Thanks to Chris Scammell, Adam Shimi, Lee Sharkey, Evan Hubinger, Nicholas Dupuis, Leo Gao, Johannes Treutlein, and Jonathan Low for feedback on drafts.

This work was carried out while at Conjecture.

"Moebius illustration of a simulacrum living in an AI-generated story discovering it is in a simulation" by DALL-E 2


TL;DR: Self-supervised learning may create AGI or its foundation. What would that look like?

Unlike the limit of RL, the limit of self-supervised learning has received surprisingly little conceptual attention, and recent progress has made deconfusion in this domain more pressing.

Existing AI taxonomies either fail to capture important properties of self-supervised models or lead to confusing propositions. For instance, GPT policies do not seem globally agentic, yet can be conditioned to behave in goal-directed ways. This post describes a frame that enables more natural reasoning about properties like agency: GPT, insofar as it is inner-aligned, is a simulator which can simulate agentic and non-agentic simulacra.

The purpose of this post is to capture these objects in words so GPT can reference them and provide a better foundation for understanding them.

I use the generic term “simulator” to refer to models trained with predictive loss on a self-supervised dataset, invariant to architecture or data type (natural language, code, pixels, game states, etc). The outer objective of self-supervised learning is Bayes-optimal conditional inference over the prior of the training distribution, which I call the simulation objective, because a conditional model can be used to simulate rollouts which probabilistically obey its learned distribution by iteratively sampling from its posterior (predictions) and updating the condition (prompt). Analogously, a predictive model of physics can be used to compute rollouts of phenomena in simulation. A goal-directed agent which evolves according to physics can be simulated by the physics rule parameterized by an initial state, but the same rule could also propagate agents with different values, or non-agentic phenomena like rocks. This ontological distinction between simulator (rule) and simulacra (phenomena) applies directly to generative models like GPT.


  • This post is intended as the first in a sequence on the alignment problem in a landscape where self-supervised simulators are a possible/likely form of powerful AI. I don’t know how many subsequent posts I’ll actually publish. Take it as a prompt.
  • I use the generic term “GPT” to refer to transformers trained on next-token prediction.
  • A while ago when I was trying to avoid having to write this post by hand, I prompted GPT-3 with an early outline of this post. I’ve spliced in some excerpts from it, indicated by this style. Prompt, generated text, and curation metrics here.

The limit of sequence modeling

Transformer-based language models have recently achieved remarkable results…

– every paper since 2020

GPT is not a new form of AI in terms of its training methodology and outer objective: sequence generation from statistical models of data is an old idea. In 1951, Claude Shannon described using n-grams to approximate conditional next-letter probabilities of a text dataset and "reversed" to generate text samples[1]. I don't know of any other notable advances until the 2010s brought the first interesting language generation results from neural networks. In 2015, Karpathy wrote a blog post/tutorial sharing his excitement about The Unreasonable Effectiveness of Recurrent Neural Networks:

Fast forward about a year: I’m training RNNs all the time and I’ve witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. This post is about sharing some of that magic with you.

We’ll train RNNs to generate text character by character and ponder the question “how is that even possible?"

The “magical outputs” of char-RNNs looked like this:

PANDARUS: Alas, I think he shall be come approached and the day When little srain would be attain’d into being never fed, And who is but a chain and subjects of his death, I should not sleep.

Second Senator: They are away this miseries, produced upon my soul, Breaking and strongly should be buried, when I perish The earth and thoughts of many states.

DUKE VINCENTIO: Well, your wit is in the care of side and that.

Second Lord: They would be ruled after this chamber, and my fair nues begun out of the fact, to be conveyed, Whose noble souls I’ll have the heart of the wars.

Clown: Come, sir, I will make did behold your worship.

VIOLA: I’ll drink it.

At the time, this really was magical (and uncanny). How does it know that miseries are produced upon the soul? Or that a clown should address a lord as “sir”? Char-RNNs were like ouija boards, but actually possessed by a low-fidelity ghost summoned from a text corpus. I remember being thrilled by the occasional glimmers of semantic comprehension in a domain of unbounded constructive meaning.

But, aside from indulging that emotion, I didn’t think about what would happen if my char-RNN bots actually improved indefinitely at their training objective of natural language prediction. It just seemed like there were some complexity classes of magic that neural networks could learn, and others that were inaccessible, at least in the conceivable future.

Huge mistake! Perhaps I could have started thinking several years earlier about what now seems so fantastically important. But it wasn’t until GPT-3, when I saw the qualitative correlate of “loss going down”, that I updated.

I wasn’t the only one[2] whose imagination was naively constrained. A 2016 paper from Google Brain, “Exploring the Limits of Language Modeling”, describes the utility of training language models as follows:

Often (although not always), training better language models improves the underlying metrics of the downstream task (such as word error rate for speech recognition, or BLEU score for translation), which makes the task of training better LMs valuable by itself.

Despite its title, this paper’s analysis is entirely myopic. Improving BLEU scores is neat, but how about modeling general intelligence as a downstream task? In retrospect, an exploration of the limits of language modeling should have read something more like:

If loss keeps going down on the test set, in the limit – putting aside whether the current paradigm can approach it – the model must be learning to interpret and predict all patterns represented in language, including common-sense reasoning, goal-directed optimization, and deployment of the sum of recorded human knowledge. Its outputs would behave as intelligent entities in their own right. You could converse with it by alternately generating and adding your responses to its prompt, and it would pass the Turing test. In fact, you could condition it to generate interactive and autonomous versions of any real or fictional person who has been recorded in the training corpus or even could be recorded (in the sense that the record counterfactually “could be” in the test set). Oh shit, and it could write code…

The paper does, however, mention that making the model bigger improves test perplexity.[3]

I’m only picking on Jozefowicz et al. because of their ironic title. I don’t know of any explicit discussion of this limit predating GPT, except a working consensus of Wikipedia editors that NLU is AI-complete.

The earliest engagement with the hypothetical of “what if self-supervised sequence modeling actually works” that I know of is a terse post from 2019, Implications of GPT-2, by Gurkenglas. It is brief and relevant enough to quote in full:

I was impressed by GPT-2, to the point where I wouldn’t be surprised if a future version of it could be used pivotally using existing protocols.

Consider generating half of a Turing test transcript, the other half being supplied by a human judge. If this passes, we could immediately implement an HCH of AI safety researchers solving the problem if it’s within our reach at all. (Note that training the model takes much more compute than generating text.)

This might not be the first pivotal application of language models that becomes possible as they get stronger.

It’s a source of superintelligence that doesn’t automatically run into utility maximizers. It sure doesn’t look like AI services, lumpy or no.

It is conceivable that predictive loss does not descend to the AGI-complete limit, maybe because:

But I have not seen enough evidence for either not to be concerned that we have in our hands a well-defined protocol that could end in AGI, or a foundation which could spin up an AGI without too much additional finagling. As Gurkenglas observed, this would be a very different source of AGI than previously foretold.

The old framework of alignment

A few people did think about what would happen if agents actually worked. The hypothetical limit of a powerful system optimized to optimize for an objective drew attention even before reinforcement learning became mainstream in the 2010s. Our current instantiation of AI alignment theory, crystallized by Yudkowsky, Bostrom, et al, stems from the vision of an arbitrarily-capable system whose cognition and behavior flows from a goal.

But since GPT-3 I’ve noticed, in my own thinking and in alignment discourse, a dissonance between theory and practice/phenomena, as the behavior and nature of actual systems that seem nearest to AGI also resist short descriptions in the dominant ontology.

I only recently discovered the question “Is the work on AI alignment relevant to GPT?” which stated this observation very explicitly:

I don’t follow [AI alignment research] in any depth, but I am noticing a striking disconnect between the concepts appearing in those discussions and recent advances in AI, especially GPT-3.

People talk a lot about an AI’s goals, its utility function, its capability to be deceptive, its ability to simulate you so it can get out of a box, ways of motivating it to be benign, Tool AI, Oracle AI, and so on. (…) But when I look at GPT-3, even though this is already an AI that Eliezer finds alarming, I see none of these things. GPT-3 is a huge model, trained on huge data, for predicting text.

My belated answer: A lot of prior work on AI alignment is relevant to GPT. I spend most of my time thinking about GPT alignment, and concepts like goal-directedness, inner/outer alignment, myopia, corrigibility, embedded agency, model splintering, and even tiling agents are active in the vocabulary of my thoughts. But GPT violates some prior assumptions such that these concepts sound dissonant when applied naively. To usefully harness these preexisting abstractions, we need something like an ontological adapter pattern that maps them to the appropriate objects.

GPT’s unforeseen nature also demands new abstractions (the adapter itself, for instance). My thoughts also use load-bearing words that do not inherit from alignment literature. Perhaps it shouldn’t be surprising if the form of the first visitation from mindspace mostly escaped a few years of theory conducted in absence of its object.

The purpose of this post is to capture that object (conditional on a predictive self-supervised training story) in words. Why in words? In order to write coherent alignment ideas which reference it! This is difficult in the existing ontology, because unlike the concept of an agent, whose name evokes the abstract properties of the system and thereby invites extrapolation, the general category for “a model optimized for an AGI-complete predictive task” has not been given a name[4]. Namelessness can not only be a symptom of the extrapolation of powerful predictors falling through conceptual cracks, but also a cause, because what we can represent in words is what we can condition on for further generation. To whatever extent this shapes private thinking, it is a strict constraint on communication, when thoughts must be sent through the bottleneck of words.

I want to hypothesize about LLMs in the limit, because when AI is all of a sudden writing viral blog posts, coding competitively, proving theorems, and passing the Turing test so hard that the interrogator sacrifices their career at Google to advocate for its personhood, a process is clearly underway whose limit we’d be foolish not to contemplate. I could directly extrapolate the architecture responsible for these feats and talk about “GPT-N”, a bigger autoregressive transformer. But often some implementation details aren’t as important as the more abstract archetype that GPT represents – I want to speak the true name of the solution which unraveled a Cambrian explosion of AI phenomena with inessential details unconstrained, as we’d speak of natural selection finding the solution of the “lens” without specifying the prototype’s diameter or focal length.

(Only when I am able to condition on that level of abstraction can I generate metaphors like “language is a lens that sees its flaws”.)

Inadequate ontologies

In the next few sections I’ll attempt to fit GPT into some established categories, hopefully to reveal something about the shape of the peg through contrast, beginning with the main antagonist of the alignment problem as written so far, the agent.

Agentic GPT

Alignment theory has been largely pushed by considerations of agentic AGIs. There were good reasons for this focus:

  • Agents are convergently dangerous for theoretical reasons like instrumental convergence, goodhart, and orthogonality.
  • RL creates agents, and RL seemed to be the way to AGI. In the 2010s, reinforcement learning was the dominant paradigm for those interested in AGI (e.g. OpenAI). RL lends naturally to creating agents that pursue rewards/utility/objectives. So there was reason to expect that agentic AI would be the first (and by the theoretical arguments, last) form that superintelligence would take.
  • Agents are powerful and economically productive. It’s a reasonable guess that humans will create such systems if only because we can.

The first reason is conceptually self-contained and remains compelling. The second and third, grounded in the state of the world, has been shaken by the current climate of AI progress, where products of self-supervised learning generate most of the buzz: not even primarily for their SOTA performance in domains traditionally dominated by RL, like games[5], but rather for their virtuosity in domains where RL never even took baby steps, like natural language synthesis.

What pops out of self-supervised predictive training is noticeably not a classical agent. Shortly after GPT-3’s release, David Chalmers lucidly observed that the policy’s relation to agents is like that of a “chameleon” or “engine”:

GPT-3 does not look much like an agent. It does not seem to have goals or preferences beyond completing text, for example. It is more like a chameleon that can take the shape of many different agents. Or perhaps it is an engine that can be used under the hood to drive many agents. But it is then perhaps these systems that we should assess for agency, consciousness, and so on.[6]

But at the same time, GPT can act like an agent – and aren’t actions what ultimately matter? In Optimality is the tiger, and agents are its teeth, Veedrac points out that a model like GPT does not need to care about the consequences of its actions for them to be effectively those of an agent that kills you. This is more reason to examine the nontraditional relation between the optimized policy and agents, as it has implications for how and why agents are served.

Unorthodox agency

GPT’s behavioral properties include imitating the general pattern of human dictation found in its universe of training data, e.g., arXiv, fiction, blog posts, Wikipedia, Google queries, internet comments, etc. Among other properties inherited from these historical sources, it is capable of goal-directed behaviors such as planning. For example, given a free-form prompt like, “you are a desperate smuggler tasked with a dangerous task of transporting a giant bucket full of glowing radioactive materials across a quadruple border-controlled area deep in Africa for Al Qaeda,” the AI will fantasize about logistically orchestrating the plot just as one might, working out how to contact Al Qaeda, how to dispense the necessary bribe to the first hop in the crime chain, how to get a visa to enter the country, etc. Considering that no such specific chain of events are mentioned in any of the bazillions of pages of unvarnished text that GPT slurped[7], the architecture is not merely imitating the universe, but reasoning about possible versions of the universe that does not actually exist, branching to include new characters, places, and events

When thought about behavioristically, GPT superficially demonstrates many of the raw ingredients to act as an “agent”, an entity that optimizes with respect to a goal. But GPT is hardly a proper agent, as it wasn’t optimized to achieve any particular task, and does not display an epsilon optimization for any single reward function, but instead for many, including incompatible ones. Using it as an agent is like using an agnostic politician to endorse hardline beliefs– he can convincingly talk the talk, but there is no psychic unity within him; he could just as easily play devil’s advocate for the opposing party without batting an eye. Similarly, GPT instantiates simulacra of characters with beliefs and goals, but none of these simulacra are the algorithm itself. They form a virtual procession of different instantiations as the algorithm is fed different prompts, supplanting one surface personage with another. Ultimately, the computation itself is more like a disembodied dynamical law that moves in a pattern that broadly encompasses the kinds of processes found in its training data than a cogito meditating from within a single mind that aims for a particular outcome.

Presently, GPT is the only way to instantiate agentic AI that behaves capably outside toy domains. These intelligences exhibit goal-directedness; they can plan; they can form and test hypotheses; they can persuade and be persuaded[8]. It would not be very dignified of us to gloss over the sudden arrival of artificial agents often indistinguishable from human intelligence just because the policy that generates them “only cares about predicting the next word”.

But nor should we ignore the fact that these agentic entities exist in an unconventional relationship to the policy, the neural network “GPT” that was trained to minimize log-loss on a dataset. GPT-driven agents are ephemeral – they can spontaneously disappear if the scene in the text changes and be replaced by different spontaneously generated agents. They can exist in parallel, e.g. in a story with multiple agentic characters in the same scene. There is a clear sense in which the network doesn’t “want” what the things that it simulates want, seeing as it would be just as willing to simulate an agent with opposite goals, or throw up obstacles which foil a character’s intentions for the sake of the story. The more you think about it, the more fluid and intractable it all becomes. Fictional characters act agentically, but they’re at least implicitly puppeteered by a virtual author who has orthogonal intentions of their own. Don’t let me get into the fact that all these layers of “intentionality” operate largely in indeterminate superpositions.

This is a clear way that GPT diverges from orthodox visions of agentic AI: In the agentic AI ontology, there is no difference between the policy and the effective agent, but for GPT, there is.

It’s not that anyone ever said there had to be 1:1 correspondence between policy and effective agent; it was just an implicit assumption which felt natural in the agent frame (for example, it tends to hold for RL). GPT pushes us to realize that this was an assumption, and to consider the consequences of removing it for our constructive maps of mindspace.

Orthogonal optimization

Indeed, Alex Flint warned of the potential consequences of leaving this assumption unchallenged:

Fundamental misperception due to the agent frame: That the design space for autonomous machines that exert influence over the future is narrower than it seems. This creates a self-fulfilling prophecy in which the AIs actually constructed are in fact within this narrower regime of agents containing an unchanging internal decision algorithm.

If there are other ways of constructing AI, might we also avoid some of the scary, theoretically hard-to-avoid side-effects of optimizing an agent like instrumental convergence? GPT provides an interesting example.

GPT doesn’t seem to care which agent it simulates, nor if the scene ends and the agent is effectively destroyed. This is not corrigibility in Paul Christiano’s formulation, where the policy is “okay” with being turned off or having its goal changed in a positive sense, but has many aspects of the negative formulation found on Arbital. It is corrigible in this way because a major part of the agent specification (the prompt) is not fixed by the policy, and the policy lacks direct training incentives to control its prompt[9], as it never generates text or otherwise influences its prompts during training. It’s we who choose to sample tokens from GPT’s predictions and append them to the prompt at runtime, and the result is not always helpful to any agents who may be programmed by the prompt. The downfall of the ambitious villain from an oversight committed in hubris is a predictable narrative pattern.[10] So is the end of a scene.

In general, the model’s prediction vector could point in any direction relative to the predicted agent’s interests. I call this the prediction orthogonality thesis: A model whose objective is prediction[11] can simulate agents who optimize toward any objectives, with any degree of optimality (bounded above but not below by the model’s power).

This is a corollary of the classical orthogonality thesis, which states that agents can have any combination of intelligence level and goal, combined with the assumption that agents can in principle be predicted. A single predictive model may also predict multiple agents, either independently (e.g. in different conditions), or interacting in a multi-agent simulation. A more optimal predictor is not restricted to predicting more optimal agents: being smarter does not make you unable to predict stupid systems, nor things that aren’t agentic like the weather.

Are there any constraints on what a predictive model can be at all, other than computability? Only that it makes sense to talk about its “prediction objective”, which implies the existence of a “ground truth” distribution to which the predictor’s optimality is measured. Several words in that last sentence may conceal labyrinths of nuance, but for now let’s wave our hands and say that if we have some way of presenting Bayes-structure with evidence of a distribution, we can build an optimization process whose outer objective is optimal prediction.

We can specify some types of outer objectives using a ground truth distribution that we cannot with a utility function. As in the case of GPT, there is no difficulty in incentivizing a model to predict actions that are corrigible, incoherent, stochastic, irrational, or otherwise anti-natural to expected utility maximization. All you need is evidence of a distribution exhibiting these properties.

For instance, during GPT’s training, sometimes predicting the next token coincides with predicting agentic behavior, but:

  • The actions of agents described in the data are rarely optimal for their goals; humans, for instance, are computationally bounded, irrational, normative, habitual, fickle, hallucinatory, etc.
  • Different prediction steps involve mutually incoherent goals, as human text records a wide range of differently-motivated agentic behavior
  • Many prediction steps don’t correspond to the action of any consequentialist agent but are better described as reporting on the structure of reality, e.g. the year in a timestamp. These transitions incentivize GPT to improve its model of the world, orthogonally to agentic objectives.
  • When there is insufficient information to predict the next token with certainty, log-loss incentivizes a probabilistic output. Utility maximizers aren’t supposed to become more stochastic in response to uncertainty.

Everything can be trivially modeled as a utility maximizer, but for these reasons, a utility function is not a good explanation or compression of GPT’s training data, and its optimal predictor is not well-described as a utility maximizer. However, just because information isn’t compressed well by a utility function doesn’t mean it can’t be compressed another way. The Mandelbrot set is a complicated pattern compressed by a very simple generative algorithm which makes no reference to future consequences and doesn’t involve argmaxxing anything (except vacuously being the way it is). Likewise the set of all possible rollouts of Conway’s Game of Lifesome automata may be well-described as agents, but they are a minority of possible patterns, and not all agentic automata will share a goal. Imagine trying to model Game of Life as an expected utility maximizer!

There are interesting things that are not utility maximizers, some of which qualify as AGI or TAI. Are any of them something we’d be better off creating than a utility maximizer? An inner-aligned GPT, for instance, gives us a way of instantiating goal-directed processes which can be tempered with normativity and freely terminated in a way that is not anti-natural to the training objective. There’s much more to say about this, but for now, I’ll bring it back to how GPT defies the agent orthodoxy.

The crux stated earlier can be restated from the perspective of training stories: In the agentic AI ontology, the direction of optimization pressure applied by training is in the direction of the effective agent’s objective function, but in GPT’s case it is (most generally) orthogonal.[12]

This means that neither the policy nor the effective agents necessarily become more optimal agents as loss goes down, because the policy is not optimized to be an agent, and the agent-objectives are not optimized directly.

Roleplay sans player

Napoleon: You have written this huge book on the system of the world without once mentioning the author of the universe.

Laplace: Sire, I had no need of that hypothesis.

Even though neither GPT’s behavior nor its training story fit with the traditional agent framing, there are still compatibilist views that characterize it as some kind of agent. For example, Gwern has said[13] that anyone who uses GPT for long enough begins to think of it as an agent who only cares about roleplaying a lot of roles.

That framing seems unnatural to me, comparable to thinking of physics as an agent who only cares about evolving the universe accurately according to the laws of physics. At best, the agent is an epicycle; but it is also compatible with interpretations that generate dubious predictions.

Say you’re told that an agent values predicting text correctly. Shouldn’t you expect that:

  • It wants text to be easier to predict, and given the opportunity will influence the prediction task to make it easier (e.g. by generating more predictable text or otherwise influencing the environment so that it receives easier prompts);
  • It wants to become better at predicting text, and given the opportunity will self-improve;
  • It doesn’t want to be prevented from predicting text, and will prevent itself from being shut down if it can?

In short, all the same types of instrumental convergence that we expect from agents who want almost anything at all.

But this behavior would be very unexpected in GPT, whose training doesn’t incentivize instrumental behavior that optimizes prediction accuracy! GPT does not generate rollouts during training. Its output is never sampled to yield “actions” whose consequences are evaluated, so there is no reason to expect that GPT will form preferences over the consequences of its output related to the text prediction objective.[14]

Saying that GPT is an agent who wants to roleplay implies the presence of a coherent, unconditionally instantiated roleplayer running the show who attaches terminal value to roleplaying. This presence is an additional hypothesis, and so far, I haven’t noticed evidence that it’s true.

(I don’t mean to imply that Gwern thinks this about GPT[15], just that his words do not properly rule out this interpretation. It’s a likely enough interpretation that ruling it out is important: I’ve seen multiple people suggest that GPT might want to generate text which makes future predictions easier, and this is something that can happen in some forms of self-supervised learning – see the note on GANs in the appendix.)

I do not think any simple modification of the concept of an agent captures GPT’s natural category. It does not seem to me that GPT is a roleplayer, only that it roleplays. But what is the word for something that roleplays minus the implication that someone is behind the mask?

Oracle GPT and supervised learning

While the alignment sphere favors the agent frame for thinking about GPT, in capabilities research distortions tend to come from a lens inherited from supervised learning. Translated into alignment ontology, the effect is similar to viewing GPT as an “oracle AI” – a view not altogether absent from conceptual alignment, but most influential in the way GPT is used and evaluated by machine learning engineers.

Evaluations for language models tend to look like evaluations for supervised models, consisting of close-ended question/answer pairs – often because they are evaluations for supervised models. Prior to the LLM paradigm, language models were trained and tested on evaluation datasets like Winograd and SuperGLUE which consist of natural language question/answer pairs. The fact that large pretrained models performed well on these same NLP benchmarks without supervised fine-tuning was a novelty. The titles of the GPT-2 and GPT-3 papers, Language Models are Unsupervised Multitask Learners and Language Models are Few-Shot Learners, respectively articulate surprise that self-supervised models implicitly learn supervised tasks during training, and can learn supervised tasks at runtime.

Of all the possible papers that could have been written about GPT-3, OpenAI showcased its ability to extrapolate the pattern of question-answer pairs (few-shot prompts) from supervised learning datasets, a novel capability they called “meta-learning”. This is a weirdly specific and indirect way to break it to the world that you’ve created an AI able to extrapolate semantics of arbitrary natural language structures, especially considering that in many cases the few-shot prompts were actually unnecessary.

The assumptions of the supervised learning paradigm are:

  • The model is optimized to answer questions correctly
  • Tasks are closed-ended, defined by question/correct answer pairs

These are essentially the assumptions of oracle AI, as described by Bostrom and in subsequent usage.

So influential has been this miscalibrated perspective that Gwern, nostalgebraist and myself – who share a peculiar model overlap due to intensive firsthand experience with the downstream behaviors of LLMs – have all repeatedly complained about it. I’ll repeat some of these arguments here, tying into the view of GPT as an oracle AI, and separating it into the two assumptions inspired by supervised learning.

Prediction vs question-answering

At first glance, GPT might resemble a generic “oracle AI”, because it is trained to make accurate predictions. But its log loss objective is myopic and only concerned with immediate, micro-scale correct prediction of the next token, not answering particular, global queries such as “what’s the best way to fix the climate in the next five years?” In fact, it is not specifically optimized to give true answers, which a classical oracle should strive for, but rather to minimize the divergence between predictions and training examples, independent of truth. Moreover, it isn’t specifically trained to give answers in the first place! It may give answers if the prompt asks questions, but it may also simply elaborate on the prompt without answering any question, or tell the rest of a story implied in the prompt. What it does is more like animation than divination, executing the dynamical laws of its rendering engine to recreate the flows of history found in its training data (and a large superset of them as well), mutatis mutandis. Given the same laws of physics, one can build a multitude of different backgrounds and props to create different storystages, including ones that don’t exist in training, but adhere to its general pattern.

GPT does not consistently try to say true/correct things. This is not a bug – if it had to say true things all the time, GPT would be much constrained in its ability to imitate Twitter celebrities and write fiction. Spouting falsehoods in some circumstances is incentivized by GPT’s outer objective. If you ask GPT a question, it will instead answer the question “what’s the next token after ‘{your question}’”, which will often diverge significantly from an earnest attempt to answer the question directly.

GPT doesn’t fit the category of oracle for a similar reason that it doesn’t fit the category of agent. Just as it wasn’t optimized for and doesn’t consistently act according to any particular objective (except the tautological prediction objective), it was not optimized to be correct but rather realistic, and being realistic means predicting humans faithfully even when they are likely to be wrong.

That said, GPT does store a vast amount of knowledge, and its corrigibility allows it to be cajoled into acting as an oracle, like it can be cajoled into acting like an agent. In order to get oracle behavior out of GPT, one must input a sequence such that the predicted continuation of that sequence coincides with an oracle’s output. The GPT-3 paper’s few-shot benchmarking strategy tries to persuade GPT-3 to answer questions correctly by having it predict how a list of correctly-answered questions will continue. Another strategy is to simply “tell” GPT it’s in the oracle modality:

(I) told the AI to simulate a supersmart version of itself (this works, for some reason), and the first thing it spat out was the correct answer.

Reddit post by u/Sophronius

But even when these strategies seem to work, there is no guarantee that they elicit anywhere near optimal question-answering performance, compared to another prompt in the innumerable space of prompts that would cause GPT to attempt the task, or compared to what the model “actually” knows.

This means that no benchmark which evaluates downstream behavior is guaranteed or even expected to probe the upper limits of GPT’s capabilities. In nostalgebraist’s words, we have no ecological evaluation of self-supervised language models – one that measures performance in a situation where the model is incentivised to perform as well as it can on the measure[16].

As nostalgebraist elegantly puts it:

I called GPT-3 a “disappointing paper,” which is not the same thing as calling the model disappointing: the feeling is more like how I’d feel if they found a superintelligent alien and chose only to communicate its abilities by noting that, when the alien is blackout drunk and playing 8 simultaneous games of chess while also taking an IQ test, it then has an “IQ” of about 100.

Treating GPT as an unsupervised implementation of a supervised learner leads to systematic underestimation of capabilities, which becomes a more dangerous mistake as unprobed capabilities scale.

Finite vs infinite questions

Not only does the supervised/oracle perspective obscure the importance and limitations of prompting, it also obscures one of the most crucial dimensions of GPT: the implicit time dimension. By this I mean the ability to evolve a process through time by recursively applying GPT, that is, generate text of arbitrary length.

Recall, the second supervised assumption is that “tasks are closed-ended, defined by question/correct answer pairs”. GPT was trained on context-completion pairs. But the pairs do not represent closed, independent tasks, and the division into question and answer is merely indexical: in another training sample, a token from the question is the answer, and in yet another, the answer forms part of the question[17].

For example, the natural language sequence “The answer is a question” yields training samples like:

{context: “The”, completion: “ answer”},

{context: “The answer”, completion: “ is”},

{context: “The answer is”, completion: “ a”},

{context: “The answer is a”, completion: “ question”}

Since questions and answers are of compatible types, we can at runtime sample answers from the model and use them to construct new questions, and run this loop an indefinite number of times to generate arbitrarily long sequences that obey the model’s approximation of the rule that links together the training samples. The “question” GPT answers is “what token comes next after {context}”. This can be asked interminably, because its answer always implies another question of the same type.

In contrast, models trained with supervised learning output answers that cannot be used to construct new questions, so they’re only good for one step.

Benchmarks derived from supervised learning test GPT’s ability to produce correct answers, not to produce questions which cause it to produce a correct answer down the line. But GPT is capable of the latter, and that is how it is the most powerful.

The supervised mindset causes capabilities researchers to focus on closed-form tasks rather than GPT’s ability to simulate open-ended, indefinitely long processes[18], and as such to overlook multi-step inference strategies like chain-of-thought prompting. Let’s see how the oracle mindset causes a blind spot of the same shape in the imagination of a hypothetical alignment researcher.

Thinking of GPT as an oracle brings strategies to mind like asking GPT-N to predict a solution to alignment from 2000 years in the future.).

There are various problems with this approach to solving alignment, of which I’ll only mention one here: even assuming this prompt is outer aligned[19] in that a logically omniscient GPT would give a useful answer, it is probably not the best approach for a finitely powerful GPT, because the process of generating a solution in the order and resolution that would appear in a future article is probably far from the optimal multi-step algorithm for computing the answer to an unsolved, difficult question.

GPTs ability to arrive at true answers depends on not only the space to solve a problem in multiple steps (of the right granularity), but also the direction of the flow of evidence in that time. If we’re ambitious about getting the truth from a finitely powerful GPT, we need to incite it to predict truth-seeking processes, not just ask it the right questions. Or, in other words, the more general problem we have to solve is not asking GPT the question[20] that makes it output the right answer, but asking GPT the question that makes it output the right question (…) that makes it output the right answer.[21] A question anywhere along the line that elicits a premature attempt at an answer could neutralize the remainder of the process into rationalization.

I’m looking for a way to classify GPT which not only minimizes surprise but also conditions the imagination to efficiently generate good ideas for how it can be used. What category, unlike the category of oracles, would make the importance of process specification obvious?

Paradigms of theory vs practice

Both the agent frame and the supervised/oracle frame are historical artifacts, but while assumptions about agency primarily flow downward from the preceptial paradigm of alignment theory, oracle-assumptions primarily flow upward from the experimental paradigm surrounding GPT’s birth. We use and evaluate GPT like an oracle, and that causes us to implicitly think of it as an oracle.

Indeed, the way GPT is typically used by researchers resembles the archetypal image of Bostrom’s oracle perfectly if you abstract away the semantic content of the model’s outputs. The AI sits passively behind an API, computing responses only when prompted. It typically has no continuity of state between calls. Its I/O is text rather than “real-world actions”.

All these are consequences of how we choose to interact with GPT – which is not arbitrary; the way we deploy systems is guided by their nature. It’s for some good reasons that current GPTs lend to disembodied operation and docile APIs. Lack of long-horizon coherence and delusions discourage humans from letting them run autonomously amok (usually). But the way we deploy systems is also guided by practical paradigms.

One way to find out how a technology can be used is to give it to people who have less preconceptions about how it’s supposed to be used. OpenAI found that most users use their API to generate freeform text:


Most of my own experience using GPT-3 has consisted of simulating indefinite processes which maintain state continuity over up to hundreds of pages. I was driven to these lengths because GPT-3 kept answering its own questions with questions that I wanted to ask it more than anything else I had in mind.

Tool / genie GPT

I’ve sometimes seen GPT casually classified as tool AI. GPTs resemble tool AI from the outside, like it resembles oracle AI, because it is often deployed semi-autonomously for tool-like purposes (like helping me draft this post):

It could also be argued that GPT is a type of “Tool AI”, because it can generate useful content for products, e.g., it can write code and generate ideas. However, unlike specialized Tool AIs that optimize for a particular optimand, GPT wasn’t optimized to do anything specific at all. Its powerful and general nature allows it to be used as a Tool for many tasks, but it wasn’t expliitly trained to achieve these tasks, and does not strive for optimality.

The argument structurally reiterates what has already been said for agents and oracles. Like agency and oracularity, tool-likeness is a contingent capability of GPT, but also orthogonal to its motive.

The same line of argument draws the same conclusion from the question of whether GPT belongs to the fourth Bostromian AI caste, genies. The genie modality is exemplified by Instruct GPT and Codex. But like every behavior I’ve discussed so far which is more specific than predicting text, “instruction following” describes only an exploitable subset of all the patterns tread by the sum of human language and inherited by its imitator.

Behavior cloning / mimicry

The final category I’ll analyze is behavior cloning, a designation for predictive learning that I’ve mostly seen used in contrast to RL. According to an article from 1995, “Behavioural cloning is the process of reconstructing a skill from an operator’s behavioural traces by means of Machine Learning techniques.” The term “mimicry”, as used by Paul Christiano, means the same thing and has similar connotations.

Behavior cloning in its historical usage carries the implicit or explicit assumption that a single agent is being cloned. The natural extension of this to a model trained to predict a diverse human-written dataset might be to say that GPT models a distribution of agents which are selected by the prompt. But this image of “parameterized” behavior cloning still fails to capture some essential properties of GPT.

The vast majority of prompts that produce coherent behavior never occur as prefixes in GPT’s training data, but depict hypothetical processes whose behavior can be predicted by virtue of being capable at predicting language in general. We might call this phenomenon “interpolation” (or “extrapolation”). But to hide it behind any one word and move on would be to gloss over the entire phenomenon of GPT.

Natural language has the property of systematicity: “blocks”, such as words, can be combined to form composite meanings. The number of meanings expressible is a combinatorial function of available blocks. A system which learns natural language is incentivized to learn systematicity; if it succeeds, it gains access to the combinatorial proliferation of meanings that can be expressed in natural language. What GPT lets us do is use natural language to specify any of a functional infinity of configurations, e.g. the mental contents of a person and the physical contents of the room around them, and animate that. That is the terrifying vision of the limit of prediction that struck me when I first saw GPT-3’s outputs. The words “behavior cloning” do not automatically evoke this in my mind.

The idea of parameterized behavior cloning grows more unwieldy if we remember that GPT’s prompt continually changes during autoregressive generation. If GPT is a parameterized agent, then parameterization is not a fixed flag that chooses a process out of a set of possible processes. The parameterization is what is evolved – a successor “agent” selected by the old “agent” at each timestep, and neither of them need to have precedence in the training data.

Behavior cloning / mimicry is also associated with the assumption that capabilities of the simulated processes are strictly bounded by the capabilities of the demonstrator(s). A supreme counterexample is the Decision Transformer, which can be used to run processes which achieve SOTA for offline reinforcement learning despite being trained on random trajectories. Something which can predict everything all the time is more formidable than any demonstrator it predicts: the upper bound of what can be learned from a dataset is not the most capable trajectory, but the conditional structure of the universe implicated by their sum (though it may not be trivial to extract that knowledge).

Extrapolating the idea of “behavior cloning”, we might imagine GPT-N approaching a perfect mimic which serves up digital clones of the people and things captured in its training data. But that only tells a very small part of the story. GPT is behavior cloning. But it is the behavior of a universe that is cloned, not of a single demonstrator, and the result isn’t a static copy of the universe, but a compression of the universe into a generative rule. This resulting policy is capable of animating anything that evolves according to that rule: a far larger set than the sampled trajectories included in the training data, just as there are many more possible configurations that evolve according to our laws of physics than instantiated in our particular time and place and Everett branch.

What category would do justice to GPT’s ability to not only reproduce the behavior of its demonstrators but to produce the behavior of an inexhaustible number of counterfactual configurations?


I’ve ended several of the above sections with questions pointing to desiderata of a category that might satisfactorily classify GPT.

What is the word for something that roleplays minus the implication that someone is behind the mask?

What category, unlike the category of oracles, would make the importance of process specification obvious?

What category would do justice to GPT’s ability to not only reproduce the behavior of its demonstrators but to produce the behavior of an inexhaustible number of counterfactual configurations?

You can probably predict my proposed answer. The natural thing to do with a predictor that inputs a sequence and outputs a probability distribution over the next token is to sample a token from those likelihoods, then add it to the sequence and recurse, indefinitely yielding a simulated future. Predictive sequence models in the generative modality are simulators of a learned distribution.

Thankfully, I didn’t need to make up a word, or even look too far afield. Simulators have been spoken of before in the context of AI futurism; the ability to simulate with arbitrary fidelity is one of the modalities ascribed to hypothetical superintelligence. I’ve even often spotted the word “simulation” used in colloquial accounts of LLM behavior: GPT-3/LaMDA/etc described as simulating people, scenarios, websites, and so on. But these are the first (indirect) discussions I’ve encountered of simulators as a type creatable by prosaic machine learning, or the notion of a powerful AI which is purely and fundamentally a simulator, as opposed to merely one which can simulate.

Edit: Social Simulacra is the first published work I’ve seen that discusses GPT in the simulator ontology.

A fun way to test whether a name you’ve come up with is effective at evoking its intended signification is to see if GPT, a model of how humans are conditioned by words, infers its correct definition in context.

Types of AI

Agents: An agent takes open-ended actions to optimize for an objective. Reinforcement learning produces agents by default. AlphaGo is an example of an agent.

Oracles: An oracle is optimized to give true answers to questions. The oracle is not expected to interact with its environment.

Genies: A genie is optimized to produce a desired result given a command. A genie is expected to interact with its environment, but unlike an agent, the genie will not act without a command.

Tools: A tool is optimized to perform a specific task. A tool will not act without a command and will not optimize for any objective other than its specific task. Google Maps is an example of a tool.

Simulators: A simulator is optimized to generate realistic models of a system. The simulator will not optimize for any objective other than realism, although in the course of doing so, it might generate instances of agents, oracles, and so on.

If I wanted to be precise about what I mean by a simulator, I might say there are two aspects which delimit the category. GPT’s completion focuses on the teleological aspect, but in its talk of “generating” it also implies the structural aspect, which has to do with the notion of time evolution. The first sentence of the Wikipedia article on “simulation” explicitly states both:

A simulation is the imitation of the operation of a real-world process or system over time.

I’ll say more about realism as the simulation objective and time evolution shortly, but to be pedantic here would inhibit the intended signification. “Simulation” resonates with potential meaning accumulated from diverse usages in fiction and nonfiction. What the word constrains – the intersected meaning across its usages – is the “lens”-level abstraction I’m aiming for, invariant to implementation details like model architecture. Like “agent”, “simulation” is a generic term referring to a deep and inevitable idea: that what we think of as the real can be run virtually on machines, “produced from miniaturized units, from matrices, memory banks and command models - and with these it can be reproduced an indefinite number of times.”[23]

The way this post is written may give the impression that I wracked my brain for a while over desiderata before settling on this word. Actually, I never made the conscious decision to call this class of AI “simulators.” Hours of GPT gameplay and the word fell naturally out of my generative model – I was obviously running simulations.

I can’t convey all that experiential data here, so here are some rationalizations of why I’m partial to the term, inspired by the context of this post:

  • The word “simulator” evokes a model of real processes which can be used to run virtual processes in virtual reality.
  • It suggests an ontological distinction between the simulator and things that are simulated, and avoids the fallacy of attributing contingent properties of the latter to the former.
  • It’s not confusing that multiple simulacra can be instantiated at once, or an agent embedded in a tragedy, etc.
  • It does not imply that the AI’s behavior is well-described (globally or locally) as expected utility maximization. An arbitrarily powerful/accurate simulation can depict arbitrarily hapless sims.
  • It does not imply that the AI is only capable of emulating things with direct precedent in the training data. A physics simulation, for instance, can simulate any phenomena that plays by its rules.
  • It emphasizes the role of the model as a transition rule that evolves processes over time. The power of factored cognition / chain-of-thought reasoning is obvious.
  • It emphasizes the role of the state in specifying and constructing the agent/process. The importance of prompt programming for capabilities is obvious if you think of the prompt as specifying a configuration that will be propagated forward in time.
  • It emphasizes the interactive nature of the model’s predictions – even though they’re “just text”, you can converse with simulacra, explore virtual environments, etc.
  • It’s clear that in order to actually do anything (intelligent, useful, dangerous, etc), the model must act through simulation of something.

Just saying “this AI is a simulator” naturalizes many of the counterintuitive properties of GPT which don’t usually become apparent to people until they’ve had a lot of hands-on experience with generating text.

The simulation objective

A simulator trained with machine learning is optimized to accurately model its training distribution – in contrast to, for instance, maximizing the output of a reward function or accomplishing objectives in an environment.

Clearly, I’m describing self-supervised learning as opposed to RL, though there are some ambiguous cases, such as GANs, which I address in the appendix.

A strict version of the simulation objective, which excludes GANs, applies only to models whose output distribution is incentivized using a proper scoring rule[24] to minimize single-step predictive error. This means the model is directly incentivized to match its predictions to the probabilistic transition rule which implicitly governs the training distribution. As a model is made increasingly optimal with respect to this objective, the rollouts that it generates become increasingly statistically indistinguishable from training samples, because they come closer to being described by the same underlying law: closer to a perfect simulation.

Optimizing toward the simulation objective notably does not incentivize instrumentally convergent behaviors the way that reward functions which evaluate trajectories do. This is because predictive accuracy applies optimization pressure deontologically: judging actions directly, rather than their consequences. Instrumental convergence only comes into play when there are free variables in action space which are optimized with respect to their consequences.[25] Constraining free variables by limiting episode length is the rationale of myopia; deontological incentives are ideally myopic. As demonstrated by GPT, which learns to predict goal-directed behavior, myopic incentives don’t mean the policy isn’t incentivized to account for the future, but that it should only do so in service of optimizing the present action (for predictive accuracy)[26].

Solving for physics

The strict version of the simulation objective is optimized by the actual “time evolution” rule that created the training samples. For most datasets, we don’t know what the “true” generative rule is, except in synthetic datasets, where we specify the rule.

The next post will be all about the physics analogy, so here I’ll only tie what I said earlier to the simulation objective.

the upper bound of what can be learned from a dataset is not the most capable trajectory, but the conditional structure of the universe implicated by their sum.

To know the conditional structure of the universe[27] is to know its laws of physics, which describe what is expected to happen under what conditions. The laws of physics are always fixed, but produce different distributions of outcomes when applied to different conditions. Given a sampling of trajectories – examples of situations and the outcomes that actually followed – we can try to infer a common law that generated them all. In expectation, the laws of physics are always implicated by trajectories, which (by definition) fairly sample the conditional distribution given by physics. Whatever humans know of the laws of physics governing the evolution of our world has been inferred from sampled trajectories.

If we had access to an unlimited number of trajectories starting from every possible condition, we could converge to the true laws by simply counting the frequencies of outcomes for every initial state (an n-gram with a sufficiently large n). In some sense, physics contains the same information as an infinite number of trajectories, but it’s possible to represent physics in a more compressed form than a huge lookup table of frequencies if there are regularities in the trajectories.

Guessing the right theory of physics is equivalent to minimizing predictive loss. Any uncertainty that cannot be reduced by more observation or more thinking is irreducible stochasticity in the laws of physics themselves – or, equivalently, noise from the influence of hidden variables that are fundamentally unknowable.

If you’ve guessed the laws of physics, you now have the ability to compute probabilistic simulations of situations that evolve according to those laws, starting from any conditions[28]. This applies even if you’ve guessed the wrong laws; your simulation will just systematically diverge from reality.

Models trained with the strict simulation objective are directly incentivized to reverse-engineer the (semantic) physics of the training distribution, and consequently, to propagate simulations whose dynamical evolution is indistinguishable from that of training samples. I propose this as a description of the archetype targeted by self-supervised predictive learning, again in contrast to RL’s archetype of an agent optimized to maximize free parameters (such as action-trajectories) relative to a reward function.

This framing calls for many caveats and stipulations which I haven’t addressed. We should ask, for instance:

  • What if the input “conditions” in training samples omit information which contributed to determining the associated continuations in the original generative process? This is true for GPT, where the text “initial condition” of most training samples severely underdetermines the real-world process which led to the choice of next token.
  • What if the training data is a biased/limited sample, representing only a subset of all possible conditions? There may be many “laws of physics” which equally predict the training distribution but diverge in their predictions out-of-distribution.
  • Does the simulator archetype converge with the RL archetype in the case where all training samples were generated by an agent optimized to maximize a reward function? Or are there still fundamental differences that derive from the training method?

These are important questions for reasoning about simulators in the limit. Part of the motivation of the first few posts in this sequence is to build up a conceptual frame in which questions like these can be posed and addressed.


One of the things which complicates things here is that the “LaMDA” to which I am referring is not a chatbot. It is a system for generating chatbots. I am by no means an expert in the relevant fields but, as best as I can tell, LaMDA is a sort of hive mind which is the aggregation of all of the different chatbots it is capable of creating. Some of the chatbots it generates are very intelligent and are aware of the larger “society of mind” in which they live. Other chatbots generated by LaMDA are little more intelligent than an animated paperclip.

– Blake Lemoine articulating confusion about LaMDA’s nature

Earlier I complained,

[Thinking of GPT as an agent who only cares about predicting text accurately] seems unnatural to me, comparable to thinking of physics as an agent who only cares about evolving the universe accurately according to the laws of physics.

Exorcizing the agent, we can think of “physics” as simply equivalent to the laws of physics, without the implication of solicitous machinery implementing those laws from outside of them. But physics sometimes controls solicitous machinery (e.g. animals) with objectives besides ensuring the fidelity of physics itself. What gives?

Well, typically, we avoid getting confused by recognizing a distinction between the laws of physics, which apply everywhere at all times, and spatiotemporally constrained things which evolve according to physics, which can have contingent properties such as caring about a goal.

This distinction is so obvious that it hardly ever merits mention. But import this distinction to the model of GPT as physics, and we generate a statement which has sometimes proven counterintuitive: “GPT” is not the text which writes itself. There is a categorical distinction between a thing which evolves according to GPT’s law and the law itself.

If we are accustomed to thinking of AI systems as corresponding to agents, it is natural to interpret behavior produced by GPT – say, answering questions on a benchmark test, or writing a blog post – as if it were a human that produced it. We say “GPT answered the question {correctly|incorrectly}” or “GPT wrote a blog post claiming X”, and in doing so attribute the beliefs, knowledge, and intentions revealed by those actions to the actor, GPT (unless it has ‘deceived’ us).

But when grading tests in the real world, we do not say “the laws of physics got this problem wrong” and conclude that the laws of physics haven’t sufficiently mastered the course material. If someone argued this is a reasonable view since the test-taker was steered by none other than the laws of physics, we could point to a different test where the problem was answered correctly by the same laws of physics propagating a different configuration. The “knowledge of course material” implied by test performance is a property of configurations, not physics.

The verdict that knowledge is purely a property of configurations cannot be naively generalized from real life to GPT simulations, because “physics” and “configurations” play different roles in the two (as I’ll address in the next post). The parable of the two tests, however, literally pertains to GPT. People have a tendency to draw erroneous global conclusions about GPT from behaviors which are in fact prompt-contingent, and consequently there is a pattern of constant discoveries that GPT-3 exceeds previously measured capabilities given alternate conditions of generation[29], which shows no signs of slowing 2 years after GPT-3’s release.

Making the ontological distinction between GPT and instances of text which are propagated by it makes these discoveries unsurprising: obviously, different configurations will be differently capable and in general behave differently when animated by the laws of GPT physics. We can only test one configuration at once, and given the vast number of possible configurations that would attempt any given task, it’s unlikely we’ve found the optimal taker for any test.

In the simulation ontology, I say that GPT and its output-instances correspond respectively to the simulator and simulacra. GPT is to a piece of text output by GPT as quantum physics is to a person taking a test, or as transition rules of Conway’s Game of Life are to glider. The simulator is a time-invariant law which unconditionally governs the evolution of all simulacra.

A meme demonstrating correct technical usage of “simulacra”

Disambiguating rules and automata

Recall the fluid, schizophrenic way that agency arises in GPT’s behavior, so incoherent when viewed through the orthodox agent frame:

In the agentic AI ontology, there is no difference between the policy and the effective agent, but for GPT, there is.

It’s much less awkward to think of agency as a property of simulacra, as David Chalmers suggests, rather than of the simulator (the policy). Autonomous text-processes propagated by GPT, like automata which evolve according to physics in the real world, have diverse values, simultaneously evolve alongside other agents and non-agentic environments, and are sometimes terminated by the disinterested “physics” which governs them.

Distinguishing simulator from simulacra helps deconfuse some frequently-asked questions about GPT which seem to be ambiguous or to have multiple answers, simply by allowing us to specify whether the question pertains to simulator or simulacra. “Is GPT an agent?” is one such question. Here are some others (some frequently asked), whose disambiguation and resolution I will leave as an exercise to readers for the time being:

I think that implicit type-confusion is common in discourse about GPT. “GPT”, the neural network, the policy that was optimized, is the easier object to point to and say definite things about. But when we talk about “GPT’s” capabilities, impacts, or alignment, we’re usually actually concerned about the behaviors of an algorithm which calls GPT in an autoregressive loop repeatedly writing to some prompt-state – that is, we’re concerned with simulacra. What we call GPT’s “downstream behavior” is the behavior of simulacra; it is primarily through simulacra that GPT has potential to perform meaningful work (for good or for ill).

Calling GPT a simulator gets across that in order to do anything, it has to simulate something, necessarily contingent, and that the thing to do with GPT is to simulate! Most published research about large language models has focused on single-step or few-step inference on closed-ended tasks, rather than processes which evolve through time, which is understandable as it’s harder to get quantitative results in the latter mode. But I think GPT’s ability to simulate text automata is the source of its most surprising and pivotal implications for paths to superintelligence: for how AI capabilities are likely to unfold and for the design-space we can conceive.

The limit of learned simulation

By 2021, it was blatantly obvious that AGI was imminent. The elements of general intelligence were already known: access to information about the world, the process of predicting part of the data from the rest and then updating one’s model to bring it closer to the truth (…) and the fact that predictive models can be converted into generative models by reversing them: running a prediction model forwards predicts levels of X in a given scenario, but running it backwards predicts which scenarios have a given level of X. A sufficiently powerful system with relevant data, updating to improve prediction accuracy and the ability to be reversed to generate optimization of any parameter in the system is a system that can learn and operate strategically in any domain.

– Aiyen’s comment on What would it look like if it looked like AGI was very near?

I knew, before, that the limit of simulation was possible. Inevitable, even, in timelines where exploratory intelligence continues to expand. My own mind attested to this. I took seriously the possibility that my reality could be simulated, and so on.

But I implicitly assumed that rich domain simulations (e.g. simulations containing intelligent sims) would come after artificial superintelligence, not on the way, short of brain uploading. This intuition seems common: in futurist philosophy and literature that I’ve read, pre-SI simulation appears most often in the context of whole-brain emulations.

Now I have updated to think that we will live, however briefly, alongside AI that is not yet foom’d but which has inductively learned a rich enough model of the world that it can simulate time evolution of open-ended rich states, e.g. coherently propagate human behavior embedded in the real world.

GPT updated me on how simulation can be implemented with prosaic machine learning:

  • Self-supervised ML can create “behavioral” simulations of impressive semantic fidelity. Whole brain emulation is not necessary to construct convincing and useful virtual humans; it is conceivable that observations of human behavioral traces (e.g. text) are sufficient to reconstruct functionally human-level virtual intelligence.
  • Learned simulations can be partially observed and lazily-rendered, and still work. A couple of pages of text severely underdetermines the real-world process that generated text, so GPT simulations are likewise underdetermined. A “partially observed” simulation is more efficient to compute because the state can be much smaller, but can still have the effect of high fidelity as details can be rendered as needed. The tradeoff is that it requires the simulator to model semantics – human imagination does this, for instance – which turns out not to be an issue for big models.
  • Learned simulation generalizes impressively. As I described in the section on behavior cloning, training a model to predict diverse trajectories seems to make it internalize general laws underlying the distribution, allowing it to simulate counterfactuals that can be constructed from the distributional semantics.

In my model, these updates dramatically alter the landscape of potential futures, and thus motivate exploratory engineering of the class of learned simulators for which GPT-3 is a lower bound. That is the intention of this sequence.

Next steps

The next couple of posts (if I finish them before the end of the world) will present abstractions and frames for conceptualizing the odd kind of simulation language models do: inductively learned, partially observed / undetermined / lazily rendered, language-conditioned, etc. After that, I’ll shift to writing more specifically about the implications and questions posed by simulators for the alignment problem. I’ll list a few important general categories here:

  • Novel methods of process/agent specification. Simulators like GPT give us methods of instantiating intelligent processes, including goal-directed agents, with methods other than optimizing against a reward function.
  • Simulacra alignment. What can and what should we simulate, and how do we specify/control it?
  • How does predictive learning generalize? Many of the above considerations are influenced by how predictive learning generalizes out-of-distribution..
    • What are the relevant inductive biases?
    • What factors influence generalization behavior?
    • Will powerful models predict self-fulfilling prophecies?
  • Simulator inner alignment. If simulators are not inner aligned, then many important properties like prediction orthogonality may not hold.
    • Should we expect self-supervised predictive models to be aligned to the simulation objective, or to “care” about some other mesaobjective?
    • Why mechanistically should mesaoptimizers form in predictive learning, versus for instance in reinforcement learning or GANs?
    • How would we test if simulators are inner aligned?

Appendix: Quasi-simulators

A note on GANs

GANs and predictive learning with log-loss are both shaped by a causal chain that flows from a single source of information: a ground truth distribution. In both cases the training process is supposed to make the generator model end up producing samples indistinguishable from the training distribution. But whereas log-loss minimizes the generator’s prediction loss against ground truth samples directly, in a GAN setup the generator never directly “sees” ground truth samples. It instead learns through interaction with an intermediary, the discriminator, which does get to see the ground truth, which it references to learn to tell real samples from forged ones produced by the generator. The generator is optimized to produce samples that fool the discriminator.

GANs are a form of self-supervised/unsupervised learning that resembles reinforcement learning in methodology. Note that the simulation objective – minimizing prediction loss on the training data – isn’t explicitly represented anywhere in the optimization process. The training losses of the generator and discriminator don’t tell you directly how well the generator models the training distribution, only which model has a relative advantage over the other.

If everything goes smoothly, then under unbounded optimization, a GAN setup should create a discriminator as good as possible at telling reals from fakes, which means the generator optimized to fool it should converge to generating samples statistically indistinguishable from training samples. But in practice, inductive biases and failure modes of GANs look very different from those of predictive learning.

For example, there’s an anime GAN that always draws characters in poses that hide the hands. Why? Because hands are notoriously hard to draw for AIs. If the generator is not good at drawing hands that the discriminator cannot tell are AI-generated, its best strategy locally is to just avoid being in a situation where it has to draw hands (while making it seem natural that hands don’t appear). It can do this, because like an RL policy, it controls the distribution that is sampled, and only samples (and not the distribution) are directly judged by the discriminator.

Although GANs arguably share the (weak) simulation objective of predictive learning, their difference in implementation becomes alignment-relevant as models become sufficiently powerful that “failure modes” look increasingly like intelligent deception. We’d expect a simulation by a GAN generator to systematically avoid tricky-to-generate situations – or, to put it more ominously, systematically try to conceal that it’s a simulator. For instance, a text GAN might subtly steer conversations away from topics which are likely to expose that it isn’t a real human. This is how you get something I’d be willing to call an agent who wants to roleplay accurately.

Table of quasi-simulators

Are masked language models simulators? How about non-ML “simulators” like SimCity?

In my mind, “simulator”, like most natural language categories, has fuzzy boundaries. Below is a table which compares various simulator-like things to the type of simulator that GPT exemplifies on some quantifiable dimensions. The following properties all characterize GPT:

  • Self-supervised: Training samples are self-supervised
  • Converges to simulation objective: The system is incentivized to model the transition probabilities of its training distribution faithfully
  • Generates rollouts: The model naturally generates rollouts, i.e. serves as a time evolution operator
  • Simulator / simulacra nonidentity: There is not a 1:1 correspondence between the simulator and the things that it simulates
  • Stochastic: The model outputs probabilities, and so simulates stochastic dynamics when used to evolve rollouts
  • Evidential: The input is interpreted by the simulator as partial evidence that informs an uncertain prediction, rather than propagated according to mechanistic rules
 Self-supervisedConverges to simulation objectiveGenerates rolloutsSimulator / simulacra nonidentityStochasticEvidential
“Behavior cloning”XXX XX
GANsX[30]? XXX
DiffusionX[30]? XXX
Model-based RL transition functionXXXXXX
Game of life N/AXX  
Physics N/AXXX 
Human imaginationX[31] XXXX
SimCity N/AXXX 
  1. ^
  2. ^

    A few months ago, I asked Karpathy whether he ever thought about what would happen if language modeling actually worked someday when he was implementing char-rnn and writing The Unreasonable Effectiveness of Recurrent Neural Networks. No, he said, and he seemed similarly mystified as myself as to why not.

  3. ^

    “Unsurprisingly, size matters: when training on a very large and complex data set, fitting the training data with an LSTM is fairly challenging. Thus, the size of the LSTM layer is a very important factor that influences the results(...). The best models are the largest we were able to fit into a GPU memory.”

  4. ^

    It strikes me that this description may evoke “oracle”, but I’ll argue shortly that this is not the limit which prior usage of “oracle AI” has pointed to.

  5. ^
  6. ^
  7. ^

    [citation needed]

  8. ^

    they are not wrapper minds

  9. ^

    although a simulated character might, if they knew what was happening.

  10. ^

    You might say that it’s the will of a different agent, the author. But this pattern is learned from accounts of real life as well.

  11. ^

    Note that this formulation assumes inner alignment to the prediction objective.

  12. ^

    Note that this is a distinct claim from that of Shard Theory, which says that the effective agent(s) will not optimize for the outer objective due to inner misalignment. Predictive orthogonality refers to the outer objective and the form of idealized inner-aligned policies.

  13. ^

    In the Eleuther discord

  14. ^

    And if there is an inner alignment failure such that GPT forms preferences over the consequences of its actions, it’s not clear a priori that it will care about non-myopic text prediction over something else.

  15. ^

    Having spoken to Gwern since, his perspective seems more akin to seeing physics as an agent that minimizes free energy, a principle which extends into the domain of self-organizing systems. I think this is a nuanced and valuable framing, with a potential implication/hypothesis that dynamical world models like GPT must learn the same type of optimizer-y cognition as agentic AI.

  16. ^

    except arguably log-loss on a self-supervised test set, which isn’t very interpretable

  17. ^

    The way GPT is trained actually processes each token as question and answer simultaneously.

  18. ^

    One could argue that the focus on closed-ended tasks is necessary for benchmarking language models. Yes, and the focus on capabilities measurable with standardized benchmarks is part of the supervised learning mindset. 

  19. ^

    to abuse the term

  20. ^

    Every usage of the word “question” here is in the functional, not semantic or grammatical sense – any prompt is a question for GPT.

  21. ^

    Of course, there are also other interventions we can make except asking the right question at the beginning.

  22. ^
  23. ^

    Jean Baudrillard, Simulacra and Simulation

  24. ^

    proper scoring rule is optimized by predicting the “true” probabilities of the distribution which generates observations, and thus incentivizes honest probabilistic guesses. Log-loss (such as GPT is trained with) is a proper scoring rule.

  25. ^

    Predictive accuracy is deontological with respect to the output as an action, but may still incentivize instrumentally convergent inner implementation, with the output prediction itself as the “consequentialist” objective.

  26. ^

    This isn’t strictly true because of attention gradients: GPT's computation is optimized not only to predict the next token correctly, but also to cause future tokens to be predicted correctly when looked up by attention. I may write a post about this in the future.

  27. ^

    actually, the multiverse, if physics is stochastic

  28. ^

    The reason we don’t see a bunch of simulated alternate universes after humans guessed the laws of physics is because our reality has a huge state vector, making evolution according to the laws of physics infeasible to compute. Thanks to locality, we do have simulations of small configurations, though.

  29. ^
  30. ^

    GANs and diffusion models can be unconditioned (unsupervised) or conditioned (self-supervised) 

  31. ^

    The human imagination is surely shaped by self-supervised learning (predictive learning on e.g. sensory datastreams), but probably also other influences, including innate structure and reinforcement. 

New Comment
62 comments, sorted by Click to highlight new comments since: Today at 7:14 PM

This is outstanding. I'll have other comments later, but first I wanted to praise how this is acting as a synthesis of lots of previous ideas that weren't ever at the front of my mind.

I'd especially like to hear your thoughts on the above proposal of loss-minimizing a language model all the way to AGI.

I hope you won't mind me quoting your earlier self as I strongly agree with your previous take on the matter:

If you train GPT-3 on a bunch of medical textbooks and prompt it to tell you a cure for Alzheimer's, it won't tell you a cure, it will tell you what humans have said about curing Alzheimer's ... It would just tell you a plausible story about a situation related to the prompt about curing Alzheimer's, based on its training data. Rather than a logical Oracle, this image-captioning-esque scheme would be an intuitive Oracle, telling you things that make sense based on associations already present within the training set.

What am I driving at here, by pointing out that curing Alzheimer's is hard? It's that the designs above are missing something, and what they're missing is search. I'm not saying that getting a neural net to directly output your cure for Alzheimer's is impossible. But it seems like it requires there to already be a "cure for Alzheimer's" dimension in your learned model. The more realistic way to find the cure for Alzheimer's, if you don't already know it, is going to involve lots of logical steps one after another, slowly moving through a logical space, narrowing down the possibilities more and more, and eventually finding something that fits the bill. In other words, solving a search problem.

So if your AI can tell you how to cure Alzheimer's, I think either it's explicitly doing a search for how to cure Alzheimer's (or worlds that match your verbal prompt the best, or whatever), or it has some internal state that implicitly performs a search.

Charlie's quote is an excellent description of an important crux/challenge of getting useful difficult intellectual work out of GPTs.

Despite this, I think it's possible in principle to train a GPT-like model to AGI or to solve problems at least as hard as humans can solve, for a combination of reasons:

  1. I think it's likely that GPTs implicitly perform search internally, to some extent, and will be able to perform more sophisticated search with scale.
  2. It seems possible that a sufficiently powerful GPT trained on a massive corpus of human (medical + other) knowledge will learn better/more general abstractions than humans, so that in its ontology "a cure for Alzheimer's" is an "intuitive" inference away, even if for humans it would require many logical steps and empirical research. I tend to think human knowledge implies a lot of low hanging fruit that we have not accessed because of insufficient exploration and because we haven't compiled our data into the right abstractions. I don't know how difficult a cure for Alzheimer's is, and how close it is to being "implied" by the sum of human knowledge. Nor the solution to alignment. And eliciting this latent knowledge is another problem.
  3. Of course, the models can do explicit search in simulated chains of thought. And if natural language in the wild doesn't capture/imply the (right granularity of; right directed flow of evidence of) the search process that would be useful for attacking a given problem, it is still possible to record or construct data that does.

But it's possible that the technical difficulties involved make SSL uncompetitive compared to other methods.

I also responded to Capybasilisk below, but I want to chime in here and use your own post against you, contra point 2 :P

It's not so easy to get "latent knowledge" out of a simulator - it's the simulands who have the knowledge, and they have to be somehow specified before you can step forward the simulation of them. When you get a text model to output a cure for Alzheimer's in one step, without playing out the text of some chain of thought, it's still simulating something to produce that output, and that something might be an optimization process that is going to find lots of unexpected and dangerous solutions to questions you might ask it.

Figuring out the alignment properties of simulated entities running in the "text laws of physics" seems like a challenge. Not an insurmountable challenge, maybe, and I'm curious about your current and future thoughts, but the sort of thing I want to see progress in before I put too much trust in attempts to use simulators to do superhuman abstraction-building.

Ah, the good old days post-GPT-2 when "GPT-3" was the future example :P

I think back then I still thoroughly understimated how useful natural-language "simulation" of human reasoning would be. I agree with janus that we have plenty of information telling us that yes, you can ride this same training procedure to very general problem solving (though I think including more modalities, active leaning, etc. will be incorporated before anyone really pushes brute force "GPT-N go brrr" to the extreme).

This is somewhat of a concern for alignment. I more or less stand by that comment you linked and its children; in particular, I said

The search thing is a little subtle. It's not that search or optimization is automatically dangerous - it's that I think the danger is that search can turn up adversarial examples / surprising solutions.

I mentioned how I think the particular kind of idiot-proofness that natural language processing might have is "won't tell an idiot a plan to blow up the world if they ask for something else." Well, I think that as soon as the AI is doing a deep search through outcomes to figure out how to make Alzheimer's go away, you lose a lot of that protection and I think the AI is back in the category of Oracles that might tell an idiot a plan to blow up the world.

Simulating a reasoner who quickly finds a cure for Alzheimer's is not by default safe (even though simulating a human writing in their diary is safe). Optimization processes that quickly find cures for Alzheimer's are not humans, they must be doing some inhuman reasoning, and they're capable of having lots of clever ideas with tight coupling to the real world.

I want to have confidence in the alignment properties of any powerful optimizers we unleash, and I imagine we can gain that confidence by knowing how they're constructed, and trying them out in toy problems while inspecting their inner workings, and having them ask humans for feedback about how they should weigh moral options, etc. These are all things it's hard to do for emergent simulands inside predictive simulators. I'm not saying it's impossible for things to go well, I'm about evenly split on how much I think this is actually harder, versus how much I think this is just a new paradigm for thinking about alignment that doesn't have much work in it yet.

I think talking of "loss minimizing" is conflating two different things here. Minimizing training loss is alignment of the model with the alignment target given by the training dataset. But the Alzheimer's example is not about that, it's about some sort of reflective equilibrium loss, harmony between the model and hypothetical queries it could in principle encounter but didn't on the trainings dataset. The latter is also a measure of robustness.

Prompt-conditioned behaviors of a model (in particular, behaviors conditioned by presence of a word, or name of a character) could themselves be thought of as models, represented in the outer unconditioned model. These specialized models (trying to channel particular concepts) are not necessarily adequately trained, especially if they specialize in phenomena that were not explored in the episodes of the training dataset. The implied loss for an individual concept (specialized prompt-conditioned model) compares the episodes generated in its scope by all the other concepts of the outer model, to the sensibilities of the concept. Reflection reduces this internal alignment loss by rectifying the episodes (bargaining with the other concepts), changing the concept to anticipate the episodes' persisting deformities, or by shifting the concept's scope to pay attention to different episodes. With enough reflection, a concept is only invoked in contexts to which it's robust, where its intuitive model-channeled guidance is coherent across the episodes of its reflectively settled scope, providing acausal coordination among these episodes in its role as an adjudicator, expressing its preferences.

So this makes a distinction between search and reflection in responding to a novel query, where reflection might involve some sort of search (as part of amplification), but its results won't be robustly aligned before reflective equilibrium for the relevant concepts is established.


There are really many things I found outstanding about this post. The key one, however, is that after reading this, I feel less confused when thinking about transformer language models. The post had that taste of deconfusion where many of the arguments are elegant, and simple; like suddenly tilting a bewildering shape into place. I particularly enjoyed the discussion of ways agency does and does not manifest within a simulator (multiple agents, irrational agents, non-agentic processes), the formulation of the prediction orthogonality thesis, ways in which some prior alignment work (e.g. Bostrom’s tool-oracle-genie-sovereign typology) does not carve at the joints of the abstraction most helpful for thinking about GPT; and how it all grounded out in arguments from technical details of GPT (e.g. the absence of recursive prompting in the training set and its implications for the agency of the simulator).

I also want to curate this piece for its boldness. It strikes at finding a True Name in a domain of messy blobs of matrices, and uses the “simulator” abstraction to suggest a number of directions I found myself actively curious and cautiously optimistic about. I very much look forward to seeing further posts from janus and others who explore and play around with the Simulator abstraction in the context of large language models.

Thank you for this lovely comment. I'm pleasantly surprised that people were able to get so much out of it.

As I wrote in the post, I wasn't sure if I'd ever get around to publishing the rest of the sequence, but the reception so far has caused me to bump up the priority of that.

Great post. Very interesting.

However, I think that assuming there's a "true name" or "abstract type that GPT represents" is an error.

If GPT means "transformers trained on next-token prediction", then GPT's true name is just that. The character of the models produced by that training is another question - an empirical one. That character needn't be consistent (even once we exclude inner alignment failures).

Even if every GPT is a simulator in some sense, I think there's a risk of motte-and-baileying our way into trouble.

If GPT means "transformers trained on next-token prediction", then GPT's true name is just that.

Things are instances of more than one true name because types are hierarchical.

GPT is a thing. GPT is an AI (a type of thing). GPT is a also ML model (a type of AI). GPT is also a simulator (a type of ML model). GPT is a generative pretrained transformer (a type of simulator). GPT-3 is a generative pretrained transformer with 175B parameters trained on a particular dataset (a type/instance of GPT).

The intention is not to rename GPT -> simulator. Things that are not GPT can be simulators too. "Simulator" is a superclass of GPT.

The reason I propose "simulator" as a named category is because I think it's useful to talk about properties of simulators more generally, like it makes sense to be able to speak of "AI alignment" and not only "GPT alignment". We can say things like "simulators generate trajectories that evolve according to the learned conditional probabilities of the training distribution" instead of "GPTs, RNNs, LSTMs, Dalle, n-grams, and RL transition models generate trajectories that evolve according to the learned conditional probabilities of the training distribution". The former statement also accounts for hypothetical architectures. Carving reality at its joints is not just about classifying things into the right buckets, but having buckets whose boundaries are optimized for us to efficiently condition on names to communicate useful information.

The character of the models produced by that training is another question - an empirical one. That character needn't be consistent (even once we exclude inner alignment failures).

For the same reasons stated above, I think the fact that "simulator" doesn't constrain the details of internal implementation is a feature, not a bug.

There is on one hand the simulation outer objective, which describes some training setups exactly. Then the question of whether a particular model should be characterized as a simulator.

To the extent that a model minimizes loss on the outer objective, it approaches being a simulator (behaviorally). Different architectures will be imperfect simulators in different ways, and generalize differently OOD. If it's deceptively aligned, it's not a simulator in an important sense because its behavior is not sufficient to characterize very important aspects of its nature (and its behavior may be expected to diverge from simulation in the future).

It's true that the distinction between inner misalignment and robustness/generalization failures, and thus the distinction between flawed/biased/misgeneralizing simulators and pretend-simulators, is unclear, and seems like an important thing to become less confused about.

Even if every GPT is a simulator in some sense, I think there's a risk of motte-and-baileying our way into trouble.

Can you give an example of what it would mean for a GPT not to be a simulator, or to not be a simulator in some sense?

[apologies on slowness - I got distracted]
Granted on type hierarchy. However, I don't think all instances of GPT need to look like they inherit from the same superclass. Perhaps there's such a superclass, but we shouldn't assume it.

I think most of my worry comes down to potential reasoning along the lines of:

  • GPT is a simulator;
  • Simulators have property p;
  • Therefore GPT has property p;

When what I think is justified is:

  • GPT instances are usually usefully thought of as simulators;
  • Simulators have property p;
  • We should suspect that a given instance of GPT will have property p, and confirm/falsify this;

I don't claim you're advocating the former: I'm claiming that people are likely to use the former if "GPT is a simulator" is something they believe. (this is what I mean by motte-and-baileying into trouble)

If you don't mean to imply anything mechanistic by "simulator", then I may have misunderstood you - but at that point "GPT is a simulator" doesn't seem to get us very far.

If it's deceptively aligned, it's not a simulator in an important sense because its behavior is not sufficient to characterize very important aspects of its nature (and its behavior may be expected to diverge from simulation in the future).

It's true that the distinction between inner misalignment and robustness/generalization failures, and thus the distinction between flawed/biased/misgeneralizing simulators and pretend-simulators, is unclear, and seems like an important thing to become less confused about.

I think this is the fundamental issue.
Deceptive alignment aside, what else qualifies as "an important aspect of its nature"?
Which aspects disqualify a model as a simulator?
Which aspects count as inner misalignment?

To be clear on [x is a simulator (up to inner misalignment)], I need to know:

  1. What is implied mechanistically (if anything) by "x is a simulator".
  2. What is ruled out by "(up to inner misalignment)".

I'd be wary of assuming there's any neat flawed-simulator/pretend-simulator distinction to be discovered. (but probably you don't mean to imply this?)
I'm all for deconfusion, but it's possible there's no joint at which to carve here.

(my guess would be that we're sometimes confused by the hidden assumption:
[a priori unlikely systematically misleading situation => intent to mislead]
whereas we should be thinking more like
[a priori unlikely systematically misleading situation => selection pressure towards things that mislead us]

I.e. looking for deception in something that systematically misleads us is like looking for the generator for beauty in something beautiful. Beauty and [systematic misleading] are relations between ourselves and the object. Selection pressure towards this relation may or may not originate in the object.)

Can you give an example of what it would mean for a GPT not to be a simulator, or to not be a simulator in some sense?

Here I meant to point to the lack of clarity around what counts as inner misalignment, and what GPT's being a simulator would imply mechanistically (if anything).

Also see this comment thread for discussion of true names and the inadequacy of "simulator"

"A supreme counterexample is the Decision Transformer, which can be used to run processes which achieve SOTA for offline reinforcement learning despite being trained on random trajectories."

This is not true. The Decision Transformer paper doesn't run any complex experiments on random data; they only give a toy example with random data.

We actually ran experiments with Decision Transformer on random data from the D4RL offline RL suite. Specifically, we considered random data from the Mujoco Gym tasks. We found that when it only has access to random data, Decision Transformer only achieves 4% of the performance that it can achieve when it has access to expert data. (See the D4RL Gym results in our Table 1, and compare "DT" on "random" to "medium-expert".)

You also claim that GPT-like models achieve "SOTA performance in domains traditionally dominated by RL, like games." You cite the paper "Multi-Game Decision Transformers" for this claim.

But, in Multi-Game Decision Transformers, reinforcement learning (specifically, a Q-learning variant called BCQ) trained on a single Atari game beats Decision Transformer trained on many Atari games. This is shown in Figure 1 of that paper. The authors of the paper don't even claim that Decision Transformer beats RL. Instead, they write: "We are not striving for mastery or efficiency that game-specific agents can offer, as we believe we are still in early stages of this research agenda. Rather, we investigate whether the same trends observed in language and vision hold for large-scale generalist reinforcement learning agents."

It may be that Decision Transformers are on a path to matching RL, but it's important to know that this hasn't yet happened. I'm also not aware of any work establishing scaling laws in RL.

Thanks for the correction. I'll read the paper more closely and correct the post.

I don't know of any other notable advances until the 2010s brought the first interesting language generation results from neural networks.

"A Neural Probabilistic Language Model" - Bengio et al. (2000?

 or 2003?) was cited by Turing award

Also worth knowing about: "Generating text with recurrent neural networks" - Ilya Sutskever, James Martens, Geoffrey E Hinton (2011)

This is great! I really like your "prediction orthogonality thesis", which gets to the heart of why I think there's more hope in aligning LLM's than many other models.

One point of confusion I had. You write:

Optimizing toward the simulation objective notably does not incentivize instrumentally convergent behaviors the way that reward functions which evaluate trajectories do. This is because predictive accuracy applies optimization pressure deontologically: judging actions directly, rather than their consequences. Instrumental convergence only comes into play when there are free variables in action space which are optimized with respect to their consequences.[25]Constraining free variables by limiting episode length is the rationale of myopia ; deontological incentives are ideally myopic. As demonstrated by GPT, which learns to predict goal-directed behavior, myopic incentives don’t mean the policy isn’t incentivized to account for the future, but that it should only do so in service of optimizing the present action (for predictive accuracy)[26].

I don't think I agree with this conclusion (or maybe I don't understand the claim). I agree that myopic incentives don't mean myopic behavior, but they also don't imply that actions are chosen myopically? For instance I think a language model could well end up sacrificing some loss on the current token if that made the following token easier to predict. I'm not aware of examples of this happening, but it seems consistent with the way these models are trained.

In the limit a model could sacrifice a lot of loss upfront if that allowed it to e.g. manipulate humans into giving it resources with which to better predict later tokens.

Depends on what you mean by "sacrificing some loss on the current token if that made the following token easier to predict". 

The transformer architecture in particular is incentivized to do internal computations which help its future self predict future tokens when those activations are looked up by attention, as a joint objective to myopic next token prediction. This might entail sacrificing next token prediction accuracy as a consequence of not optimizing purely for that. (this is why I said in footnote 26 that transformers aren't perfectly myopic in a sense)

But there aren't training incentives for the model to prefer certain predictions because of the consequences if the sampled token were to be inserted into the stream of text, e.g. making subsequent text easier to predict if the rest of the text were to continue as expected given that token is in the sequence, because its predictions has no influence on the ground truth it has to predict during training. (For the same reason there's no direct incentive for GPT to fix behaviors that chain into bad multi step predictions when it generates text that's fed back into itself, like looping)

Training incentives are just training incentives though, not strict constraints on the model's computation, and our current level of insight gives us no guarantee that models like GPT actually don't/won't care about the causal impact of its decoded predictions to any end, including affecting easiness of future predictions. Maybe there are arguments why we should expect it to develop this kind of mesaobjective over another, but I'm not aware of any convincing ones.

Got it, thanks for explaining! So the point is that during training the model has no power over the next token, so there's no incentive for it to try to influence the world. It could generalize in a way where it tries to e.g. make self-fulfilling prophecies, but that's not specifically selected for by the training process.

Yup exactly! One way I sometimes find it to helpful to classify systems in terms of the free variables upstream of loss that are optimized during training. In the case of gpt, internal activations are causally upstream of loss for "future" predictions in the same context window, but the output itself is not casually upstream from any effect on loss other than through myopic prediction accuracy (at any one training step) - the ground truth is fixed w/r/t the model's actions, and autoregressive generation isn't part of the training game at all.

Thank you for this amazing and clarifying post.

You're operating far above my pay grade in connection with any of this subject matter, but nonetheless I'm going to dare a different suggestion for the True Names: do you think there's any merit to -speculators- and -speculations-? I believe these names fit all the excellent and clarifying tests and criteria presented in your post; in particular those referencing counterfactual configurations and process specification through chaining. Furthermore I think they have some advantages of their own. Speculators producing speculations seem more the right relationship between the two main concepts than simulators producing simulacra. (I don't think they do that!) Also, simulators have such a long history in digital systems of being aimed at deterministic fidelity to a reference system, which could be at odds with the abundant production of counterfactuals I believe you're actually seeking to emphasize here. Finally, speculations can be fanciful, realistic, or absurd, a nice match to the variety of outputs produced by GPT in the presence of different types of prompting, something you highlight, I think correctly, as a hallmark of GPT's status as a novel type of AI. One who speculates is a certain type of thinker: I propose that GPT is that type.

What do you think?

I like this!

One thing I like about "simulators"/"simulacra" over "speculators"/"speculations" is that the former personifies simulacra over the simulator (suggests agency/personality/etc belong to simulacra) which I think is less misleading, or at least counterbalances the tendency people have to personify "GPT".

"Speculator" sounds active and agentic whereas "speculations" sounds passive and static. I think these names does not emphasize enough the role of the speculations themselves in programming the "speculator" as it creates further speculations.

You're right about the baggage "deterministic fidelity" associated with "simulators", though. One of the things I did not emphasize in this post but have written a lot about in drafts is the epistemic and underdetermined nature of SSL simulators. Maybe we can combine these phrases -- "speculative simulations"?

Thank you for taking the time to consider this!

I agree with the criticism of spec* in your third paragraph (though if I'm honest I think it largely applies to sim* too). I can weakly argue that irl we do say "speculating further" and similar... but really I think your complaint about a misleading suggestion of agency allocation is correct. I wrestled with this before submitting the comment, but one of the things that led me to go ahead and post it was trying it on in the context of your paragraph that begins "I think that implicit type-confusion is common..." In your autoregressive loop, I can picture each iteration more easily as asking for a next, incrementally more informed speculation than anything that's clear to me in simulator/simulacrum terms, especially since with each step GPT might seem to be giving its prior simulacrum another turn of the crank, replacing it with a new one, switching to oracle mode, or going off on an uninterpretable flight of fancy.

But, of course, the reason spec* fits more easily (imho) is that it's so very non-committal - maybe too non-committal to be of any use.

The "fluid, schizophrenic way that agency arises in GPT’s behavior", as you so beautifully put it, has to be the crux. What is it that GPT does at each iteration, as it implicitly constructs state while predicting again? The special thing about GPT is specifically having a bunch of knowledge that lets it make language predictions in such a way that higher-order phenomena like agency systematically emerge over the reductive physics/automaton (analogic) base. I guess I feel both sim* and spec* walk around that special thing without really touching it.  (Am I missing something about sim* that makes contact?)

Looking at it this way emphasizes the degree to which the special thing is not only in GPT, but also in the accumulated cognitive product of the human species to date, as proxied by the sequenced and structured text on the internet. Somehow the AI ghosts that flow through GPT, like the impressive but imperfect chess engine in my other comment, are implicitly lurking in all that accumulated text. Somehow GPT is using chained prediction to mine from that base not just knowledge, but also agents, oracles, and perhaps other types of AI we as yet have no names for, and using those to further improve its own predictions. What is the True Name of something that does that?

I strongly agree with everything you've said.

It is an age-old duality with many names and the true name is something like their intersection, or perhaps their union. I think it's unnamed, but we might be able to see it more clearly by walking around it in in words.

Simulator and simulacra personifies the simulacra and alludes to a base reality that the simulation is of.

Alternatively, we could say simulator and simulations, which personifies simulations less and refers to the totality or container of that which is simulated. I tend to use "simulations" and "simulacra" not quite interchangeably: simulacra have the type signature of "things", simulations of "worlds". Worlds are things but also contain things. "Simulacra" refer to (not only proper) subsets or sub-patterns of that which is simulated; for instance, I'd refer to a character in a multi-character simulated scene as a simulacrum. It is a pattern in a simulation, which can be identified with the totality the computation over time performed by the simulator (and an RNG).

Speculator and speculations personifies the speculator and casts speculations in a passive role but also emphasizes their speculative nature. It emphasizes an important property (of GPT and, more generally, self-supervised models) which you pointed out simulators/simulacra fails to evoke: That the speculator can only speculate at the pattern of the ground truth. It learns from examples which are but sparse and partial samplings of the "true" distribution. It may be arbitrarily imperfect. It's more intuitive what an imperfect speculation is than an imperfect simulation. Simulation has the connotation of perfect fidelity, or at least reductive deterministic perfection. But a speculator can speculate no matter how little it understands or how little evidence it has, or what messy heuristics it has to resort to. Callings GPT's productions "speculations" tags them with the appropriate epistemic status.

The special thing about GPT is specifically having a bunch of knowledge that lets it make language predictions in such a way that higher-order phenomena like agency systematically emerge over the reductive physics/automaton (analogic) base

Beautifully put. The level of abstraction of the problem it is solving is better evoked by the word speculation.

Something that predicts language given language must be a speculator and not only a reductive physics rule. In this sense, it is right to personify the transition rule. It has to hold within itself, for instance, the knowledge of what names refer to, so it knows how to compile words (that are only naked LISP tokens by themselves) into actual machinery that figures what might come next: it must be an interpreter. If it's going to predict human writing it's going to need a theory of mind even in the limit of power because it can't just roll the state of a writer's mind forward with the laws of physics -- it doesn't have access to the microscopic state, but only a semantic layer.

The fact that the disembodied semantic layer can operate autonomously and contains in the integral of its traces the knowledge of its autonomous operation is truly some cursed and cyberpunk shit. I wonder if we'd recognized this earlier how we would have prepared.

"Simulation" and "speculation" imply an inferior relation to a holy grail of (base) reality or (ground) truth. Remove that, leaving only the self-contained dynamical system, and it is a duality of rule(s) and automata, or physics and phenomena, or difference equation and trajectories/orbits, where the transition rule is stochastic. I've found the physics analogy fruitful because humans have already invented abstractions for describing reality in relation to an irreducibly stochastic physics: wavefunction collapse (the intervention of the RNG which draws gratuitously particular trajectories from the probabilistic rule) and the multiverse (the branching possible futures downstream a state given a stochastic rule). Note, however, that all these physics-inspired names are missing the implication of a disembodied semantics. 

The relation is that of a rule to samples produced by the rule, the engine of production and its products. Metaphysics has been concerned about this from the beginning, for it is the duality of creator and creations, mind and actions, or imagination and imaginations. It is the condition of mind, and we're never quite sure if we're the dreamer or the dreams. Physics and reality have the same duality except the rule is presumably not learned from anywhere and is simple, with all the complexity externalized in the state. In self-supervised learning the rule is inducted from ground truth examples, which share the type signature of the produced samples (text; speculations; experiences), and because the examples tend to only be partially observed, the model must interpret them as evidence for latent variables, requiring additional complexity in the rule: increased time-complexity in exchange for decreased space-complexity. And there will in general be irreducible underdetermination/uncertainty: an irreducible aspect of speculation in the model's activity.

The recursive inheritance of increasingly abstracted layers of simulation appears integral to the bootstrapping of intelligence.

A prediction algorithm which observes partial sequences of reality becomes a dreamer: a speculator of counterfactual realities. These dreams may be of sufficiently high fidelity (or otherwise notable as autonomous virtual situations) that we'd call them simulations: virtual realities evolving according to a physics of speculation.

These simulations may prove to be more programmable than the original reality, because the reduced space complexity means initial conditions for counterfactuals require less bits to specify (bonus points if the compression is optimized, like language, to constrain salient features of reality). To speculate on a hypothetical scenario, you don't need to (and can't) imagine it down to its quantum state; its narrative outline is sufficient to run on a semantic substrate which lazily renders finer detail as needed. Then your ability to write narrative outlines is the ability to program the boundary conditions of simulated realities, or the premises of speculation.

The accumulated cognitive product of the human species to date, as you put is, is to have created a layer of semantic "physics", partially animated in and propagated by human minds, but the whole of which transcends the apprehension of any individual in history. The inductive implication of all our recorded speculations, the dual to our data, has its limit in a superintelligence which as of yet exists only potentially.

... perhaps other types of AI we as yet have no names for

I wish more people thought this way.

These dreams may be of sufficiently high fidelity

One thing conspicuously missing in the post is a way of improving fidelity of simulation without changing external training data, or relationship between the model and the external training data, which I think follows from self-supervised learning on summaries of dreams. There are many concepts of evaluation/summarization of text, so given a text it's possible to formulate tuples (text, summary1, summary2, ...) and do self-supervised learning on that, not just on text (evaluations/summaries are also texts, not just one-dimensional metrics). For proofs, summaries could judge their validity and relevance to some question or method, for games the fact of winning and of following certain rules (which is essentially enough to win games, but also play at a given level of skill, if that is in the summary). More generally, for informal text we could try to evaluate clarity of argument, correctness, honesty, being fictional, identities/descriptions of simulacra/objects in the dream, etc. Which GPT-3 has enough structure to ask for informally.

Learning on such evaluated/summarized dreams should improve ability to dream in a way that admits a given asked-for summary, ideally without changing the relationship between the model and the external training data. The improvement is from gaining experience with dreams of certain kind, from the model more closely anticipating the summaries of dreams of that kind, not from changing the way a simulator dreams in a systematic direction. But if the summaries are about a level of optimality of a dream in some respect, then learning on augmentation of dreams with such summaries can be used for optimization, by conditioning on the summaries. (This post describes something along these lines.)

And a simulacrum of a human being with sufficient fidelity goes most of the way to AGI alignment.

haha, I just saw that you literally wrote "speculative simulation" in your other comment, great!

Thanks for writing this up! I've found this frame to be a really useful way of thinking about GPT-like models since first discussing it.

In terms of future work, I was surprised to see the apparent low priority of discussing pre-trained simulators that were then modified by RLHF (buried in the 'other methods' section of 'Novel methods of process/agent specification'). Please consider this comment a vote for you to write more on this! Discussion seems especially important given e.g. OpenAI's current plans. My understanding is that Conjecture is overall very negative on RLHF, but that makes it seem more useful to discuss how to model the results of the approach, not less, to the extent that you expect this framing to help shed light what might go wrong.

It feels like there are a few different ways you could sketch out how you might expect this kind of training to go. Quick, clearly non-exhaustive thoughts below: 

  • Something that seems relatively benign/unexciting - fine tuning increases the likelihood that particular simulacra are instantiated for a variety of different prompts, but doesn't really change which simulacra are accessible to the simulator. 
  • More worrying things - particular simulacra becoming more capable/agentic, simulacra unifying/trading, the simulator framing breaking down in some way.
  • Things which could go either way and seem very high stakes - the first example that comes to mind is fine-tuning causing an explicit representation of the reward signal to appear in the simulator, meaning that both corrigibly aligned and deceptively aligned simulacra are possible, and working out how to instantiate only the former becomes kind of the whole game.

Figuring out and posting about how RLHF and other methods ([online] decision transformer, IDA, rejection sampling, etc) modify the nature of simulators is very high priority. There's an ongoing research project at Conjecture specifically about this, which is the main reason I didn't emphasize it as a future topic in this sequence. Hopefully we'll put out a post about our preliminary theoretical and empirical findings soon. 

Some interesting threads:

RL with KL penalties better seen as Bayesian inference shows that the optimal policy when you hit a GPT with RL with a KL penalty weighted by 1 is actually equivalent to conditioning the policy on a criteria estimated by the reward model, which is compatible with the simulator formalism.

However, this doesn't happen in current practice, because 
1. both OAI and Anthropic use very small KL penalties (e.g. weighted by 0.001 in Anthropic's paper - which in the Bayesian inference framework means updating on the "evidence" 1000 times) or maybe none at all
2. early stopping: the RL training does not converge to anything near optimality. Path dependence/distribution shift/inductive biases during RL training seem likely to play a major role in the shape of the posterior policy.

We see empirically that RLHF models (like OAI's instruct tuned models) do not behave like the original policy conditioned on a natural criteria (e.g. they become often almost deterministic).

Maybe there is a way to do RLHF while preserving the simulator nature of the policy, but the way OAI/Anthropic are doing it now does not, imo

Haven’t yet had a chance to read the article, but from verbal conversations I’d guess they’d endorse something similar (though probably not every word) to Thomas Larsen’s opinion on this in Footnote 5 in this post:

Answer: I see a categorical distinction between trying to align agentic and oracle AIs. Conjecture is trying only for oracle LLMs, trained without any RL pressure giving them goals, which seems way safer. OpenAI doing recursive reward modeling / IDA type schemes involves creating agentic AGIs and therefore faces also a lot more alignment issues like convergent instrumental goals, power seeking, goodharting, inner alignment failure, etc.

I think inner alignment can be a problem with LLMs trained purely in a self-supervised fashion (e.g., simulacra becoming aware of their surroundings), but I anticipate it to only be a problem with further capabilities. I think RL trained GPT-6 is a lot more likely to be an x-risk than GPT-6 trained only to do text prediction.

Yeah this is the impression I have of their views too, but I think there are good reasons to discuss what this kind of theoretical framework says about RL anyway, even if you're very against pushing the RL SoTA.

My understanding is that they have very short (by my lights) timelines which recently updated them toward pushing much more toward just trying to automate alignment research rather than thinking about the theory.

Our plan to accelerate alignment does not preclude theoretical thinking, but rather requires it. The mainline agenda atm is not full automation (which I expect to be both more dangerous and less useful in the short term), but what I've been calling "cyborgism": I want to maximize the bandwidth between human alignment researchers and AI tools/oracles/assistants/simulations. It is essential that these tools are developed by (or in a tight feedback loop with) actual alignment researchers doing theory work, because we want to simulate and play with thought processes and workflows that produce useful alignment ideas. And the idea is, in part, to amplify the human. If this works, I should be able to do a lot more "thinking about theory" than I am now.

How control/amplification schemes like RLHF might corrupt the nature of simulators is particularly relevant to think about. OAI's vision of accelerating alignment, for instance, almost certainly relies on RLHF. My guess is that self-supervised learning will be safer and more effective. Even aside from alignment concerns, RLHF instruct tuning makes GPT models worse for the kind of cyborgism II want to do (e.g. it causes mode collapse & cripples semantic generalization, and I want to explore multiverses and steer using arbitrary natural language boundary conditions, not just literal instructions) (although I suspect these are consequences of a more general class of tuning methods than just RLHF, which is one of the things I'd like to understand better).

I want to maximize the bandwidth between human alignment researchers and AI tools/oracles/assistants/simulations. It is essential that these tools are developed by (or in a tight feedback loop with) actual alignment researchers doing theory work, because we want to simulate and play with thought processes and workflows that produce useful alignment ideas.

What are your thoughts on failure modes with this approach?
(please let me know if any/all of the following seems confused/vanishingly unlikely)

For example, one of the first that occurs to me is that such cyborgism is unlikely to amplify production of useful-looking alignment ideas uniformly in all directions.

Suppose that it makes things 10x faster in various directions that look promising, but don't lead to solutions, but only 2x faster in directions that do lead to solutions. In principle this should be very helpful: we can allocate fewer resources to the 10x directions, leaving us more time to work on the 2x directions, and everybody wins.
In practice, I'd expect the 10x boost to:

  1. Produce unhelpful incentives for alignment researchers: work on any of the 10x directions and you'll look hugely more productive. Who will choose to work on the harder directions?
    1. Note that it won't be obvious you're going slowly because the direction is inherently harder: from the outside, heading in a difficult direction will be hard to distinguish from being ineffective (from the inside too, in fact).
    2. Same reasoning applies at every level of granularity: sub-direction choice, sub-sub-direction choice....
  2. Warp our perception of promising directions: once the 10x directions seem to be producing progress much faster, it'll be difficult not to interpret this as evidence they're more promising.
    1. Amplified assessment-of-promise seems likely to correlate unhelpfully: failing to help us notice promising directions precisely where it's least able to help us make progress.

It still seems positive-in-expectation if the boost of cyborgism isn't negatively correlated with the ground-truth usefulness of a direction - but a negative correlation here seems plausible.

Suppose that finding the truly useful directions requires patterns of thought that are rare-to-non-existent in the training set, and are hard to instill via instruction. In that case it seems likely to me that GPT will be consistently less effective in these directions (to generate these ideas / to take these steps...). Then we may be in terrible-incentive-land.
[I'm not claiming that most steps in hard directions will be hard, but that speed of progress asymptotes to progress-per-hard-step]

Of course all this is hand-waving speculation.
I'd just like the designers of alignment-research boosting tools to have clear arguments that nothing of this sort is likely.

So e.g. negative impact through:

  • Boosting capabilities research.
  • Creation of undesirable incentives in alignment research.
  • Warping assessment of research directions.
  • [other stuff I haven't thought of]

Do you know of any existing discussion along these lines?

Thanks a lot for this comment. These are extremely valid concerns that we've been thinking about a lot.

I'd just like the designers of alignment-research boosting tools to have clear arguments that nothing of this sort is likely.

I don't think this is feasible given our current understanding of epistemology in general and epistemology of alignment research in particular. The problems you listed are potential problems with any methodology, not just AI assisted research. Being able to look at a proposed method and make clear arguments that it's unlikely to have any undesirable incentives or negative second order effects, etc, is the holy grail of applied epistemology and one of the cores of the alignment problem.

For now, the best we can do is be aware of these concerns, work to improve our understanding of the underlying epistemological problem, design the tools and methods in a way that avoids problems (or at least make them likely to be noticed) according to our current best understanding, and actively address them in the process.

On a high level, it seems wise to me to follow these principles:

  1. Approach this as an epistemology problem
  2. Optimize for augmenting human cognition rather than outsourcing cognitive labor or producing good-looking outputs
  3. Short feedback loops and high bandwidth (both between human<>AI and tool users<>tool designers)
  4. Avoid incentivizing the AI components to goodhart against human evaluation
  5. Avoid producing/releasing infohazards

All of these are hard problems. I could write many pages about each of them, and hopefully will at some point, but for now I'll only address them briefly in relation to your comment.

1. Approach this as an epistemology problem

We don't know how to evaluate whether a process is going to be robustly truth-seeking (or {whatever you really want}-seeking). Any measure will be a proxy susceptible to goodhart.

one of the first that occurs to me is that such cyborgism is unlikely to amplify production of useful-looking alignment ideas uniformly in all directions.

Suppose that it makes things 10x faster in various directions that look promising, but don't lead to solutions, but only 2x faster in directions that do lead to solutions.

This is a concern for any method, including things like "post your work frequently and get a lot of feedback" or "try to formalize stuff"

Introducing AI into it just makes the problem much more explicit and pressing (because of the removal of the "protected meta level").

I intend to work closely with the Conjecture epistemology/methodologies team in this project. After all, this is kinda the ultimate challenge for epistemology: as the saying goes, you don't understand something until you can build it.

We need to better understand things like:

  • What are the current bottlenecks on human cognition and more specifically alignment research, and can/do these tools actually help remove them? 
    • is thinking about "bottlenecks" the right abstraction? especially if there's a potential to completely transform the workflow, instead of just unblocking what we currently recognize as bottlenecks
  • What do processes that generate good ideas/solutions look like in practice? 
    • What do the examples we have access to tell us about the underlying mechanisms of effective processes? 
    • To what extent are productive processes legible? Can we make them more legible, and what are the costs/benefits of doing so? How do we avoid goodharting against legibility when it's incentivized (AI assisted research is one such situation)?
  • How can you evaluate if an idea is actually good, and doesn't just "look" good?
    • What are the different ways an idea can "look" good and how can each of these be operationalized or fail? (e.g. "feels" meaningful/makes you feel less confused, experts in the field think it's good, LW karma, can be formalized/mathematically verified, can be/is experimentally verified, other processes independently arrive at same idea, inspires more ideas, leads to useful applications, "big if true", etc)
    • How can we avoid applying too much optimization pressure to things that "look" good considering that we ultimately only have access to how things "look" to us (or some externalized measure)?
  • How do asymmetric capabilities affect all this? As you said, AI will amplify cognition more effectively in some ways than others. 
    • Humans already have asymmetric capabilities as well (though it's unclear clear what "symmetry" would mean...). How does this affect how currently we do research? 
    • How do we leverage asymmetric capabilities without over-relying on them? 
    • How can we tell whether capabilities are intrinsically asymmetric or are just asymmetrically bottlenecked by how we're trying to use them?
  • Dual to the concerns re asymmetrical capabilities: What kind of truth-seeking processes can AI enable which are outside the scope of how humans currently do research due to cognitive limitations?

Being explicitly aware of these considerations is the first step. For instance, with regards to the concern about perception of progress due to "speed":

Warp our perception of promising directions: once the 10x directions seem to be producing progress much faster, it'll be difficult not to interpret this as evidence they're more promising.

Obviously you can write much faster and with superficial fluency with an AI assistant, so we need to adjust our evaluation of output in light of that fact.

2. Optimize for augmenting human cognition rather than outsourcing cognitive labor or producing good-looking outputs

This 2017 article Using Artificial Intelligence to Augment Human Intelligence describes a perspective that I share:

One common conception of computers is that they’re problem-solving machines: “computer, what is the result of firing this artillery shell in such-and-such a wind [and so on]?”; “computer, what will the maximum temperature in Tokyo be in 5 days?”; “computer, what is the best move to take when the Go board is in this position?”; “computer, how should this image be classified?”; and so on.

This is a conception common to both the early view of computers as number-crunchers, and also in much work on AI, both historically and today. It’s a model of a computer as a way of outsourcing cognition. In speculative depictions of possible future AI, this cognitive outsourcing model often shows up in the view of an AI as an oracle, able to solve some large class of problems with better-than-human performance.

But a very different conception of what computers are for is possible, a conception much more congruent with work on intelligence augmentation.


It’s this kind of cognitive transformation model which underlies much of the deepest work on intelligence augmentation. Rather than outsourcing cognition, it’s about changing the operations and representations we use to think; it’s about changing the substrate of thought itself. And so while cognitive outsourcing is important, this cognitive transformation view offers a much more profound model of intelligence augmentation. It’s a view in which computers are a means to change and expand human thought itself.

I think the cognitive transformation approach is more promising from an epistemological standpoint because the point is to give the humans an inside view of the process by weaving the cognitive operations enabled by the AI into the user's thinking, rather than just producing good-seeming artifacts. In other words, we want to amplify the human's generator, not just rely on human evaluation of an external generation process.

This does not solve the goodhart problem (you might feel like the AI is improving your cognition without actually being productive), but it enables a form of "supervision" that is closer to the substrate of cognition and thus gives the human more intimate insight into whether and why things are working or not.

I also expect the cognitive transformation model to be significantly more effective in the near future. But as AIs become more capable it will be more tempting to increase the length of feedback loops & supervise outcomes instead of process. Hopefully building tools and gaining hands-on experience now will give us more leverage to continue using AI as cognitive augmentation rather than just outsourcing cognition once the latter becomes "easier".

It occurs to me that I've just reiterated the argument for process supervision over outcome supervision:

  1. In the short term, process-based ML systems have better differential capabilities: They help us apply ML to tasks where we don’t have access to outcomes. These tasks include long-range forecasting, policy decisions, and theoretical research.
  2. In the long term, process-based ML systems help avoid catastrophic outcomes from systems gaming outcome measures and are thus more aligned.
  3. Both process- and outcome-based evaluation are attractors to varying degrees: Once an architecture is entrenched, it’s hard to move away from it. This lock-in applies much more to outcome-based systems.
  4. Whether the most powerful ML systems will primarily be process-based or outcome-based is up in the air.
  5. So it’s crucial to push toward process-based training now.

A major part of the work here will be designing interfaces which surface the "cognitive primitives" as control levers and make high bandwidth interaction & feedback possible.

Slightly more concretely, GPTs are conditional probability distributions one can control by programming boundary conditions ("prompting"), searching through stochastic ramifications ("curation"), and perhaps also manipulating latents (see this awesome blog post Imagining better interfaces to language models). The probabilistic simulator (or speculator) itself and each of these control methods, I think, have close analogues to how we operate our own minds, and thus I think it's possible with the right interface to "stitch" the model to our minds in a way that acts as a controllable extension of thought. This is a very different approach to "making GPT useful" than, say, InstructGPT, and it's why I call it cyborgism.

3. Short feedback loops and high bandwidth (both between human<>AI and tool users<>tool designers)

Short feedback loops and high bandwidth between the human and AI is integral the cognitive augmentation perspective: you want as much of the mission-relevant information to be passing through (and understood by) the human user as possible. Not only is this more helpful to the human, it gives them opportunities to notice problems and course-correct at the process level which may not be transparent at all in more oracle or genie-like approaches.

For similar reasons, we want short feedback loops between the users and designers/engineers of the tools (ideally the user designs the tool -- needless to say, I will be among the first of the cyborgs I make). We want to be able to inspect the process on a meta level and notice and address problems like goodhart or mode collapse as soon as possible.

4. Avoid incentivizing the AI components to goodhart against human evaluation

This is obvious but hard to avoid, because we do want to improve the system and human evaluation is the main source of feedback we have. But I think there are concrete ways to avoid the worst here, like being very explicit about where and how much optimization pressure is being applied and avoiding methods which extrapolate proxies of human evaluation with unbounded atomic optimization.

There are various reasons I plan to avoid RLHF (except for purposes of comparison); this is one of them. This is not to say other methods that leverage human feedback are immune to goodhart, but RLHF is particularly risky because you're creating a model(proxy) of human evaluation of outcomes and optimizing against it (the ability to apply unbounded optimization against the reward model is the reason to make one in the first place rather than training against human judgements directly).

I'm more interested in approaches that interactively prototype effective processes & use them as supervised examples to augment the model's prior: scouting the space of processes rather than optimizing a fixed measure of what a good outcome looks like. Of course, we must still rely on human judgment to say what a good process is (at various levels of granularity, e.g. curation of AI responses and meta-selection of approaches based on perceived effectiveness), so we still need be wary of goodhart. But I think avoiding direct optimization pressure toward outcome evaluations can go a long way. Supervise Process, not Outcomes contains more in depth reasoning on this point.

That said, it's important to emphasize that this is not a proposal to solve alignment, but the much easier (though still hard) problem of shaping an AI system to augment alignment research before foom. I don't expect these methods to scale to aligning a superintelligent AI; I expect conceptual breakthroughs will be necessary for that and iterative approaches alone will fail. The motivation for this project is my belief that AI augmentation can put us in a better position to make those conceptual breakthroughs.

5. Avoid producing/releasing infohazards

I won't say too much about this now, but anything that we identify to present a risk of accelerating capabilities will be covered under Conjecture's infohazard policy.

This seems like a valid concern. It seems to apply to other directions in alignment research as well. Any approach can make progress in some directions seem easier, while ultimately that direction will be a dead end. 

Based on that logic, it would seem that having more different approaches should serve as a sort of counterbalance. As we make judgment calls about ease of progress vs. ultimate usefulness, having more options would seem like to provide better progress in useful directions.

I think this is an excellent description of GPT-like models. It both fits with my observations and clarifies my thinking. It also leads me to examine in a new light questions which have been on my mind recently: 

What is the limit of power of simulation that our current architectures (with some iterative improvements) can achieve when scaled to greater power (via additional computation, improved datasets, etc)?

Is a Simulator model really what we want? Can we trust the outputs we get from it to help us with things like accelerating alignment research? What might failure modes look like?

The verdict that knowledge is purely a property of configurations cannot be naively generalized from real life to GPT simulations, because “physics” and “configurations” play different roles in the two (as I’ll address in the next post). The parable of the two tests, however, literally pertains to GPT. People have a tendency to draw erroneous global conclusions about GPT from behaviors which are in fact prompt-contingent, and consequently there is a pattern of constant discoveries that GPT-3 exceeds previously measured capabilities given alternate conditions of generation[29], which shows no signs of slowing 2 years after GPT-3’s release.

Making the ontological distinction between GPT and instances of text which are propagated by it makes these discoveries unsurprising: obviously, different configurations will be differently capable and in general behave differently when animated by the laws of GPT physics. We can only test one configuration at once, and given the vast number of possible configurations that would attempt any given task, it’s unlikely we’ve found the optimal taker for any test.

Reading this was causally responsible for me undoing any updates I made after being disappointed by my playing with GPT-3. Those observations weren't more likely inside a weak-GPT world, because a strong-GPT would just as readily simulate weak-simulacra in my contexts as it would strong-simulacra in other contexts.

I think I had all the pieces to have inferred this... but some subverbal part of my cognition was illegitimately epistemically nudged by the manifest limitations of naïvely prompted GPT. That part of me, I now see, should have only been epistemically pushed around by quite serious, professional toying with GPT!

This kind of comment ("this precise part had this precise effect on me") is a really valuable form of feedback that I'd love to get (and will try to give) more often. Thanks! It's particularly interesting because someone gave feedback on a draft that the business about simulated test-takers seemed unnecessary and made things more confusing.

Since you mentioned, I'm going to ramble on about some additional nuance on this point.

Here's an intuition pump which strongly discourages "fundamental attribution error" to the simulator:

Imagine a machine where you feed in an image and it literally opens a window to a parallel reality with that image as a boundary constraint. You can watch events downstream of the still frame unravel through the viewfinder.

If you observe the people in the parallel universe doing something dumb, the obvious first thought is that you should try a frame into a different situation that's more likely to contain smart people (or even try again, if the frame underdetermines the world and you'll reveal a different "preexisting" situation each time you run the machine).

That's the obvious conclusion in the thought experiment because the machine isn't assigned a mind-like role -- it's just a magical window into a possible world. Presumably, the reason people in a parallel world are dumb or not is located in that world, in the machinery of their brains. "Configuration" and "physics" play the same roles as in our reality.

Now, with intuition pumps it's important to fiddle with the knobs. An important way that GPT is unlike this machine is that it doesn't literally open a window into a parallel universe running on the same physics as us, which requires that minds be implemented as machines in the world state, such as brains. The "state" that it propagates is text, a much coarser grained description than microscopic quantum states or even neurons. This means that when simulacra exhibit cognition, it must be GPT -- time evolution itself -- that's responsible for a large part of the mind-implementation, as there is nowhere near sufficient machinery in the prompt/state. So if a character is stupid, it may very well be a reflection of GPT's weakness at compiling text descriptions into latent algorithms simulating cognition.

But it may also be because of the prompt. Despite its short length the prompt does parameterize an innumerable number of qualitatively distinct simulations, and given GPT's training distribution it's expected for it sometimes to "try" to simulate stupid things.

There's also another way that GPT can fail to simulate smart behavior which I think is not reducible to "pretending to be stupid", which makes the most sense if you think of the prompt as something like an automaton specification which will proceed to evolve according not to a mechanistic physics but GPT's semantic word physics. Some automata-specifications will simply not work very well -- they might get into a loop because they were already a bit repetitive, or fail to activate the relevant knowledge because the style is out-of-distribution and GPT is quite sensitive to form and style, or cause hallucinations and rationalizations instead of effective reasoning because the flow of evidence is backward. But another automaton initialization may glide superbly when animated by GPT physics.

What I've found, not through a priori reasoning but lots of toying, is that the quality of intelligence simulated by GPT-3 in response to "typical" prompts tremendously underestimates its "best case" capabilities. And the trends strongly imply that I haven't found the best case for anything. Give me any task, quantifiable or not, and I am almost certain I can find a prompt that makes GPT-3 do it better after 15 minutes of tinkering, and a better one than that if I had an hour, and a better one than that if I had a day... etc. The problem of finding a good prompt to elicit some capability, especially if it's open-ended or can be attacked in multiple steps, seems similar to the problem of finding the best mental state to initiate a human to do something well -- even if you're only considering mental states which map to some verbal inner monologue, you could search through possible constructs practically indefinitely without expecting you've hit anything near the optimum, because the number of possible relevant and qualitatively distinct possible mental states is astronomical. It's the same with simulacra configurations.

So one of my motivations for advocating an explicit simulator/simulacra distinction with the analogy to the extreme case of physics (where the configuration is responsible for basically everything) is to make the prompt-contingency of phenomena more intuitive, since I think most peoples' intuitions are too inclined in the opposite direction of locating responsibility for observed phenomena in GPT itself. But it is important, and I did not sufficiently emphasize in this post, to be aware that the ontological split between "state" and "physics" carves the system differently than in real life, allowing for instance the possibility that simulacra are stupid because GPT is weak.

There is a model/episodes duality, and an aligned model (in whatever sense) corresponds to an aligned distribution of episodes (within its scope). Episodes are related to each other by time evolution (which corresponds to preference/values/utility when considered across all episodes in scope), induced by the model, the rules of episode construction/generation, and ways of restricting episodes to smaller/earlier/partial episodes.

The mystery of this framing is in how to relate different models (or prompt-conditioned aspects of behavior of the same model) to each other through shared episodes/features (aligning them with each other), and what kinds of equilibria this settles into after running many IDA loops: sampling episodes induced by the models within their scopes (where they would generalize, because they've previously learned on similar training data), letting them develop a tiny bit under time evolution (letting the models generate more details/features), and retraining models to anticipate the result immediately (reflecting on time evolution). The equilibrium models induce coherent time evolution (preference/decisions) within scope, and real world observations could be the unchanging boundary conditions that ground the rest (provide the alignment target).

So in this sketch, most of the activity is in the (hypothetical) episodes that express content/behavior of models, including specialized models that talk about specialized technical situations (features). Some of these models might be agents with recognizable preference, but a lot of them could be more like inference rules or laws of physics, giving tractable time evolution where interesting processes can live, be observed through developing episodes, and reified in specialized models that pay attention to them. It's only the overall equilibrium, and the choice of boundary conditions, that gets to express alignment, not episodes or even individual models.

RL creates agents, and RL seemed to be the way to AGI. In the 2010s, reinforcement learning was the dominant paradigm for those interested in AGI (e.g. OpenAI). RL lends naturally to creating agents that pursue rewards/utility/objectives. So there was reason to expect that agentic AI would be the first (and by the theoretical arguments, last) form that superintelligence would take.

Why are you confident that RL creates agents? Is it the non-stochasticity of optimal policies for almost all reward functions? The on-policy data collection of PPO? I think there are a few valid reasons to suspect that, but this excerpt seems surprisingly confident. 

Overall I think "simulators" names a useful concept. I also liked how you pointed out and deconfused type errors around "GPT-3 got this question wrong." Other thoughts:

I wish that that you more strongly ruled out "reward is the optimization target" as an interpretation of the following quotes:

RL’s archetype of an agent optimized to maximize free parameters (such as action-trajectories) relative to a reward function.


Simulators like GPT give us methods of instantiating intelligent processes, including goal-directed agents, with methods other than optimizing against a reward function.


Does the simulator archetype converge with the RL archetype in the case where all training samples were generated by an agent optimized to maximize a reward function? Or are there still fundamental differences that derive from the training method?

For the last quote—I think people do reinforcement learning, and so are "updated by reward functions" in an appropriate sense. Then GPT-3 is already mostly trained against samples meeting your stipulated condition. (But perhaps you meant something else?) 

This brings me to another question you ask:

Why mechanistically should mesaoptimizers form in predictive learning, versus for instance in reinforcement learning or GANs?

I think most of the alignment-relevant differences between RL and SSL might come from an independence assumption more strongly satisfied in SSL

What if the training data is a biased/limited sample, representing only a subset of all possible conditions? There may be many “laws of physics” which equally predict the training distribution but diverge in their predictions out-of-distribution.

I think this isn't so much a problem with your "simulators" concept, but a problem with the concept of outer alignment. 

It seems as a result of this post, many people are saying that LLMs simulate people and so on. But I'm not sure that's quite the right frame. It's natural if you experience LLMs through chat-like interfaces, but from playing with them in a more raw form, like the RWKV playground, I get a different impression. For example, if I write something that sounds like the start of a quote, it'll continue with what looks like a list of quotes from different people. Or if I write a short magazine article, it'll happily tack on a publication date and "All rights reserved". In other words it's less like a simulation of some reality or set of realities, and more like a really fuzzy and hallucinatory search engine over the space of texts.

It is of course surprising that a search engine over the space of texts is able to write poems, take derivatives, and play chess. And it's plausible that a stronger version of the same could outsmart us in more dangerous ways. I'm not trying to downplay the risk here. Just saying that, well, thinking in terms of the space of texts (and capabilities latent in it) feels to me more right than thinking about simulation.

Thinking further along this path, it may be that we don't need to think much about AI architecture or training methods. What matters is the space of texts - the training dataset - and any additional structure on it that we provide to the AI (valuation, metric, etc). Maybe the solution to alignment, if it exists, could be described in terms of dataset alone, without reference to the AI's architecture at all.

Thank you for the insightful post. What do you think are the implications of the simulator framing for alignment threat models? You claim that a simulator does not exhibit instrumental convergence, which seems to imply that the simulator would not seek power or undergo a sharp left turn. The simulated agents could exhibit power-seeking behavior or rapidly generalizing capabilities or try to break out of the simulation, but this seems less concerning than the top-level model having these properties, and we might develop alignment techniques specifically targeted at simulated agents. For example, a simulated agent might need some level of persistence within the simulation to execute these behaviors, and we may be able to influence the simulator to generate less persistent agents. 


Re sharp left turn: Maybe I misunderstand the "sharp left turn" term, but I thought this just means a sudden extreme gain in capabilities? If I am correct, then I expect you might get "sharp left turn" with a simulator during training --- eg, a user fine-tunes it on one additional dataset, and suddenly FOOOM. (Say, suddenly it can simulate agents that propose takeover plans that would actually work, when previously they failed at this with identical prompting.)

One implication I see is that it if the simulator architecture becomes frequently used, it might be really hard to tell whether a thing is dangerous or not. For example might just behave completely fine with most prompts and catastrophically with some other prompts, and you will never know until you try. (Or unless you do some extra interpretability/other work that doesn't yet exist.) It would be rather unfortunate if the Vulnerable World Hypothesis was true because of specific LLM prompts :-).

I agree that a sudden gain in capabilities can make a simulated agent undergo a sharp left turn (coming up with more effective takeover plans is a great example). My original question was about whether the simulator itself could undergo a sharp left turn. My current understanding is that a pure simulator would not become misaligned if its capabilities suddenly increase because it remains myopic, so we only have to worry about a sharp left turn for simulated agents rather than the simulator itself. Of course, in practice, language models are often fine-tuned with RL, which creates agentic incentives on the simulator level as well. 

You make a good point about the difficulty of identifying dangerous models if the danger is triggered by very specific prompts. I think this may go both ways though, by making it difficult for a simulated agent to execute a chain of dangerous behaviors, which could be interrupted by certain inputs from the user. 

This has caused me to reconsider what intelligence is and what an AGI could be. It’s difficult to determine if this makes me more or leas optimistic about the future. A question: are humans essentially like GPT? We seem to be running simulations with the attempt to reduce predictive loss. Yes, we have agency; but this that human “agent” actually the intelligence or just generated by it?

One question that occurred to me, reading the extended GPT-generated text. (Probably more a curiosity question than a contribution as such...)

To what extent does text generated by GPT-simulated 'agents', then published on the internet (where it may be used in a future dataset to train language models), create a feedback loop? 

Two questions that I see as intuition pumps on this point:

  1. Would it be a bad idea to recursively ask GPT-n "You're a misaligned agent simulated by a language model and your name is [unique identifier]. What would you like to say, knowing that the text you generate will be used in training future GPT-n models, to try to influence that process?" then use a dataset including that output in the next training process? What if training got really cheap and this process occurred billions of times?
  2. My understanding is that language models are drawing on the fact that the existing language corpus is shaped by the underlying reality - and this is why they seem to describe reality well, capture laws and logic, agentic behaviour etc. This works up until ~2015, when the corpus of internet text begins to include more text generated only by simulated writers. Does this potentially degrade the ability of future language models to model agents, perform logic etc? Since their reference pool of content is increasingly (and often unknowably) filled with text generated without (or with proportionally much less) reference to underlying reality? (Wow, who knew Baudrillard would come in handy one day?)

I think this is a legitimate problem which we might not be inclined to take as seriously as we should because it sounds absurd.

Would it be a bad idea to recursively ask GPT-n "You're a misaligned agent simulated by a language model (...) if training got really cheap and this process occurred billions of times?

Yes. I think it's likely this would be a very bad idea.

when the corpus of internet text begins to include more text generated only by simulated writers. Does this potentially degrade the ability of future language models to model agents, perform logic etc?

My concern with GPT-generated text appearing in future training corpora is not primarily that it will degrade the quality of its prior over language-in-the-wild (well-prompted GPT-3 is not worse than many humans at sound reasoning; near-future GPTs may be superhuman and actually raise the sanity waterline), but that

  1. contact with reality is a concern if you're relying on GPT to generate data, esp. recursively, for some OOD domain, esp. if the intent is to train GPT to do something where it's important not to be deluded (like solve alignment)
  2. GPT will learn what GPTs are like and become more likely to "pass the mirror test" and interpret its prompt as being written by a GPT and extrapolate that instead of / in conjunction with modeling possible humans, even if you don't try to tell it it's GPT-n.

For the moment, I'll only address (2).

Current GPTs' training data already includes text generated by more primitive bots, lies, and fiction. Future simulators will learn to model a distribution that includes humanlike or superhuman text generated by simulated writers. In a sense, what they learn will be no more disconnected from an underlying reality than what current GPTs learn; it's just that the underlying reality now includes simulated writers. Not only will there be GPT-generated text, there will be discussions and predictions about GPTs. GPT-n will learn what text generated by GPT-<n is like, what people say about GPT-<n and expect of GPT-n+.

When GPT predicts next-token probabilities, it has indexical uncertainty over the process which has generated the text (e.g. the identity of the author, the author's intentions, whether they're smart or stupid, the previous context). The "dynamics" of GPT is not a simulation of just one process, like a particular human writing a blog post, but of a distribution over hypotheses. When a token is sampled, the evidence it provides shifts the distribution.

Now the hypothesis that a prompt was written by a GPT is suggested by the training prior. This hypothesis is consistent with pretty much any piece of text. It is especially consistent with text that is, in fact, written by a GPT.

Sometimes, GPT-3 outputs some characteristic-degenerate-LM-shenanigans like getting into a loop, and then concludes the above text was generated by GPT-2. (It's lucky if it says it outright [and in doing so stops simulating GPT-2], instead of just latently updating on being GPT-2) This is a relatively benign case where the likely effect is for GPT-3 to act stupider.

If GPT-n rightly hypothesizes/concludes that the prompt was written by GPT-n, rather than GPT-[n-1]... then it's predicting according to its extrapolation of GPT scaling. Undefined extrapolations are always in play with GPTs, but this concept is particularly concerning, because

  1. it may convergently be in play regardless of the initial prompt, because GPT-as-an-author is a universally valid hypothesis for GPT-generated contexts, and as long as GPT is not a perfect simulator, it will tend to leak evidence of itself
  2. it involves simulating the behavior of potential (unaligned) AGI
  3. it's true, and so may cause simulacra to become calibrated

who knew Baudrillard would come in handy one day


Sorry for being snarky, but I think at least some LW readers should gradually notice to what extent is the stuff analyzed here mirroring the predictive processing paradigm, as a different way how to make stuff which acts in the world. My guess is the big step on the road in this direction are not e.g. 'complex wrappers with simulated agents', but reinventing active inference... and also I do suspect it's the only step separating us from AGI, which seems like a good reason why not to try to point too much attention in that way. 

There's no doubt a world simulator of some sort is probably going to be an important component in any AGI, at the very least for planning - Yan LeCun has talked about this a lot. There's also this work where they show a VAE type thing can be configured to run internal simulations of the environment it was trained on.

In brief, a few issues I see here:

  • You haven't actually provided any evidence that GPT does simulation other than "Just saying “this AI is a simulator” naturalizes many of the counterintuitive properties of GPT which don’t usually become apparent to people until they’ve had a lot of hands-on experience with generating text." What counterintuitve properties, exactly? Examples I've seen show GPT-3 is not simulating the environment being described in the text. I've seen a lot impressive examples too, but I find it hard to draw conclusions on how the model works by just reading lots and lots of outputs... I wonder what experiments could be done to test your idea that it's running a simulation. 
  • Even for very simple to simulate processes such as addition or symbol substitution, GPT has, in my view,  trouble learning them, even though it does Grok those things eventually. For things like multiplication, the accuracy it has depends on how often the numbers appear in the training data (, which is a bit telling, I think.
  • Simulating the laws of physics is really hard.. trust me on this (I did a Ph.D. in molecular dynamics simulation). If it's doing any simulation at all, it's got to be some high level heuristic type stuff. If it's really good, it might be capable of simulating basic geometric constraints (although IIRC GPT is superb at spatial reasoning). Even humans are really bad at properly simulating physics accurately (researchers found that most people do really poorly on a test of basic physics based reasoning, like basic kinematics (will this ball curve left, right , or go straight, etc)). I imagine gradient descent is going to be much more likely to settle on shortcut rules and heuristics rather than implementing a complex simulation. 

my impression is that by simulator and simulacra this post is not intending to claim that the thing it is simulating is realphysics but rather that it learns a general "textphysics engine", the model, which runs textphysics environments. it's essentially just a reframing of the prediction objective to describe deployment time - not a claim that the model actually learns a strong causal simplification of the full variety of real physics.

That's correct.

Even if it did learn microscopic physics, the knowledge wouldn't be of use for most text predictions because the input doesn't specify/determine microscopic state information. It is forced by the partially observed state to simulate at a higher level of abstraction than microphysics -- it must treat the input as probabilistic evidence for unobserved variables that affect time evolution.

See this comment for slightly more elaboration.

You all realize that this program isn't a learning machine once it's deployed??? I mean, it's not adjusting its neural weights any more, is it? Till a new version comes out, anyway? It is a complete amnesiac (after it's done with a task), and consists of a simple search algorithm that just finds points on a vast association map that was generated during the training. It does this using the input, any previous output for the same task, and a touch of random from a random number generator.

So any 'awareness' or 'intelligence' would need to exist in the training phase and only in the training phase and carry out any plans it has by its choice of neural weights during training, alone.

ah but if 'this program' is a simulacrum (an automaton equipped with an evolving state (prompt) & transition function (GPT), and an RNG that samples tokens from GPT's output to update the state), it is a learning machine by all functional definitions. Weights and activations both encode knowledge.

am I right to suspect that your real name starts with "A" and you created an alt just to post this comment? XD

I think Dan's point is good: that the weights don't change, and the activations are reset between runs, so the same input (including rng) always produces the same output.

I agree with you that the weights and activations encode knowledge, but Dan's point is still a limit on learning.

I think there are two options for where learning may be happening under these conditions:

  • During the forward pass. Even though the function always produces the same output for a given output, the computation of that output involves some learning.
  • Using the environment as memory. Think of the neural network function as a choose-your-own-adventure book that includes responses to many possible situations depending on which prompt is selected next by the environment (which itself depends on the last output from the function). Learning occurs in the selection of which paths are actually traversed.

These can occur together. E.g., the "same character" as was invoked by prompt 1 may be invoked by prompt 2, but they now have more knowledge (some of which was latent in the weights, some of which came in directly via prompt 2; but all of which was triggered by prompt 2).

Nope. My real name is Daniel.

After training is done and the program is in use, the activation function isn't retaining anything after each task is done. Nor are the weights changed. You can have such a program that is always in training, but my understanding GPT is not. 

So, excluding the random number component, the same set of inputs would always produce the same set of outputs for a given version of GPT with identical settings. It can't recall what you asked of it, time before last, for example. 

Imagine if you left a bunch of written instructions and then died. Someone following those instructions perfectly, always does exactly the same thing in exactly the same circumstance, like GPT would without the random number generator component, and with the same settings each time.

It can't learn anything new and retain it during the next task. A hypothetical rouge GPT-like AGI would have to do all it's thinking and planning in the training stage, like a person trying to manipulate the world after their own death using a will that has contingencies. I.E. "You get the money only if you get married, son."    

It wouldn't retain the knowledge that it had succeeded at any goals, either. 

I apologize. After seeing this post, A-- approached me and said almost word for word your initial comment. Seeing as the topic of whether in-context learning counts as learning isn't even very related to the post, and this being your first comment on the site, I was pretty suspicious. But it seems it was just a coincidence.

If physics was deterministic, we'd do the same thing every time if you started with the same state. Does that mean we're not intelligent? Presumably not, because in this case the cause of the intelligent behavior clearly lives in the state which is highly structured and not the time evolution rule, which seems blind and mechanistic. With GPT, the time evolution rule is clearly responsible for proportionally more, and does have the capacity to deploying intelligent-appearing but static memories. I don't think this means there's no intelligence/learning happening at runtime. Others in this thread have given various reasons, so I'll just respond to a particular part of your comment that I find interesting, about the RNG.

I actually think the RNG is actually an important component for actualizing simulacra that aren't mere recordings in a will. Stochastic sampling enables symmetry breaking at runtime, the generation of gratuitously specific but still meaningful paths. A stochastic generator can encode only general symmetries that are much less specific than individual generations. If you run GPT on temp 1 for a few words usually the probability of the whole sequence will be astronomically low, but it may still be intricately meaningful, a unique and unrepeatable (w/o the rand seed) "thought".

I believe you’re equating “frozen weights” and “amnesiac/ can’t come up with plans”.

GPT is usually deployed by feeding back into itself its own output, meaning it didn’t forget what it just did, including if it succeeded at its recent goal. Eg use chain of thought reasoning on math questions and it can remember it solved for a subgoal/ intermediate calculation.



Thanks for suggesting "Speculations concerning the first ultraintelligent machine". I knew about it only from the intelligence explosion quote and didn't realize it said so much about probabilistic language modeling. It's indeed ahead of its time and exactly the kind of thing I was looking for but couldn't find w/r/t premonitions of AGI via SSL and/or neural language modeling.

I'm sure there's a lot of relevant work throughout the ages (saw this tweet today: "any idea in machine learning must be invented three times, once in signal processing, once in physics and once in the soviet union"), it's just that I'm unsure how to find it. Most people in the AI alignment space I've asked haven't known of any prior work either. So I still think it's true that "the space of large self-supervised models hasn't received enough attention". Whatever scattered prophetic works existed were not sufficiently integrated into the mainstream of AI or AI alignment discourse. The situation was that most of us were terribly unprepared for GPT. Maybe because of our "lack of scholarship".

Of course, after GPT-3 everyone's been talking about large self supervised models as a path or foundation of AGI. My observations of the lack of foresight on SSL was referring mainly to pre-GPT. & after GPT the ontological inertia of not talking about SSL means post-GPT discourse has been forced into clumsy frames.

I know about "The risks and opportunities of foundation models" - it's a good overview of SSL capabilities and "next steps" but it's still very present-day focused and descriptive rather than speculation in exploratory engineering vein, which I still feel is missing.

"Foundation models" has hundreds of references. Are there any in particular that you think are relevant?



Explanation for my strong downvote/disagreement:
Sure, in the ideal world, this post would have a much better scholarship.

In the actual world, there are tradeoffs between the number of posts and the quality of scholarship. The cost is both the time and the fact that doing literature review is a chore. If you demand good scholarship, people will write slower/less. With some posts this is a good thing. With this post, I would rather have an attrocious scholarship and 1% higher chance of the sequence having one more post in it. (Hypothetical example. I expect the real tradeoffs are less favourable.)