May 4, 2023 01:09 AM
https://www.vice.com/en/article/wxjdg5/s...study-says
INTRO: In a new paper, Stanford researchers say they have shown that so-called "emergent abilities" in AI models—when a large model suddenly displays an ability it ostensibly was not designed to possess—are actually a "mirage" produced by researchers.
Many researchers and industry leaders, such as Google CEO Sundar Pichai, have perpetuated the idea that large language models like GPT-4 and Google's Bard can suddenly spit out knowledge that it wasn’t programmed to know. A 60 Minutes segment from April 16 claimed that Bard was able to translate Bengali even though it was not trained to. 60 Minutes claimed that AI models are "teaching themselves skills that they weren't expected to have."
Microsoft researchers, too, claimed that OpenAI's GPT-4 language model showed “sparks of artificial general intelligence,” saying that the AI could “solve novel and difficult tasks…without needing any special prompting.” Such concerns not only hype up the AI models that companies hope to profit from, but stoke fears of losing control of an AI that suddenly eclipses human intelligence.
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Co-authors Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo present an explanation for emergent abilities in their paper, posted to the arXiv preprint server on Friday. The authors write that “for a particular task and model family, when analyzing fixed model outputs, one can choose a metric which leads to the inference of an emergent ability or another metric which does not.”
Though the model the researchers discuss is a previous iteration in the GPT family, GPT-3, they compare their findings to previous papers that also focused on GPT-3 when defining its emergent abilities. The researchers found that AI abilities only appeared to suddenly emerge when people used specific metrics to measure the task.
The researchers wrote that a person’s choice of a "non-linear" or "discontinuous" measurement can result in what appear to be sharp and unpredictable changes that are then falsely labeled as emergent abilities when in reality the performance curve is increasing smoothly. The authors write that this is compounded by researchers not having enough data on small models—perhaps they really are capable of the supposedly emergent task—and not enough on large models, either... (MORE - details)
INTRO: In a new paper, Stanford researchers say they have shown that so-called "emergent abilities" in AI models—when a large model suddenly displays an ability it ostensibly was not designed to possess—are actually a "mirage" produced by researchers.
Many researchers and industry leaders, such as Google CEO Sundar Pichai, have perpetuated the idea that large language models like GPT-4 and Google's Bard can suddenly spit out knowledge that it wasn’t programmed to know. A 60 Minutes segment from April 16 claimed that Bard was able to translate Bengali even though it was not trained to. 60 Minutes claimed that AI models are "teaching themselves skills that they weren't expected to have."
Microsoft researchers, too, claimed that OpenAI's GPT-4 language model showed “sparks of artificial general intelligence,” saying that the AI could “solve novel and difficult tasks…without needing any special prompting.” Such concerns not only hype up the AI models that companies hope to profit from, but stoke fears of losing control of an AI that suddenly eclipses human intelligence.
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Co-authors Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo present an explanation for emergent abilities in their paper, posted to the arXiv preprint server on Friday. The authors write that “for a particular task and model family, when analyzing fixed model outputs, one can choose a metric which leads to the inference of an emergent ability or another metric which does not.”
Though the model the researchers discuss is a previous iteration in the GPT family, GPT-3, they compare their findings to previous papers that also focused on GPT-3 when defining its emergent abilities. The researchers found that AI abilities only appeared to suddenly emerge when people used specific metrics to measure the task.
The researchers wrote that a person’s choice of a "non-linear" or "discontinuous" measurement can result in what appear to be sharp and unpredictable changes that are then falsely labeled as emergent abilities when in reality the performance curve is increasing smoothly. The authors write that this is compounded by researchers not having enough data on small models—perhaps they really are capable of the supposedly emergent task—and not enough on large models, either... (MORE - details)