>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)
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.
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.
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.
1 - https://www.drools.org 2- https://en.wikipedia.org/wiki/CLIPS