I can totally see where you are coming from and I like the wink and nod to the article!
I guess...hrm... if someone asked me what classical AI was today... I would answer with something like A*, decision trees, or Markov model-style algorithms (but never DFS). All of these have 'worked' for a long time but still feel like classical (ie no back-prop) AI.
To your second point, I'd be the first to admit I'm no expert (I don't do any ML professionally). In my limited understanding, it feels odd to separate MLPs from 'modern' AI. It's all still back-prop, yeah? I get that universal approximators and (idealized) Turing machine approximators like LSTMs are old hat. But Attention-based (key, value) query models just seem like the next step on the same ladder of "increasingly complex computing paradigms we've figured out a way to back-prop through."
Not something uniquely separate. First, we could do functions, then we could do Turing machines, and now we can do data queries.
But again, I'm probably just showing how green I am. I would, time permitting, honestly appreciate clarification/correction.