As an analogy, it would be strange if psychology was a part of physics. Physicists would (I think) not be amused if their work literature became flooded with papers about the human psyche. Even if the underlying "hardware" is physics.
The newcomers with their strong opinions seem to have become confused because programmers hit APIs and think they're doing "AI".
Software architecture and neural network architecture are not synonyms.
IOW, an intellectually-linked discipline that drives so much revenue (and thus has so much work to be done in it) that it comprises its own field (about 75% of which is unique "applications-of-thing" problems).
I don't know that this particular aspect of the "AI revolution" needs some disruptive paradigm shift.
I'm hesitant to appropriate "engineer" into what we do. There are certainly people who work with code who earn that title. There are also many who don't.
I do think it would be healthy for people who work with ML to separate more decisively from people who work with general purpose code. There are enough unique problems and solutions in ML that a clear community would better serve the field's maturation.
As opposed to getting an endless summer of "Why don't you just" software developers fouling things up, because it's "similar".
Within a few years the fraction of papers in AI about new architectures, training, hyperparamter optimization, etc will be dwarfed by papers about things like controlnets, LoRA combinators, multi-model dispatch networks and few-shot embedding methods.
Popularity and status quo doesn't change the definition of the underlying theory. I will push back forever on some demotion of the importance of ML (i.e. theory) as distinct from some hype-driven notion of AI.
Then the focus should be to identify something like a fundamental unit of intelligence in order to formalize a higher-order science out of the foundations. An analogy can be drawn between physics and chemistry: we needed to properly identify "the atom" and its component parts to get anywhere with the science of chemistry. But it took a whole lot of physics to get to that point. It seems similar with the ML-cum-AI transition where we'll still need to dig very deep into statistics and information theory before being able to abstract them away in favor of higher-order concepts.
To me it seems we're really far from anything like that yet. Like at least a couple decades if not more. Friston's got some cool ideas that make me think he may have his name on some stuff later on but again the theory on learning systems is barely getting started.
Doesn't that support the argument even more? CS and math share a lot more then CS and AI, yet CS and math are different disciplines.
Neural nets came from biology and rely on math.
The art of application
Of course, computing is closer to pure math than applied maths. Computing is applied pure maths, perhaps.
The majority of what computer science practitioners do is general purpose coding, which doesn't have nearly as much to do with the underlying math.
Or, in other words, how proficient would an applied math person be at building a front end? And how many of their skills would they be able to leverage?
That's the overlap, or lack thereof.