kaarelh AT gmail DOT com

Wiki Contributions


First, suppose GPT-n literally just has a “what a human would say” feature and a “what do I [as GPT-n] actually believe” feature, and those are the only two consistently useful truth-like features that it represents, and that using our method we can find both of them. This means we literally only need one more bit of information to identify the model’s beliefs. 

One difference between “what a human would say” and “what GPT-n believes” is that humans will know less than GPT-n. In particular, there should be hard inputs that only a superhuman model can evaluate; on these inputs, the “what a human would say” feature should result in an “I don’t know” answer (approximately 50/50 between “True” and “False”), while the “what GPT-n believes” feature should result in a confident “True” or “False” answer.[2] This would allow us to identify the model’s beliefs from among these two options.

For  such that GPT- is superhuman, I think one could alternatively differentiate between these two options by checking which is more consistent under implications, by which I mean that whenever the representation says that the propositions  and  are true, it should also say that  is true. (Here, for a language model,  and  could be ~whatever assertions written in natural language.) Or more generally, in addition to modus ponens, also construct new propositions with ANDs and ORs, and check against all the inference rules of zeroth-order logic, or do this for first-order logic or whatever. (Alternatively, we can also write down versions of these constraints that apply to probabilities.) Assuming [more intelligent => more consistent] (w.r.t. the same set of propositions), for a superhuman model, the model's beliefs would probably be the more consistent feature. (Of course, one could also just add these additional consistency constraints directly into the loss in CCS instead of doing a second deductive step.) 

I think this might even be helpful for differentiating the model's beliefs from what it models some other clever AI as believing or what it thinks would be true in some fake counterfactual world, because presumably it makes sense to devote less of one's computation to ironing out incoherence in these counterfactuals – for humans, it certainly seems computationally much easier to consistently tell the truth than to consistently talk about what would be the case in some counterfactual of similar complexity to reality (e.g. to lie).

Hmm, after writing the above, now that I think more of it, I guess it seems plausible that the feature most consistent under negations is already more likely to be the model's true beliefs, for the same reasons as what's given in the above paragraph. I guess testing modus ponens (and other inference rules) seems much stronger though, and in any case that could be useful for constraining the search.


(There are a bunch of people that should be thanked for contributing to the above thoughts in discussions, but I'll hopefully have a post up in a few days where I do that – I'll try to remember to edit this comment with a link to the post when it's up.)

I think does not have to be a variable which we can observe, i.e. it is not necessarily the case that we can deterministically infer the value of from the values of and . For example, let's say the two binary variables we observe are and . We'd intuitively want to consider a causal model where is causing both, but in a way that makes all triples of variable values have nonzero probability (which is true for these variables in practice). This is impossible if we require to be deterministic once is known.

I agree with you regarding 0 lebesgue. My impression is that the Pearl paradigm has some [statistics -> causal graph] inference rules which basically do the job of ruling out causal graphs for which having certain properties seen in the data has 0 lebesgue measure. (The inference from two variables being independent to them having no common ancestors in the underlying causal graph, stated earlier in the post, is also of this kind.) So I think it's correct to say "X has to cause Y", where this is understood as a valid inference inside the Pearl (or Garrabrant) paradigm.  (But also, updating pretty close to "X has to cause Y" is correct for a Bayesian with reasonable priors about the underlying causal graphs.)

(epistemic position: I haven't read most of the relevant material in much detail)

I don't understand why 1 is true – in general, couldn't the variable $W$ be defined on a more refined sample space? Also, I think all $4$ conditions are technically satisfied if you set $W=X$ (or well, maybe it's better to think of it as a copy of $X$).

I think the following argument works though. Note that the distribution of $X$ given $(Z,Y,W)$ is just the deterministic distribution $X=Y \xor Z$ (this follows from the definition of Z). By the structure of the causal graph, the distribution of $X$ given $(Z,Y,W)$ must be the same as the distribution of $X$ given just $W$. Therefore, the distribution of $X$ given $W$ is deterministic. I strongly guess that a deterministic connection is directly ruled out by one of Pearl's inference rules.

The same argument also rules out graphs 2 and 4.


I took the main point of the post to be that there are fairly general conditions (on the utility function and on the bets you are offered) in which you should place each bet like your utility is linear, and fairly general conditions in which you should place each bet like your utility is logarithmic. In particular, the conditions are much weaker than your utility actually being linear, or than your utility actually being logarithmic, respectively, and I think this is a cool point. I don't see the post as saying anything beyond what's implied by this about Kelly betting vs max-linear-EV betting in general.

(By the way, I'm pretty sure the position I outline is compatible with changing usual forecasting procedures in the presence of observer selection effects, in cases where secondary evidence which does not kill us is available. E.g. one can probably still justify [looking at the base rate of near misses to understand the probability of nuclear war instead of relying solely on the observed rate of nuclear war itself].)

I'm inside-view fairly confident that Bob should be putting a probability of 0.01% on surviving conditional on many worlds being true, but it seems possible I'm missing some crucial considerations having to do with observer selection stuff in general, so I'll phrase the rest of this as more of a question.

What's wrong with saying that Bob should put a probability of 0.01% of surviving conditional on many-worlds being true – doesn't this just follow from the usual way that a many-worlder would put probabilities on things, or at least the simplest way for doing so (i.e. not post-normalizing only across the worlds in which you survive)? I'm pretty sure that the usual picture of Bayesianism as having a big (weighted) set of possible worlds in your head and, upon encountering evidence, discarding the ones which you found out you were not in, also motivates putting a probability of 0.01% on surviving conditional on many-worlds. (I'm assuming that for a many-worlder, weights on worlds are given by squared amplitudes or whatever.)

This contradicts a version of the conservation of expected evidence in which you only average over outcomes in which you survive (even in cases where you don't survive in all outcomes), but that version seems wrong anyway, with Leslie's firing squad seeming like an obvious counterexample to me, https://plato.stanford.edu/entries/fine-tuning/#AnthObje .

A big chunk of my uncertainty about whether at least 95% of the future’s potential value is realized comes from uncertainty about "the order of magnitude at which utility is bounded". That is, if unbounded total utilitarianism is roughly true, I think there is a <1% chance in any of these scenarios that >95% of the future's potential value would be realized. If decreasing marginal returns in the [amount of hedonium -> utility] conversion kick in fast enough for 10^20 slightly conscious humans on heroin for a million years to yield 95% of max utility, then I'd probably give >10% of strong utopia even conditional on building the default superintelligent AI. Both options seem significantly probable to me, causing my odds to vary much less between the scenarios.

This is assuming that "the future’s potential value" is referring to something like the (expected) utility that would be attained by the action sequence recommended by an oracle giving humanity optimal advice according to our CEV. If that's a misinterpretation or a bad framing more generally, I'd enjoy thinking again about the better question. I would guess that my disagreement with the probabilities is greatly reduced on the level of the underlying empirical outcome distribution.

Great post, thanks for writing this! In the version of "Alignment might be easier than we expect" in my head, I also have the following:

  • Value might not be that fragile. We might "get sufficiently many bits in the value specification right" sort of by default to have an imperfect but still really valuable future.
    • For instance, maybe IRL would just learn something close enough to pCEV-utility from human behavior, and then training an agent with that as the reward would make it close enough to a human-value-maximizer. We'd get some misalignment on both steps (e.g. because there are systematic ways in which the human is wrong in the training data, and because of inner misalignment), but maybe this is little enough to be fine, despite fragility of value and despite Goodhart.
    • Even if deceptive alignment were the default, it might be that the AI gets sufficiently close to correct values before "becoming intelligent enough" to start deceiving us in training, such that even if it is thereafter only deceptively aligned, it will still execute a future that's fine when in deployment.
    • It doesn't seem completely wild that we could get an agent to robustly understand the concept of a paperclip by default. Is it completely wild that we could get an agent to robustly understand the concept of goodness by default?
    • Is it so wild that we could by default end up with an AGI that at least does something like putting 10^30 rats on heroin? I have some significant probability on this being a fine outcome.
    • There's some distance  from the correct value specification such that stuff is fine if we get AGI with values closer than . Do we have good reasons to think that  is far out of the range that default approaches would give us?

(But here's some reasons not to expect this.)

I still disagree / am confused. If it's indeed the case that , then why would we expect ? (Also, in the second-to-last sentence of your comment, it looks like you say the former is an equality.) Furthermore, if the latter equality is true, wouldn't it imply that the utility we get from [chocolate ice cream and vanilla ice cream] is the sum of the utility from chocolate ice cream and the utility from vanilla ice cream? Isn't  supposed to be equal to the utility of ?

My current best attempt to understand/steelman this is to accept , to reject , and to try to think of the embedding as something slightly strange. I don't see a reason to think utility would be linear in current semantic embeddings of natural language or of a programming language, nor do I see an appealing other approach to construct such an embedding. Maybe we could figure out a correct embedding if we had access to lots of data about the agent's preferences (possibly in addition to some semantic/physical data), but it feels like that might defeat the idea of this embedding in the context of this post as constituting a step that does not yet depend on preference data. Or alternatively, if we are fine with using preference data on this step, maybe we could find a cool embedding, but in that case, it seems very likely that it would also just give us a one-step solution to the entire problem of computing a set of rational preferences for the agent.

A separate attempt to steelman this would be to assume that we have access to a semantic embedding pretrained on preference data from a bunch of other agents, and then to tune the utilities of the basis to best fit the preferences of the agent we are currently dealing with. That seems like it a cool idea, although I'm not sure if it has strayed too far from the spirit of the original problem.

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