Norvig and Russell's textbook (one of the current go-to AI books) calls the big "if-else AI" a "simple reflex agent". It observes the environment in a rudimentary way and then goes through the if-then chain. One of the first things students (should) learn is how inefficient this is for more challenging problems.

My students were just given an assignment where they build AI to play Connect 4. Some will try to make a simple reflex solution because they want to avoid recursion, then come to office hours asking how to make it work better. It... can't. There really is an observable upper-bound on if-then performance.

Meanwhile decision tree ensembles are still one of the most performant models for tabular data, with gradient boosted trees particularly hard to beat.

> There really is an observable upper-bound on if-then performance.

Only on if-else chains you can code by hand.

There's a lot of machine learning methods that can be seen as using data to generate large networks of if-else decision points. There are methods that perform (a discrete simulation of) continuous generalization of if-else chains. And fundamentally, if-else chains with a loop thrown in the mix is a Turing-complete system, so it can do anything.

The problem here is that if-else chains are a really inefficient way for humans to model reality with. We can do much better with different approaches and abstractions, but since they all are equivalent to a finite sequence of if-else branches, it's not the if-else where the problem is - it's our own cognitive capacity.

Yeah, XGBoost (and all tree methods) are basically if-else on steroids.