2018年06月六级第2套仔细阅读 Passage 1 原文翻译及答案解析

Human memory is notoriously unreliable. Even people with the sharpest facial-recognition skills can only remember so much.

人类记忆的不可靠性众所周知。即使拥有最敏锐面部识别能力的人，记忆容量也有限。

It's tough to quantify how good a person is at remembering. No one really knows how many different faces someone can recall, for example, but various estimates tend to hover in the thousands-based on the number of acquaintances a person might have.

量化一个人的记忆力好坏很困难。例如，没有人真正知道一个人能回忆起多少张不同的面孔，但各种估计往往在数千张左右——基于一个人可能拥有的熟人数量。

Machines aren't limited this way. Give the right computer a massive database of faces, and it can process what it sees-then recognize a face it's told to find-with remarkable speed and precision. This skill is what supports the enormous promise of facial-recognition software in the 21st century. It's also what makes contemporary surveillance systems so scary.

机器不受这种方式的限制。给合适的计算机一个庞大的面孔数据库，它就能以惊人的速度和精度处理所见内容——然后识别出被告知要找的面孔。这项技能支撑着面部识别软件在21世纪的巨大前景，也正是它使得当代监控系统如此可怕。

The thing is, machines still have limitations when it comes to facial recognition. And scientists are only just beginning to understand what those constraints are. To begin to figure out how computers are struggling, researchers at the University of Washington created a massive database of faces- they call it MegaFace- and tested a variety of facial-recognition algorithms (算法) as they scaled up in complexity. The idea was to test the machines on a database that included up to 1 million different images of nearly 700,000 different people-and not just a large database featuring a relatively small number of different faces, more consistent with what's been used in other research.

问题在于，机器在面部识别方面仍然存在局限性。科学家们才刚刚开始理解这些限制是什么。为了初步了解计算机的困难，华盛顿大学的研究人员创建了一个庞大的面部数据库——他们称之为MegaFace——并测试了各种随着复杂性增加的面部识别算法。这个想法是在一个包含近70万人的高达100万张不同图像的数据库上测试机器，而不仅仅是一个拥有相对较少不同面孔的大型数据库，这与其他研究中使用的更一致。

As the databases grew, machine accuracy dipped across the board. Algorithms that were right 95% of the time when they were dealing with a 13, 000-image database, for example, were accurate about 70% of the time when confronted with 1 million images. That's still pretty good, says one of the researchers, Ira Kemelmacher-Shlizerman."Much better than we expected, "she said.

随着数据库的增长，机器准确率全面下降。例如，在处理13,000张图像数据库时正确率为95%的算法，在面对100万张图像时准确率约为70%。一位研究人员Ira Kemelmacher-Shlizerman说，这仍然相当不错。"比我们预期的要好得多，"她说。

Machines also had difficulty adjusting for people who look a lot alike-either doppelgangers(长相极相似的人), whom the machine would have trouble identifying as two separate people, or the same person who appeared in different photos at different ages or in different lighting, whom the machine would incorrectly view as separate people.

机器也难以调整那些长相非常相似的人——无论是双胞胎（长相极相似的人），机器会难以将他们识别为两个不同的人，还是同一个人出现在不同年龄或不同光线下的照片中，机器会错误地将他们视为不同的人。

"Once we scale up, algorithms must be sensitive to tiny changes in identities and at the same time invariant to lighting, pose, age, "Kemelmacher-Shlizerman said.

Kemelmacher-Shlizerman说："一旦我们扩大规模，算法必须对身份的细微变化敏感，同时对光线、姿势、年龄保持不变性。"

The trouble is, for many of the researchers who'd like to design systems to address these challenges, massive datasets for experimentation just don't exist--at least, not in formats that are accessible to academic researchers. Training sets like the ones Google and Facebook have are private. There are no public databases that contain millions of faces. MegaFace's creators say it's the largest publicly available facial-recognition dataset out there.

麻烦的是，对于许多希望设计系统来应对这些挑战的研究人员来说，用于实验的大规模数据集根本不存在——至少，不是以学术研究人员可访问的格式存在。像Google和Facebook拥有的训练集是私有的。没有包含数百万张面孔的公开数据库。MegaFace的创建者说它是目前最大的公开可用面部识别数据集。

"An ultimate face recognition algorithm should perform with billions of people in a dataset, "the researchers wrote.

研究人员写道："一个终极的面部识别算法应该能在包含数十亿人的数据集中表现良好。"