>> Sorry, I did misunderstand what you were saying.

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.