听力文本

This is Scientific American — 60-Second Science. I'm Christopher Intagliata.
Google and Facebook both do a nice job identifying your friends in photos—a testament to how good machines have gotten at studying human faces. But how well would an algorithm fare, when pitted against a forensic facial examiner... the experts that testify in court?
"Well it turns out the best algorithm is comparable to the best humans." Jonathan Phillips, a facial recognition scientist at the National Institute of Standards and Technology.
He and his colleagues presented 20 very difficult image pairs to human experts, and a range of algorithms. And the most up-to-date algorithms did indeed perform as well as the skilled humans.
But when Phillips and his team asked for input from two humans, or a human and an algorithm, it was the combined judgment of humans and machines that won out... providing near perfect results. Which suggests the pooled strengths and weaknesses of human brains and computer code add up to superior accuracy. The study is in the Proceedings of the National Academy of Sciences.

Phillips says now it's now up to the facial recognition community to use these findings to improve the tests in real-world settings. And don't worry: human recognizers won't be out of a job anytime soon.
"Just because an algorithm says i give you a high score you don't just accept the word of that black box... you develop ways of integrating human judgment into the decision you get out of an algorithm itself." After all someone—preferably, a human—still has to explain the findings in court.
Thanks for listening for Scientific American — 60-Second Science. I'm Christopher Intagliata.

参考译文

这里是科学美国人——60秒科学84~mG^d|]y%.k_K&d+]。我是克里斯托弗·因塔利亚塔Er~2cAwa^F,+IYg5。
在用照片识别出你的朋友这方面，谷歌和脸谱网的表现都很不错，这证明机器在研究人脸方面拥有突出能力t2B!^B2Y2(_=ZDz7。那在与面部检察官这种专家在法庭对质时，算法的表现又如何呢？
“事实证明，最好的算法可以与最优秀的人类专家相媲美BjU*uGssA]7Q。”乔纳森·菲利普斯是美国国家标准与技术研究院的面部识别科学家Q.;9!@J@HLRzGx+|EdxX。
他和同事给人类专家和一系列算法提出了20个极为难识别的图像对k8vNA^EGBAdBxmjg^3X。最先进算法的表现的确和技术过硬的人类专家一样出色Td+78)Lknp0HxE。
而当菲利普斯和团队让两个人一组或一个人与一个算法一组进行图像识别时，结果发现人类与机器的联合判断胜出，其识别结果近乎完美*qOu@5HjC#C。这表明，人脑与计算机代码的综合优劣势互补之后，得出的结果具有卓越的精确性PEw3eK=lR^&_9b2QI。这项研究结果发表在《美国国家科学院学报》上!v&vZOAn(+8rLX&。
菲利普斯说，现在使用面部识别的团体应该利用这些研究发现来改善现实世界中的测试Z1IVlfH98Pj0m@P4eBz+。不过别担心：人类识别者短时间内不会失业[h-#plv38t。
“只因一个算法给了你高分，而你不接受那个未知系统的判断，就开发出一种方法，将人类判断融入算法所得出的结论中9]Du71CeQWn_E_=Suv。”毕竟，有人——最好是真人——仍要在法庭上解释这项发现Al%v50QzcY#-&Uyg。
谢谢大家收听科学美国人——60秒科学zT=7vj.)c-HGq0|L-s。我是克里斯托弗·因塔利亚塔4,6fX(aJJJ~q,zm6。

译文为可可英语翻译，未经授权请勿转载！

重点讲解

重点讲解：
1. be comparable to 可比的；可比较的；
We decided that even the economy model had to be comparable to the luxury and performance versions.
我们决定：即使是经济型也要比得上豪华型和超性能型F5UR.heprb[lDGpBLF。
2. win out (经过比赛或斗争)获胜，胜出；
Sometimes perseverance does win out.
有时候坚持就是胜利AQY.fFqe=Mj~。
3. integrate into (使)加入；(使)融入；
Once upon a time , the euro was supposed to help integrate Europe's nations into one seamless, fully-integrated economy.
曾经一段时间，人们认为欧元的一项使命就是无缝隙将欧洲各国融入到统一的经济之中nA~mS-L17Bm。
4. after all 毕竟；终究；
Things weren't so bad, after all. I was among friends again.
情况还不是那么糟糕，毕竟我又回到了朋友当中,L2J7g1X=._Aq7w_。