range.

I'm not sure why this isn't a very general counterexample. Once we've decided that the human imitator is simpler and faster to compute, don't all further approaches (e.g., penalizing inconsistency) involve a competitiveness hit along these general lines? Aren't they basically designed to drag the AI away from a fast, simple human imitator toward a slow, complex reporter? If so, why is that better than dragging the AI from a foreign ontology toward a familiar ontology?

3Mark Xu9mo
There is a distinction between the way that the predictor is reasoning and the way that the reporter works. Generally, we imagine that that the predictor is trained the same way the "unaligned benchmark" we're trying to compare to is trained, and the reporter is the thing that we add onto that to "align" it (perhaps by only training another head on the model, perhaps by finetuning). Hopefully, the cost of training the reporter is small compared to the cost of the predictor (maybe like 10% or something) In this frame, doing anything to train the way the predictor is trained results in a big competitiveness hit, e.g. forcing the predictor to use the same ontology as a human is potentially going to prevent it from using concepts that make reasoning much more efficient. However, training the reporter in a different way, e.g. doubling the cost of training the reporter, only takes you from 10% of the predictor to 20%, which not that bad of a competitiveness hit (assuming that the human imitator takes 10% of the cost of the original predictor to train). In summary, competitiveness for ELK proposals primarily means that you can't change the way the predictor was trained. We are already assuming/hoping the reporter is much cheaper to train than the predictor, so making the reporter harder to train results in a much smaller competitiveness hit.

Can you explain this: "In Section: specificity we suggested penalizing reporters if they are consistent with many different reporters, which effectively allows us to use consistency to compress the predictor given the reporter." What does it mean to "use consistency to compress the predictor given the reporter" and how does this connect to penalizing reporters if they are consistent with many different predictors?

1Mark Xu9mo
A different way of phrasing Ajeya's response, which I think is roughly accurate, is that if you have a reporter that gives consistent answers to questions, you've learned a fact about the predictor, namely "the predictor was such that when it was paired with this reporter it gave consistent answers to questions." if there were 8 predictor for which this fact was true then "it's the [7th] predictor such that when it was paired with this reporter it gave consistent answers to questions" is enough information to uniquely determine the reporter, e.g. the previous fact + 3 additional bits was enough. if the predictor was 1000 bits, the fact that it was consistent with a reporter "saved" you 997 bits, compressing the predictor into 3 bits. The hope is that maybe the honest reporter "depends" on larger parts of the predictor's reasoning, so less predictors are consistent with it, so the fact that a predictor is consistent with the honest reporter allows you to compress the predictor more. As such, searching for reporters that most compressed the predictor would prefer the honest reporter. However, the best way for a reporter to compress a predictor is to simply memorize the entire thing, so if the predictor is simple enough and the gap between the complexity of the human-imitator and the direct translator is large enough, then the human-imitator+memorized predictor is the simplest thing that maximally compresses the predictor.
2Ajeya Cotra9mo
Warning: this is not a part of the report I'm confident I understand all that well; I'm trying anyway and Paul/Mark can correct me if I messed something up here. I think the idea here is like: * We assume there's some actual true correspondence between the AI Bayes net and the human Bayes net (because they're describing the same underlying reality that has diamonds and chairs and tables in it). * That means that if we have one of the Bayes nets, and the true correspondence, we should be able to use that rederive the other Bayes net. In particular the human Bayes net plus the true correspondence should let us reconstruct the AI Bayes net; false correspondences that just do inference from observations in the human Bayes net wouldn't allow us to do this since they throw away all the intermediate info derived by the AI Bayes net. * If you assume that the human Bayes net plus the true correspondence are simpler than the AI Bayes net, then this "compresses" the AI Bayes net because you just wrote down a program that's smaller than the AI Bayes net which "unfolds" into the AI Bayes net. * This is why the counterexample in that section focuses on the case where the AI Bayes net was already so simple to describe that there was nothing left to compress, and the human Bayes net + true correspondence had to be larger.

Here are a couple of hand-wavy "stub" proposals that I sent over to ARC, which they thought were broadly intended to be addressed by existing counterexamples. I'm posting them here so they can respond and clarify why these don't qualify.

*Proposal 1: force ontological compatibility*

On page 34 of the ELK gdoc, the authors talk about the possibility that training an AI hard enough produces a model that has deep mismatches with human ontology - that is, it has a distinct "vocabulary of basic concepts" (or nodes in a Bayes net) that are distinct from the ones h... (read more)

2Paul Christiano9mo
I think that a lot depends on what kind of term you include. If you just say "find more interesting things" then the model will just have a bunch of neurons designed to look interesting. Presumably you want them to be connected in some way to the computation, but we don't really have any candidates for defining that in a way that does what you want. In some sense I think if the digital neuroscientists are good enough at their job / have a good enough set of definitions, then this proposal might work. But I think that the magic is mostly being done in the step where we make a lot of interpretability progress, and so if we define a concrete version of interpretability right now it will be easy to construct counterexamples (even if we define it in terms of human judgments). If we are just relying on the digital neuroscientists to think of something clever, the counterexample will involve something like "they don't think of anything clever." In general I'd be happy to talk about concrete proposals along these lines. (I agree with Ajeya and Mark that the hard case for this kind of method is when the most efficient way of thinking is totally alien to the human. I think that can happen, and in that case in order to be competitive you basically just need to learn an "interpreted" version of the alien model. That is, you need to basically show that if there exists an alien model with performance X, there is a human-comprehensible model with performance X, and the only way you'll be able to argue that for any model we can define a human-comprehensible model with similar complexity and the same behavior.)

Again trying to answer this one despite not feeling fully solid. I'm not sure about the second proposal and might come back to it, but here's my response to the first proposal (force ontological compatibility):

The counterexample "Gradient descent is more efficient than science" should cover this proposal because it implies that the proposal is uncompetitive. Basically, the best Bayes net for making predictions could just turn out to be the super incomprehensible one found by unrestricted gradient descent, so if you force ontological compatibility then you ... (read more)

Regarding this:

The bad reporter needs to specify the entire human model, how to do inference, and how to extract observations. But the complexity of this task depends only on the complexity of the human’s Bayes net.

If the predictor's Bayes net is fairly small, then this may be much more complex than specifying the direct translator. But if we make the predictor's Bayes net very large, then the direct translator can become more complicated — and there is no obvious upper bound on how complicated it could become. Eventually direct translation will be more co

... (read more)
2Paul Christiano10mo
Yes, I agree that something similar applies to complexity as well as computation time. There are two big reasons I talk more about computation time: * It seems plausible we could generate a scalable source of computational difficulty, but it's less clear that there exists a scalable source of description complexity (rather than having some fixed upper bound on the complexity of "the best thing a human can figure out by doing science.") * I often imagine the assistants all sharing parameters with the predictor, or at least having a single set of parameters. If you have lots of assistant parameters that aren't shared with the predictor, then it looks like it will generally increase the training time a lot. But without doing that, it seems like there's not necessarily that much complexity the predictor doesn't already know about. (In contrast, we can afford to spend a ton of compute for each example at training time since we don't need that many high-quality reporter datapoints to rule out the bad reporters. So we can really have giant ratios between our compute and the compute of the model.) But I don't think these are differences in kind and I don't have super strong views on this.

(Note: I read an earlier draft of this report and had a lot of clarifying questions, which are addressed in the public version. I'm continuing that process here.)

I get the impression that you see most of the "builder" moves as helpful (on net, in expectation), even if there are possible worlds where they are unhelpful or harmful. For example, the "How we'd approach ELK in practice" section talks about combining several of the regularizers proposed by the "builder." It also seems like you believe that combining multiple regularizers would create a "stacking... (read more)

5Paul Christiano10mo
This is because of the remark on ensembling---as long as we aren't optimizing for scariness (or diversity for diversity's sake), it seems like it's way better to have tons of predictors and then see if any of them report tampering. So adding more techniques improves our chances of getting a win. And if the cost of fine-tuning a reporters is small relative to the cost of training the predictor, we can potentially build a very large ensemble relatively cheaply. (Of course, having more techniques also helps because you can test many of them in practice and see which of them seem to really help.) This is also true for data---I'd be scared about generating a lot of riskier data, except that we can just do both and see if either of them reports tampering in a given case (since they appear to fail for different reasons). I believe this in a few cases (especially combining "compress the predictor," imitative generalization, penalizing upstream dependence, and the kitchen sink of consistency checks) but mostly the stacking is good because ensembling means that having more and more options is better and better. I don't think the kind of methodology used in this report (or by ARC more generally) is very well-equipped to answer most of these questions. Once we give up on the worst case, I'm more inclined to do much messier and more empirically grounded reasoning. I do think we can learn some stuff in advance but in order to do so it requires getting really serious about it (and still really wants to learn from early experiments and mostly focus on designing experiments) rather than taking potshots. This is related to a lot of my skepticism about other theoretical work. I do expect the kind of research we are doing now to help with ELK in practice even if the worst case problem is impossible. But the particular steps we are taking now are mostly going to help by suggesting possible algorithms and difficulties; we'd then want to give those as one input into that much messier