I mean, we ground that in external verification (or, use math, which in theory is accessible to everyone, but also has an "empirical" loop now via lean).
I'm not saying AI has remotely solved this problem, not even close. But plenty of philosophy, especially in the 1950s+, from analytic philosophers that these kinds of statements aren't coherent.
Symbolic and formal AI has failed as a general path to intelligence, heuristic bash with deep learning won. There needs to be some kind of actual breakthrough on the actual structure of how we formalize things - we can either describe processes and just let them run (physics, CS), or verify well (math), but if you look at any messy, real-life domain, how "knowledge" is encoded is a bajillion heuristics that would not really be the "rules" of the domain. The heuristics being linear algebra rather than SAT equations doesn't change how bad the "knowledge" is compared to what humans idealize as "knowledge" or even empirically, the kinds of things humans cut at for "knowledge".
I'm not saying AI has remotely solved this problem, not even close. But plenty of philosophy, especially in the 1950s+, from analytic philosophers that these kinds of statements aren't coherent.
Symbolic and formal AI has failed as a general path to intelligence, heuristic bash with deep learning won. There needs to be some kind of actual breakthrough on the actual structure of how we formalize things - we can either describe processes and just let them run (physics, CS), or verify well (math), but if you look at any messy, real-life domain, how "knowledge" is encoded is a bajillion heuristics that would not really be the "rules" of the domain. The heuristics being linear algebra rather than SAT equations doesn't change how bad the "knowledge" is compared to what humans idealize as "knowledge" or even empirically, the kinds of things humans cut at for "knowledge".