We're then going to use a small amount of RL (like, 10 training episodes) to try to point it in this direction. We're going to try to use the RL to train: "Act exactly like [a given alignment researcher] would act."
Why are we doing RL if we just want imitation? Why not SFT on expert demonstrations?
Also, if 10 episodes suffices, why is so much post-training currently done on base models?
If the agent follows EDT, it seems like you are giving it epistemically unsound credences. In particular, the premise is that it's very confident it will go left, and the consequence is that it in fact goes right. This was the world model's fault, not EDT's fault. (It is notable though that EDT introduces this loopiness into the world model's job.)
The idea of dividing failure stories into "failures involving rogue deployments" and "other failures" seems most useful if the following argument goes through:
1. Catastrophes require a very large (superhuman?) quantity and/or quality of intellectual labor
2. Either this labor is done by AIs in approved scaffolds, or it is done in "rogue deployments"
3. Hence the only easy-by-default disaster route is through a rogue deployment
4. Hence if we rule out rogue deployments and very impressive/difficult malicious labor in our scaffolds, we are safe
This seems true f...
This google search seems to turn up some interesting articles (like maybe this one, though I've just started reading it).
Paul [Christiano] called this “problems of the interior” somewhere
Since it's slightly hard to find: Paul references it here (ctrl+f for "interior") and links to this source (once again ctrl+f for "interior"). Paul also refers to it in this post. The term is actually "position of the interior" and apparently comes from military strategist Carl von Clausewitz.
Can you clarify what figure 1 and figure 2 are showing?
I took the text description before figure 1 to mean {score on column after finetuning on 200 from row then 10 from column} - {score on column after finetuning on 10 from column}. But then the text right after says "Babbage fine-tuned on addition gets 27% accuracy on the multiplication dataset" which seems like a different thing.
Here's a fun thing I noticed:
There are 16 boolean functions of two variables. Now consider an embedding that maps each of the four pairs {(A=true, B=true), (A=true, B=false), ...} to a point in 2d space. For any such embedding, at most 14 of the 16 functions will be representable with a linear decision boundary.
For the "default" embedding (x=A, y=B), xor and its complement are the two excluded functions. If we rearrange the points such that xor is linearly represented, we always lose some other function (and its complement). In fact...
The variance of the multivariate uniform distribution is largest along the direction , which is exactly the direction which we would want to represent a AND b.
The variance is actually the same in all directions. One can sanity-check by integration that the variance is 1/12 both along the axis and along the diagonal.
In fact, there's nothing special about the uniform distribution here: The variance should be independent of direction for any N-dimensional joint distribution where the N constituent distributions are ind...
This will initially boost relative to because it will suddenly be joined to a network with is correctly transmitting but which does not understand at all.
However, as these networks are trained to equilibrium the advantage will disappear as a steganographic protocol is agreed between the two models. Also, this can only be used once before the networks are in equilibrium.
Why would it be desirable to do this end-to-end training at all, rather than simply sticking the two networks together and doing no furthe...
I've been asked to clarify a point of fact, so I'll do so here:
My recollection is that he probed a little and was like "I'm not too worried about that" and didn't probe further.
This does ring a bell, and my brain is weakly telling me it did happen on a walk with Nate, but it's so fuzzy that I can't tell if it's a real memory or not. A confounder here is that I've probably also had the conversational route "MIRI burnout is a thing, yikes" -> "I'm not too worried, I'm a robust and upbeat person" multiple times with people other than Nate.
In private ...
In database design, sometimes you have a column in one table whose entries are pointers into another table - e.g. maybe I have a Users table, and each User has a primaryAddress field which is a pointer into an Address table. That keeps things relatively compact and often naturally represents things - e.g. if several Users in a family share a primary address, then they can all point to the same Address. The Address only needs to be represented once (so it's relatively compact), and it can also be changed once for everyone if that's a thing someone wants to ...
Interesting, I find what you are saying here broadly plausible, and it is updating me (at least toward greater uncertainity/confusion). I notice that I don't expect the 10x effect, or the Von Neumann effect, to be anywhere close to purely genetic. Maybe some path-dependency in learning? But my intuition (of unknown quality) is that there should be some software tweaks which make the high end of this more reliably achievable.
Anyway, to check that I understand your position, would this be a fair dialogue?:
...Person: "The jump from chimps to hu
In your view, who would contribute more to science -- 1000 Einsteins, or 10,000 average scientists?[1]
"IQ variation is due to continuous introduction of bad mutations" is an interesting hypothesis, and definitely helps save your theory. But there are many other candidates, like "slow fixation of positive mutations" and "fitness tradeoffs[2]".
Do you have specific evidence for either:
Or do you believe these things just because ...
In your view, who would contribute more to science -- 1000 Einsteins, or 10,000 average scientists?
I vaguely agree with your 90%/60% split for physics vs chemistry. In my field of programming we have the 10x myth/meme, which I think is reasonably correct but it really depends on the task.
For the 10x programmers it's some combination of greater IQ/etc but also starting programming earlier with more focused attention for longer periods of time, which eventually compounds into the 10x difference.
But it really depends on the task distribution - there are s...
POV: I'm in an ancestral environment, and I (somehow) only care about the rewarding feeling of eating bread. I only care about the nice feeling which comes from having sex, or watching the birth of my son, or being gaining power in the tribe. I don't care about the real-world status of my actual son, although I might have strictly instrumental heuristics about e.g. how to keep him safe and well-fed in certain situations, as cognitive shortcuts for getting reward (but not as terminal values).
Would such a person sacrifice themselves for their children (in situations where doing so would be a fitness advantage)?
You could use all of world energy output to have a few billion human speed AGI, or a millions that think 1000x faster, etc.
Isn't it insanely transformative to have millions of human-level AIs which think 1000x faster?? The difference between top scientists and average humans seems to be something like "software" (Einstein isn't using 2x the watts or neurons). So then it should be totally possible for each of the "millions of human-level AIs" to be equivalent to Einstein. Couldn't a million Einstein-level scientists running at 1000x speed ...
See Godel's incompleteness theorems. For example, consider the statement "For all A, (ZFC proves A) implies A", encoded into a form judgeable by ZFC itself. If you believe ZFC to be sound, then you believe that this statement is true, but due to Godel stuff you must also believe that ZFC cannot prove it. The reasons for believing ZFC to be sound are reasons from "outside the system" like "it looks logically sound based on common sense", "it's never failed in practice", and "no-one's found a valid issue". Godel's theorems let us conv...
Agreed. To give a concrete toy example: Suppose that Luigi always outputs "A", and Waluigi is {50% A, 50% B}. If the prior is {50% luigi, 50% waluigi}, each "A" outputted is a 2:1 update towards Luigi. The probability of "B" keeps dropping, and the probability of ever seeing a "B" asymptotes to 50% (as it must).
This is the case for perfect predictors, but there could be some argument about particular kinds of imperfect predictors which supports the claim in the post.
Context windows could make the claim from the post correct. Since the simulator can only consider a bounded amount of evidence at once, its P[Waluigi] has a lower bound. Meanwhile, it takes much less evidence than fits in the context window to bring its P[Luigi] down to effectively 0.
Imagine that, in your example, once Waluigi outputs B it will always continue outputting B (if he's already revealed to be Waluigi, there's no point in acting like Luigi). If there's a context window of 10, then the simulator's probability of Waluigi never goes below 1/1025, w...
It would help if you specified which subset of "the community" you're arguing against. I had a similar reaction to your comment as Daniel did, since in my circles (AI safety researchers in Berkeley), governance tends to be well-respected, and I'd be shocked to encounter the sentiment that working for OpenAI is a "betrayal of allegiance to 'the community'".
To be clear, I do think most people who have historically worked on "alignment" at OpenAI have probably caused great harm! And I do think I am broadly in favor of stronger community norms against working at AI capability companies, even in so called "safety positions". So I do think there is something to the sentiment that Critch is describing.
In ML terms, nearly-all the informational work of learning what “apple” means must be performed by unsupervised learning, not supervised learning. Otherwise the number of examples required would be far too large to match toddlers’ actual performance.
I'd guess the vast majority of the work (relative to the max-entropy baseline) is done by the inductive bias.
Beware, though; string theory may be what underlies QFT and GR, and it describes a world of stringy objects that actually do move through space
I think this contrast is wrong.[1] IIRC, strings have the same status in string theory that particles do in QFT. In QM, a wavefunction assigns a complex number to each point in configuration space, where state space has an axis for each property of each particle.[2] So, for instance, a system with 4 particles with only position and momentum will have a 12-dimensional configuration space.[3] I...
As I understand Vivek's framework, human value shards explain away the need to posit alignment to an idealized utility function. A person is not a bunch of crude-sounding subshards (e.g. "If
food nearby
andhunger>15
, then be more likely togo to food
") and then also a sophisticated utility function (e.g. something like CEV). It's shards all the way down, and all the way up.[10]
This read to me like you were saying "In Vivek's framework, value shards explain away .." and I was confused. I now think you mean "My take on Vivek's is that value s...
"Well, what if I take the variables that I'm given in a Pearlian problem and I just forget that structure? I can just take the product of all of these variables that I'm given, and consider the space of all partitions on that product of variables that I'm given; and each one of those partitions will be its own variable.
How can a partition be a variable? Should it be "part" instead?
ETA: Koen recommends reading Counterfactual Planning in AGI Systems before (or instead of) Corrigibility with Utility Preservation
Update: I started reading your paper "Corrigibility with Utility Preservation".[1] My guess is that readers strapped for time should read {abstract, section 2, section 4} then skip to section 6. AFAICT, section 5 is just setting up the standard utility-maximization framework and defining "superintelligent" as "optimal utility maximizer".
Quick thoughts after reading less than half:
AFAICT,[2] this is a mathematica...
OK, Below I will provide links to few mathematically precise papers about AGI corrigibility solutions, with some comments. I do not have enough time to write short comments, so I wrote longer ones.
This list or links below is not a complete literature overview. I did a comprehensive literature search on corrigibility back in 2019 trying to find all mathematical papers of interest, but have not done so since.
I wrote some of the papers below, and have read all the rest of them. I am not linking to any papers I heard about but did not read (yet).
Math-based w...
- Try to improve my evaluation process so that I can afford to do wider searches without taking excessive risk.
Improve it with respect to what?
My attempt at a framework where "improving one's own evaluator" and "believing in adversarial examples to one's own evaluator" make sense:
Yeah, the right column should obviously be all 20s. There must be a bug in my code[1] :/
I like to think of the argmax function as something that takes in a distribution on probability distributions on with different sigma algebras, and outputs a partial probability distribution that is defined on the set of all events that are in the sigma algebra of (and given positive probability by) one of the components.
Take the following hypothesis :
If I add this into with weight , then the middle column is still near...
Now, let's consider the following modification: Each hypothesis is no longer a distribution on , but instead a distribution on some coarser partition of . Now is still well defined
Playing around with this a bit, I notice a curious effect (ETA: the numbers here were previously wrong, fixed now):
The reason the middle column goes to zero is that hypothesis A puts 60% on the rightmost column, and hypothesis B puts 40% on the leftmost, and neither cares about the middle column specifically.
But philosophically, what d...
Do you want to try playing this game together sometime?