I disagree. Frankly, for a lot of people and a lot of contexts, I don't think the halting problem is particularly important. You're using understanding of it as a shibboleth for exposure to common curricula about theoretical computation. But you can even know a lot about practical computation and not know anything about the halting problem. Curious: has your knowledge of the halting problem ever actually saved you time or effort in your work? If so, how?

Turing's work on the limitations of his machine are interesting, and I'm sure people with a deep understanding of them can advance the study of computation.

I think you're just being dismissive of skillsets which aren't your own. I think you're just bothered by the fact that AI and ML are being advanced more by people with more knowledge of linear algebra and statistics than computer science. And realize that it's the arrogant among them that will dismiss you as "just a technician."

Anyone who is looking down on either "scientists" or "technicians" should get over themselves.

> Curious: has your knowledge of the halting problem ever actually saved you time or effort in your work? If so, how?

Not OP, but I'm working a lot with ontologies. Some ontologies representations are undecidable, while other languages are not very expressive but can be manipulated in polynomial time. Had I not known that, I would still be like "crap, why does it take so long? I must have a bug somewhere, maybe I should switch to C".

> AI and ML are being advanced more by people with more knowledge of linear algebra and statistics than computer science.

Just answered OP about that, but actually, symbolic AI is pure computer science. It does not get as much publicity as ML currently, but believe me, it's everywhere: at the core of almost all package managers, like debian's apt-get or maven, at the core of most advanced static code analyzers, etc.

This is a recent shift lead by the ML trend. Traditionally (like 5 years ago), ML and AI were two different things, AI being the term for symbol manipulation. Expert systems, inference engines, constraint programming, SAT solving for instance. These domains are typical CS stuff: inference, complexity classes, low-level representation of data, etc. You don't need that much knowledge in math/statistics to be proficient in those fields, but you rather know what the halting problem is.

I'm working in the symbolic AI field, and sometimes use ML techniques. They are complementary. To me, ML is about induction, AI is abut deduction. They don't solve the same kinds of problems and they tend to work pretty well together.

I guess "dynamic programming" must really bother you. That field was named completely arbitrarily, to secure funding.

The more you look around, the more you find science concepts are named for marketing purposes.

Heck, "data scientist" is a bit of nonsense.

Does this just come down to a semantic idea that if something isn't in pursuit of AGI, its not really AI? That feels unfair to most of these researchers who absolutely disagree with that.

And to consider these algorithms to not "learn" is similarly unfair. They do. They learn to solve specific problems (at least right now), but they do learn.