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上期翻译答案
In the United States, initially buoyant public and political support for President Barack Obama’s strategyerodedwithin just a couple of years, following accusations of squandered public funds in a government-backed loan to Solyndra, a California solar panel company.
在美国，公众和政治界最初对巴拉克·奥巴马总统战略的满怀信心的态度短短几年内就被消耗殆尽了，因为有人指责政府支持的对Solyndra公司的贷款浪费了公共资金，这是加利福尼亚的一家太阳能电池板公司。
《本期内容》
导读
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我们可能认为：相比于笨拙的人类，我们对功能失常的应用程序或设计糟糕的算法会更为苛刻。然而这只是一种模糊的猜测。
双语阅读
Over the last few years, together with my team, I ran dozens of experiments where thousands of Americans reacted to actions performed by humans and machines. The experiments consisted of scenarios that could be presented as a human or a machine action, like anexcavatoraccidentallydigging upa grave or atsunamialert system failing to alert acoastal town. These comparisons allowed us to go beyond the way humans judge AI, and focus instead on how our judgment of machines compares to our judgment of humans.
在过去的几年里，我和我的团队一起做了几十个实验，让数千美国人对人类和机器的行为表现做出反应。这些实验包括的场景是由人类或机器表现出的行为，比如挖掘机不小心挖出了一个坟墓，或者海啸预警系统没能向沿海城镇发出警报。这些比较让我们超越人类评判人工智能的方式，专注于比较我们对机器与对人类评判方式的差别。
This difference may seem subtle, but it forces us to judge machines in a more realistic frame of reference. We tend to compare AI with perfection instead of comparing it with how we react to a humancarrying outthe same action with the same result. So, what did the experiments teach us? Even the first data points showed that people did not react to humans and machines equally. For instance, people were less forgiving of machines than of humans in accidental scenarios, especially when they resulted in physical harm.
差别可能似乎很细微，但它迫使我们在一个更现实的参考框架下对机器进行评判。我们倾向于把人工智能与最完美的标准进行比较，而不是将其与我们对人类执行相同行动得到同样结果的反应进行比较。那么，这些实验教会了我们什么？即使是第一个数据点也表明，人们对人类和机器的反应并不一样。例如，发生意外事故时，人们对机器的宽容度比对人类的更低，特别是当这些事故造成身体伤害时。
But since we had thousands of data points, we could go beyond anecdotal observations. So, we decided to build a statistical model explaining how people judged humans and machines. The model predicted how people scored a scenario,in terms ofmoral wrongness, as a function of how they scored it based on harm and on perceived intention.
但由于我们有成千上万的数据点，我们能做的不止于轶事观察。因此，我们决定建立一个统计模型，解释人们如何评判人类和机器。翻译划线句，长按文末小程序码打卡，答案下期公布~
To our surprise, the model showed that people did not just judge humans less severely than machines, but that we used a different moral philosophy to judge them. The figure nearby summarizes this finding. The blue plane shows how people judge—on average—other people. The red plane shows how humans judge—on average—machines.
出乎我们意料的是，该模型显示，人们不只是对人类的评判不如对机器的那么严厉，而且我们会用不同的道德哲学标准来评判他们。旁边的图总结了这一发现。蓝色的平面展示的是人们对其他人评判的平均水平。红色平面展示的是人类如何对机器评判的平均水平。
You can clearly see that the planes are not parallel, and that there is a rotation between the red and blue plane. This is because, when judging machines, people seem to care mostly about the outcome of a scenario. In this case, the perceived level of harm. But when people judge other people—the blue plane—we find acurvature. This time, the growth is along the diagonal, representing the interaction between harm and perceived intention. This explains why machines are judged more harshly in accidental scenarios.
可以清楚地看到，这些平面并不平行，而且红色和蓝色平面之间有一个旋转。这是因为，在评判机器的表现时，人们主要关心的似乎是场景的结果。在本例中即为感知到的伤害程度。但是当人们评判其他人时，我们在蓝色平面上发现了一个曲率。这一次，增长是沿着对角线的，代表了伤害和感知伤害背后的意图之间的相关关系。这就解释了为什么发生意外情况时，机器受到的评判更为严苛。
本文节选自：Scientific American（科学美国人）
作者：César A. Hidalgo
原文标题：Why We Forgive Humans More Readily Than Machines
词汇积累
英[ˈekskəveɪtə(r)]美[ˈekskəveɪtər]
n. 挖掘机；开凿者
英 [tsuːˈnɑːmi]美[tsuːˈnɑːmi]
n. 海啸
英 [ˈkɜːvətʃə(r)]美[ˈkɜːrvətʃər]
n.弯曲;曲度;曲率;
词组搭配
写作句总结
You can clearly see that the planes are not parallel, and that there is a rotation between the red and blue plane.
结构:You can clearly see that ...
可以清楚地看到...
例句:You can clearly see that the ecological environment is worse than before.
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