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<blockquote data-quote="kigmatzomat" data-source="post: 9273465" data-attributes="member: 9254">One other aspect is bias.&nbsp; Everything has bias because no random number generator is perfect. Most dice are imperfect and do not have a perfect distribution curve after a million rolls, but they are usually close enough.&nbsp; But some dice are really badly weighted or have some kind of damage or defective that makes some numbers less likely to come up.&nbsp; By the same token, most computer RNGs are perfectly adequate for most uses but there are cases where their bias can be exploited.An LLM or whatever flavor of AI is based on training data that adds its own skew.&nbsp; Imagine an AI trained on data that never included the number 4.&nbsp; It might have a sentence or two of counting so it can say &quot;2+2=four&quot;but if asked &quot;what's 4x2?&quot;it would have no clue.&nbsp; Alternately, &quot;lucky number 7&quot; could appear so often that it's the most probable result of any numeric request.And that gets to the icky part. A random generator uses an RNG to select from lists of items assembled by a person.&nbsp; Generative AI is using patterns found in the training data to extrapolate a list.&nbsp; If you do that on some public domain work like Shakespeare, that's ok.But most LLMs are full of internet data, ebooks, who knows what, but it's often pretty current meaning not public domain.&nbsp; Maybe the lists it generates were scraped from some other list created by some person and its just regurgitating that.&nbsp; Or it had articles about Shakespeare and used the words that appeared most often (which could result in references to the Globe Theater).Personally, I look forward to an LLM based on Project Gutenberg and a visual transformer exclusively made on pre-20th century art that I can use guilt free to glitz up the games I run.&nbsp; Until then, I shun it.&nbsp; A full fledged shunnin'.</blockquote>

[QUOTE="kigmatzomat, post: 9273465, member: 9254"] One other aspect is bias. Everything has bias because no random number generator is perfect. Most dice are imperfect and do not have a perfect distribution curve after a million rolls, but they are usually close enough. But some dice are really badly weighted or have some kind of damage or defective that makes some numbers less likely to come up. By the same token, most computer RNGs are perfectly adequate for most uses but there are cases where their bias can be exploited. An LLM or whatever flavor of AI is based on training data that adds its own skew. Imagine an AI trained on data that never included the number 4. It might have a sentence or two of counting so it can say "2+2=four"but if asked "what's 4x2?"it would have no clue. Alternately, "lucky number 7" could appear so often that it's the most probable result of any numeric request. And that gets to the icky part. A random generator uses an RNG to select from lists of items assembled by a person. Generative AI is using patterns found in the training data to extrapolate a list. If you do that on some public domain work like Shakespeare, that's ok. But most LLMs are full of internet data, ebooks, who knows what, but it's often pretty current meaning not public domain. Maybe the lists it generates were scraped from some other list created by some person and its just regurgitating that. Or it had articles about Shakespeare and used the words that appeared most often (which could result in references to the Globe Theater). Personally, I look forward to an LLM based on Project Gutenberg and a visual transformer exclusively made on pre-20th century art that I can use guilt free to glitz up the games I run. Until then, I shun it. A full fledged shunnin'. [/QUOTE]

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