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If all the worst people you know are supporting something... (thelemmy.club)
submitted 6 days ago by to c/fuck_ai@lemmy.world
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[–] 2 points 6 days ago (2 children)

Oh boy, did that rub everyone the wrong way.

Either way, you could have your own LLM agent and run it locally. That way your recommendations wouldn’t be steered towards greatest quarterly revenue. Instead, it would be something that actually serves your purposes.

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  • [–] 6 points 6 days ago* (last edited 6 days ago) (1 child)

    Either way, you could have your own LLM agent and run it locally

    Ignoring everything else, that wouldn't work. Your local llm instance, to recommend you new movies, would have to scour the internet to summarize information about them.

    Boom, thats the vector of it getting steered towards greatest quarterly revenue.

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  • [–] -1 points 6 days ago (1 child)

    Yeah that’s true. Search results, movie reviews and blogs definitely can be used to steer it in any direction. You just need to keep the LLM on a very short leash and keep a close eye on its diet. Takes some effort to do this properly.

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  • [–] 4 points 6 days ago (1 child)

    That defeats the purpose then.

    If the goal is to say "hi, my local LLM, recommend me a new movie that I like, because god forbid I'll take a risk and grow", then you having to curate the data feed going into LLM to describe new movies is in practice this:

    • you'll have to find sources of data (websites, threads, communicties)
    • verify each and everyone for poisoning BEFORE adding to your deterministic vectorization (see what Netflix did for recommendation algorithm recently) and applying custom LLM-like in architecture model.

    Tl;dr;

    Either you can use LLM and will be steered towards greatest quarterly revenue OR you have to put the same effort as if reading all reviews from multiple sources by yourself

    And in both cases you still end up with the same outcome "maybe you'll like it" :P

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  • [–] 2 points 5 days ago

    Unless there are community based information sources. Adblock works mainly by blacklisting. Foss agents could use a combination of blacklisting and whitelisting in the same way. Yes, it takes effort, but spread over a community, it's minimal. In the days before internet, it meant buying a magazine or newspaper. There has always been a certain amount of effort to find reviews, other than word of mouth, which will continue.

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  • [–] 3 points 6 days ago (2 children)

    Or even better, don’t be lazy and curate your own interests.

    My high school motto was “Faber Quisque Fortunae” (each man creates their own destiny). That means that You need to take responsibility for your own actions and decisions. You don’t outsource living.

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  • [–] 0 points 5 days ago (1 child)

    Curating your own interests involves some aapect of information seeking. Llms can be used for that. It's not a case that anyone should trust ai to make a decision. It's that ai can help surface information to allow them to make a decision. The problem is too many people are letting the ai make a decision for them, or using it instead of other information that is better.

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  • [–] 2 points 5 days ago (1 child)

    Most of the raw information has been deliberately obfuscated in with malicious ads.

    If we were able to dismantle the toxic malicious web advertising apparatus, we wouldn’t need them. At all!

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  • [–] 2 points 5 days ago

    That's a problem that precedes llms. There used to be information online but it was normally informed. Since the social web, blogs, reviews, business reviews, Google reviews etc etc are packed so full of junk and nonsense that they are unhelpful.

    The hope of the internet was to make information available to all. Spread ideas. Instead we as a species spread vapid half thoughts that drowns out the useful. LLMs are an extension of that and likely will make it much worse. But they can also be used to filter it.

    The question is who decides which data is accurate and how do we verify that with these black boxes that are poorly understood by most of us.

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