...Distinguish two types of shutdown goals: temporary and permanent. These types of goals may differ with respect to entrenchment. AGIs that seek temporary shutdown may be incentivized to protect themselves during their temporary shutdown. Before shutting down, the AGI might set up cyber defenses that prevent humans from permanently disabling it while ‘asleep’. This is especially pressing if the AGI has a secondary goal, like paperclip manufacturing. In that case, protection from permanent disablement increases its expected goal satisfaction. On the other han
Another related work: Concept Algebra for Text-Controlled Vision Models (Discloser: while I did not author this paper, I am in the PhD lab who did, under Victor Veitch at UChicago. Any mistakes made in this comment are my own). We haven't prioritized a blog post about the paper so it makes sense that this community isn't familiar with it.
The concept algebra paper demonstrates that for text-to-image models like Stable Diffusion, there exist linear subspaces in the score embedding space, on which you can do the same manner of concept editing/control as...
Also, just to make sure we share a common understanding of Schölkopf 2021: Wouldn't you agree that asking "how do we do causality when we don't even know what level abstraction on which to define causal variables?" is beyond the "usual pearl causality story" as usually summarized in FFS posts? It certainly goes beyond Pearl's well-known works.
I don't think my claim is that "FFS is already subsumed by work in academia": as I acknowledge, FFS is a different theoretical framework than Pearl-based causality. I view them as two distinct approaches, but my claim is that they are motivated by the same question (that is, how to do causal representation learning).
It was intentional that the linked paper is an intro survey paper to the Pearl-ish approach to causal rep. learning: I mean to indicate that there are already lots of academic researchers studying the question "what does it mean to ...
Strongly upvoting this for being a thorough and carefully cited explanation of how the safety/alignment community doesn't engage enough with relevant literature from the broader field, likely at the cost of reduplicated work, suboptimal research directions, and less exchange and diffusion of important safety-relevant ideas
Ditto. I've recently started moving into interpretability / explainability and spent the past week skimming the broader literature on XAI, so the timing of this carefully cited post is quite impactful for me.
I see similar things happening...
Under the "reward as selection" framing, I find the behaviour much less confusing:
- We use reward to select for actions that led to the agent reaching the coin.
- This selects for models implementing the algorithm "move towards the coin".
- However, it also selects for models implementing the algorithm "always move to the right".
- It should therefore not be surprising you can end up with an agent that always moves to the right and not necessarily towards the coin.
I've been reconsidering the coin run example as well recently from a causal perspective, and your ...
I'd be interested in seeing other matrix factorizations explored as well. Specifically, I would recommend trying nonnegative matrix factorizations: to quote the Wikipedia article:
This non-negativity makes the resulting matrices easier to inspect. Also, in applications such as processing of audio spectrograms or muscular activity, non-negativity is inherent to the data being considered.
The added constraint may help eliminate spurious patterns: for instance, I suspect the positive/negative singular value distinction might be a red herring (based on past projects I've worked on).
[Warning: "cyclic" overload. I think in this post it's referring to the dynamical systems definition, i.e. variables reattain the same state later in time. I'm referring to Pearl's causality definition: variable X is functionally dependent on variable Y, which is itself functionally dependent on variable X.]
Turns out Chaos is not Linear...
I think the bigger point (which is unaddressed here) is that chaos can't arise for acyclic causal models (SCMs). Chaos can only arise when there is feedback between the variables right? Hence the characterization of chaos...
As I understand it, the proof in the appendix only assumes we're working with Bayes nets (so just factorizations of probability distributions). That is, no assumption is made that the graphs are causal in nature (they're not necessarily assumed to be the causal diagrams of SCMs) although of course the arguments still port over if we make that stronger assumption.
Is that correct?
Could you clarify how this relates to e.g. the PC (Peter-Clark) or FCI (Fast Causal Inference) algorithms for causal structure learning?
Like, are you making different assumptions (than e.g. minimality, faithfulness, etc)?