Human memory is notoriously （眾所周知地） unreliable.Even people with the sharpest facial recognition skills can only remember so much.
It's tough to quantify how good a person is （1）atat 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 （2）may/mightmay/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 （3）is toldis told （tell） to find – with remarkable speed and precision.This skill is （4）whatwhat supports the enormous promise of facial-recognition software in the 21st century.It is also what makes contemporary surveillance （監(jiān)控） systems so scary.
The thing is，machines sill have limitations when it comes to facial recognition.And scientists are only just beginning to understand what those constraints are. （5）To figureTo figure （figure） out how computers are struggling，researchers at the University of Washington created a massive database of faces – they call it MegaFace – and （6）testedtested （test） a variety of facial-recognition algorithms（算法） as they scales up in complexity.The idea was to test the machines on a database that included up to 1 million different images of nearly 7，000 different people – and not just a large database （7）featuringfeaturing （feature） 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 （8）which/thatwhich/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 （9）facedfaced （face） 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 （長相極相似的人），whom the machine would have trouble （10）identifyingidentifying （identify） 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.
【考點(diǎn)】人與自我．
【答案】at；may/might；is told；what；To figure；tested；featuring；which/that；faced；identifying
【解答】
【點(diǎn)評(píng)】
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發(fā)布：2024/12/30 1:0:8組卷：4引用：1難度：0.5
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G．Some location services operate like Facebook，where you invite and accept friends．發(fā)布：2025/1/1 18:0:1組卷：7引用：4難度：0.7 -
2．"Me".It's a small word with big meaning，and that meaning is as individual to each of us as the way we look.Through interviews，Dr Hart，a psychologist，was able to discover what "me" means to other people，and how they have learnt to（1 ）
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Jade has become（2）to selfie apps to beautify herself.She spends hours every day （3）（ edit） her selfies，removing her freckles and enlarging her eyes so as to boost her （4）.Dr Hart says obsessive online photo editing indicates a（5）of self-confidence and a desire for attention.
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3．Dear Laura，
Thanks for letting us to stay at your seaside house． Now， you might have heard from your neighbors about which happened on Saturday evening． When I was out a walk， my younger son， Tom， played football before your house． He made so many noise that the neighbors got quite angrily． Even worse， Tom broke one of the window of Mr． Wilson's house． Tom and I apologized to all the neighbors for the unhappy happenings， but promised to have the broken window repair soon． Tom said that we would not do such silly things any more． I am glad that your neighbors had forgiven us． They are really nice people．
Yours，
Betty發(fā)布：2025/1/30 8:0:1組卷：12引用：2難度：0.5
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