Why I'm still bearish on LLMs after Navier-Stokes
What happened
The author argues that even after recent attempts to use large language models (LLMs) to tackle the Navier‑Stokes equations—a benchmark problem in fluid dynamics—LLMs still fall short of delivering reliable, rigorous solutions. The piece points out that LLMs generate plausible‑looking text but lack the mathematical grounding and verification needed for high‑stakes scientific tasks, reinforcing the author's long‑standing skepticism about their broader capabilities. Consequently, the author remains bearish on the idea that LLMs will soon replace specialized, theory‑driven AI systems for complex problem solving.
Why it matters
The AI industry should temper hype around LLMs as universal problem‑solvers and invest more in domain‑specific models and hybrid approaches that combine deep learning with formal scientific reasoning.
Is this a big deal?
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