Alternately, we need formal language to talk about these concepts or we risk another "evolution is just a theory."
Having information does not imply having knowledge of it. For example, an engineer may not know which material can withstand a given environment (temperature, corrosion, pressure, etc) but they may be able to look it up in a book and select the appropriate material.
Knowledge is similar, except it's within the mind. For example, an engineer may know that the thermal coefficients for steel and concrete are similar, so the expected deformation under temperature change should be similar. But this doesn't mean that they necessarily understand why these two materials have similar coefficients, only that they do.
Understanding is a matter of cognitive appreciation of the underlying situation and is built around the parameters of a situation. For example, someone drowning understands that they need to grab something buoyant in order to breath. It's built on a base of knowledge, but it doesn't necessarily imply that they have knowledge of the complete situation. Even in science or software, there are things that one can know without understanding. Personal example: There was once a bug in ruby when built from source. I was tracking down a bug in production and I was unable to replicate it locally. So I finally got someone to shell into prod and run three lines of code for me. That code crashed even though it worked fine on literally every other computer I tried it on, including staging. I knew that the issue was with the production server, but I didn't understand why. It took further work to go from knowledge to understanding.
Wisdom is squishy. It correlates with experience, yes, but there are other factors that play into it as well. A child can be wise and choose not to associate with, say, a gang not because they've experienced it before, but because of something else. An inner reflection of the dangerousness of the situation and the courage or inner resolve to avoid it.
Yes, at some level both are just memorising something. But in the way I like to think about it, they are different things.
For example, you could figure out if someone is overweight by memorising a whole pile of weight & height stats. If someone height 170cm and they are over 75kg, they're overweight. If they are 180cm and over 81kg they're overweight, and so on. That's knowledge.
But if you understand being overweight is a statement about height versus weight, and memorise the formula for BMI you've compressed all that knowledge into a few bytes worth. That's understanding. Both are just memorising something.
Taken a bit further, what AI's turn the knowledge encoded in whole pile of specific examples (the training data) into a understanding (ie, a formula encapsulating the knowledge).
The key to both you and the article is that the base memorization of discrete rules and facts is not sufficient -- the real value comes from being able to dynamically connect the rules yourself in new contexts (rather than having them simply handed to you, to be memorized)
Basically, these are the same as the building blocks of library and information science.
Wish I could figure out the original source. Do you know?
We often mislabel 'information' as 'knowledge'. But information is just information. A 'knowledge base' is no such thing; it's an information base. Knowledge is not 'memory of facts'.
Knowledge == information + understanding.
Wisdom == information + understanding + experience.
Example: Python syntax is information. Understanding and using Python syntax is knowledge. Knowing when Python isn't what you need is wisdom.