Thanks for the question.
In the paper "A proof of the impossibility of inductive probability." by Popper and Miller, they demonstrated that the truth of a theory cannot be supported by observation.
The knowledge in AI models is created through a kind of induction (the knowledge is a generalization of observations). AI models lack discrete theory statements, as in Bayesian reasoning and in the Popper/Miller critique; the knowledge structure is continuous.
Therefore, your comment does not point to a contradiction. It does, however, point to the problem I am trying to express: that modern AI methods cannot produce a coherent deductive knowledge structure. I try to communicate an alternate method.
This is my first post on LessWrong and I apologize for the length. I thought it would be possible that someone here is interested in reading or critiquing it.
The blog post is my attempt to explain why we do not yet have AGI and a possible short path for getting there. The ideas are extrapolated from an interpretation of Karl Popper and David Miller's critique of inductive probability. My view is that a world model composed of formal statements (theories) can only be constrained by observation (including observations in the form of induction); theories cannot be supported by evidence, they can only be consistent or not.
I try to clearly define two categories... (read more)
I have not found any persuasive rebuttals of Popper's argument. If you have found one that has convinced you, I am interested to know it.