单选题
Human memory is notoriously unreliable. Even people with the sharpest facial-recognition skills can only remember so much.
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.
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.
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 I 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.
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.
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.
"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.
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.
"An ultimate face recognition algorithm should perform with billions of people in a dataset," the researchers wrote.
单选题
Compared with human memory, machines can ______.
【正确答案】
C
【答案解析】[解析] 根据题干信息词compared with human memory，machines可以把答案线索定位到前三段。
文章前两段指出人类的记忆是不可靠的，根据可能拥有的熟人数量来估计，一个人可以回忆出的不同面孔的数量在数千左右。接着第三段前两句提到：机器则不受这样的限制。为一台合适的计算机提供一个庞大的人脸数据库，它就可以处理它所见到的所有人脸信息。即机器不受人脸数量的限制，可以记住无限多的人脸。故C项正确。文中并未提及机器比人类能“更有效地识别人脸”“将朋友和仅是认识的人区分开”或是“感知人眼看不见的图像”，因此排除其他三项。
单选题
Why did researchers create MegaFace?
【正确答案】
C
【答案解析】[解析] 根据题干信息词create MegaFace，可以把答案线索定位到第四段第三句。
该句中的不定式To begin to figure out提示本句即答案所在。该句指出：为了着手弃清楚(To begin to figure out)计算机是如何努力(struggle)识别人脸的，华盛顿大学的研究人员创建了一个庞大的人脸数据库——他们称其为MegaFace。其中struggle的意思是“努力；艰难地行进”，结合上一句中提到的“在面部识别方面，机器仍然有其局限性。科学家们才刚刚开始了解这些制约因素是什么”，可知计算机在人脸识别方面受到一些制约因素的限制，识别人脸时存在问题和困难，MegaFace的创建正是为了了解计算机识别人脸时存在的问题，因此C项正确。其他三项均与创建MegaFace的目的无关，故均排除。
单选题
What does the passage say about machine accuracy?