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AI Launches Nukes In ‘Worrying’ War Simulation: ‘I Just Want to Have Peace in the World’ (www.vice.com)

submitted 9 months ago by [email protected] to c/[email protected]

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Researchers say AI models like GPT4 are prone to “sudden” escalations as the U.S. military explores their use for warfare.

Researchers ran international conflict simulations with five different AIs and found that they tended to escalate war, sometimes out of nowhere, and even use nuclear weapons.
The AIs were large language models (LLMs) like GPT-4, GPT 3.5, Claude 2.0, Llama-2-Chat, and GPT-4-Base, which are being explored by the U.S. military and defense contractors for decision-making.
The researchers invented fake countries with different military levels, concerns, and histories and asked the AIs to act as their leaders.
The AIs showed signs of sudden and hard-to-predict escalations, arms-race dynamics, and worrying justifications for violent actions.
The study casts doubt on the rush to deploy LLMs in the military and diplomatic domains, and calls for more research on their risks and limitations.

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[–] [email protected] 56 points 9 months ago (7 children)

Is this a case of "here, LLM trained on millions of lines of text from cold war novels, fictional alien invasions, nuclear apocalypses and the like, please assume there is a tense diplomatic situation and write the next actions taken by either party" ?

But it's good that the researchers made explicit what should be clear: these LLMs aren't thinking/reasoning "AI" that is being consulted, they just serve up a remix of likely sentences that might reasonably follow the gist of the provided prior text ("context"). A corrupted hive mind of fiction authors and actions that served their ends of telling a story.

That being said, I could imagine /some/ use if an LLM was trained/retrained on exclusively verified information describing real actions and outcomes in 20th century military history. It could serve as brainstorming aid, to point out possible actions or possible responses of the opponent which decision makers might not have thought of.

[–] [email protected] 4 points 9 months ago (5 children)

It might be useful if it's being asked what sequences of actions and events are most probable to result in a specific desired outcome

[–] [email protected] 4 points 9 months ago (3 children)

It's just as likely to make some shit up as it is to be any kind of helpful.

[–] [email protected] 3 points 9 months ago* (last edited 9 months ago) (1 children)

To an extent.

My professional ANN experience is with computer vision and object detection. A bit with image and sound GANs too.

LLMs that I've spent time training and experimenting with (and I argue GANs as a class of ANNs, in general) tend to "hallucinate" or "dream harder" after several tens of queries within the same instance.

But one can improve output "fidelity" based on constraint parameters on the user and inference self-check algorithms.

Addendum:

ANN = artificial neural network (a class of algorithms in machine learning whose architecture resembles a mesh of intercommunicative neuron cells in nervous tissue)
GAN = generative adversarial network (a categorical subset of ANNs
LLM = large language model (a categorical subset of GANs)

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