• recapitated@lemmy.world
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    8 months ago

    That anyone would ask language models to analyze circumstances, perform logic and reason or conjure an application of knowledge and skill is kind of their own fault.

    It is a language model, it excels at rephrasing given ideas.

    If you put nuke buttons under a flock of pigeons or toddlers just to see what happens, they might launch. It’s not much of a study.

  • breadsmasher@lemmy.world
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    8 months ago

    This sounds like the result of feeding it tons of literature that denotes having nuclear weapons, and the world we live in now being “peaceful” (as the ai claimed to want)

  • Car@lemmy.dbzer0.com
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    8 months ago

    Interesting. There was a study put out some time ago that had 40 or so game theorists develop algorithms to compete against each other. The most successful algorithm cooperated with the opponent until they defected, at which point they would defect the next round.

    They never performed a first strike. Only one retaliation strike for each attack their opponent performed. After the retaliation, it was back to cooperating with no long term ill will.

    • Ech@lemm.ee
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      8 months ago

      I think I saw something about it that. It was an extended prisoner’s dilemma game, right? I wouldn’t say that’s directly applicable to every gaming genre.

      • Car@lemmy.dbzer0.com
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        8 months ago

        Without being in the room, we can only go off what the article lays out. These are wargaming scenarios though, so escalation is a very real concern. If both sides are running these models to provide recommendations and both are pushing for greater conflict, you find yourself in a prisoner’s dilemma real quick.

        • fidodo@lemmy.world
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          8 months ago

          These aren’t simulations that are estimating results, they’re language models that are extrapolating off a ton of human knowledge embedded as artifacts into text. It’s not necessarily going to pick the best long term solution.

            • fidodo@lemmy.world
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              8 months ago

              I want to be careful about how the word reasoning is used because when it comes to AI there’s a lot of nuance. LLMs can recall text that has reasoning in it as an artifact of human knowledge stored into that text. It’s a subtle but important distinction that’s important for how we deploy LLMs.

  • RedstoneValley@sh.itjust.works
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    8 months ago

    Don’t want to spoil your little circlejerk here, but that should not surprise anyone, considering chatbots are trained on vast amounts of human data input. Humans have a rich history of violence with only brief excursions into “collaborating for the good of mankind and the planet we live on”. So unless you build a chatbot that focuses on those values the result will inevitably be a mirror image of us human shitbags.

    • ormr@feddit.de
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      8 months ago

      Humans have a history of violence as well as altruism. And with an increasing degree of societal complexity, humans also have a consistent record of violence reduction. See e.g. “The better angels of our nature” (Pinker, 2011).

      Painting humans as intrinsically violent is not backed by evidence.

      • RedstoneValley@sh.itjust.works
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        8 months ago

        Ok, maybe it helps to be more specific. We have an LLM which is based on a broad range of human data input, like news, internet chatter, stories but also books of all kinds including those about philosophy, diplomacy, altruism etc. But if the topic at hand is “conflict resolution” the overwhelming data will be about violent solutions. It’s true that humans have developed means for peaceful conflict resolution. But at the same time they also have a natural tendency to focus on “bad news” so there is much more data available on the shitty things that happen in the world which is then fed to the chatbot.

        To fix this, you would have to train an LLM specifically to have a bias towards educational resources and a moral code based on established principles.

        But current implementations (like ChatGPT) don’t work that way. Quite the opposite, in fact: In training, first we ingest all the data that we can get our hands on (including all the atrocities in the world) and then in a second step we fine-tune the LLM to make it “better”.