Skip Navigation
32 comments
  • Because they've sunk billions into the hype train, and it's clear most people don't really want it. So it's being force-fed to everyone in every product to try to get some kind of ROI.

    That, and the more interactions it can get, the more data it can suck up to train on and/or sell.

  • Allow me to add in an extract from the film "The Cube" which addresses this point

    WORTH

    It's maybe hard for you to understand, but there's no conspiracy. Nobody

    is in charge. It's a headless blunder operating under the illusion of a masterplan.

    Can you grasp that? Big brother is not watching you.

    QUENTIN

    What kind of fucking explanation is that?

    WORTH

    It's the best you�re gonna get. I looked and the only explanation I can

    come to is that there is nobody up there.

    QUENTIN

    Somebody had to say yes to this thing.

    WORTH

    What thing? Only we know what it is.

    QUENTIN

    We have no idea, what it is.

    WORTH

    We know more than anybody else. I mean somebody might have known

    sometime, before they got fired or voted out or sold it. But if this place ever had a

    purpose, then it got miscommunicated or lost in the shuffle. This is an accident, a

    forgotten propetual, public, worksproject. Do you think anybody wants to ask

    questions? All they want is a clear conscience and a fat paycheck. I mean, I lead on

    my desk for months. This was a great job!

    QUENTIN

    Why put people in it?

    WORTH

    Because it's here. you have to use it or admit it's pointless.

    QUENTIN

    But it is pointless!

    WORTH

    Quentin... That's my point.

  • Money and incentives are very powerful, but also remember that these organizations are made of humans. And humans are vain.

    Amassing station and power can scarcely be divorced from the history of human civilization, and even fairly trivial things like the job title of "AI engineer" or whatever might be alluring to those aspiring for it.

    To that end, it's not inhuman to pursue "the next big thing", however misguided that thing may be. All good lies are wrapped in a kernel of truth, and the fact is that machine learning and LLMs have been in development for decades and do have a few concrete contributions to scientific endeavors. But that's the small kernel, and surrounding it is a soup of lies, exaggerations, and inexactitudes which somehow keep drawing more entities into the fold.

    Governments, businesses, and universities seem eager to get on the bandwagon before it departs the station, but where is it heading? Probably nowhere good. But hey, it's new and shiny, and when nothing else suggests a quick turnaround for systemic political, economic, or academic issues (usually caused by colonialism, fascism, debt, racism, or social change), then might as well hitch onto the bandwagon and pray for the best.

  • Because it's useful. Have you tried? But the LLM has to be able to use conventional search engines as well. I tell my LLM agent to prioritize certain kinds of websites and present a compressed answer with references. Usually works way better than a standard Google search (which only produce AI generated junk results anyway).

    You can get very good answers or search results by utilizing RAG.

    • I've not used retrieval augmented generation as far as I'm aware, so my reference point is what's been pushed to the masses so far (dunno if any of it incorporates RAG, correct me if I'm mistaken).

      Looking it up I can see how it may mitigate some issues, however I still don't have much confidence that this is a wise application since at base it's still generative text. What I've tried so far has reinforced this view, as it's not served as a good research aid.

      Anything it has generated for me has typically been superficial, i.e. info I can easily find on my own, because it's on the sites right there in the first page of search results. In other situations the source articles cited seem not to exist, as attempts to verify them turn up nothing.

      • My only advice is that you stick to one model and give it some time. You need to "learn" your model. I will probably never go back. I had huge issues with getting good results from Google and my subjective experience is that this is far better. However, I do still use conventional search engines as a complement. It's not all or nothing.

    • Can you please share a simple prompt? I’ve heard of RAG, but was unaware how you could use it in this case. Is this something you can only do with a local LLM? Or can you plug this into GitHub copilot or the like?

      • This is the one I use: https://docs.mistral.ai/capabilities/agents/

        Just tell the modell to do web searches in the system prompt.

        All providers should have something similar. With a user friendly UI.

        If you code your own agent and utilize an API or run a model locally, you can of course do even more. There are many tools if you want to do RAG on your own local data.

32 comments