Thank you for your honesty and there's no need to apologise.
Let me explain my point above, if I can. You said that search, planning, expert systems, etc, are "not AI, and they never will be". I understand that as saying that such systems are not artificial intelligences, in the sense of an intelligent machine created by humans out of whole cloth (without trying to define what "intelligent" means).
That is certainly true, but it is also uncontroversial that the above are sub-fields of the field of research that is known as "AI". That is, there is a field that researches approaches that could lead to the creation of intelligent machines and that is called "AI" and then there's the ultimate target of that field which is to create "AI".
My original comment bemoans the fact that in some sectors, "AI", as a field of research, has become synonymous with only one sub-sub-field of AI research, that is, deep learning.
Contrary to what you state, these "GOFAI" fields (symbolic AI, if you will) are still active and far from having "failed" in any way, they are "SOTA" in their respective tasks. For example, the field of automated theorem proving is dominated by systems that employ the resolution rule, a logic-based approach and while recent efforts have been made to make inroads into theorem proving with deep neural nets (e.g. a recent attempt to use transformers) results are still very far from being comparable to the traditional approaches. I know more about automated theorem proving that I know e.g. about planning or search (because my PhD is in a subject close to the former) but my understanding is that in those fields too, traditional techniques dominate- which is why research in them is still active.
If I am permitted to tout my own horn, my broader subject area can be described as "program learning", i.e. machine learning of arbitrary programs from examples of their inputs and outputs. In this area also, deep learning systems are hopelessly outclassed by symbolic approaches, not least because these approaches learn precise representations of target programs (rather than approximations) from a mere handful of examples (four or five, etc).
And so on. These are just some examples of AI research that is ongoing, that is not deep learning, and that is state of the art for its respective tasks. In view of that, I consider that using "AI" to mean "deep learning" (as the article above does) is not only misinformed but also misinforming and actively harmful. In the sense of harming people's understanding, that is.
As to your comment about how GOFAI "failed", I'm afraid this opinion, which is common, is also misinformed. Here, too, a knowledge of the history of the field helps, but to summarise, the last winter happened because of strictly political reasons and for no reason that had anything to do with the scientific merits, or practical successes of the relevant approaches. In fact, expert systems, now widely considered a failure, were one of the first big success stories of AI; a success story that was cut short only because, again, of political reasons.
I could talk about the AI winter subject for hours (it's a favourit subject of mine) but a good starting point is this article by the editor of the IEEE journal Intelligent Systems: (Avoiding Another Winter) https://www.computer.org/csdl/magazine/ex/2008/02/mex2008020.... The wikipedia page on https://en.wikipedia.org/wiki/AI_winter also has a decent summary and some sources. Finally, see the wikipedia article on the Lighthill Report https://en.wikipedia.org/wiki/Lighthill_report which is more relevant to AI research in the UK (the Report killed AI research in the UK, dead) and this review of the report by John McCarthy: http://www-formal.stanford.edu/jmc/reviews/lighthill/lighthi... (man who coined the term AI and also created LISP on the side).
>> As much as you argue I should adopt the subject's history, I'm saying you should adopt its present.
More to the point, I'm recommending that you should know a subject's history before forming a strong opinion about its present and future. As to me, I'm firmly rooted to the present. About half of the literature I read is neural networks- and that's not even the subject of my research. But if you think about it, in the era where the trend is to use deep learning, the most interesting results can only come from not using deep learning. In a gold rush, if everyone is digging up Widow's Creek, then Widow's Creek is the last place to dig for gold.