The article discusses the mysterious nature of large language models and their remarkable capabilities, focusing on the challenges of understanding why they work. Researchers at OpenAI stumbled upon unexpected behavior while training language models, highlighting phenomena such as “grokking” and “double descent” that defy conventional statistical explanations. Despite rapid advancements, deep learning remains largely trial-and-error, lacking a comprehensive theoretical framework. The article emphasizes the importance of unraveling the mysteries behind these models, not only for improving AI technology but also for managing potential risks associated with their future development. Ultimately, understanding deep learning is portrayed as both a scientific puzzle and a critical endeavor for the advancement and safe implementation of artificial intelligence.
Yeah pretty much this. My understanding of the way LLMs function is that they operate on statistical associations of words which would amount to categories in Category Theory. Basically the training phase is classifying words into categories based on the examples in the training input. Then when you feed it a prompt it just uses those categories to parse and “solve” your prompt. It’s not “mysterious” it’s just opaque because it’s an incredibly complicated model. Exactly the sort of thing that people are really bad at working with, but which computers are really good with.