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Text 2 In a former leather factory just off Euston Road in London, a hopeful firm is starting up...
Text 2
In a former leather factory just off Euston Road in London, a hopeful firm is starting up. BenevolentAI's main room is large and open-plan. In it, scientists and coders sit busily on benches, plyinS their various trades. I\e firm's star, though, has a private, temperature.controlled office.Thal star is a powerful computer that runs the software which sits at the heart of BenevolentAl's business. This software is an artificial-intelligence system.
AI, as it is known for short, comes in several forms. But BenevolentAI's version of it is a form of machine learning that can draw inferences about wh8t it has leamed. In particular, it can process natural language and formulate new ideas from what it reads. Its job is to sift through vast chemical libraries, medical databases and conventionally presented scientific papers, looking for potential drug molecules.
Nor is BenevolentAI a one-off. More and more people and firms believe that AI is well placed to help unpick biology and advance human health. Indeed, as Chris Bishop of Microsoft Research, in Cambridge, England, observes, one way of thinking aboui living organisms is to recognize that they are, in essence. complex systems which process informalion using a combination of hardware and software.
That thought has consequences. Whether it is the new Chan Zuckerberg Initiative ( CZI) , from the founder of Facebook and his wife, or the biological subsidiaries being set up by firms such as Alphabet ( Google's parent company) , IBM and Microsofi, the new Big Idea in Silicon Valley is that in the worlds of biology and disease there are problems its software engineers can solve.
The discovery of new drugs is an early test of the belief that AI has much to offer biology and medicine. Pharmaceutical companies are finding il increasingly difficult lo make headway in their search for novel products. The conventional approach is to screen larf;e numbers of molecules for signs of relative biological effect, and then weed out the useless partin a series of more and more expensive tests and trials, in the hope of coming up with a golden nugget at the end. This way of doing things is, however, declining in productivity and rising in cost.
27. According to Paragraph 2, BenevolentAI's version of Al can .
In a former leather factory just off Euston Road in London, a hopeful firm is starting up. BenevolentAI's main room is large and open-plan. In it, scientists and coders sit busily on benches, plyinS their various trades. I\e firm's star, though, has a private, temperature.controlled office.Thal star is a powerful computer that runs the software which sits at the heart of BenevolentAl's business. This software is an artificial-intelligence system.
AI, as it is known for short, comes in several forms. But BenevolentAI's version of it is a form of machine learning that can draw inferences about wh8t it has leamed. In particular, it can process natural language and formulate new ideas from what it reads. Its job is to sift through vast chemical libraries, medical databases and conventionally presented scientific papers, looking for potential drug molecules.
Nor is BenevolentAI a one-off. More and more people and firms believe that AI is well placed to help unpick biology and advance human health. Indeed, as Chris Bishop of Microsoft Research, in Cambridge, England, observes, one way of thinking aboui living organisms is to recognize that they are, in essence. complex systems which process informalion using a combination of hardware and software.
That thought has consequences. Whether it is the new Chan Zuckerberg Initiative ( CZI) , from the founder of Facebook and his wife, or the biological subsidiaries being set up by firms such as Alphabet ( Google's parent company) , IBM and Microsofi, the new Big Idea in Silicon Valley is that in the worlds of biology and disease there are problems its software engineers can solve.
The discovery of new drugs is an early test of the belief that AI has much to offer biology and medicine. Pharmaceutical companies are finding il increasingly difficult lo make headway in their search for novel products. The conventional approach is to screen larf;e numbers of molecules for signs of relative biological effect, and then weed out the useless partin a series of more and more expensive tests and trials, in the hope of coming up with a golden nugget at the end. This way of doing things is, however, declining in productivity and rising in cost.
27. According to Paragraph 2, BenevolentAI's version of Al can .
参考答案: A
参考解析: 细节题。根据关键词BenevolenLAI's version of AI定位到第二段第一行：But BenevolentAI's version of it is a form of machine learning that can draw inferences about what it has learned.选项[A]make some inferences“作出一些推断”=draw inferences about what it has learned．故该项为答案。选项[B] think like human beings“像人类一样思考”，该项属于无中生有。选项[C] teach machines to learn“教授机器学习”是针对machine learning“机器学习”的干扰，其中teach 一词无中生有。选项[D] leam complex language“学习复杂语言”，该项与iI can process natural language -句相关，但是并不等同，leam“学习”不等于“处理”，故该项也与原文不符。综上，本题选择[A]。
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