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AlphaZero seems to express insight.It plays like no computer ever has, intuitively and beautifully, with a romantic, attacking style.It plays gambits and takes risks.In some games it paralyzed Stockfish and toyed with it.While conducting its attack in Game 10, AlphaZero retreated its queen back into the corner of the board on its own side，far from Stockfish’s king, not normally where an attacking queen should be placed.
Yet this peculiar retreat was venomous: No matter how Stockfish replied, it was doomed.It was almost as if AlphaZero was waiting for Stockfish to realize, after billions of brutish calculations, how hopeless its position truly was, so that the beast could relax and expire peacefully, like a vanquished bull before a matador.Grandmasters had never seen anything like it.AlphaZero had the finesse of a virtuoso and the power of a machine.It was humankind’s first glimpse of an awesome new kind of intelligence.
When AlphaZero was first unveiled, some observers complained that Stockfish had been lobotomized by not giving it access to its book of memorized openings.This time around, even with its book, it got crushed again.And when AlphaZero handicapped itself by giving Stockfish ten times more time to think, it still destroyed the brute.Tellingly, AlphaZero won by thinking smarter, not faster; it examined only 60 thousand positions a second, compared to 60 million for Stockfish.It was wiser, knowing what to think about and what to ignore.By discovering the principles of chess on its own, AlphaZero developed a style of play that “reflects the truth” about the game rather than “the priorities and prejudices of programmers,” Mr.Kasparov wrote in a commentary accompanying the Science article.
The question now is whether machine learning can help humans discover similar truths about the things we really care about: the great unsolved problems of science and medicine, such as cancer and consciousness; the riddles of the immune system, the mysteries of the genome.
The early signs are encouraging.Last August, two articles in Nature Medicine explored how machine learning could be applied to medical diagnosis.In one, researchers at DeepMind teamed up with clinicians at Moorfields Eye Hospital in London to develop a deep-learning algorithm that could classify a wide range of retinal pathologies as accurately as human experts can. (Ophthalmology suffers from a severe shortage of experts who can interpret the millions of diagnostic eye scans performed each year; artificially intelligent assistants could help enormously.)
The other article concerned a machine-learning algorithm that decides whether a CT scan of an emergency-room patient shows signs of a stroke, an intracranial hemorrhage or other critical neurological event.For stroke victims, every minute matters; the longer treatment is delayed, the worse the outcome tends to be.(Neurologists have a grim saying: “Time is brain.”)The new algorithm flagged these and other critical events with an accuracy comparable to human experts but it did so 150 times faster.A faster diagnostician could allow the most urgent cases to be triaged sooner, with review by a human radiologist.
What is frustrating about machine learning, however, is that the algorithms can't articulate what they're thinking.We don't know why they work, so we don't know if they can be trusted. AlphaZero gives every appearance of having discovered some important principles about chess, but it can't share that understanding with us. Not yet, at least.As human beings, we want more than answers.We want insight.This is going to be a source of tension in our interactions with computers from now on.
In fact, in mathematics, it's been happening for years already.Consider the longstanding math problem called the four-color map theorem.It proposes that, under certain reasonable constraints, any map of contiguous countries can always be colored with just four colors such that no two neighboring countries are colored the same.
Although the four-color theorem was proved in 1977 with the help of a computer, no human could check all the steps in the argument.Since then, the proof has been validated and simplified, but there are still parts of it that entail brute-force computation, of the kind employed by AlphaZero’s chess-playing computer ancestors.This development annoyed many mathematicians.They didn’t need to be reassured that the four-color theorem was true; they already believed it.They wanted to understand why it was true, and this proof didn't help.
单选题第1题From the first paragraph we can infer that（）.
A.Stockfish is a human world champion on chess
B.Stockfish is a computer software on chess
C.AlphaZero has won 10 games
D.AlphaZero does not play like a computer
参考答案：B
答案解析：推断题。根据第1段可知，AlphaZero是一款计算机软件，而文中也用it指代Stockfish，可见也是计算机软件，故正确答案为B。
单选题第2题In the clause “so that the beast could relax and expire peacefully”，the author uses the rhetorical device termed（）.
A.simile
B.personification
C.metaphor
D.exaggeration
参考答案：C
答案解析：修辞题。根据上下文可知，此处beast指代Stockfish，因此运用的修辞手法为暗喻，故正确答案为C。
单选题第3题The word “virtuoso” underlined in Paragraph 2 most probably means（）.
A.master
B.singer
C.architect
D.designer
参考答案：A
答案解析：语义题。virtuoso意为“艺术大师”，故正确答案为A（大师）。
单选题第4题According to Paragraph 3, which of the following statements was NOT true of AlphaZero in comparison with Stockfish（）?
A.When AlphaZero was first launched, Stockfish did not have its book.
B.Even with its book, Stockfish was still defeated by AlphaZero.
C.AlphaZero defeated Stockfish because it was cleverer and faster.
D.AlphaZero worked out the principles of chess while Stockfish followed the priorities and prejudices of programmers.
参考答案：C
答案解析：推断题。根据选项定位原文，仔细比对后可知C与原文不符，因原文表明Stockfish的运算速度快于AlphaZero，故正确答案为C。
单选题第5题The word “algorithm” underlined in Paragraph 5 most probably means（）.
A.computer program
B.medical treament
C.scientific diagnosis
D.mathematical device
参考答案：A
答案解析：语义题。algorithm意为“运算法则，计算程序”，故A为正确答案。
单选题第6题In Paragraph 6, by saying: “Time is brain.”, neurologists imply that for stroke victims（）.
A.time is as important as brain
B.time plays the most important role
C.time is more important than brain
D.time can save brain
参考答案：B
答案解析：推断题。此句是针对中风病人治疗特点的描述，即治疗越及时，获救的机会越大。因此，时间至关重要，故正确答案为B。
单选题第7题According to Paragraph 6, which of the following statements is NOT true of the new algorithm（）?
A.It could diagnose neurological events as accurately as human experts.
B.It could diagnose neurological events faster than human experts.
C.It could allow the most urgent cases to be triaged sooner.
D.It could allow review by a human radiologist.
参考答案：D
答案解析：推断题。根据选项定位第6段，仔细比对后可知D与原文不符，因为原文with review by a human radiologist并未限定执行者为algorithm，故正确答案为D。
单选题第8题In Paragraph 8, the four-color map theorem is mentioned to show that（）.
A.the theorem was already proved
B.humans cannot solve such problems
C.the algorithms can't tell us what they are thinking
D.the computer is of great help in solving such problems
参考答案：C
答案解析：推断题。根据语篇逻辑，此处举例的目的是为了证明前文提到的观点，即the algorithms can't articulate what they're thinking（程序无法说明其工作思路），故正确答案为C。
单选题第9题The best title for the passage is（）.
A.AlphaZero and Stockfish
B.AlphaZero: the Best Machine in the World
C.AlphaZero and Machine Learning
D.Machine Learning: Merits and Demerits
参考答案：D
答案解析：主旨题。本文以AlphaZero与Stockfish之间的对弈为切入点，引出机器学习这一主题，阐述了其优势与不足。根据相关性和全面性的原则，D为最 佳题目。
单选题第10题The overall tone of the passage is（）.
A.subjective
B.objective
C.indifferent
D.optimistic
参考答案：B
答案解析：态度题。作者既肯定了机器学习的长处，也指出了其不足，故正确答案为B（客观的）。
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