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Joined 1 year ago
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Cake day: June 24th, 2023

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  • More appropriate tools to detect AI generated text you mean?

    It’s not a thing. I don’t think it will ever be a thing. Certainly not reliably, and never as a 100% certainty tool.

    The punishment for a teacher deciding you cheated on a test or an assignment? I don’t know, but I imagine it sucks. Best case, you’d probably be at risk of failing the class and potentially the grade/semester. Worst case you might get expelled for being a filthy cheater. Because an unreliable tool said so and an unreliable teacher chose to believe it.

    If you’re asking what’s the answer teachers should know to defend against AI generated content, I’m afraid I don’t have one. It’s akin to giving students math homework assignments but demanding that they don’t use calculators. That could have been reasonable before calculators were a thing, but not anymore and so teachers don’t expect that to make sense and don’t put those rules on students.





  • The term AI was coined many decades ago to encompass a broad set of difficult problems, many of which have become less difficult over time.

    There’s a natural temptation to remove solved problems from the set of AI problems, so playing chess is no longer AI, diagnosing diseases through a set of expert system rules is no longer AI, processing natural language is no longer AI, and maybe training and using large models is no longer AI nowadays.

    Maybe we do this because we view intelligence as a fundamentally magical property, and anything that has been fully described has necessarily lost all its magic in the process.
    But that means that “AI” can never be used to label anything that actually exists, only to gesture broadly at the horizon of what might come.


  • I’ll note that there are plenty of models out there that aren’t LLMs and that are also being trained on large datasets gathered from public sources.

    Image generation models, music generation models, etc.
    Heck, it doesn’t even need to be about generation. Music recognition and image recognition models can also be trained on the same sort of datasets, and arguably come with similar IP right questions.

    It’s definitely a broader topic than just LLMs, and attempting to enumerate exhaustively the flavors of AIs/models/whatever that should be part of this discussion is fairly futile given the fast evolving nature of the field.


  • One of my guilty pleasures is to rewrite trivial functions to be statements free.

    Since I’d be too self-conscious to put those in a PR, I keep those mostly to myself.

    For example, here’s an XPath wrapper:

    const $$$ = (q,d=document,x=d.evaluate(q,d),a=[],n=x.iterateNext()) => n ? (a.push(n), $$$(q,d,x,a)) : a;
    

    Which you can use as $$$("//*[contains(@class, 'post-')]//*[text()[contains(.,'fedilink')]]/../../..") to get an array of matching nodes.

    If I was paid to write this, it’d probably look like this instead:

    function queryAllXPath(query, doc = document) {
        const array = [];
        const result = doc.evaluate(query, doc);
        let node= result.iterateNext();
        while (node) {
            array.push(node);
            n = result.iterateNext();
        }
        return array;
    }
    

    Seriously boring stuff.

    Anyway, since var/let/const are statements, I have no choice but to use optional parameters instead, and since loops are statements as well, recursion saves the day.

    Would my quality of life improve if the lambda body could be written as => if n then a.push(n), $$$(q,d,x,a) else a ? Obviously, yes.