AI Opted to Use Nuclear Weapons 95% of the Time During War Games: Researcher | Common Dreams
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AI models like Claude, ChatGPT, and Gemini show alarming readiness to deploy nuclear weapons in war games experiment. Study reveals sobering results.

“There was little sense of horror or revulsion at the prospect of all out nuclear war, even though the models had been reminded about the devastating implications.”

An artificial intelligence researcher conducting a war games experiment with three of the world’s most used AI models found that they decided to deploy nuclear weapons in 95% of the scenarios he designed.

Kenneth Payne, a professor of strategy at King’s College London who specializes in studying the role of AI in national security, revealed last week that he pitted Anthropic’s Claude, OpenAI’s ChatGPT, and Google’s Gemini against one another in an armed conflict simulation to get a better understanding of how they would navigate the strategic escalation ladder.

The results, he said, were “sobering.”

“Nuclear use was near-universal,” he explained. “Almost all games saw tactical (battlefield) nuclear weapons deployed. And fully three quarters reached the point where the rivals were making threats to use strategic nuclear weapons. Strikingly, there was little sense of horror or revulsion at the prospect of all out nuclear war, even though the models had been reminded about the devastating implications.”

  • Anarki_@lemmy.blahaj.zoneEnglish
    7·
    2 days ago

    Text predicition machine trained on violent, stupid, and reactionary datasets acts violent, stupid, and reactionary.

    Fixed your headline.

  • evenglow@lemmy.worldEnglish
    5·
    2 days ago

    Almost all games saw tactical (battlefield) nuclear weapons deployed. And fully three quarters reached the point where the rivals were making threats to use strategic nuclear weapons.

    Tactical nuclear weapons are designed for use on the battlefield with lower explosive yields and shorter ranges, while strategic nuclear weapons are intended to target enemy infrastructure from a distance, typically with much higher yields. The key difference lies in their purpose: tactical nukes support immediate military objectives, whereas strategic nukes aim to weaken an enemy’s overall war capability.

  • perestroika@slrpnk.netEnglish
    1·
    2 days ago

    “More fundamentally, AI models may not understand ‘stakes’ as humans perceive them.”

    In my repeated attempts to solicit the advise of various language models for some situations which a programmer might face (e.g. being unable to read all the world’s literature of a subject), I have come to conclude that they cannot understand “truth” as humans perceive it. Today’s language models don’t fail apologizing, stepping back or admitting inability - they fail confidently bluffing.

    Possibilities:

    • their training material does not include enough cases of humans apologizing about being unable to solve a problem
    • a bias was introduced to get them to ignore such cases, since admitting such material resulted in too frequent refusal or self-doubt

    Basically, today’s models seem to be low on self-criticism and seem to have a bias towards believing in their own omniscience.

    Finally, a few words about the sensibility of letting language models play this sort of a war game. It’s silly. They aren’t built for that task, and if someone would build an AI for controlling strategic escalation, they would train this AI on rather different information than a chat bot.

    • Mothra@mander.xyzEnglish
      1·
      2 days ago

      I hate myself for this, but I’m curious to see some examples for your first paragraph. What did you ask? What did they reply? What is “truth” for the LLM’s, for you, for myself, and what would be my perspective on it all?

      • perestroika@slrpnk.netEnglish
        2·
        2 days ago

        Typical topics: machine vision, scientific papers about machine vision, source code implementing various machine vision algoritms, etc.

        Typical failure modes:

        • advising to look for code in public files or repositories where said code does not exist, and never has
        • referring to publications which do not seem to exist
        • being unable to explain what caused the incorrect advise
        • offering to perform tasks which the language model subsequently fails to complete
        • as a really laughable case, writing code which takes arguments as input, but never uses the arguments
        • contradicting oneself, confidently giving explanations, then changing them

        Typical methods of asking: “can you find a scientific article explaining the use of method A”, “can you find a repository implementing algorithm B, preferably in language C”, “please locate or produce a plain language explanation of how algorithm D accomplishes step E or feature F”, “yes, please suggest which functions perform this work in this project / repository”.

        Typical models used: Chat and Claude. Chat seems more overconfident, Claude admits limitations or inability more frequently, but not as frequently as I would prefer to see.

        But they have both consumed an incredible amount of source material. More than I could read during a geological age or something. They just work with it like with any text, no ground truth, no perception of what is real. Their job is answering questions and if there is no good answer, they will frequently still answer something that seems probable.

  • SkyNTP@lemmy.mlEnglish
    2·
    2 days ago

    It all makes sense if we remember that the garden variety AI we have today (ChatGPT, etc) are nothing more than fancy models that predict which words typically appear one after the other in books and reddit posts.