Yes I remember from university in the literature these were referred to as Expert Systems. Vertical AI seems to be a new rebranding for new wave of more advance expert systems.
I don't see these things as equal, but I see where you are going with the comparison. Both require an understanding of a specific domain and problem(s) that need to be solved in that domain.
I really think striving to find specific problems where AI can add value is critical to making money in the space, and I agree with this article strongly.
The term "AI" is so overused that it can mean anything. Expert systems are considered "AI" for that matter. They may not necessarily use the most computationally expensive methods like neural networks, but "AI" doesn't prescribe any specific technique.
I can see expert systems as hybrids of deep learning and predicate logic. For example: how to boil an egg: find egg carton in refrigerator (train deep neural net to recognize egg cartons). Find expiration date ( deep neural net ocr ). If egg carton expired, discard and terminate and alert. Otherwise continue etc.
Some ML applications will replicate expert behavior (e.g. document discovery in law) or improve on it (quant trading). A lot of the gains will be at the other end of the skill spectrum though.
Universal human abilities like speech and face recognition have made some low-skill jobs harder to automate than expert ones. We've only just invented algorithms that solve those problems adequately. If your vertical ML application is a machine that picks fruit when it's ripe, it's a stretch to call it an expert system.
How does the machine recognize fruit? It must have some training data set to classify what is fruit and what is not, and which are ripe. Humans may be "experts" at simple stuff like this. One cannot easily generalize it to a machine that also harvests potatoes, without providing "expert" knowledge about potatoes.
The brain itself is also a collection of specialized systems - you could call them expert systems, connected at the middle with reinforcement learning.
I'd suggest that there is more to it (hopefully) than an Expert System. Expert System defines the AI, but my take-away from the write-up (possibly adding my own thought here) is that the author was looking at a complete system, which is possible due to an Expert System, but which incorporates a user vertical, which would likely include UX, sales and marketing, etc.
Expert Systems of the past had user interfaces, though crude ones such as Java applets and basic HTML forms. UI evolves too fast and new means of HCI emerged like VR/AR, IoT, etc. Most likely the human interaction will have to be made by humans, but the more fundamental advancements can and will be automated. Being a "front-end engineer" on whatever platform, be it web, mobile, embedded, seems to be a pretty stable career in the future, at least for now.
Nope, drools [1] is a Java Expert System. It's fairly old now, but it's maintained and advanced.
Heck, I used PHP to interface with CLIPS [2] to provide a simple suggestion engine for a college project back in 2002. It had a simple wizard flow where it would ask a few questions, shell out to CLIPS for the next series of questions, do that for a bit, and return a suggested product.
>a piece of software which uses databases of expert knowledge to offer advice or make decisions.
The methods of inference have improved from predicate logic to statistics and "machine learning" now that computers have gotten much faster.
(I'm bootstrapping an expert system myself)