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How Generative AI's work
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<blockquote data-quote="Clint_L" data-source="post: 9292626" data-attributes="member: 7035894"><p>I'm not sure how you got that out of it.</p><p></p><p>Well, it's science - scientists are always going to frame results in terms of probability and leave room for alternative hypotheses. However, the assessment by an independent scientist is pretty unequivocal: “[Generative AIs] cannot be just mimicking what has been seen in the training data,” said <a href="https://www.microsoft.com/en-us/research/people/sebubeck/" target="_blank">Sébastien Bubeck</a>, a mathematician and computer scientist at Microsoft Research who was not part of the work. “That’s the basic insight."</p><p></p><p>And later: "...Hinton thinks the work lays to rest the question of whether LLMs are stochastic parrots. “It is the most rigorous method I have seen for showing that GPT-4 is much more than a mere stochastic parrot,” he said. “They demonstrate convincingly that GPT-4 can generate text that combines skills and topics in ways that almost certainly did not occur in the training data.”</p><p></p><p>Note that this is far from the only experiment that has been conducted. There is a raft of research currently being done on generative AI, as you might expect, and collectively they indicate a number of unexpected ways that models like Chat seem to go beyond stochastic parroting. And, as I note above, we don't understand the human mind very well, so it is a challenging point of comparison. For example, to what extent is <em>human</em> thought "merely" stochastic parroting?</p></blockquote><p></p>
[QUOTE="Clint_L, post: 9292626, member: 7035894"] I'm not sure how you got that out of it. Well, it's science - scientists are always going to frame results in terms of probability and leave room for alternative hypotheses. However, the assessment by an independent scientist is pretty unequivocal: “[Generative AIs] cannot be just mimicking what has been seen in the training data,” said [URL='https://www.microsoft.com/en-us/research/people/sebubeck/']Sébastien Bubeck[/URL], a mathematician and computer scientist at Microsoft Research who was not part of the work. “That’s the basic insight." And later: "...Hinton thinks the work lays to rest the question of whether LLMs are stochastic parrots. “It is the most rigorous method I have seen for showing that GPT-4 is much more than a mere stochastic parrot,” he said. “They demonstrate convincingly that GPT-4 can generate text that combines skills and topics in ways that almost certainly did not occur in the training data.” Note that this is far from the only experiment that has been conducted. There is a raft of research currently being done on generative AI, as you might expect, and collectively they indicate a number of unexpected ways that models like Chat seem to go beyond stochastic parroting. And, as I note above, we don't understand the human mind very well, so it is a challenging point of comparison. For example, to what extent is [I]human[/I] thought "merely" stochastic parroting? [/QUOTE]
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