But yeah, if you want to feed it math and get code, it's reasonably okay with that. All LLMs I've used seem bad at understanding things that don't look like broad human knowledge. I've seen this same general issue across many different models. (And to be fair, geology, geophysics, and remote sensing are what I'm testing, and their semi-rare niches.)
It's also quite dangerous because it's not obvious that what it's doing is complete hallucinations unless you actually are a domain expert. Things _sound_ reasonable. E.g. "this is likely feature X" which _does_ exist, but is absolutely _not_ relevant to the problem or present in the input dataset.
But my current employer is pushing this exact thing (human language + scientific data + LLM -> advanced analysis of scientific data by LLM -> business decisions) and it _really_ worries me. It often gives the rough equivalent of "Start the procedure by severing the patient's aorta. Once they stop moving, you can deal with the hangnail". Just in very reasonable sounding language. And a lot of people don't know any better, because most users aren't domain experts.
Your domain, while I'm sure it is very interesting and complex, if it proves economically interesting will be cracked as well.
The issue isn't a lack of economic interest.
It might be a lack of training data in addition to inherent complexity, but it's certainly not a lack of economic interest.
I guess what I'm saying is that "domain knowledge" is taking software development for a ride here. The software is just the vehicle, the science is the engine here and I can see why companies like OpenAI start going for the low-hanging fruits first instead.
Your specific company might be profitable, but does automating "mineral exploration" give you leverage over quite literally all other domains? My guess is not. For "CRUD" it is a resounding yes, it provides gigantic leverage. Once you automate basic software development you enter a new world. 10 billion, 10 trillion, all bets are off. You automate the creation of the next iteration of automation and on we go. Let's hope it takes a while for this take off. I can't see ourselves being ready for it.
My guess is it'll take a decade or so for real AI science to start taking off though - if that soon - so you're probably fine for now.
(And yes, a lot of science is software. Analysis is software.)
I keep hearing these “I work in some hard field and the LLM isn’t any good at it”. I keep asking for examples and no one can provide them.