Post AwYjeyfBfvHznOm5Ls by ryanjyoder@techhub.social
(DIR) More posts by ryanjyoder@techhub.social
(DIR) Post #AwYjevsu0TTfB4AUoS by paco@infosec.exchange
2025-07-25T14:54:06Z
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A friend sent me the story of the LLM deleting a database during a code freeze and said "it lied when asked about it." I assert that a generative AI cannot lie. These aren't my original thoughts. But if you read Harry Frankfurt's famous essay On Bullshit (downloadable PDF here), he makes a very reasoned definition of bullshit. And this paragraph near the end of the essay explains why an LLM cannot lie.It is impossible for someone to lie unless he thinks he knows the truth. Producing bullshit requires no such conviction. A person who lies is thereby responding to the truth, and he is to that extent respectful of it. When an honest man speaks, he says only what he believes to be true; and for the liar, it is correspondingly indispensable that he consider his statements to be false. For the bullshitter, however, all these bets are off: he is neither on the side of the true nor on the side of the false. His eye is not on the facts at all, as the eyes of the honest man and of the liar are, except insofar as they may be pertinent to his interest in getting away with what he says. He does not care whether the things he says describe reality correctly. He just picks them out, or makes them up, to suit his purpose.And that's a generative artificial intelligence algorithm. Whether generating video, image, text, network traffic, whatever. It has no reference to the truth and is unaware of what truth is. It just says things. Sometimes they turn out to be true. Sometimes not. But that's irrelevant to an LLM. It doesn't know.#bullshit #ai #llm #genai
(DIR) Post #AwYjewpkTgY67ZhUXI by ryanjyoder@techhub.social
2025-07-25T15:37:42Z
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@paco Maybe this is semantics but Meta AI appears to be able to lie.
(DIR) Post #AwYjexvSPwi3VZNZ4a by glc@mastodon.online
2025-07-25T16:47:32Z
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@ryanjyoder @paco Nope.Just another illustration of the original point.
(DIR) Post #AwYjeyfBfvHznOm5Ls by ryanjyoder@techhub.social
2025-07-25T17:15:07Z
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@glc @paco If an llm can't lie, then it seems true by only a technical definition. I can ask Meta ai basic questions about geography and get correct answers reliably. And I can ask it to lie about basic geography and I can reliably get incorrect answers.
(DIR) Post #AwYjezJbFfcDojgMLI by ralfmaximus@mastodon.social
2025-07-25T19:48:58Z
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@ryanjyoder @glc @paco There are billions geography facts on the internet. This includes fictional data sets too from popular SF/fantasy.There are millions of human<->human interactions where geography facts are discussed, some of them accurately, many of them not.Train a LLM on all that. Ask it geography questions.The results will be the most likely next chain of text based on all those weighted interactions. That's it. There's no lying, no truth. It's making predictions.
(DIR) Post #AwYjezzQk94luTFlXk by ryanjyoder@techhub.social
2025-07-27T12:38:08Z
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@ralfmaximus @glc @paco If you didn't know the implementation of a specific model would there be any way to determine if it was capable of telling a lie?
(DIR) Post #AwYjf0ZaZi01icAdu4 by paco@infosec.exchange
2025-07-27T12:53:52Z
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@ryanjyoderAll models work fundamentally in the same way: predicting a series of output tokens based on a series of input tokens. If you don’t understand this basic implementation mechanism, the rest of this conversation is inaccessible.It doesn’t store facts. It doesn’t have a representation of “true” or “false.” It isn’t a database. It splits written text into tokens and does colossally huge, environmentally damaging, and fabulously expensive “training” on that data using billions of parameters to arrive at a statistical model of tokens that follow other tokens. The model can then be queried to produce statistically likely replies to inputs.Given an input like “tell me a lie about the capital of France” the most statistically improbable reply is “the capital of France is Paris.” Other replies like “wear a seatbelt” are also super improbable. The size of these models and the probabilities they work with are really difficult to get one’s head around. But it returned a statement that was a probabilistically likely reply to that input. That’s all it did. When models make up legal cases that don’t exist, books that don’t exist, programming APIs that don’t exist, etc, they are simply outputting likely results. Text that fits the probability distribution of their input data. That’s why it is not a “bug” when an LLM bullshits. It’s not an error. It is working as designed.There is nowhere to report to an LLM company the factually incorrect outputs its model produced because there is nothing they can do with that. It is working as designed.@ralfmaximus @glc