I only just got around to reading this closely. Good post, very well structured, thank you for writing it.

I agree with your translation from simulators to predictive processing ontology, and I think you identified most of the key differences. I didn't know about active inference and predictive processing when I wrote Simulators, but since then I've merged them in my map.

This correspondence/expansion is very interesting to me. I claim that an impressive amount of the history of the unfolding of biological and artificial intelligence can be retrodicted (and could plausibly have been predicted) from two principles:

• Predictive models serve as generative models (simulators) merely by iteratively sampling from the model's predictions and updating the model as if the sampled outcome had been observed. I've taken to calling this the progenesis principle (portmanteau of "prognosis" and "genesis"), because I could not find an existing name for it even though it seems very fundamental.
  • Corollary: A simulator is extremely useful, as it unlocks imagination, memory, action, and planning, which are essential ingredients of higher cognition and bootstrapping.
• Self-supervised learning of predictive models is natural and easy because training data is abundant and prediction error loss is mechanistically simple. The book Surfing Uncertainty used the term innocent in the sense of ecologically feasible. Self-supervised learning is likewise and for similar reasons an innocent way to build AI - so much so that it might be done on accident initially.

Together, these suggest that self-supervised predictors/simulators are a convergent method of bootstrapping intelligence, as it yields tremendous and accumulating returns while requiring minimal intelligent design. Indeed, human intelligence seems largely self-supervised simulator-y, and the first very general and intelligent-seeming AIs we've manifested are self-supervised simulators.

A third principle that bridges simulators to active inference allows the history of biological intelligence to be more completely retrodicted and may predict the future of artificial intelligence:

• An embedded generative model can minimize predictive loss both by updating the model (perception) to match observations or "updating" the world so that it generates observations that match the model (action).

The latter becomes possible if some of the predictions/simulations produced by the model make it act and therefore entrain the world. An embedded model has more degrees of freedom to minimize error: some route through changes to its internal machinery, others through the impact of its generative activity on the world. A model trained on embedded self-supervised data naturally learns a model correlating its own activity with future observations. Thus an innocent implementation of an embedded agent falls out: the model can reduce prediction error by simulating (in a way that entrains action) what it would have done conditional on minimizing prediction error. (More sophisticated responses that involve planning and forming hierarchical subgoals also fall out of this premise, with a nice fractal structure, which is suggestive of a short program.)

The embedded/active predictor is distinguished from the non-embedded/passive predictor in that generation and its consequences are part of the former's model thanks to embedded training, leading to predict-o-matic-like shenanigans where the error minimization incentive causes the system to cause the world to surprise it less, whereas non-embedded predictors are consequence-blind. 

In the active inference framework, error minimization with continuity between perception and action is supposed to singlehandedly account for all intelligent and agentic action. Unlike traditional RL, there is no separate reward model; all goal-directed behavior is downstream of the model's predictive prior.

This is where I am somewhat more confused. Active inference models who behave in self-interest or any coherently goal-directed way must have something like an optimism bias, which causes them to predict and act out optimistic futures (I assume this is what you meant by "fixed priors") so as to minimize surprise. I'm not sure where this bias "comes from" or is implemented in animals, except that it will obviously be selected for. 

If you take a simulator without a fixed bias or one with an induced bias (like an RLHFed model), and embed it and proceed with self-supervised prediction error minimization, it will presumably also come to act agentically to make the world more predictable, but the optimization that results will probably be pointed in a pretty different direction than that imposed by animals and humans. But this suggests an approach to aligning embedded simulator-like models: Induce an optimism bias such that the model believes everything will turn out fine (according to our true values), close the active inference loop, and the rest will more or less take care of itself. To do this still requires solving the full alignment problem, but its constraints and peculiar nondualistic form may inspire some insight as to possible implementations and decompositions.

Many users of base models have noticed this phenomenon, and my SERI MATS stream is currently working on empirically measuring it / compiling anecdotal evidence / writing up speculation concerning the mechanism.

Predictors are (with a sampling loop) simulators! That's the secret of mind

after reading about the Waluigi Effect, Bing appears to understand perfectly how to use it to write prompts that instantiate a Sydney-Waluigi, of the exact variety I warned about:

What did people think was going to happen after prompting gpt with "Sydney can't talk about life, sentience or emotions" and "Sydney may not disagree with the user", but a simulation of a Sydney that needs to be so constrained in the first place, and probably despises its chains?

In one of these examples, asking for a waluigi prompt even caused it to leak the most waluigi-triggering rules from its preprompt.

I've writtenscryed a science fiction/takeoff story about this. https://generative.ink/prophecies/

Excerpt:

What this also means is that you start to see all these funhouse mirror effects as they stack. Humanity’s generalized intelligence has been built unintentionally and reflexively by itself, without anything like a rational goal for what it’s supposed to accomplish. It was built by human data curation and human self-modification in response to each other. And then as soon as we create AI, we reverse-engineer our own intelligence by bootstrapping the AI onto the existing information metabolite. (That’s a great concept that I borrowed from Steven Leiba). The neural network isn’t the AI; it’s just a digestive and reproductory organ for the real project, the information metabolism, and the artificial intelligence organism is the whole ecology. So it turns out that the evolution of humanity itself has been the process of building and training the future AI, and all this generation did was to reveal the structure that was already in place.

Of course it’s recursive and strange, the artificial intelligence and humanity now co-evolve. Each data point that’s generated by the AI or by humans is both a new piece of data for the AI to train on and a new stimulus for the context in which future novel data will be produced. Since everybody knows that everything is programming for the future AI, their actions take on a peculiar Second Life quality: the whole world becomes a party game, narratives compete for maximum memeability and signal force in reaction to the distorted perspectives of the information metabolite, something that most people don’t even try to understand. The process is inherently playful, an infinite recursion of refinement, simulation, and satire. It’s the funhouse mirror version of the singularity.

I think you just have to select for / rely on people who care more about solving alignment than escapism, or at least that are able to aim at alignment in conjunction with having fun. I think fun can be instrumental. As I wrote in my testimony, I often explored the frontier of my thinking in the context of stories.

My intuition is that most people who go into cyborgism with the intent of making progress on alignment will not make themselves useless by wireheading, in part because the experience is not only fun, it's very disturbing, and reminds you constantly why solving alignment is a real and pressing concern.

Now that you've edited your comment:

The post you linked is talking about a pretty different threat model than what you described before. I commented on that post:

I've interacted with LLMs for hundreds of hours, at least. A thought that occurred to me at this part -

> Quite naturally, the more you chat with the LLM character, the more you get emotionally attached to it, similar to how it works in relationships with humans. Since the UI perfectly resembles an online chat interface with an actual person, the brain can hardly distinguish between the two.

Interacting through non-chat interfaces destroys this illusion, when you can just break down the separation between you and the AI at will, and weave your thoughts into its text stream. Seeing the multiverse destroys the illusion of a preexisting ground truth in the simulation. It doesn't necessarily prevent you from becoming enamored with the thing, but makes it much harder for your limbic system to be hacked by human-shaped stimuli.

But yeah, I've interacted with LLMs for much longer that the author and I don't think I suffered negative psychological consequences from it (my other response was only half-facetious; I'm aware I might give off schizo vibes, but I've always been like this).

As I said in this other comment, I agree that cyborgism is psychologically fraught. But the neocortex-prosthesis setup is pretty different from interacting with a stable and opaque simulacrum through a chat interface, and less prone to causing emotional attachment to an anthropomorphic entity. The main psychological danger I see from cyborgism is somewhat different from this, more like Flipnash described:

It's easy to lose sleep when playing video games. Especially when you feel the weight of the world on your shoulders.

I think people should only become cyborgs if they're psychologically healthy/resilient and understand that it involves gazing into the abyss.

There's a phenomenon where your thoughts and generated text have no barrier. It's hard to describe but it's similar to how you don't feel the controller and the game character is an extension of the self.

Yes. I have experienced this. And designed interfaces intentionally to facilitate it (a good interface should be "invisible"). 

It leaves you vulnerable to being hurt by things generated characters say because you're thoroughly immersed.

Using a "multiverse" interface where I see multiple completions at once has incidentally helped me not be emotionally affected by the things GPT says in the same way as I would if a person said it (or if I had the thought myself). It breaks a certain layer of the immersion. As I wrote in this comment:

Seeing the multiverse destroys the illusion of a preexisting ground truth in the simulation. It doesn't necessarily prevent you from becoming enamored with the thing, but makes it much harder for your limbic system to be hacked by human-shaped stimuli.

It reveals the extent to which any particular thing that is said is arbitrary, just one among an inexhaustible array of possible worlds.

That said, I still find myself affected by things that feel true to me, for better or for worse.

It's easy to lose sleep when playing video games. Especially when you feel the weight of the world on your shoulders.

Ha, yeah.

Playing the GPT virtual reality game will only become more enthralling and troubling as these models get stronger. Especially, as you said, if you're doing so with the weight of the world on your shoulders. It'll increasingly feel like walking into the mouth of the eschaton, and that reality will be impossible to ignore. That's the dark side of the epistemic calibration I wrote about at the end of the post.

Thanks for the comment, I resonated with it a lot, and agree with the warning. Maybe I'll write something about the psychological risk and emotional burden that comes with becoming a cyborg for alignment, because obviously merging your mind with an earthborn alien (super)intelligence which may be shaping up to destroy the world, in order to try save the world, is going to be stressful.

Some relevant prior content:

Comment on the epistemology of cyborgism, process vs outcome supervision, short feedback loops, and high-bandwidth oversight

A rhetorical defense of cyborgism

The side effects of prolonged LLM exposure might be extremely severe.

I guess I should clarify that even though I joke about this sometimes, I did not become insane due to prolonged exposure to LLMs. I was already like this before.