I recently realized that meaning cannot be done using pattern matching only, which is what (current) machine learning is about. The key insight came from artic

I recently realized that meaning cannot be done using pattern matching only, which is what (current) machine learning is about. The key insight came from article on bullshit: https://news.ycombinator.com/item?id=17764348 https://news.ycombinator.com/item?id=17764348 I believe understanding (things having meaning) comes down to whether you can produce an example from the given description of the situation. If you can produce an example, then you can say you understand the description. The problem is, you can have situation descriptions that are contradictory (have no examples, thus have no meaning) yet they are arbitrarily close to situation descriptions that have examples (and so have meaning). A good example are those Escher-like impossible objects, which look very much like real objects, but humans can easily see they are meaningless (they cannot be interpreted, and thus imagined, in 3D). Another good examples are sentences from the above paper. The bullshit sentences are the ones for which you cannot construct a mental example in your head. I suspect this happens for the famous flaws in deep learning as well, the deep learning network cannot learn that some inputs are contradictory. I believe that this is actually ultimately related to the boolean satisfiability problem. In theory, one could determine whether some learning agent is only using pattern matching or is actually having understanding (is able to recognize logical inconsistency in the input) by teaching it different SAT instances, and the agent that would be able to learn the difference between arbitrarily similar SAT instances with and without solutions could be considered to have understanding.