A new artificial intelligence created by researchers at the Massachusetts Institute of Technology pulls off a staggering feat: by analyzing only a short audio clip of a person's voice, it reconstructs what they might look like in real life.
麻省理工学院的研究人员创造的一种新的人工智能取得了惊人的成就：通过仅分析一个人声音的短片段，可以重建他们在现实生活中的样子。
The AI's results aren't perfect, but they're pretty good - a remarkable and somewhat terrifying example of how a sophisticated AI can make incredible inferences from tiny snippets of data.
人工智能的结果并不完美，但它们已经相当不错了 - 这是一个细思恐极例子，说明复杂的人工智能如何从微小的数据片段中做出令人难以置信的推断。
In a paper published this week to the preprint server arXiv, the team describes how it used trained a generative adversarial network to analyze short voice clips and "match several biometric characteristics of the speaker," resulting in "matching accuracies that are much better than chance."
在本周发布给预打印服务器arXiv的一篇论文中，该团队描述了如何使用经过训练的生成对抗网络来分析短语音片段并“匹配说话者的几种生物特征”，从而使“匹配准确性大大提高”。
图片来源：视觉中国
That's the carefully-couched language of the researchers. In practice, the Speech2Face algorithm seems to have an uncanny knack for spitting out rough likenesses of people based on nothing but their speaking voices.
这是由研究人员精心打造的语言系统。在实际操作中，Speech2Face算法似乎有一个神秘的技巧，它只能根据他们的说话声音产生人们大概的肖像。
The MIT researchers urge caution on the project's GitHub page, acknowledging that the tech raises worrisome questions about privacy and discrimination.
麻省理工学院的研究人员敦促对该项目的GitHub页面提出警告，承认该技术引发了关于隐私和歧视的问题令人担忧。
"Although this is a purely academic investigation, we feel that it is important to explicitly discuss in the paper a set of ethical considerations due to the potential sensitivity of facial information," they wrote, suggesting that "any further investigation or practical use of this technology will be carefully tested to ensure that the training data is representative of the intended user population."
“虽然这是纯粹的学术调查，但我们认为，由于面部信息的潜在敏感性，在文章中明确讨论一系列的道德因素很重要，”他们写道，“这表明‘对此进行任何进一步调查或实际应用我们都会对此进行严谨的技术测试，以确保实际数据能够代表预期的用户群。’”
