Thanks for the reply. A couple remarks:
“indifference over infinite bitstrings” is a misnomer in an important sense, because it’s literally impossible to construct a normalized probability measure over infinite bitstrings that assigns equal probability to each one. What you’re talking about is the length weighted measure that assigns exponentially more probability mass to shorter programs. That’s definitely not an indifference principle, it’s baking in substantive assumptions about what’s more likely.
I don’t see why we should expect any of this reasoning about Turing machines to transfer over to neural networks at all, which is why I didn’t cast the counting argument in terms of Turing machines in the post. In the past I’ve seen you try to run counting or simplicity arguments in terms of parameters. I don’t think any of that works, but I at least take it more seriously than the Turing machine stuff.
If we’re really going to assume the Solomonoff prior here, then I may just agree with you that it’s malign in Christiano’s sense and could lead to scheming, but I take this to be a reductio of the idea that we can use Solomonoff as any kind of model for real world machine learning. Deep learning does not approximate Solomonoff in any meaningful sense.
Terminological point: it seems like you are using the term “simple” as if it has a unique and objective referent, namely Kolmogorov-simplicity. That’s definitely not how I use the term; for me it’s always relative to some subjective prior. Just wanted to make sure this doesn’t cause confusion.
(Didn’t consult Quintin on this; I speak for myself)
I flatly deny that our arguments depend on AGI being anything like an LLM. I think the arguments go through in a very wide range of scenarios, basically as long as we’re using some kind of white-box optimization to align them, rather than e.g. carrot-and-stick incentives or prompt engineering. Even if we only relied on prompt engineering, I think we’d be in a better spot than with humans (because we can run many controlled experiments).
I’m pretty confused by this claim. Why should we expect the human reward system to overwrite all secret desires? Also how do we know it’s not doing that? Your desires are just causal effects of a bunch of stuff including your reward circuitry.
This is just generally a pretty weak argument. You don’t seem to be contesting the fact that we have full sensory control for AI and we don’t have full sensory control for humans. It’s just a claim that this doesn’t matter. Maybe this ends up being a brute clash of intuitions, but it seems obvious to me that full sensory control matters a lot, even if the AI is doing a lot of long running cognition without supervision.
With AI we can choose to cut its reasoning short whenever we want, force it to explain itself in human language, roll it back to a previous state, etc. We just have a lot more control over this ongoing reasoning process for AIs and it’s baffling to me that you seem to think this mostly doesn’t matter.
You can just include online learning in your experimentation loop. See what happens when you let the AI online learn for a bit in different environments. I don’t think online learning changes the equation very much. It’s known to be less stable than offline RL, but that instability hurts capabilities as well as alignment, so we’d need a specific argument that it will hurt alignment significantly more than capabilities, in ways that we wouldn’t be able to notice during training and evaluation.
It just means we are directly updating the AI’s neural circuitry with white box optimizers. This will be true across a very wide range of scenarios, including (IIUC) your brain-like AGI scenario.
I don’t see why any of the differences you listed are relevant for safety.
I basically deny this, especially if you’re stipulating that it’s a “clean” distinction. Obviously folk psychology has a fuzzy distinction between beliefs and desires in it, but it’s also well-known both in common sense and among neuroscientists etc. that beliefs and desires get mixed up all the time and there’s not a particularly sharp divide.