I think it ought to be possible for someone to always be present. [I’m also not sure it would be necessary.]
I think I don’t understand what you’re imagining here. Are you imagining a human manually overseeing all outputs of something like ChatGPT, or Microsoft Copilot, before those outputs are sent to the end user (or, worse yet, put directly into production)?
[I also think I don’t understand why you make the bracketed claim you do, but perhaps hashing that out isn’t a conversational priority.]
As I understand this thought experiment, we’re doing next-token prediction on e.g. a book written by a philosopher, and in order to predict the next token using QFT, the obvious method is to use QFT to simulate the philosopher. But that’s not quite enough—you also need to read the next token out of that QFT-based simulation if you actually want to predict it.
It sounds like your understanding of the thought experiment differs from mine. If I were to guess, I’d guess that by “you” you’re referring to someone or something outside of the model, who has access to the model’s internals, and who uses that access to, as you say, “read” the next token out of the model’s ontology. However, this is not the setup we’re in with respect to actual models (with the exception perhaps of some fairly limited experiments in mechanistic interpretability)—and it’s also not the setup of the thought experiment, which (after all) is about precisely what happens when you can’t read things out of the model’s internal ontology, because it’s too alien to be interpreted.
In other words: “you” don’t read the next token out of the QFT simulation. The model is responsible for doing that translation work. How do we get it to do that, even though we don’t know how to specify the nature of the translation work, much less do it ourselves? Well, simple: in cases where we have access to the ground truth of the next token, e.g. because we’re having it predict an existing book passage, we simply penalize it whenever its output fails to match the next token in the book. In this way, the model can be incentivized to correctly predict whatever we want it to predict, even if we wouldn’t know how to tell it explicitly to do whatever it’s doing.
(The nature of this relationship—whereby humans train opaque algorithms to do things they wouldn’t themselves be able to write out as pseudocode—is arguably the essence of modern deep learning in toto.)
For one thing, in a standard train/dev/test setup, the model is arguably not really doing prediction, it’s doing retrodiction. It’s making ‘predictions’ about things which already happened in the past. The final model is chosen based on what retrodicts the data the best.
Yes, this is a reasonable description to my eyes. Moreover, I actually think it maps fairly well to the above description of how a QFT-style model might be trained to predict the next token of some body of text; in your terms, this is possible specifically because the text already exists, and retrodictions of that text can be graded based on how well they compare against the ground truth.
Also, usually the data is IID rather than sequential—there’s no time component to the data points (unless it’s a time-series problem, which it usually isn’t).
This, on the other hand, doesn’t sound right to me. Yes, there are certainly applications where the training regime produces IID data, but next-token prediction is pretty clearly not one of those? Later tokens are highly conditionally dependent on previous tokens, in a way that’s much closer to a time series than to some kind of IID process. Possibly part of the disconnect is that we’re imagining different applications entirely—which might also explain our differing intuitions w.r.t. deployment?
The fact that we’re choosing a model which retrodicts well is why the presence/absence of a human is generally assumed to be irrelevant, and emphasizing this factor sounds wacky to my ML engineer ears.
Right, so just to check that we’re on the same page: do we agree that after a (retrodictively trained) model is deployed for some use case other than retrodicting existing data—for generative use, say, or for use in some kind of online RL setup—then it’ll doing something other than retrodicting? And that in that situation, the source of (retrodictable) ground truth that was present during training—whether that was a book, a philosopher, or something else—will be absent?
If we do actually agree about that, then that distinction is really all I’m referring to! You can think of it as training set versus test set, to use a more standard ML analogy, except in this case the “test set” isn’t labeled at all, because no one labeled it in advance, and also it’s coming in from an unpredictable outside world rather than from a folder on someone’s hard drive.
Why does that matter? Well, because then we’re essentially at the mercy of the model’s generalization properties, in a way we weren’t while it was retrodicting the training set (or even the validation set, if one of those existed). If it gets anything wrong, there’s no longer any training signal or gradient to penalize it for being “wrong”—so the only remaining question is, just how likely is it to be “wrong”, after being trained for however long it was trained?
And that’s where the QFT model comes in. It says, actually, even if you train me for a good long while on a good amount of data, there are lots of ways for me to generalize “wrongly” from your perspective, if I’m modeling the universe at the level of quantum fields. Sure, I got all the retrodictions right while there was something to be retrodicted, but what exactly makes you think I did that by modeling the philosopher whose remarks I was being trained on?
Maybe I was predicting the soundwaves passing through a particularly region of air in the room he was located—or perhaps I was predicting the pattern of physical transistors in the segment of memory of a particular computer containing his works. Those physical locations in spacetime still exist, and now that I’m deployed, I continue to make predictions using those as my referent—except, the encodings I’m predicting there no longer resemble anything like coherent moral philosophy, or coherent anything, really.
The philosopher has left the room, or the computer’s memory has been reconfigured—so what exactly are the criteria by which I’m supposed to act now? Well, they’re going to be something, presumably—but they’re not going to be something explicit. They’re going to be something implicit to my QFT ontology, something that—back when the philosopher was there, during training—worked in tandem with the specifics of his presence, and the setup involving him, to produce accurate retrodictions of his judgements on various matters.
Now that that’s no longer the case, those same criteria describe some mathematical function that bears no meaningful correspondence to anything a human would recognize, valuable or not—but the function exists, and it can be maximized. Not much can be said about what maximizing that function might result in, except that it’s unlikely to look anything like “doing right according to the philosopher”.
That’s why the QFT example is important. A more plausible model, one that doesn’t think natively in terms of quantum amplitudes, permits the possibility of correctly compressing what we want it to compress—of learning to retrodict, not some strange physical correlates of the philosopher’s various motor outputs, but the actual philosopher’s beliefs as we would understand them. Whether that happens, or whether a QFT-style outcome happens instead, depends in large part on the inductive biases of the model’s architecture and the training process—inductive biases on which the natural abstraction hypothesis asserts a possible constraint.
If I were to guess, I’d guess that by “you” you’re referring to someone or something outside of the model, who has access to the model’s internals, and who uses that access to, as you say, “read” the next token out of the model’s ontology.
Was using a metaphorical “you”. Probably should’ve said something like “gradient descent will find a way to read the next token out of the QFT-based simulation”.
Yes, there are certainly applications where the training regime produces IID data, but next-token prediction is pretty clearly not one of those?
I suppose I should’ve said various documents are IID to be more clear. I would certainly guess they are.
Right, so just to check that we’re on the same page: do we agree that after a (retrodictively trained) model is deployed for some use case other than retrodicting existing data—for generative use, say, or for use in some kind of online RL setup—then it’ll doing something other than retrodicting?
Generally speaking, yes.
And that’s where the QFT model comes in. It says, actually, even if you train me for a good long while on a good amount of data, there are lots of ways for me to generalize “wrongly” from your perspective, if I’m modeling the universe at the level of quantum fields. Sure, I got all the retrodictions right while there was something to be retrodicted, but what exactly makes you think I did that by modeling the philosopher whose remarks I was being trained on?
Well, if we’re following standard ML best practices, we have a train set, a dev set, and a test set. The purpose of the dev set is to check and ensure that things are generalizing properly. If they aren’t generalizing properly, we tweak various hyperparameters of the model and retrain until they do generalize properly on the dev set. Then we do a final check on the test set to ensure we didn’t overfit the dev set. If you forgot or never learned this stuff, I highly recommend brushing up on it.
In principle we could construct a test set or dev set either before or after the model has been trained. It shouldn’t make a difference under normal circumstances. It sounds like maybe you’re discussing a scenario where the model has achieved a level of omniscience, and it does fine on data that was available during its training, because it’s able to read off of an omniscient world-model. But then it fails on data generated in the future, because the translation method for its omniscient world-model only works on artifacts that were present during training. Basically, the time at which the data was generated could constitute a hidden and unexpected source of distribution shift. Does that summarize the core concern?
(To be clear, this sort of acquired-omniscience is liable to sound kooky to many ML researchers. I think it’s worth stress-testing alignment proposals under these sort of extreme scenarios, but I’m not sure we should weight them heavily in terms of estimating our probability of success. In this particular scenario, the model’s performance would drop on data generated after training, and that would hurt the company’s bottom line, and they would have a strong financial incentive to fix it. So I don’t know if thinking about this is a comparative advantage for alignment researchers.)
BTW, the point about documents being IID was meant to indicate that there’s little incentive for the model to e.g. retrodict the coordinates of the server storing a particular document—the sort of data that could aid and incentivize omniscience to a greater degree.
In any case, I would argue that “accidental omniscience” characterizes the problem better than “alien abstractions”. As before, you can imagine an accidentally-omniscient model that uses vanilla abstractions, or a non-omniscient model that uses alien ones.
Well, if we’re following standard ML best practices, we have a train set, a dev set, and a test set. The purpose of the dev set is to check and ensure that things are generalizing properly. If they aren’t generalizing properly, we tweak various hyperparameters of the model and retrain until they do generalize properly on the dev set. Then we do a final check on the test set to ensure we didn’t overfit the dev set. If you forgot or never learned this stuff, I highly recommend brushing up on it.
(Just to be clear: yes, I know what training and test sets are, as well as dev sets/validation sets. You might notice I actually used the phrase “validation set” in my earlier reply to you, so it’s not a matter of guessing someone’s password—I’m quite familiar with these concepts, as someone who’s implemented ML models myself.)
Generally speaking, training, validation, and test datasets are all sourced the same way—in fact, sometimes they’re literally sourced from the same dataset, and the delineation between train/dev/test is introduced during training itself, by arbitrarily carving up the original dataset into smaller sets of appropriate size. This may capture the idea of “IID” you seem to appeal to elsewhere in your comment—that it’s possible to test the model’s generalization performance on some held-out subset of data from the same source(s) it was trained on.
In ML terms, what the thought experiment points to is a form of underlying distributional shift, one that isn’t (and can’t be) captured by “IID” validation or test datasets. The QFT model in particular highlights the extent to which your training process, however broad or inclusive from a parochial human standpoint, contains many incidental distributional correlates to your training signal which (1) exist in all of your data, including any you might hope to rely on to validate your model’s generalization performance, and (2) cease to correlate off-distribution, during deployment.
This can be caused by what you call “omniscience”, but it need not; there are other, more plausible distributional differences that might be picked up on by other kinds of models. But QFT is (as far as our current understanding of physics goes) very close to the base ontology of our universe, and so what is inferrable using QFT is naturally going to be very different from what is inferrable using some other (less powerful) ontology. QFT is a very powerful ontology!
If you want to call that “omniscience”, you can, although note that strictly speaking the model is still just working from inferences from training data. It’s just that, if you feed enough data to a model that can hold entire swaths of the physical universe inside of its metaphorical “head”, pretty soon hypotheses that involve the actual state of that universe will begin to outperform hypotheses that don’t, and which instead use some kind of lossy approximation of that state involving intermediary concepts like “intent”, “belief”, “agent”, “subjective state”, etc.
In principle we could construct a test set or dev set either before or after the model has been trained. It shouldn’t make a difference under normal circumstances. It sounds like maybe you’re discussing a scenario where the model has achieved a level of omniscience, and it does fine on data that was available during its training, because it’s able to read off of an omniscient world-model. But then it fails on data generated in the future, because the translation method for its omniscient world-model only works on artifacts that were present during training. Basically, the time at which the data was generated could constitute a hidden and unexpected source of distribution shift. Does that summarize the core concern?
You’re close; I’d say the concern is slightly worse than that. It’s that the “future data” never actually comes into existence, at any point. So the source of distributional shift isn’t just “the data is generated at the wrong time”, it’s “the data never gets externally generated to begin with, and you (the model) have to work with predictions of what the data counterfactually would have been, had it been generated”.
(This would be the case e.g. with any concept of “human approval” that came from a literal physical human or group of humans during training, and not after the system was deployed “in the wild”.)
In any case, I would argue that “accidental omniscience” characterizes the problem better than “alien abstractions”. As before, you can imagine an accidentally-omniscient model that uses vanilla abstractions, or a non-omniscient model that uses alien ones.
The problem is that “vanilla” abstractions are not the most predictively useful possible abstractions, if you’ve got access to better ones. And models whose ambient hypothesis space is broad enough to include better abstractions (from the standpoint of predictive accuracy) will gravitate towards those, as is incentivized by the outer form of the training task. QFT is the extreme example of a “better abstraction”, but in principle (if the natural abstraction hypothesis fails) there will be all sorts and shapes of abstractions, and some of them will be available to us, and some of them will be available to the model, and these sets will not fully overlap—which is a concern in worlds where different abstractions lead to different generalization properties.
QFT is the extreme example of a “better abstraction”, but in principle (if the natural abstraction hypothesis fails) there will be all sorts and shapes of abstractions, and some of them will be available to us, and some of them will be available to the model, and these sets will not fully overlap—which is a concern in worlds where different abstractions lead to different generalization properties.
Indeed. I think the key thing for me is, I expect the model to be strongly incentivized to have a solid translation layer from its internal ontology to e.g. English language, due to being trained on lots of English language data. Due to Occam’s Razor, I expect the internal ontology to be biased towards that of an English-language speaker.
It’s just that, if you feed enough data to a model that can hold entire swaths of the physical universe inside of its metaphorical “head”, pretty soon hypotheses that involve the actual state of that universe will begin to outperform hypotheses that don’t, and which instead use some kind of lossy approximation of that state involving intermediary concepts like “intent”, “belief”, “agent”, “subjective state”, etc.
I’m imagining something like: early in training the model makes use of those lossy approximations because they are a cheap/accessible way to improve its predictive accuracy. Later in training, assuming it’s being trained on the sort of gigantic scale that would allow it to hold swaths of the physical universe in its head, it loses those desired lossy abstractions due to catastrophic forgetting. Is that an OK way to operationalize your concern?
I’m still not convinced that this problem is a priority. It seems like a problem which will be encountered very late if ever, and will lead to ‘random’ failures on predicting future/counterfactual data in a way that’s fairly obvious.
I think I don’t understand what you’re imagining here. Are you imagining a human manually overseeing all outputs of something like ChatGPT, or Microsoft Copilot, before those outputs are sent to the end user (or, worse yet, put directly into production)?
[I also think I don’t understand why you make the bracketed claim you do, but perhaps hashing that out isn’t a conversational priority.]
It sounds like your understanding of the thought experiment differs from mine. If I were to guess, I’d guess that by “you” you’re referring to someone or something outside of the model, who has access to the model’s internals, and who uses that access to, as you say, “read” the next token out of the model’s ontology. However, this is not the setup we’re in with respect to actual models (with the exception perhaps of some fairly limited experiments in mechanistic interpretability)—and it’s also not the setup of the thought experiment, which (after all) is about precisely what happens when you can’t read things out of the model’s internal ontology, because it’s too alien to be interpreted.
In other words: “you” don’t read the next token out of the QFT simulation. The model is responsible for doing that translation work. How do we get it to do that, even though we don’t know how to specify the nature of the translation work, much less do it ourselves? Well, simple: in cases where we have access to the ground truth of the next token, e.g. because we’re having it predict an existing book passage, we simply penalize it whenever its output fails to match the next token in the book. In this way, the model can be incentivized to correctly predict whatever we want it to predict, even if we wouldn’t know how to tell it explicitly to do whatever it’s doing.
(The nature of this relationship—whereby humans train opaque algorithms to do things they wouldn’t themselves be able to write out as pseudocode—is arguably the essence of modern deep learning in toto.)
Yes, this is a reasonable description to my eyes. Moreover, I actually think it maps fairly well to the above description of how a QFT-style model might be trained to predict the next token of some body of text; in your terms, this is possible specifically because the text already exists, and retrodictions of that text can be graded based on how well they compare against the ground truth.
This, on the other hand, doesn’t sound right to me. Yes, there are certainly applications where the training regime produces IID data, but next-token prediction is pretty clearly not one of those? Later tokens are highly conditionally dependent on previous tokens, in a way that’s much closer to a time series than to some kind of IID process. Possibly part of the disconnect is that we’re imagining different applications entirely—which might also explain our differing intuitions w.r.t. deployment?
Right, so just to check that we’re on the same page: do we agree that after a (retrodictively trained) model is deployed for some use case other than retrodicting existing data—for generative use, say, or for use in some kind of online RL setup—then it’ll doing something other than retrodicting? And that in that situation, the source of (retrodictable) ground truth that was present during training—whether that was a book, a philosopher, or something else—will be absent?
If we do actually agree about that, then that distinction is really all I’m referring to! You can think of it as training set versus test set, to use a more standard ML analogy, except in this case the “test set” isn’t labeled at all, because no one labeled it in advance, and also it’s coming in from an unpredictable outside world rather than from a folder on someone’s hard drive.
Why does that matter? Well, because then we’re essentially at the mercy of the model’s generalization properties, in a way we weren’t while it was retrodicting the training set (or even the validation set, if one of those existed). If it gets anything wrong, there’s no longer any training signal or gradient to penalize it for being “wrong”—so the only remaining question is, just how likely is it to be “wrong”, after being trained for however long it was trained?
And that’s where the QFT model comes in. It says, actually, even if you train me for a good long while on a good amount of data, there are lots of ways for me to generalize “wrongly” from your perspective, if I’m modeling the universe at the level of quantum fields. Sure, I got all the retrodictions right while there was something to be retrodicted, but what exactly makes you think I did that by modeling the philosopher whose remarks I was being trained on?
Maybe I was predicting the soundwaves passing through a particularly region of air in the room he was located—or perhaps I was predicting the pattern of physical transistors in the segment of memory of a particular computer containing his works. Those physical locations in spacetime still exist, and now that I’m deployed, I continue to make predictions using those as my referent—except, the encodings I’m predicting there no longer resemble anything like coherent moral philosophy, or coherent anything, really.
The philosopher has left the room, or the computer’s memory has been reconfigured—so what exactly are the criteria by which I’m supposed to act now? Well, they’re going to be something, presumably—but they’re not going to be something explicit. They’re going to be something implicit to my QFT ontology, something that—back when the philosopher was there, during training—worked in tandem with the specifics of his presence, and the setup involving him, to produce accurate retrodictions of his judgements on various matters.
Now that that’s no longer the case, those same criteria describe some mathematical function that bears no meaningful correspondence to anything a human would recognize, valuable or not—but the function exists, and it can be maximized. Not much can be said about what maximizing that function might result in, except that it’s unlikely to look anything like “doing right according to the philosopher”.
That’s why the QFT example is important. A more plausible model, one that doesn’t think natively in terms of quantum amplitudes, permits the possibility of correctly compressing what we want it to compress—of learning to retrodict, not some strange physical correlates of the philosopher’s various motor outputs, but the actual philosopher’s beliefs as we would understand them. Whether that happens, or whether a QFT-style outcome happens instead, depends in large part on the inductive biases of the model’s architecture and the training process—inductive biases on which the natural abstraction hypothesis asserts a possible constraint.
Was using a metaphorical “you”. Probably should’ve said something like “gradient descent will find a way to read the next token out of the QFT-based simulation”.
I suppose I should’ve said various documents are IID to be more clear. I would certainly guess they are.
Generally speaking, yes.
Well, if we’re following standard ML best practices, we have a train set, a dev set, and a test set. The purpose of the dev set is to check and ensure that things are generalizing properly. If they aren’t generalizing properly, we tweak various hyperparameters of the model and retrain until they do generalize properly on the dev set. Then we do a final check on the test set to ensure we didn’t overfit the dev set. If you forgot or never learned this stuff, I highly recommend brushing up on it.
In principle we could construct a test set or dev set either before or after the model has been trained. It shouldn’t make a difference under normal circumstances. It sounds like maybe you’re discussing a scenario where the model has achieved a level of omniscience, and it does fine on data that was available during its training, because it’s able to read off of an omniscient world-model. But then it fails on data generated in the future, because the translation method for its omniscient world-model only works on artifacts that were present during training. Basically, the time at which the data was generated could constitute a hidden and unexpected source of distribution shift. Does that summarize the core concern?
(To be clear, this sort of acquired-omniscience is liable to sound kooky to many ML researchers. I think it’s worth stress-testing alignment proposals under these sort of extreme scenarios, but I’m not sure we should weight them heavily in terms of estimating our probability of success. In this particular scenario, the model’s performance would drop on data generated after training, and that would hurt the company’s bottom line, and they would have a strong financial incentive to fix it. So I don’t know if thinking about this is a comparative advantage for alignment researchers.)
BTW, the point about documents being IID was meant to indicate that there’s little incentive for the model to e.g. retrodict the coordinates of the server storing a particular document—the sort of data that could aid and incentivize omniscience to a greater degree.
In any case, I would argue that “accidental omniscience” characterizes the problem better than “alien abstractions”. As before, you can imagine an accidentally-omniscient model that uses vanilla abstractions, or a non-omniscient model that uses alien ones.
(Just to be clear: yes, I know what training and test sets are, as well as dev sets/validation sets. You might notice I actually used the phrase “validation set” in my earlier reply to you, so it’s not a matter of guessing someone’s password—I’m quite familiar with these concepts, as someone who’s implemented ML models myself.)
Generally speaking, training, validation, and test datasets are all sourced the same way—in fact, sometimes they’re literally sourced from the same dataset, and the delineation between train/dev/test is introduced during training itself, by arbitrarily carving up the original dataset into smaller sets of appropriate size. This may capture the idea of “IID” you seem to appeal to elsewhere in your comment—that it’s possible to test the model’s generalization performance on some held-out subset of data from the same source(s) it was trained on.
In ML terms, what the thought experiment points to is a form of underlying distributional shift, one that isn’t (and can’t be) captured by “IID” validation or test datasets. The QFT model in particular highlights the extent to which your training process, however broad or inclusive from a parochial human standpoint, contains many incidental distributional correlates to your training signal which (1) exist in all of your data, including any you might hope to rely on to validate your model’s generalization performance, and (2) cease to correlate off-distribution, during deployment.
This can be caused by what you call “omniscience”, but it need not; there are other, more plausible distributional differences that might be picked up on by other kinds of models. But QFT is (as far as our current understanding of physics goes) very close to the base ontology of our universe, and so what is inferrable using QFT is naturally going to be very different from what is inferrable using some other (less powerful) ontology. QFT is a very powerful ontology!
If you want to call that “omniscience”, you can, although note that strictly speaking the model is still just working from inferences from training data. It’s just that, if you feed enough data to a model that can hold entire swaths of the physical universe inside of its metaphorical “head”, pretty soon hypotheses that involve the actual state of that universe will begin to outperform hypotheses that don’t, and which instead use some kind of lossy approximation of that state involving intermediary concepts like “intent”, “belief”, “agent”, “subjective state”, etc.
You’re close; I’d say the concern is slightly worse than that. It’s that the “future data” never actually comes into existence, at any point. So the source of distributional shift isn’t just “the data is generated at the wrong time”, it’s “the data never gets externally generated to begin with, and you (the model) have to work with predictions of what the data counterfactually would have been, had it been generated”.
(This would be the case e.g. with any concept of “human approval” that came from a literal physical human or group of humans during training, and not after the system was deployed “in the wild”.)
The problem is that “vanilla” abstractions are not the most predictively useful possible abstractions, if you’ve got access to better ones. And models whose ambient hypothesis space is broad enough to include better abstractions (from the standpoint of predictive accuracy) will gravitate towards those, as is incentivized by the outer form of the training task. QFT is the extreme example of a “better abstraction”, but in principle (if the natural abstraction hypothesis fails) there will be all sorts and shapes of abstractions, and some of them will be available to us, and some of them will be available to the model, and these sets will not fully overlap—which is a concern in worlds where different abstractions lead to different generalization properties.
Indeed. I think the key thing for me is, I expect the model to be strongly incentivized to have a solid translation layer from its internal ontology to e.g. English language, due to being trained on lots of English language data. Due to Occam’s Razor, I expect the internal ontology to be biased towards that of an English-language speaker.
I’m imagining something like: early in training the model makes use of those lossy approximations because they are a cheap/accessible way to improve its predictive accuracy. Later in training, assuming it’s being trained on the sort of gigantic scale that would allow it to hold swaths of the physical universe in its head, it loses those desired lossy abstractions due to catastrophic forgetting. Is that an OK way to operationalize your concern?
I’m still not convinced that this problem is a priority. It seems like a problem which will be encountered very late if ever, and will lead to ‘random’ failures on predicting future/counterfactual data in a way that’s fairly obvious.