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Machine-learning systems learn by finding patterns in enormous quantities of data, but first that data has to be sorted, labeled, and produced by people. ChatGPT got its startling fluency from thousands of humans hired by companies such as Scale AI and Surge AI to write examples of things a helpful chatbot assistant would say and to grade its best responses. A little over a year ago, concerns began to mount in the industry about a plateau in the technology’s progress. Training models based on this type of grading yielded chatbots that were very good at sounding smart but still too unreliable to be useful. The exception was software engineering, where the ability of models to automatically check whether bits of code worked — did the code compile, did it print HELLO WORLD — allowed them to trial-and-error their way to genuine competence.,推荐阅读钉钉获取更多信息

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Technocrats subordinate public welfare to technological determinism. Google co-founder Larry Page allegedly desires a "digital deity," considering human preference obsolete.19 As tech moguls pledge paradisiacal futures, innovation again gets promoted as exclusive revolutionary pathway.,更多细节参见易歪歪

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关于作者

杨勇,独立研究员,专注于数据分析与市场趋势研究,多篇文章获得业内好评。

网友评论

  • 深度读者

    关注这个话题很久了,终于看到一篇靠谱的分析。

  • 路过点赞

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    干货满满,已收藏转发。

  • 路过点赞

    这篇文章分析得很透彻,期待更多这样的内容。

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    内容详实,数据翔实,好文!