One of the most pleasing things about probability and expected utility theory is that there are many coherence arguments that suggest that these are the “correct” ways to reason. If you deviate from what the theory prescribes, then you must be executing a dominated strategy. There must be some other strategy that never does any worse than your strategy, but does strictly better than your strategy with certainty in at least one situation. There’s a good explanation of these arguments here.

We shouldn’t expect mere humans to be able to notice any failures of coherence in a superintelligent agent, since if we could notice these failures, so could the agent. So we should expect that powerful agents appear coherent to us. (Note that it is possible that the...

There has been a lot of discussion recently of policy proposals around monitorability, such as Redwood's proposal and my counter-proposal. I'd like to say a bit more generally about how I'm thinking about monitorability. Note that nothing here is an official Anthropic position; these are just my personal thoughts.

First, a categorization. I currently think about four different types of monitorability:

1. Control Monitorability: the extent to which Control and monitoring techniques would likely catch catastrophic sabotage actions, like escaping a sandbox or setting up a rogue internal deployment. We can measure this via reference to sabotage evaluations like LinuxArena or any evaluation that measures the model’s capability to take dangerous actions like escaping a sandbox while looking to a monitor like it's doing some innocuous task (like a cyber

Context: I created this to share before the start of courses I facilitate to provide a refresher on recent events, but also as a hook to draw students into engaging with some of ideas we think about and debate in AI safety early. I expect the topics I cover in my “Setting the Scene” post to change frequently, so this is less polished than some of my other content. I emphasise to students that these posts are to be considered as bonus content—not core curriculum content—and that as such they are really just there for students who want to go deeper, before or after the course. I also provide a lot of links; this is to make it easy for students to follow up anything that catches...

[Epistemic status: somewhat lower confidence based on the fact that I haven't worked out a detailed theory along the lines I've suggested, yet.]

I've felt like the problem of counterfactuals is "mostly settled" (modulo some math working out) for about a year, but I don't think I've really communicated this online. Partly, I've been waiting to write up more formal results. But other research has taken up most of my time, so I'm not sure when I would get to it.

So, the following contains some "shovel-ready" problems. If you're convinced by my overall perspective, you may be interested in pursuing some of them. I think these directions have a high chance of basically solving the problem of counterfactuals (including logical counterfactuals).

Another reason for posting this rough write-up is...

Suppose we have an AI CEO, ruthlessly devoted to maximising shareholder value. Fortunately, we haven't been completely stupid, and have given it a rich definition of shareholder value, encompassing the success, flourishing, safety, preference satisfaction, and moral values of the shareholders, not just the stock price. So it won't destroy the company - or the world - to build a giant stock-ticker with a swiftly increasing number on it.

This rich definition is contained in a sophisticated LLM, able to analyse the current situation and answer any question about the objective. The LLM is an integral part of the AI's system and motivation, not something it can simply set aside or hack directly.

Unfortunately, as discussed, there are going to be zones in the space of possible situations where...