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.

  • @Redacted@lemmy.worldEnglish
    5•4 months ago

    This article, along with others covering the topic, seem to foster an air of mystery about machine learning which I find quite offputting.

    Known as generalization, this is one of the most fundamental ideas in machine learning—and its greatest puzzle. Models learn to do a task—spot faces, translate sentences, avoid pedestrians—by training with a specific set of examples. Yet they can generalize, learning to do that task with examples they have not seen before.

    Sounds a lot like Category Theory to me which is all about abstracting rules as far as possible to form associations between concepts. This would explain other phenomena discussed in the article.

    Like, why can they learn language? I think this is very mysterious.

    Potentially because language structures can be encoded as categories. Any possible concept including the whole of mathematics can be encoded as relationships between objects in Category Theory. For more info see this excellent video.

    He thinks there could be a hidden mathematical pattern in language that large language models somehow come to exploit: “Pure speculation but why not?”

    Sound familiar?

    models could seemingly fail to learn a task and then all of a sudden just get it, as if a lightbulb had switched on.

    Maybe there is a threshold probability of a positied association being correct and after enough iterations, the model flipped it to “true”.

    I’d prefer articles to discuss the underlying workings, even if speculative like the above, rather than perpetuating the “It’s magic, no one knows.” narrative. Too many people (especially here on Lemmy it has to be said) pick that up and run with it rather than thinking critically about the topic and formulating their own hypotheses.

    • @orclev@lemmy.worldEnglish
      5•4 months ago

      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.

    • @PipedLinkBotBEnglish
      2•4 months ago

      Here is an alternative Piped link(s):

      this excellent video

      Piped is a privacy-respecting open-source alternative frontend to YouTube.

      I’m open-source; check me out at GitHub.