Makes a lot of sense AI would nuke disproportionately. For an AI, if you do not set a value for something, it is worth zero. This is actually the base problem for AI: Alignment.
For a human, there’s a mushy vagueness about it but our cultural upbringing says that even in war, it’s bad to kill indiscriminately. And we value the future humans who do not yet exist, we recognize that after the war is over, people will want to live in the nuked place and they can’t if it’s radioactive. There’s a self-image issue where we want to be seen as a good person by our peers and the history books. There is value there which is overlooked by programmers.
An AI will trade infinite things worth 0 for a single thing worth 1. So if nukes increase your win percentage by .1%, and they don’t have the deterrence of being labeled history’s greatest monster, they will nuke as many times as they can.
That explanation is obviously based on traditional chess AI. This is about role-playing with chatbots (LLMs). Think SillyTavern.
LLMs are made for text production, not tactical or strategic reasoning. The text that LLMs produce favors violence, because the text that humans produce (and want) favors violence.
Especially if its training material included comments from the early 00s. There was a lot of “nuke it from orbit” and “glass parking lot” comments about the Middle East in the wake of 911.
And with the glorified text predictors that LLMs are, you could probably adjust the wording of the question to get the opposite results. Like, “what should we do about the Middle East?” might get a “glass parking lot” response, while “should we turn the middle East into a glass parking lot?” might get a “no, nuking the middle East is a bad idea and inhumane” because that’s how those conversations (using the term loosely) would go.
For AGI, sure, those kinds of game theory explanations are plausible. But an LLM (or any other kind of statistical model) isn’t extracting concepts, forming propositions, and estimating values. It never gets beyond the realm of tokens.
Makes a lot of sense AI would nuke disproportionately. For an AI, if you do not set a value for something, it is worth zero. This is actually the base problem for AI: Alignment.
For a human, there’s a mushy vagueness about it but our cultural upbringing says that even in war, it’s bad to kill indiscriminately. And we value the future humans who do not yet exist, we recognize that after the war is over, people will want to live in the nuked place and they can’t if it’s radioactive. There’s a self-image issue where we want to be seen as a good person by our peers and the history books. There is value there which is overlooked by programmers.
An AI will trade infinite things worth 0 for a single thing worth 1. So if nukes increase your win percentage by .1%, and they don’t have the deterrence of being labeled history’s greatest monster, they will nuke as many times as they can.
That explanation is obviously based on traditional chess AI. This is about role-playing with chatbots (LLMs). Think SillyTavern.
LLMs are made for text production, not tactical or strategic reasoning. The text that LLMs produce favors violence, because the text that humans produce (and want) favors violence.
Especially if its training material included comments from the early 00s. There was a lot of “nuke it from orbit” and “glass parking lot” comments about the Middle East in the wake of 911.
And with the glorified text predictors that LLMs are, you could probably adjust the wording of the question to get the opposite results. Like, “what should we do about the Middle East?” might get a “glass parking lot” response, while “should we turn the middle East into a glass parking lot?” might get a “no, nuking the middle East is a bad idea and inhumane” because that’s how those conversations (using the term loosely) would go.
That’s not necessarily true, there is a lot of violent fiction.
For AGI, sure, those kinds of game theory explanations are plausible. But an LLM (or any other kind of statistical model) isn’t extracting concepts, forming propositions, and estimating values. It never gets beyond the realm of tokens.