Why I'm still bearish on LLMs after Navier-Stokes

TL;DR

The author remains pessimistic about large language models (LLMs) following the successful application of traditional mathematical methods to solve the Navier-Stokes equations, highlighting that LLMs did not contribute to this breakthrough. This matters because it questions the current hype around LLMs and their ability to solve complex problems outside natural language tasks.

Detailed Summary

The author remains pessimistic about large language models (LLMs) following their performance in solving the Navier-Stokes equations, which are crucial for fluid dynamics. This skepticism is based on observed limitations in LLMs' ability to handle complex mathematical problems that require deep understanding and precise calculations. The broader impact suggests potential challenges for LLMs in scientific and technical fields where accuracy and nuance are paramount.

Key Points

• • The author remains skeptical about the general applicability of large language models.
• • Concerns over the limitations of current LLMs in handling complex mathematical problems were not alleviated.
• • There is a need for more specialized models to address specific domains effectively.