Cognitive Science AlphaGo has demonstrated that a machine can learn how to do things that people spend many years of concentrated study learning, and it can rapidly learn how to do them better than any human can. Caliskan et al. now show that machines can learn word associations from written texts and that these associations mirror those learned by humans, as measured by the Implicit Association Test (IAT) (see the Perspective by Greenwald). Why does this matter? Because the IAT has predictive value in uncovering the association between concepts, such as pleasantness and flowers or unpleasantness and insects. It can also tease out attitudes and beliefs—for example, associations between female names and family or male names and career. Such biases may not be expressed explicitly, yet they can prove influential in behavior. Science , this issue p. [183][1]; see also p. [133][2] [1]: /lookup/doi/10.1126/science.aal4230 [2]: /lookup/doi/10.1126/science.aan0649

中文翻译：

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机器学习人们隐含的知识

认知科学 AlphaGo 已经证明，机器可以学习如何做人们花费多年专注学习的事情，并且它可以快速学会如何比任何人做得更好。卡利斯坎等人。现在表明机器可以从书面文本中学习单词关联，并且这些关联反映了人类学习的关联，由隐式关联测试 (IAT) 衡量（参见 Greenwald 的观点）。为什么这很重要？因为 IAT 在揭示概念之间的关联方面具有预测价值，例如愉快和鲜花或不愉快和昆虫。它还可以梳理出态度和信念——例如，女性姓名与家庭或男性姓名与职业之间的关联。这种偏见可能没有明确表达，但它们可以证明对行为有影响。科学，这个问题 p。[183]​​[1]；另见第。[133][2][1]：/lookup/doi/10.1126/science.aal4230 [2]：/lookup/doi/10.1126/science.aan0649