I think the only time a system can be truly be said to understand something is when its answers are derived from logic (such as old school symbolic AI). No matt

I think the only time a system can be truly be said to understand something is when its answers are derived from logic (such as old school symbolic AI). No matter how good current statistical approaches get, they won't meet that bar. However, I do believe we see evidence of approximate logical reasoning in these models, as well as the concept of abstraction. Furthermore we can take statements generated with statistical techniques and validate them mechanically with older techniques. This is basically what recent work in automated theorem proving using deep learning is about. Generating logical statements using heuristics and then validating them mechanically also sounds like a reasonable approximation of what a human often does, speaking as a human.