Comment on Lutris now being built with Claude AI, developer decides to hide it after backlash

• moseschrute@lemmy.world ⁨9⁩ ⁨hours⁩ ago

  Most people on Lemmy probably haven’t given it a single minute let alone 5 minutes.

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• Vlyn@lemmy.zip ⁨18⁩ ⁨hours⁩ ago

  It’s not really that simple. Yes, it’s a great tool when it works, but in the end it boils down to being a text prediction machine.

  So a nice helper to throw shit at, but I trust the output as much as a random Stackoverflow reply with no votes :)

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  • dream_weasel@sh.itjust.works ⁨10⁩ ⁨hours⁩ ago

    I feel like there needs to be a post (and I don’t want to write it, but maybe I eventually will) that outlines what a model really is. It is not just a statistical text prediction machine unless you are being so loose with the definition of “statistical” that it doesn’t even mean anything anymore.

    A decent example of a statistical text prediction machine is the middle word suggested by your phone when you’re using the keyboard. An LLM is not that.

    In the most general terms, this kind of language model tokenizes a corpus of text based on a vocabulary (which is probably more than just the words in the dictionary), uses an embedding model to translate these tokens into a vector of semantic “meaning” which minimized loss in a bidirectional encoding (probably), that is then trained against a rubric for one or more topic area questions, retrained for instruction and explainability, retrained with reinforcement learning and human feedback to provide guardrails, and retrained again to make use of supplemental materials not part of the original training corpus (resource augmented generation), then distilled, then probably scaled and fine tuned against topic areas of choice (like coding or Korean or whatever) and maybe THEN made available to people to use. There are generally more parts to curriculum learning even than that but it’s a representative-ish start.

    My point being that, yes, it would be nuts to pose ANY question to a predictor that says “with 84% probability, the word that is most likely follows ‘I really like’ is ‘gooning’ on reddit”, but even Grok is wildly more sophisticated than that and Grok is terrible.

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    • Vlyn@lemmy.zip ⁨10⁩ ⁨hours⁩ ago

      The training is sophisticated, but inference is unfortunately really a text prediction machine. Technically token prediction, but you get the idea.

      For every single token/word. You input your system prompt, context, user input, then the output starts.

      The

      Feed the entire context back in and add the reply “The” at the end.

      The capital

      Feed everything in again with “The capital”

      The capital of

      Feed everything in again…

      The capital of Austria

      …

      It literally works like that, which sounds crazy :)

      The only control you as a user can have is the sampling, like temperature, top-k and so on. But that’s just to soften and randomize how deterministic the model is.

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  • p03locke@lemmy.dbzer0.com ⁨18⁩ ⁨hours⁩ ago

    but in the end it boils down to being a text prediction machine.

    And we’re barely smarter than a bunch of monkeys throw piles of shit at each other. Being reductive about its origins doesn’t really explain anything.

    I trust the output as much as a random Stackoverflow reply with no votes :)

    Yeah, but that’s why there’s unit tests. Let it run its own tests and solve its own bugs. How many mistakes have you or I made because we hate making unit tests? At least the LLM has no problems writing the tests, after you know it works.

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    • svtdragon@lemmy.world ⁨9⁩ ⁨hours⁩ ago

      I’ve had better luck with using it in a TDD style. “Write a test for this issue, watch it fail, then make it pass.”

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