I’d say there’s a meaningful distinction between literalism and what I’m advocating. I’m not arguing for rigid formalism or abandoning all metaphor. I’m calling for clarity, accessibility, and a prioritisation of core arguments, especially when communicating with people outside the field.
Your first critique concerns my statement that “Current utility functions do not optimise toward values in which humans are treated as morally valuable.” I agree this could have been phrased more precisely, for example: “Current AI systems are not necessarily trained to pursue goals that treat humans as morally valuable.” That’s a fair point. I was using “utility function” loosely to refer to optimisation objectives (e.g., loss functions, reward signals) not in the strict agent-theoretic sense.
But the purpose of my post isn’t to adjudicate whether the conclusions drawn by Yudkowsky and others are true or not. I fully acknowledge that the arguments rest on assumptions and that there’s room for serious debate about their validity. I should (and probably will) think more about that (after my exams).
What I am addressing in this post is a communication issue. Even if we accept the core arguments about the risks of developing powerful misaligned AI systems, such as those based on instrumental convergence and the orthogonality thesis, I believe these risks are often communicated in ways that obscure rather than clarify. This is particularly true when metaphors become the primary framing, which can confuse people who are encountering these ideas for the first time.
So to clarify: I’m not trying to resolve the epistemic status of AI risk claims. I’m making a narrower point about how they’re presented, and how this presentation may hinder public understanding or uptake. That’s the focus of the post.
I’d say there’s a meaningful distinction between literalism and what I’m advocating. I’m not arguing for rigid formalism or abandoning all metaphor. I’m calling for clarity, accessibility, and a prioritisation of core arguments, especially when communicating with people outside the field.
Your first critique concerns my statement that “Current utility functions do not optimise toward values in which humans are treated as morally valuable.” I agree this could have been phrased more precisely, for example: “Current AI systems are not necessarily trained to pursue goals that treat humans as morally valuable.” That’s a fair point. I was using “utility function” loosely to refer to optimisation objectives (e.g., loss functions, reward signals) not in the strict agent-theoretic sense.
But the purpose of my post isn’t to adjudicate whether the conclusions drawn by Yudkowsky and others are true or not. I fully acknowledge that the arguments rest on assumptions and that there’s room for serious debate about their validity. I should (and probably will) think more about that (after my exams).
What I am addressing in this post is a communication issue. Even if we accept the core arguments about the risks of developing powerful misaligned AI systems, such as those based on instrumental convergence and the orthogonality thesis, I believe these risks are often communicated in ways that obscure rather than clarify. This is particularly true when metaphors become the primary framing, which can confuse people who are encountering these ideas for the first time.
So to clarify: I’m not trying to resolve the epistemic status of AI risk claims. I’m making a narrower point about how they’re presented, and how this presentation may hinder public understanding or uptake. That’s the focus of the post.