Sorted by New


Transparency and AGI safety

Thanks Rohin! Agree with and appreciate the summary as I mentioned before. 

 I don’t agree with motivation 1 as much: if I wanted to improve AI timeline forecasts, there are a lot of other aspects I would investigate first. (Specifically, I’d improve estimates of inputs into <@this report@>(@Draft report on AI timelines@).) Part of this is that I am less uncertain than the author about the cruxes that transparency could help with, and so see less value in investigating them further.

I'm curious: does this mean that you're on board with the assumption in Ajeya’s report that 2020 algorithms and datasets + "business as usual" in algorithm and dataset design will scale up to strong AI, with compute being the bottleneck? I feel both uncertain about this assumption and uncertain about how to update on it one way or the other. (But this probably belongs more in a discussion of that report and is kind of off topic here.)

  • The "alien in a box" hypothetical made sense to me (mostly), but I didn't understand the "lobotomized alien" hypothetical. I also didn't see how this was meant to be analogous to machine learning. One concrete question: why are we assuming that we can separate out the motivational aspect of the brain? (That's not my only confusion, but I'm having a harder time explaining other confusions.)

A more concrete version of the “lobotomized alien" hypothetical might be something like this: There’s this neuroscience model that sometimes gets discussed around here that human cognition works by running some sort of generative model over the neocortex, with a loss function that's modulated by stuff going on in the midbrain (see e.g. https://www.lesswrong.com/posts/diruo47z32eprenTg/my-computational-framework-for-the-brain). Suppose that you buy this theory, and now suppose that we’re the AIs being trained in a simulation by a more advanced alien race. Then one way that the aliens could try to get us to do stuff for them might be to reinstantiate just a human neocortex and train it from scratch on a loss function + dataset of their choice, as some sort of souped-up unsupervised learning algorithm.

In this example, I’m definitely just assuming by fiat that the cognition and motivation parts of the brain are well-separated (and moreover, that the aliens are able to discover this, say by applying some coarse-grained transparency tools). So it’s just a toy model for how things *could* go, not necessarily how they *will* go.

  • It feels like your non-agentic argument is too dependent on how you defined "AGI". I can believe that the first powerful research accelerator will be limited to language, but that doesn't mean that other AI systems deployed at the same time will be limited to language.

Hmm. I think I agree that this is a weak point of the argument and it's not clear how to patch it. I think I had some intuition like, even once we have some sort of pretrained AGI algorithm (like an RL agent trained in simulation), we would have to fine-tune it on real-world tasks one at a time by coming up with a curriculum for each of those tasks; this seems easier to do for simple bounded tasks than for more open-ended ones (though in some sense that needs to be made more precise, and is maybe already assuming some things about alignment); and "research acceleration" seems like a much narrower task with a relatively well-defined training set of papers, books, etc. than "AI agent that competently runs a company", so might still come first on those grounds. But even then it would have to come first by a large enough margin for insights from the research accelerator to actually be implemented, for this argument to work. So there's at least a gap there...

  • It seems like there's a pretty clear argument for language models to be deceptive -- the "default" way to train them is to have them produce outputs that humans like; this optimizes for being convincing to humans, which is not necessarily the same as being true. (However, it's more plausible to me that the first such model won't cause catastrophic risk, which would still be enough for your conclusions.)

Yeah, fair enough. I should have said that I don't see a path for language models to get selection pressure in the direction of being catastrophically deceptive like in the old "AI getting out of the box" stories, so I think we agree.