In Tolkien's Silmarillion, the creator of the palantiri had a great foe, Morgoth. Join the resistance side of the Lord of the Rigs meme war at http://silmarils.tech
We're fighting lord of the rings memes with silmarillion memes and ontologies with ologs (category theory) over at http://silmarils.tech , join the resistance!
If you have a compositional system, such as a programming language, you can use category theory to predict its behavior, in the same way that if you have a symmetric system, such as particles in a box, you can predict its behavior. In the former, maybe you prove that your programming language "can't go wrong"; in the latter, maybe you prove your physical system "conserves energy". (Or, do both at once: https://conferences.inf.ed.ac.uk/clapscotland/atkey.pdf). In any case, the point of category theory is to model compositional systems, which may or may not be worthwhile in any given context.
Putting topology aside, and recognizing that 'ease' is subjective, imo Moggi's use of monads to model the denotational semantics of I/O in lazy functional languages such as Haskell is a common textbook example; the creators of Haskell had tried many solutions that did not work in practice before monads cracked it open. Even now this solution is more widely adopted than the alternatives (streaming I/O, linear I/O types, etc) and Moggi's paper remains a classic.
Modern algebraic topology, especially homological algebra, more or less requires category theory... intro textbooks such as Rotman's will contain primers on category theory for this reason.
We built a correctness checker for LLM-generated SQL code for the military before LLMs were commercially available, it is going live soon on http://sql.ai . Some people do care about this problem, but it is hard to solve; even for SQL alone, this requires significant computer algebra, automated theorem proving, having to define what 'correct' even means, and much else etc.
programming is the art of being able to implement what one doesn't understand, and math is the art of being able to understand what one cannot implement
Symbolica landed a $33 million investment earlier this year led by Khosla Ventures. Traditional symbolic AI solves tasks by defining symbol-manipulating rule sets dedicated to particular jobs, such as editing lines of text in word processor software. That’s as opposed to neural networks, which try to solve tasks through statistical approximation and learning from examples. Symbolica aims to leverage the best of both worlds.
Generative symbolic AI occurs all the time in data integration, when you have to generate new identifiers (e.g. create new targets for foreign keys that don't exist in any source), recursively merge identified entities together along their attributes, and so on. But now that this process is mathematically well-understood it is no longer called "AI" but rather "logic programming".
That symbolic AI (vs machine learning) can also be generative (for example, using model completion algorithms to generate new information during ETL/data warehousing cf https://silmarils.tech/https://www.categoricaldata.net/)