(Related text posted to Twitter; this version is edited and has a more advanced final section.)
Imagine yourself in a box, trying to predict the next word - assign as much probability mass to the next token as possible - for all the text on the Internet.
Koan: Is this a task whose difficulty caps out as human intelligence, or at the intelligence level of the smartest human who wrote any Internet text? What factors make that task easier, or harder? (If you don't have an answer, maybe take a minute to generate one, or alternatively, try to predict what I'll say next; if you do have an answer, take a moment to review it inside your mind, or maybe say the words out loud.)
Consider that somewhere on the internet is probably a list of thruples: <product of 2 prime numbers, first prime, second prime>.
GPT obviously isn't going to predict that successfully for significantly-sized primes, but it illustrates the basic point:
There is no law saying that a predictor only needs to be as intelligent as the generator, in order to predict the generator's next token.
Indeed, in general, you've got to be more intelligent to predict particular X, than to generate realistic X. GPTs are being trained to a much harder task than GANs.
Same spirit: <Hash, plaintext> pairs, which you can't predict without cracking the hash algorithm, but which you could far more easily generate typical instances of if you were trying to pass a GAN's discriminator about it (assuming a discriminator that had learned to compute hash functions).
Consider that some of the text on the Internet isn't humans casually chatting. It's the results section of a science paper. It's news stories that say what happened on a particular day, where maybe no human would be smart enough to predict the next thing that happened in the news story in advance of it happening.
As Ilya Sutskever compactly put it, to learn to predict text, is to learn to predict the causal processes of which the text is a shadow.
Lots of what's shadowed on the Internet has a *complicated* causal process generating it.
Consider that sometimes human beings, in the course of talking, make errors.
GPTs are not being trained to imitate human error. They're being trained to *predict* human error.
Consider the asymmetry between you, who makes an error, and an outside mind that knows you well enough and in enough detail to predict *which* errors you'll make.
If you then ask that predictor to become an actress and play the character of you, the actress will guess which errors you'll make, and play those errors. If the actress guesses correctly, it doesn't mean the actress is just as error-prone as you.
Consider that a lot of the text on the Internet isn't extemporaneous speech. It's text that people crafted over hours or days.
GPT-4 is being asked to predict it in 200 serial steps or however many layers it's got, just like if a human was extemporizing their immediate thoughts.
A human can write a rap battle in an hour. A GPT loss function would like the GPT to be intelligent enough to predict it on the fly.
Or maybe simplest:
Imagine somebody telling you to make up random words, and you say, "Morvelkainen bloombla ringa mongo."
Imagine a mind of a level - where, to be clear, I'm not saying GPTs are at this level yet -
Imagine a Mind of a level where it can hear you say 'morvelkainen blaambla ringa', and maybe also read your entire social media history, and then manage to assign 20% probability that your next utterance is 'mongo'.
The fact that this Mind could double as a really good actor playing your character, does not mean They are only exactly as smart as you.
When you're trying to be human-equivalent at writing text, you can just make up whatever output, and it's now a human output because you're human and you chose to output that.
GPT-4 is being asked to predict all that stuff you're making up. It doesn't get to make up whatever. It is being asked to model what you were thinking - the thoughts in your mind whose shadow is your text output - so as to assign as much probability as possible to your true next word.
Figuring out that your next utterance is 'mongo' is not mostly a question, I'd guess, of that mighty Mind being hammered into the shape of a thing that can simulate arbitrary humans, and then some less intelligent subprocess being responsible for adapting the shape of that Mind to be you exactly, after which it simulates you saying 'mongo'. Figuring out exactly who's talking, to that degree, is a hard inference problem which seems like noticeably harder mental work than the part where you just say 'mongo'.
When you predict how to chip a flint handaxe, you are not mostly a causal process that behaves like a flint handaxe, plus some computationally weaker thing that figures out which flint handaxe to be. It's not a problem that is best solved by "have the difficult ability to be like any particular flint handaxe, and then easily figure out which flint handaxe to be".
GPT-4 is still not as smart as a human in many ways, but it's naked mathematical truth that the task GPTs are being trained on is harder than being an actual human.
And since the task that GPTs are being trained on is different from and harder than the task of being a human, it would be surprising - even leaving aside all the ways that gradient descent differs from natural selection - if GPTs ended up thinking the way humans do, in order to solve that problem.
GPTs are not Imitators, nor Simulators, but Predictors.
Bailey: GPTs are Predictors, not Imitators (nor Simulators).
Motte: The training task for GPTs is a prediction task.
The title and the concluding sentence both plainly advocate for (1), but it's not really touched by the overall post, and I think it's up for debate (related: reward is not the optimization target). Instead there is an argument for (2). Perhaps the intention of the final sentence was to oppose Simulators? If that's the case, cite it, be explicit. This could be a really easy thing for an editor to fix.
Does this look like a motte-and-bailey to you?
Bailey: The task that GPTs are being trained on is ... harder than the task of being a human.
Motte: Being an actual human is not enough to solve GPT's task.
As I read it, (1) is false, the task of being a human doesn't cap out at human intelligence. More intelligent humans are better at minimizing prediction error, achieving goals, inclusive genetic fitness, whatever you might think defines "the task of being a human". In the comments, Yudkowsky retreats to (2), which is true. But then how should I understand this whole paragraph from the post?
And since the task that GPTs are being trained on is different from and harder than the task of being a human, it would be surprising - even leaving aside all the ways that gradient descent differs from natural selection - if GPTs ended up thinking the way humans do, in order to solve that problem.
If we're talking about how natural selection trained my genome, why are we talking about how well humans perform the human task? Evolution is optimizing over generations. My human task is optimizing over my lifetime. Also, if we're just arguing for different thinking, surely it mostly matters whether the training task is different, not whether it is harder?
Overall I think "Is GPT-N bounded by human capabilities? No." is a better post on the mottes and avoids staking out unsupported baileys. This entire topic is becoming less relevant because AIs are getting all sorts of synthetic data and RLHF and other training techniques thrown at them. The 2022 question of the capabilities of a hypothetical GPT-N that was only trained on the task of predicting human text is academic in 2024. On the other hand, it's valuable for people to practice on this simpler question before moving on to harder ones.
(Related text posted to Twitter; this version is edited and has a more advanced final section.)
Imagine yourself in a box, trying to predict the next word - assign as much probability mass to the next token as possible - for all the text on the Internet.
Koan: Is this a task whose difficulty caps out as human intelligence, or at the intelligence level of the smartest human who wrote any Internet text? What factors make that task easier, or harder? (If you don't have an answer, maybe take a minute to generate one, or alternatively, try to predict what I'll say next; if you do have an answer, take a moment to review it inside your mind, or maybe say the words out loud.)
Consider that somewhere on the internet is probably a list of thruples: <product of 2 prime numbers, first prime, second prime>.
GPT obviously isn't going to predict that successfully for significantly-sized primes, but it illustrates the basic point:
There is no law saying that a predictor only needs to be as intelligent as the generator, in order to predict the generator's next token.
Indeed, in general, you've got to be more intelligent to predict particular X, than to generate realistic X. GPTs are being trained to a much harder task than GANs.
Same spirit: <Hash, plaintext> pairs, which you can't predict without cracking the hash algorithm, but which you could far more easily generate typical instances of if you were trying to pass a GAN's discriminator about it (assuming a discriminator that had learned to compute hash functions).
Consider that some of the text on the Internet isn't humans casually chatting. It's the results section of a science paper. It's news stories that say what happened on a particular day, where maybe no human would be smart enough to predict the next thing that happened in the news story in advance of it happening.
As Ilya Sutskever compactly put it, to learn to predict text, is to learn to predict the causal processes of which the text is a shadow.
Lots of what's shadowed on the Internet has a *complicated* causal process generating it.
Consider that sometimes human beings, in the course of talking, make errors.
GPTs are not being trained to imitate human error. They're being trained to *predict* human error.
Consider the asymmetry between you, who makes an error, and an outside mind that knows you well enough and in enough detail to predict *which* errors you'll make.
If you then ask that predictor to become an actress and play the character of you, the actress will guess which errors you'll make, and play those errors. If the actress guesses correctly, it doesn't mean the actress is just as error-prone as you.
Consider that a lot of the text on the Internet isn't extemporaneous speech. It's text that people crafted over hours or days.
GPT-4 is being asked to predict it in 200 serial steps or however many layers it's got, just like if a human was extemporizing their immediate thoughts.
A human can write a rap battle in an hour. A GPT loss function would like the GPT to be intelligent enough to predict it on the fly.
Or maybe simplest:
Imagine somebody telling you to make up random words, and you say, "Morvelkainen bloombla ringa mongo."
Imagine a mind of a level - where, to be clear, I'm not saying GPTs are at this level yet -
Imagine a Mind of a level where it can hear you say 'morvelkainen blaambla ringa', and maybe also read your entire social media history, and then manage to assign 20% probability that your next utterance is 'mongo'.
The fact that this Mind could double as a really good actor playing your character, does not mean They are only exactly as smart as you.
When you're trying to be human-equivalent at writing text, you can just make up whatever output, and it's now a human output because you're human and you chose to output that.
GPT-4 is being asked to predict all that stuff you're making up. It doesn't get to make up whatever. It is being asked to model what you were thinking - the thoughts in your mind whose shadow is your text output - so as to assign as much probability as possible to your true next word.
Figuring out that your next utterance is 'mongo' is not mostly a question, I'd guess, of that mighty Mind being hammered into the shape of a thing that can simulate arbitrary humans, and then some less intelligent subprocess being responsible for adapting the shape of that Mind to be you exactly, after which it simulates you saying 'mongo'. Figuring out exactly who's talking, to that degree, is a hard inference problem which seems like noticeably harder mental work than the part where you just say 'mongo'.
When you predict how to chip a flint handaxe, you are not mostly a causal process that behaves like a flint handaxe, plus some computationally weaker thing that figures out which flint handaxe to be. It's not a problem that is best solved by "have the difficult ability to be like any particular flint handaxe, and then easily figure out which flint handaxe to be".
GPT-4 is still not as smart as a human in many ways, but it's naked mathematical truth that the task GPTs are being trained on is harder than being an actual human.
And since the task that GPTs are being trained on is different from and harder than the task of being a human, it would be surprising - even leaving aside all the ways that gradient descent differs from natural selection - if GPTs ended up thinking the way humans do, in order to solve that problem.
GPTs are not Imitators, nor Simulators, but Predictors.