Question

3．"There are many ways of being smart that aren't smart like us．"These are the words of Patrick Winston，a leading voice in the field of artificial intelligence．Although his idea is simple，its significance has been lost on most people thinking about the future of work．Yet this is the feature of Al that ought to preoccupy us the most．
   From the 1950s to the 1980s．it was generally thought that the best way to build systems capable of performing tasks to the level of human experts or higher was to copy the way that experts worked．But there was a problem：human experts often struggled to explain how they performed many tasks．Chess-playing was a good example．When asked to explain how they played chess well，some grandmasters appealed to"intuition"（真爱），others to"experience"．Many said they did not really know at all．How could researchers build a chess-playing system to beat a grandmaster if the best players themselves could not explain how they were so good？
  A turning point came in 1997．Deep Blue，IBM's supercomputer，beat Garry Kasparov，the ihen world chess champion，in a six-game match．What was most remarkable was how the system did it．Deep Blue did not share Mr Kasparov's"intuition"or"experience"．It won by means of absolute processing power and massive data storage ability．
  Consider the legal world．Daniel Martin Katz，a law professor，has designed a system to predict the voting behaviour of the US Supreme Court．It can perform as well as most specialists，but it does not mirror the judgement of a human being．Instead it draws on data that capture six decades of Court behaviour．
  We see similiar developments in other parts of life．Surely，all these point to a future that is very different from the one most experts are predicting．It is often said that there are many tasks that will always require human beings to perform because machines cannot"think"，"reason"or"feel"like human beings．But this is to fail to grasp that tomorrow's systems will handle many tasks not by copying us but by working in entirely different，unhuman ways．The set of tasks reserved particularly for human beings is likely to be much smaller than many expect．

46．The example of chess-playing mainly shows thatC．
A．it's important for a chess-playing system to beat a grandmaster
B．human experts nave various responses for their good performance
C．it's difficult to build systems by copying the way human experts work
D．the way human experts work can be of great benefit to building systems
47．According to the passage，what's the reason for Deep Blue's success over Garry？D
A．It shared Garry's intuition and experience．
B．It's designed to think like a human expert．
C．Garry was not able enough lo beat supercomputer．
D．It was equipped with a powerful data processing system．
4S．What's ihe author's purpose of writing Paragraph 4？B
A．To present more details．
B．To offer another example．
C．To give further explanation．
D．To share deeper understanding．
49．Which of the following best shows ihe structure of the passage？C
A．①②③--④⑤
B．①②--③④--⑤
C．①--②③④--⑤
D．①--②③--④⑤
50．What can we infer from the last paragraph？B
A．Systems are expected to be more humanlike．
B．More tasks are likely to be handled by systems．
C．Human beings are certain to be replaced by systems．
D．Tasks tomorrow are supposed to be more demanding．

Answer 1

分析 本文主要写了建立系统可以帮助我们干很多的事情的．

解答 46．C 细节理解题．根据句子human experts often struggled to explain how they performed many tasks．How could researchers build a chess-playing system to beat a grandmaster if the best players themselves could not explain how they were so good？人类专家经常努力解释他们是如何完成许多任务的．如何建立一个国际象棋博弈系统研究打败大师即使最好的球员本身不能解释他们是如何这么好，所以答案选C．
47．D 细节理解题．根据句子DIt won by means of absolute processing power and massive data storage ability．它通过绝对的处理能力和海量的数据存储能力．所以答案选D．
48．B 考虑到法律世界，作者写了法律教授Daniel Martin Katz设计了一个系统来预测美国最高法院的投票行为，可见本段是作者给出的另一个例子，所以答案选B．
49．C 本文第一段提出观点There are many ways of being smart that aren't smart like us，二三四段通过举例说明有很多的聪明的方法．最后一段We see similiar developments in other parts of life延伸到生活的其他领域，所以答案为C、
50．B 推理判断题．根据句子But this is to fail to grasp that tomorrow's systems will handle many tasks not by copying us but by working in entirely different，unhuman ways．但这是无法把握明天的系统会处理很多任务不是通过复制我们的工作方式，而是在完全不同的，非人的工作方式中，可以推断将来有更多的任务是由系统处理的．所以答案选B．．

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