lewis smith

• lewis smith 11 Feb 2025 17:32 UTC

  4 points

  0

  in reply to: lewis smith’s comment on: A Prob­lem to Solve Be­fore Build­ing a De­cep­tion Detector

  I think it’s important to push back against the assumption that this will always happen, or that something like the refusal direction has to exist for every possible state of interest.

  and to expand on this a little bit more: it seems important that we hedge against this possibility by at least spending a bit of time thinking about plans that don’t rhyme with ‘I sure hope everything turns out to be a simple correspondence’! I think eleni and i feel that this is a suprisingly widespread move in interpretability plans, which is maybe why some of the post is quite forceful in arguing against it

• lewis smith 11 Feb 2025 17:06 UTC

  4 points

  0

  in reply to: eggsyntax’s comment on: A Prob­lem to Solve Be­fore Build­ing a De­cep­tion Detector

  I think this is along the right sort of lines. Indeed I think this plan is the sort of thing I hoped to prompt people to think about with the post. But I think there are a few things wrong with it:

  • i think premise 1 is big if true, but I think I doubt that it is at easy as this: see the deepmind fact-finding sequence for some counter-evidence. It’s also easy to imagine this being true for some categories of static facts about the external world (e.g paris being in france) but you need to be careful about extending this to the category of all propositional statements (e.g the model thinks that this safeguard is adequate, or the model can’t find any security flaws in this program).

  • relatedly, your second bullet point assumes that you can identify the ‘fact’ related to what the model is currently outputing unambiguously, and look it up in the model; does this require you to find all the fact representations in advance, or is this computed on-the-fly?

  • I think that detecting/​preventing models from knowingly lying would be a good research direction and it’s clearly related to strategic deception, but I’m not actually sure that it’s a superset (consider a case when I’m bullshitting you rather than lying; I predict what you want to hear me say and I say it, and I don’t know or care whether what I’m saying is true or false or whatever).

  but yeah I think this is a reasonable sort of thing to try, but I think you need to do a lot of work to convince me of premise 1, and indeed I think I doubt premise 1 is true a priori though I am open to persuasion on this. Note that premise 1 being true of some facts is a very different claim to it being true of every fact!

• lewis smith 11 Feb 2025 11:00 UTC

  2 points

  0

  in reply to: eggsyntax’s comment on: A Prob­lem to Solve Be­fore Build­ing a De­cep­tion Detector

  I don’t think we actually disagree very much?

  I think that it’s totally possible that there do turn out to be convenient ‘simple correspondences’ for some intentional states that we care about (as you say, we have some potential examples of this already), but I think it’s important to push back against the assumption that this will always happen, or that something like the refusal direction has to exist for every possible state of interest.

  re.

  Even in the case of irreducible complexity, it seems too strong to call it a category mistake; there’s still an algorithmic implementation of (eg) recognizing a good chess move, it might just not be encapsulable in a nicely simple description. In the most extreme case we can point to the entire network as the algorithm underlying the intentional state.

  This seems like a restatement of what I would consider an important takeaway from this post; that this sort of emergence is at least a conceptual possibility. I think if this is true, it is a category mistake to think about the intentional states as being implemented by a part or a circuit in the model; they are just implemented by the model as a whole.

  I don’t think that a takeaway from our argument here is that you necessarily need to have like a complete account of how intentional states emerge from algorithmic ones (e.g see point 4. in the conclusion). I think our idea is more to point out that this conceptual distinction between intentional and algorithmic states is important to make, and that it’s an important thing to think about looking for empirically. See also conclusion/​suggestion 2: we aren’t arguing that interpretability work is hopeless, we are trying to point it at the problems that matter for building a deception detector, and give you some tools for evaluating existing or planned research on that basis.

A Prob­lem to Solve Be­fore Build­ing a De­cep­tion Detector

• lewis smith 29 Jan 2025 15:50 UTC

  3 points

  2

  in reply to: eggsyntax’s comment on: eggsyn­tax’s Shortform

  i do agree with that, although ‘step 1 is identify the problem’

• lewis smith 29 Jan 2025 12:27 UTC

  4 points

  0

  in reply to: Lucius Bushnaq’s comment on: eggsyn­tax’s Shortform

  I think I agree that there are significant quibbles you can raise with the picture chalmers outlines, but in general I think he’s pointing at an important problem for interpretability; that it’s not clear what the relationship between a circuit-level algorithmic understanding and the kind of statements we would like to rule out (e.g this system is scheming against me) is.

• lewis smith 23 Jan 2025 14:32 UTC

  1 point

  0

  in reply to: Brent’s comment on: Sher­lock­ian Ab­duc­tion Master List

  re. the article saying it’s hard to observe; I think the short nails are pretty hard to spot (many people keep their nails short) but the long fingerstyle nails are quite unusual looking, though also not that common.

• lewis smith 8 Jan 2025 10:14 UTC

  1 point

  0

  in reply to: SL’s comment on: The ‘strong’ fea­ture hy­poth­e­sis could be wrong

  well spotted

• lewis smith 22 Dec 2024 0:42 UTC

  1 point

  0

  in reply to: leogao’s comment on: leogao’s Shortform

  i mean i think that its’ definitely an update (anything short of 95% i think would have been quite surprising to me)

• lewis smith 19 Dec 2024 9:24 UTC

  3 points

  −10

  in reply to: leogao’s comment on: leogao’s Shortform

  not to be ‘i trust my priors more than your data’, but i have to say that i find the AGI thing quite implausible; my impression is that most AI researchers (way more than 60%), even ones working in like something very non-deep learning adjacent, have heard of the term AGI, but many of them are/​were quite dismissive of it as an idea or associate it strongly (not entirely unfairly) with hype /​bullshit, hence maybe walking away from you when you ask them about it.

  e.g deepmind and openAI have been massive producers of neurips papers for years now (at least since I started a phd in 2016), and both organisations explictly talked about AGI fairly often for years.

  maybe neurips has way more random attendees now (i didn’t go this year), but I still find this kind of hard to believe; I think I’ve read about AGI in the financial times now.

• lewis smith 3 Dec 2024 14:22 UTC

  1 point

  0

  in reply to: Logan Zoellner’s comment on: How should TurnTrout han­dle his Deep­Mind equity situ­a­tion?

  your example agreement with a friend is obviously a derivative, which is just a contract whose value depends on the value of an underlying asset (google stock in this case). If it’s not a formal derivative contract you might be less likely to get in trouble for it compared to doing it on robinhood or whatever (not legal advice!) but it doesn’t seem like a very good idea.

• lewis smith 2 Dec 2024 16:13 UTC

  5 points

  0

  in reply to: Logan Zoellner’s comment on: How should TurnTrout han­dle his Deep­Mind equity situ­a­tion?

  like at many public companies, google has anti-insider trading policies that prohibit employees from trading in options and other derivatives on the company stock, or shorting it.

• lewis smith 15 Oct 2024 15:01 UTC

  1 point

  0

  in reply to: jake_mendel’s comment on: Cir­cuits in Su­per­po­si­tion: Com­press­ing many small neu­ral net­works into one

  yeah that makes sense I think

• lewis smith 15 Oct 2024 13:51 UTC

  1 point

  0

  on: Cir­cuits in Su­per­po­si­tion: Com­press­ing many small neu­ral net­works into one

  with later small networks taking the outputs of earlier small networks as their inputs.

  what’s the distinction between two small networks connected in series with the first taking the output of the previous one as input and one big network? what defines the boundaries of the networks here?

• lewis smith 15 Sep 2024 13:22 UTC

  5 points

  2

  in reply to: AnthonyC’s comment on: The ‘strong’ fea­ture hy­poth­e­sis could be wrong

  I kind of agree that Dennett is right about this, but I think it’s important to notice that the idea he’s attacking—that all representation is explicit representation—is an old and popular one in philosophy of mind that was, at one point, seen as natural and inevitable by many people working in the field, and one which I think still seems somewhat natural and obvious to many people who maybe haven’t thought about the counterarguments much (e.g I think you can see echos of this view in a post like this one, or the idea that there will be some ‘intelligence algorithm’ which will be a relatively short python program). The idea that a thought is always or mostly something like a sentence in ‘mentalese’ is, I think, still an attractive one to many people of a logical sort of bent, as is the idea that formalised reasoning captures the ‘core’ of cognition.

• lewis smith 14 Sep 2024 21:41 UTC

  1 point

  0

  in reply to: AnthonyC’s comment on: The ‘strong’ fea­ture hy­poth­e­sis could be wrong

  I guess you are thinking about holes with the p-type semiconductor?

  I don’t think I agree (perhaps obviously) that it’s better to think about the issues in the post in terms of physics analogies than in terms of the philosophy of mind and language. If you are thinking about how a mental representation represents some linguistic concept, then Dennett and Wittgenstein (and others!) are addressing the same problem as you! in a way that virtual particles are really not

• lewis smith 31 Aug 2024 15:43 UTC

  4 points

  0

  in reply to: Neel Nanda’s comment on: lewis smith’s Shortform

  I think atom would be a pretty good choice as well but I think the actual choice of terminology is less important than the making the distinction. I used latent here because that’s what we used in our paper

• lewis smith 30 Aug 2024 9:51 UTC

  59 points

  49

  on: lewis smith’s Shortform

  ‘Feature’ is overloaded terminology

  In the interpretability literature, it’s common to overload ‘feature’ to mean three separate things:

  1. Some distinctive, relevant attribute of the input data. For example, being in English is a feature of this text.

  2. A general activity pattern across units which a network uses to represent some feature in the first sense. So we might say that a network represents the feature (1) that the text is in English with a linear feature (2) in layer 32.

  3. The elements of some process for discovering features, like an SAE. An SAE learns a dictionary of activation vectors, which we hope correspond to the features (2) that the network actually uses. It is common to simply refer to the elements of the SAE dictionary as ‘features’ as well. For example, we might say something like ’the network represents features of the input with linear features in its representation space, which are recovered well by feature 3242 in our SAE.

  This seems bad; at best it’s a bit sloppy and confusing, at worst it’s begging the question about the interpretability or usefulness of SAE features. It seems important to carefully distinguish between them in case these don’t coincide. We think that it’s probably worth making a bit of an effort to carefully distinguish between all of these different concepts by giving them different names. A terminology that we prefer is to reserve ‘feature’ for the conceptual senses of the word (1 and 2) and use alternative terminology for case 3, like ‘SAE latent’ instead of ‘SAE feature’. So we might say, for example, that a model uses a linear representation for a Golden Gate Bridge feature, which is recovered well by an SAE latent. We have tried to follow this terminology in our recent Gemma Scope report. We think that this added precision is helpful in thinking about feature representations, and SAEs more clearly. To illustrate what we mean with an example, we might ask whether a network has a feature for numbers—i.e, whether it has any kind of localized representation of numbers at all. We can then ask what the format of this feature representation is ; for example, how many dimensions does it use, where is it located in the model, does it have any kind of geometric structure, etc. We could then separately ask whether it is discovered by a feature discovery algorithm; i.e is there a latent in a particular SAE that describes it well? We think it’s important to recognise that these are all distinct questions, and use a terminology that is able to distinguish between them.

  We (the deepmind language model interpretability team) started using this terminology in the GemmaScope report, but we didn’t really justify the decision much there and I thought it was worth making the argument separately.

lewis smith’s Shortform

• lewis smith 28 Aug 2024 10:14 UTC

  1 point

  0

  in reply to: debrevitatevitae’s comment on: The ‘strong’ fea­ture hy­poth­e­sis could be wrong

  I’m not that confident about the statistical query dimension (I assume that’s what you mean by SQ dimension?) But I don’t think it’s applicable; SQ dimension is about the difficulty of a task (e.g binary parity), wheras explicit vs tacit representations are properties of an implementation, so it’s kind of apples to oranges.

  To take the chess example again, one way to rank moves is to explicitly compute some kind of rule or heuristic from the board state, and another is to do some kind of parallel search, and yet another is to use a neural network or something similar. The first one is explicit, the second is (maybe?) more tacit, and the last is unclear. I think stronger variations of the LRH kind of assume that the neural network must be ‘secretly’ explicit, but I’m not really sure this is neccesary.

  But I don’t think any of this is really affected by the SQ dimension because it’s the same task in all three cases (and we could possibly come up with examples which had identical performance?)

  but maybe i’m not quite understanding what you mean