No, just that every other type of successful AI ends up being called something else.

Do you have any examples? I'm charged with figuring out how my organization can benefit from AI, and hearing that there are non-ML options is very relieving. I assume it's not quite so simple as to include "any algorithm," right?

PageRank (search ranking), A* algorithm (pathfinding), simplex (linear optimization), branch prediction (in CPUs), autocomplete

There is also a large set of algorithms for adversarial search problems, such as playing board games, that traditionally don't use machine learning. Minimax is the simplest one, and then there are negamax, alpha-beta pruning, negascout, etc. I believe Stockfish (the world's strongest chess program, or close to it) has actually improved its evaluation function using machine learning, but this is a fairly recent development.

Any form of constraint solving tech. SAT solvers (used in hardware-synthesis, software verification, math proofs, etc.), Mixed Integer Solvers (usually sold for tens of thousands of dollars) that are used for hardcore optimization problems, Google's Operations Research toolkit (OR-tools), etc.

Unlike most algorithms, these things are general purpose, they can solve any* NP-complete problem (*usually in a useful amount of time).

My manager refers to these things as "Machine Reasoning" in contrast with "Machine Learning", since they start from the rules instead of from examples.

If you've used a GPS navigator you've used AI, Pathfinding is a type of AI. Saw mills use planning algorithms to extract the maximum amount of useful planks from lumber, that's AI.

The most popular text book in the subject might be a good starting place: https://aima.cs.berkeley.edu/

No personal experience here, but my understanding is that expert systems were considered a type of AI and were in the early days mostly implemented as a collection of "if-then" statements.

Not at all.

AI in games (of many different types) is extremely successful.

AI in games (well at least NPC style AI) is also kind of unique in that it's actually good to try to trick the end users into thinking it's more intelligent than it is.

ML is the intersection of the set of successful things and the set of things we call AI today.

There are things we used to call AI, like inference engines, that were and are phenomenally successful (not to mention easier to implement). Type inference in modern programming languages, for instance, still uses the GOFAI technique of unification to solve for unspecified types of variables in a program.

That's why I found it funny the article said "There are companies claiming their products are powered by AI, when they're really powered by IF statements." Back in the day, AI was itself powered by IF statements.

> There are things we used to call AI, like inference engines, that were and are phenomenally successful (not to mention easier to implement). Type inference in modern programming languages, for instance, still uses the GOFAI technique of unification to solve for unspecified types of variables in a program.

Okay. I don't see how these application domains have anything to do with “AI” in the sense of something that resembles human reasoning in any given subarea (like pattern recognition, language).