A decent workmanlike overview. If I had to criticize: Reducing things to quasi-orthogonal categories (here quality and scope) is a human habit that's good for

A decent workmanlike overview. If I had to criticize: Reducing things to quasi-orthogonal categories (here quality and scope) is a human habit that's good for learning and characterizing, but perhaps not for accuracy. I would instead have hoped for some algorithmic categories (math, semantics, reasoning (deductive, inductive, probabilistic/weighted...), evaluation (summarization, synthesis), interactivity, etc. That seems closer to the creature's natural joints (to use Plato's phrase). "Emergence" is mentioned as prior art, but is otherwise largely ignored. To me, that's a feature that distinguishes intelligence from calculation/algorithm. (That it's hard to operationalize makes it no less key.) Usability is only mentioned as part of UI for autonomy for hybrid human-AI systems. But the overall question is about measuring AGI for purposes of assessing utility and deployment risk. To me, usability is the metric that determines how valuable AGI would be, in broadly-available applications. Its relative unimportance suggests that people are mainly concerned about AI's use in highly-targeted (if also not highly-leveraged) applications, like market or election influence. Finally, the notion of "progress" itself is a somewhat charged but relatively unnecessary concept. I believe the goal is to adopt the lens that will highlight effectiveness in the high-value and high-risk applications (something like heightened scrutiny in legal cases). In that case, we'd enumerate the value and fault models before deciding on principles and constraining the ontology.