Vivek Hebbar

Karma: 1,046

Vivek Hebbar Apr 14, 2025, 1:29 PM
4 points
2
in reply to: Vivek Hebbar’s comment on: ‘Empiricism!’ as Anti-Epistemology
Slightly more spelled-out thoughts about bounded minds:
1. We can’t actually run the hypotheses of Solomonoff induction. We can only make arguments about what they will output.
2. In fact, almost all of the relevant uncertainty is logical uncertainty. The “hypotheses” (programs) of Solomonoff induction are not the same as the “hypotheses” entertained by bounded Bayesian minds. I don’t know of any published formal account of what these bounded hypotheses even are and how they relate to Solomonoff induction. But informally, all I’m talking about are ordinary hypotheses like “the Ponzi guy only gets money from new investors”.
3. In addition to “bounded hypotheses” (of unknown type), we also have “arguments”. An argument is a thing whose existence provides fallible evidence for a claim.
4. Arguments are made of pieces which can be combined “conjuctively” or “disjunctively”. The conjunction of two subarguments is weaker evidence for its claim than each subargument was for its subclaim. This is the sense in which “big arguments” are worse.

Vivek Hebbar Apr 14, 2025, 1:29 PM
4 points
2
on: ‘Empiricism!’ as Anti-Epistemology
I suspect there is some merit to the Scientist’s intuition (and the idea that constant returns are more “empirical”) which nobody has managed to explain well. I’ll try to explain it here.^[1]
The Epistemologist’s notion of simplicity is about short programs with unbounded runtime which perfectly explain all evidence. The [non-straw] empiricist notion of simplicity is about short programs with heavily-bounded runtime which approximately explain a subset of the evidence. The Epistemologist is right that there is nothing of value in the empiricist’s notion if you are an unbounded Solomonoff inductor. But for a bounded mind, two important facts come into play:
1. The implications of hypotheses can only be guessed using “arguments”. These “arguments” become less reliable the more conjunctive they are.
2. Induction over runtime-bounded programs turns out for some reason to agree with Solomonoff induction way more than the maximum entropy prior does, despite not containing any “correct” hypotheses. This is a super important fact about reality.
Therefore a bounded mind will sometimes get more evidence from “fast-program induction on local data” (i.e. just extrapolate without a gears-level model) than from highly conjunctive arguments about gears-level models.
1. ^
  FWIW, I agree with the leading bit of Eliezer’s position—that we should think about the object-level and not be dismissive of arguments and concretely imagined gears-level models.

Vivek Hebbar Apr 14, 2025, 11:01 AM
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on: Shah and Yudkowsky on alignment failures
I’d be capable of helping aliens optimize their world, sure. I wouldn’t be motivated to, but I’d be capable.
@So8res How many bits of complexity is the simplest modification to your brain that would make you in fact help them? (asking for an order-of-magnitude wild guess)
(This could be by actually changing your values-upon-reflection, or by locally confusing you about what’s in your interest, or by any other means.)

How training-gamers might function (and win)

Vivek HebbarApr 11, 2025, 9:26 PM

88 points

4 comments12 min readLW link

Vivek Hebbar Apr 10, 2025, 7:00 AM
3 points
0
in reply to: Raemon’s comment on: Arguments about fast takeoff
Sigmoid is usually what “straight line” should mean for a quantity bounded at 0 and 1. It’s a straight line in logit-space, the most natural space which complies with that range restriction.
(Just as exponentials are often the correct form of “straight line” for things that are required to be positive but have no ceiling in sight.)

Vivek Hebbar Oct 13, 2024, 12:14 AM
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in reply to: Daniel Kokotajlo’s comment on: Discussion with Nate Soares on a key alignment difficulty
Do you want to try playing this game together sometime?

Vivek Hebbar Oct 12, 2024, 11:57 PM
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on: Discussion with Nate Soares on a key alignment difficulty
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?

Vivek Hebbar Aug 14, 2024, 5:38 PM
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in reply to: Diffractor’s comment on: CDT Dutch Book
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.)

Vivek Hebbar Jun 28, 2024, 8:55 PM
1 point
0
in reply to: ryan_greenblatt’s comment on: An issue with training schemers with supervised fine-tuning
[resolved]

Different senses in which two AIs can be “the same”

Vivek Hebbar and Buck

Jun 24, 2024, 3:16 AM

68 points

2 comments4 min readLW link

Vivek Hebbar Jun 12, 2024, 11:39 PM
1 point
0
in reply to: tgb’s comment on: Worked Examples of Shapley Values
Superadditivity seems rare in practice. For instance, workers should have subadditive contributions after some point. This is certainly true in the unemployment example in the post.

Vivek Hebbar Jun 4, 2024, 1:06 PM
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on: AI catastrophes and rogue deployments
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 for most catastrophes which only need outsider affordances (such as “order the proteins etc for a pandemic or a nanofactory”). Catastrophes with outsider affordances can usually be presumed difficult because rogue humans don’t randomly end the world.

However, this argument for #1 doesn’t work for routes which use insider affordances or responsibilities. Nor for routes which go through work done by future generations of AIs. The central case of both is routes that involve using insider privileges to backdoor or intentionally misalign future AIs. For example:
- Planting backdoors in the codebase which allow safety training code to be turned off or misconfigured at a later time
- Poisoning datasets (idk if insider access helps though)
- Intentionally creating and defending flawed alignment plans

These routes get future generations of AIs to do the difficult work required for the final catastrophe. But they aren’t necessarily rogue deployments. Consider a backdoored future AI running only in authorized scaffolds, where the safety case for the future AI was an alignment case (not a control case). The alignment case fails due to the subversive actions of the earlier AI. So the earlier AI has caused a catastrophe without doing the difficult work of directly taking over the world, and also without a rogue deployment.

One could separately argue that these routes are also “fundamentally hard” (even if not as hard as directly causing a catastrophe), but I don’t see a clear blanket reason.

Vivek Hebbar Apr 16, 2024, 8:14 AM
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in reply to: Vivek Hebbar’s comment on: AXRP Episode 27 - AI Control with Buck Shlegeris and Ryan Greenblatt
This google search seems to turn up some interesting articles (like maybe this one, though I’ve just started reading it).

Vivek Hebbar Apr 16, 2024, 8:08 AM
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on: AXRP Episode 27 - AI Control with Buck Shlegeris and Ryan Greenblatt
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.

Vivek Hebbar Feb 9, 2024, 11:30 PM
6 points
2
on: Transfer learning and generalization-qua-capability in Babbage and Davinci (or, why division is better than Spanish)
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.

Vivek Hebbar Feb 7, 2024, 2:07 AM
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on: Survey for alignment researchers!
Note: The survey took me 20 mins (but also note selection effects on leaving this comment)

Vivek Hebbar Jan 15, 2024, 1:54 AM
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on: What’s up with LLMs representing XORs of arbitrary features?
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, there are 7 meaningfully distinct colinearity-free embeddings, each of which excludes a different pair of functions.^[1]
I wonder how this situation scales for higher dimensions and variable counts. It would also make sense to consider sparse features (which allow superposition to get good average performance).
1. ^
  The one unexcludable pair is (“always true”, “always false”).
  These are the seven embeddings:

Vivek Hebbar Jan 8, 2024, 11:19 PM
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in reply to: Sam Marks’s comment on: What’s up with LLMs representing XORs of arbitrary features?
Oops, I misunderstood what you meant by unimodality earlier. Your comment seems broadly correct now (except for the variance thing). I would still guess that unimodality isn’t precisely the right well-behavedness desideratum, but I retract the “directionally wrong”.

Vivek Hebbar Jan 8, 2024, 7:02 PM
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in reply to: Sam Marks’s comment on: What’s up with LLMs representing XORs of arbitrary features?
The variance of the multivariate uniform distribution $U ([0, 1] \times [0, 1])$ is largest along the direction $x_{1} + x_{2}$ , 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 independent and have equal variance.^[1]
The diagram in the post showing that “and” is linearly represented works if the features are represented discretely (so that there are exactly 4 points for 2 binary features, instead of a distribution for each combination). As soon as you start defining features with thresholds like DanielVarga did, the argument stops going through in general, and the claim can become false.
The stuff about unimodality doesn’t seem relevant to me, and in fact seems directionally wrong.
1. ^
  I have a not-fully-verbalized proof which I don’t have time to write out

Vivek Hebbar Jan 7, 2024, 6:07 AM
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on: What’s up with LLMs representing XORs of arbitrary features?
Maybe models track which features are basic and enforce that these features be more salient
Couldn’t it just write derivative features more weakly, and therefore not need any tracking mechanism other than the magnitude itself?

Vivek Hebbar

How train­ing-gamers might func­tion (and win)

Differ­ent senses in which two AIs can be “the same”

How training-gamers might function (and win)

Different senses in which two AIs can be “the same”