this post was submitted on 07 Sep 2025
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The issue is the overloading of the word “AI”.
“Machine Learning using Neural Networks” is a technique that can come up with decent but rough solutions to problems where it’s hard to come up with any solution.
“Large Language Models” is the application of “Machine Learning using Neural Networks” to natural language processing, and it is incredibly good at that.
The problem comes when people apply models trained for natural language processing onto other random problems just because you can formulate anything as a natural language problem.
That's a fair distinction. That being said, at their core llms are just big functions. You could cover a dartboard in subfields of physics, toss a dart randomly, and I'd bet money you hit a field that finds use for the bessel functions for instance. I am not informed enough on the specifics of llms to say either way, but there's definitely precedent for "we found this really powerful function and it turns out it accurately predicts 10 shitloads of unrelated systems."
To double down on my devils advocacy, the projects I have personally seen or been consulted for that fit the form "use llm to solve non-nlp problem" are 99% propelled by "funding for Ai buzzwords flows freely" and "understanding of the limitations of different kinds of ai is rare"