[nl]

I don't see why this is hilarious at all.

The problem with expert systems (and most KG-type applications) has always been that translating unconstrained natural language into the system requires human-level intelligence.

It's been completely obvious that LLMs are a technology that let us bridge that gap for years, and many of the best applications of LLMs are doing exactly that (eg code generation)

[Metricon]

To be clear, my amusement isn't that I find this technique to not be useful for the purpose it was created, but that 40 years later, we find ourselves in pursuit for the advancement of AI to be somewhat back where we already were; albeit, in a more semi-automated fashion as someone still has to create the underlying rule-set.

I do feel that the introduction of generative neural network models in both natural language and multi-media creation has been a tremendous boon for the advancement of AI, it just amuses me to see that which was old is new again.

[rustastra]

Same with symbolic systems!

[fzzzy]

Seems likely that we were on the right track, it just took 40 years for computers to get good enough.

[Animats]

Right. The trouble with that approach is that it's great on the easy cases and degrades rapidly with scale.

This sounds like is a fix for a very specific problem. An airline chatbot told a customer that some ticket was exchangeable. The airline claimed it wasn't. The case went to court. The court ruled that the chatbot was acting as an agent of the airline, and so ordinary rules of principal-agent law applied. The airline was stuck with the consequence of their chatbot's decision.[1]

Now, if you could reduce the Internal Revenue Code to rules in this way, you'd have something.

[1] https://www.bbc.com/travel/article/20240222-air-canada-chatb...

[nl]

Yes, as I said in another comment: "By constraining the field it is trying to solve it makes grounding the natural language question in a knowledge graph tractable."

IRS rules should be tractable!