Hi Aleksei, thanks for your comment!
You are right: thare are many scalable systems that don't need to use everything I described. As you said in your other comment, there are no silver bullets. Using so many techniques together brings all their advantages and their drawbacks together.
Now about the AI vs. ML discussion, I often find people describing ML as a subset of AI, maybe better described by the name of "statistical learning". In my understanding, AI is a wider field that uses different mathematical frameworks (another example, first order logic and graph theory) to create automated decision making. What are your thoughts?
Here in Brazil we have a quite large fintech ecosystem where more companies, in particular banks, are applying machine learning for risk evaluation, fraud detection, etc. Other interesting use cases I saw in other domains was the use of ML in the infrastructure level to optimize resource usage.
Surely, as you said, many systems don't need to apply everything to be highly scalable (bank systems running in mainframes or chatting systems built with Erlang are nice examples). However I wanted to highlight some systems use all those concepts together and we could give a name to them.
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