Well that depends on your definition of AI. Which isn't well defined. We call AI what we perceive as "magic". Black box algorithms have a higher chance of being perceived that way (e.g. neural nets). When you get some insight into how an algorithm works (easier for transparent box algos, but same holds for black box algorithms), you start to see it less and less as "magic", and, consequently, you're less likely to refer to it as an (artificial) intelligence. Because ultimately, that's what we mean by intelligence -- magic. When we say that something is intelligent, we liken it to ourselves: it evokes a sense of identification. It all comes back to a sense of humans being fundamentally separate from "the other" (computers in this case). If we saw the mathematical models and algorithms as just that, we wouldn't call them AI. Also, if we didn't think of our intelligence as more than the behaviour of our biological computer, we wouldn't be enchanted by the concept of non-biological systems mimicking some of our behaviour.

A professor once told in class "when it works and you don't understand why, it's called AI; when you do, it's called algorithm"

I disagree.

We don't find these systems intelligent because, on inspection, they arent.

We are intelligent. Not "magically", but actually nevertheless.

Our intelligence, and that of dogs (, mice, etc.) consists in the ability to operate on partial models of environments; dynamically responsive to them; and to skilfully respond to changes in them.

This sort of intelligence requires the environment to physically reconstitue the animal in order to non-cognitively develop skills.

It is skillful action we are interested in; and precisely what I missing in naive rule-based models of congition.

You provided an illustration of "magic". It's important to realise that you don't need a complex algorithm to produce complex behaviour (see Stephen Wolfram and his work on cellular automata).

In my understanding AI is an even broader term and means "any solution that imitates intelligent behavior". E.g. expert systems which are pretty much a bunch of if-then rules are also considered AI.

It's my understanding as well, many things that a modern programmer thinks in term of "computation" were once considered to be "AI". Lisp and Prolog were "AI", even the A* algorithm is still considered a rudimentary form of "AI" in textbooks just because it uses heuristics. There's a joke that says "every time AI researchers figure out a piece of it, it stops being AI" [0].

It's why I use "AI" and "ML" interchangeably although I know it's technically incorrect - the formal definition doesn't match what people are currently thinking.

[0] https://en.wikipedia.org/wiki/AI_effect

There have traditionally been different approaches and definitions for AI. Some emphasize behaviour while others emphasize the logic behind the behaviour. (In some sense, while expert systems of course were an attempt at getting practical results, they might also have been an attempt to implement what was seen as human reasoning, while e.g. black box machine learning could be more about just getting the behaviour we want.) Some approaches view agents as intelligent if their action resembles humans or other beings that we consider intelligent, while other approaches are merely interested in whether they perform well at a specified task, perhaps more so than humans.

So yes, "any solution that imitates intelligent behaviour" is probably right, but with nuances with regard to what that actually means.

That's not symbolic AI though. That's only statistical methods. The statistical methods are all the rage now, but explainable AI that can reason is an important area of computer science (and research) and uses formal methods.

Edit: yeah, you can downvote this, but current AI research splits right along this line, whether it's symbolic or statistical. Some AI courses will use NNs, others will use Prolog and ASP. You can't just dismiss a whole field of research by reducing AI to statistical methods.

"Expert systems" were the hot research area in AI prior to machine learning (data driven methods, basically). Old methods and problems from that era like automated reasoning still have some research and applications going on, but aren't remotely as big an area as machine learning.

When I see "symbolic AI" I immediately think of Gary Marcus and immediately feel disdain towards the topic because of his behaviour on Twitter and other places.

I don't know the dude. I "only" know that my field of research is deductive reasoning in interactive applications and that this area falls under "Logic Programming" and LP is an area of AI.

I know that AI researchers are usually a bit dismissive about the other area. I don't like statistics either. Reducing the whole of AI research to statistical approaches (and NNs are one of those) is disingenious and dismisses hundreds of researchers doing important work.

You may not want to have rule-based image recognition, but if your car decides to run over somebody, I feel we better have an explanation for this behaviour based on reasoning and logic.

I don’t think anyone is dismissing symbolic AI. As far as I can see, it’s just not beating current SOTA results of NNs? It’s not really about ideology, it’s about what currently has superior performance. Model interpretability is not always a requirement.